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Canadian hospital admissions systems : a simulation approach Lim, Timothy Warren 1973

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c . l CANADIAN HOSPITAL ADMISSIONS SYSTEMS: A SIMULATION APPROACH TIMOTHY WARREN LIM ( B . S c , U.B.C., 1967) A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN BUSINESS ADMINISTRATION i n the Department o f O p e r a t i o n s Research We a c c e p t t h i s t h e s i s as c o n f o r m i n g t o t h e r e q u i r e d s t a n d a r d THE UNIVERSITY OF BRITISH COLUMBIA O c t o b e r 1973 In presenting t h i s t h e s i s in p a r t i a l f u l f i l m e n t o f the requirements f o r an advanced degree at the U n i v e r s i t y of B r i t i s h Columbia, I agree that the L i b r a r y s h a l l make i t f r e e l y a v a i l a b l e f o r reference and study. I f u r t h e r agree tha permission f o r extensive copying of t h i s t h e s i s f o r s c h o l a r l y purposes may be granted by the Head of my Department or by h i s r e p r e s e n t a t i v e s . I t i s understood that copying or p u b l i c a t i o n of t h i s t h e s i s f o r f i n a n c i a l gain s h a l l not be allowed without my w r i t t e n permission. Department The U n i v e r s i t y of B r i t i s h Columbia Vancouver 8, Canada i i ABSTRACT This study attempts to improve the delivery of health services by applying operations research techniques to hospital admission systems. Although this study applies to hospital admissions systems in general, the admission system of one ward of one hospital was chosen to be the central object in the study. A computer simulation model was formulated to examine the - r e s u l t s of various p o l i c i e s . In the model, the admission of patients i s determined primarily by the scheduling of the operating theatre and secondarily by the a v a i l a b i l i t y of beds. The three standard p r i o r i t i e s f o r hospital admissions ( e l e c t i v e , urgent and emergent) are given separate considerations as would be the case in real l i f e ; because scheduling can be much more f l e x i b l e f o r , e l e c t i v e patients, while time must be set aside f o r emergent patients although the hospital has no advance information about them. The general results of this study led to two suggestions that would improve most existing admission systems. The f i r s t requires that the hospital set up a special class of patients, the "quickcall patients," who would be w i l l i n g to be admitted f o r surgery on short notice. It was shown that this procedure s i g n i f i c a n t l y reduced the waiting time f o r e l e c t i v e surgery. The second requires that the hospital l i m i t each physician to a fixed number of requests f o r e l e c t i v e surgery at any given time, so that the hospital need not keep extensive f i l e s . i i i The model could be extended to examine (1) the s e n s i t i v i t y of the schedule to r e f e r r a l patients, (2) the higher u t i l i z a t i o n of the operating theatre and (3) waiting p r i o r i t y based on patient need and/or u t i l i t y . In conclusion the simulation study indicated that these p o l i c i e s i f implemented would s i g n i f i c a n t l y reduce the waiting time ( 29% in the model), and increase the hospital's effectiveness in assessing the order of admission f o r patients. i v ACKNOWLEDGEMENT The author i s indebted to Professor D.H. Uyeno of the Faculty of Commerce and Business Administration, University of B r i t i s h Columbia, f o r his invaluable guidance and encouragement in the develop-ment of this t hesis. He i s also grateful f o r the assistance and co-operation provided by Mr. E.H. 0'Del 1 and Mrs. M. Gagnon and her s t a f f . This work has been supported by National Health and Welfare Grant #610-21-24. V TABLE OF CONTENTS Chapter Page 1. INTRODUCTION 1 1.1 General ^ 1.2 Literature Review 1 1.3 Overview 3 2. HOSPITAL ADMISSIONS SYSTEMS (HAS) 5 2.1 Types of Admission Systems . 5 2.2 Statement of Objectives of Admissions Systems 7 2.3 Waiting L i s t s : The Tool of HAS 10 2.4 Daily Problems of HAS 14 2.5 General Flow of Patients . 15 3. DESCRIPTION OF HOSPITAL USED IN SIMULATION . . . . . . 18 3.1 General 18 3.2 Admission Flow 18 3.3 Hospital Data Bank 23 4. THE SIMULATION MODEL 24 4.1 Choosing a Hospital Subsystem 24 4.2 Choosing an Admission Subsystem (The Ward) 2 5 4.3 Data Gathering 2 6 4.4 The Simulation Language Used 2 7 4.5 General Model Structure 31 vi Chapter Page 4.6 Detail Model Structure 34 4.7 Model Sophistication 50 4.8 Physical Characteristics of the Model 52 4.9 Validation of the Model 53 • 5. EXPERIMENTAL RESULTS 58 5.1 General 58 5.2 Quickcalls 58 5.3 Constant Queue ..... 60 5.4 Minimum Queue 6 3 5.5 Different Standard Schedules 6 4 5.6 Excess Surgery Time 6 6 5.7 The Maui Experiment 67 6. CONCLUSIONS 6 9 BIBLIOGRAPHY 7 4 APPENDIX A - Computer Program Printout 76 APPENDIX B - User's Manual Procedure to Do Admission Study (Manual Method) 1 6 4 ' & /' APPENDIX C - User's Manual Procedure to Do Admission , , , Study (Computer Method) 1 6 8 APPENDIX D - Questionnaire 1 7 2 •' —• \ APPENDIX E - Experimental Numerical Output 179 , ^ v i i LIST OF FIGURES Figure Page 1. Schematic View of Admission Waiting L i s t , 12, 2. Transaction Schematic 30 3. Block Diagram of Simulation Program 35 4. A Typical Clock 37 5. The Weekly Surgery Time Allotment Clock 38 6. The Patient Generator 39 7. Urgent Medical Patient Flow in Model 44 8. Emergent Patient Flow in Model 46 9. Urgent Surgical Patient Flow in Model . 47 10. El e c t i v e Patient Flow i n Model 49 11. Schematic Diagram of Explanation on Reducing Line Length. A Physician's Waiting L i s t f o r : (a) F i r s t Week (b) Second Week (c) Third Week 62 v i i i LIST OF TABLES Table Page 1. Surgery Physician Time Factor 42 2. Results of a Standard Run . . . . . . . . . . 55 3. Summary of Experimental Results . 5 7 1 CHAPTER 1 INTRODUCTION 1.1 General This study was undertaken in the hope that the techniques of operations research might reveal ways to reduce the mounting cost of medical s e r v i c e s . This study considers how the control of patient input i n t o the hospital might make the hospital more productive. To achieve t h i s we have set the following objectives for the study: 1.. to study hospital admission systems in order to identify some practices that may cause non-optimal utilization of re-sources; 2. to develop admission procedures that will make i t possible to use hospital resources more effectively; 3- to investigate whether i t is feasible to introduce these procedures without disturbing the existing system. 1.2 L i t e r a t u r e Review Operations research techniques have been used in the study df medical services f o r some time and numerous survey articles have been published. Milsum et al_. [7] presented.a comprehensive summary of existing articles on hospital admissions systems. In their recent book, Stimson and Stimson [9] surveyed several facets of operations 2 research in the health f i e l d . Michigan University has published abstracts on studies on any operations research applied to a l l facets of health care [6]. More s p e c i f i c a l l y , the following operations research techniques have been applied to the study of medical services and in p a r t i c u l a r , the hospital admission system: queueing theory [10], p r o b a b i l i t y and s t a t i s t i c s [11], break-even analysis [3], scheduling [5], and simulation [10]. Thompson, et al_. [10] used queueing theory to predict the number of patients in the hospital delivery suite Whitston, [11] analyzed s t a t i s t i c a l models in studying the scheduling problem. Goldman and Knappenberger [3] used computer simulation to gn'n information f o r a breakeven analysis model, whereas Kenny and Murray [5] used simulation to show the results of d i f f e r e n t types of schedules. None of the above models used both beds and operating theatre time as concomitant constraints. Barnoon and Wolfe [1], on the otherhand, introduced a model which uses both a surgery and a bed constraint. However, because there was excess operating theatre time, the only actual constraint was bed a v a i l a b i l i t y . The present study i s d i s t i n c t from any of the foregoing in that i t uses a simulation model in which there are two e f f e c t i v e con-s t r a i n t s on the admission of e l e c t i v e patients—operating theatre time and the a v a i l a b i l i t y of beds. 3 1.3 Overview Chapter 1 begins with a general introduction followed by a b r i e f l i t e r a t u r e review. A overview completes the f i r s t chapter. Chapter 2 sets out the objectives and workings of admission systems, to make i t c l e a r how admission systems a f f e c t hospital productivity and to show enough d e t a i l about such systems that the model in this study can be compared to the r e a l i t y . To t h i s end, i t discusses possible a l t e r n a t i v e admission systems, the general objectives of a l l admission systems, the uses of waiting l i s t s , the d a i l y routine of admission systems, and the general flow of patients through the h o s p i t a l . Chapter 3 i s a description of the hospital used i n the simulation and i t s s p e c i f i c patient admission flow, including the differences in procedure used f o r patients of d i f f e r e n t p r i o r i t i e s . A discussion of the hospital data bank or data storage which was a primary source of data f o r this study completes th i s chapter. The rationale behind the model in this study and the d e t a i l s of i t s construction are explained in Chapter 4. It begins by examining the factors influencing the choice of a s p e c i f i c subsystem of the h o s p i t a l , the patient admission system. It was deemed s u f f i c i e n t to study a unit of t h i s subsystem, the ward. The choice of the ward i s considered next. Description of data gathering procedure, the simulation language, and the general model structure are followed by a detailed description along with flow charts to explain the model of the hospital used in t h i s study; a b r i e f discussion i s then given 4 on model s o p h i s t i c a t i o n . F i n a l l y , the chapter terminates with a physical description and an account of the computer model. Chapter 5 describes and gives the results of each of the experiments performed on the simulation model. Table 3 summarizes them and allows a comparison of them. F i n a l l y , in Chapter 6 the conclusions produced by this study are outlined, followed by suggestions f o r extended uses of this model. 5 CHAPTER 2 HOSPITAL ADMISSIONS SYSTEMS 2.1 Types of Admission Systems One way of improving the admission system presently in use would be to substitute a more efficient system that is already working in another country. It can be shown, however, that none of the -possible substitutes are really suitable for our hospital system. While there have been several detailed articles written on the different approaches to hospital admission systems [7], perhaps the following brief summary will be more helpful and informative. There are 3 general types of admission systems in use today: 1. the one used in North America where physicians are granted admitting privileges to one hospital ( or sometimes a small set of hospitals); 2. the one used in Europe where the hospital is staffed by hospital-board specialists to which the general practitioner refers patients; 3. The Central Admissions Bureau where all physicians refer their patients to a central office which finds the "best" hospital for the patient. In the North American system, specialists have practices in the community. These specialists acquire the privilege of admitting their patients to two or three hospitals. In general, the patient 6 retains one primary physician from i n i t i a l diagnosis to recovery and periodic check-up. This treatment continuity prevents duplication of e f f o r t and provides the patient with the comforting assurance that one physician becomes completely f a m i l i a r with his or her case. This physician i s closely associated with his hospital or hospitals. Therefore, he i s presumed to be f u l l y aware of the strengths and weak-nesses of each and w i l l try to match the patient's requirements to the resources available. The disadvantages of the North American system are clear. When the physician t r i e s to match patients with available hospitals he has only a few alternatives which allow him to preserve operational control. He i s forced to choose one i n which he has admitting privileges when in f a c t another hospital in the same area may be a better choice. In the European system, the physician in the community i s usually a general p r a c t i t i o n e r . He can refer his patients to almost any hospital in the area. A l l s p e c i a l i s t s work in the hospitals. The hospital s p e c i a l i s t s then review the acceptability of the case because they must be responsible f o r the case. The advantage of this system i s that the r e f e r r i n g physician and the patient have a great deal of choice. If either objects to any p a r t i c u l a r hospital another can be chosen. The major disadvantage i s that i t i s v i r t u a l l y impossible for each general p r a c t i t i o n e r to keep informed on which hospitals are overcrowded or low on resources and which have excess capacity. The t h i r d system, the Central Admission Bureau or CAB, i s currently used in Holland. A l l physicians refer t h e i r patients to 7 t h i s central o f f i c e which matches patient needs with resource a v a i l a -b i l i t y . The advantage over the European system i s obvious: a better u t i l i z a t i o n of resources and a s e l e c t i o n of hospital on the basis of the patient's needs. The CAB has disadvantages too. Without a good information system duplication of e f f o r t may become a problem. Neither physician nor patient has a real choice in the selection of a h o s p i t a l , more study and perhaps, extensive modifications, are necessary before i t can be incorporated into the North American structure. Moreover, because of the difference in hospital s t a f f i n g , the CAB may not bo e a s i l y incorporated in the present North American hospital structure. One probable problem i s the a l l o c a t i o n of a p a r t i c u l a r physician's patients to several hospitals. The travel time to t r e a t patients would become excessively large and uneconomical. Since neither of the alternatives i s suitable in the North American system, the productivity of our hospitals can only be increased by making improvements within the existing structure. 2.2 Statement of Objectives of Admission Systems Before attempting to f u l f i l l our goals we must understand the goals of the admission system. The admission or scheduling system f o r a hospital t r i e s to s a t i s f y many objectives. These are: 1. To optimize u t i l i z a t i o n of hospital resources, 2. To optimize u t i l i z a t i o n of a physician's available time, 8 3. To schedule the admission at a time convenient f o r the patient. The above order does not necessarily represent the hospital's ranking of p r i o r i t i e s . For instance, the patient may be admitted at the requested time', even though t h i s may reduce the margin necessary to allow for emergency patients. Before discussing each objective further, the catagories which the hospital must use to describe patient's needs should be made clear. An emergent admission i s a patient that must be given immediate entry to the h o s p i t a l . Failure to do so would r e s u l t in a serious or a disastrous outcome. An urgent admission i s a patient who must be admitted to the hospital within a few weeks. An e l e c t i v e admission i s a patient who can be scheduled f o r admittance up to si x months away. (The exact time periods vary from hospital to hospital.) 1. Optimal U t i l i z a t i o n of Hospital Resources A hospital has many resources. Some of these are: physician, nurses, non-medical s t a f f , administrative personnel, beds, sp e c i a l i z e d equipment drugs and medication. Some of these resources may be best used by not pre-arranging f u l l employment of t h e i r capacity. For example, i f 100% of the beds were u t i l i z e d , emergencies might have to be diverted to another hospital further away. Slack allows f o r emergencies, however, too much slack may cause non-emergent patients to wait excessively. A good scheduling system balances non-emergent need against necessary slack. Some slack can be gained by the substitution of bed type. For instance a female patient needing a bed in the orthopedic 9 ward may be scheduled for a bed in the gynaecology ward. This patient can be transferred to a bed in the proper ward as soon as one becomes available. A good scheduling system provides slack in sub-stitutable faci l i t ies . Another function of the scheduling system is to choose proper patient mix. The patients must be chosen so that no one resource is overworked or depleted while others are underutilized. If this were not observed, one ward might be partially occupied by a constant flow of another ward's patients even i f they both had the same number of patients needing service. Clearly, a good scheduling system can help ensure that the hospital provides proper care for the maximum number of patients. 2. To Optimize the Use of Physician's Available Time Non-resident physicians spend most of their time outside the hospital. These physicians usually have their practices in the community nearby. They monitor recovery, perform surgery, administer medication etc., to patients in hospital only on certain days. There are ways of optimizing the utilization of available time on the days that the physician visits the hospital. For example grouping his patients in one part of the hospital reduces travel time between calls. If patients can be located promptly to replace cancellations in surgery schedules, the operating physician's time will be better used, and so will the time available in the operating theatre. 10 3. To Schedule the Admission at a Time Convenient f o r the  Patient Scheduling e l e c t i v e admissions at times convenient to the patient as well as making his l i f e easier reduces the number of cancellations f o r the ho s p i t a l . This consequently reduces the number of replacements that must be found f o r cancellations as well as reducing administrative work. Furthermore, i t results in a more stable schedule. 2.3 Waiting L i s t s : The Tools of Hospital Admissions Systems Some hospitals maintain a waiting l i s t of patients. With the waiting l i s t they can predict the u t i l i z a t i o n of s p e c i f i c resources and compare i t with the previous year's. This allows proper a l l o c a t i o n of f a c i l i t i e s , purchase of supplies, and many other functions necessary to the e f f i c i e n t operation of the hospital. Another use of the waiting l i s t i s allowing the hospital to choose the proper mix of the incoming patients. This permits control of resource u t i l i z a t i o n . I f one ward i s overworked then the mix can be changed to reduce the i n f l u x of patients to that p a r t i c u l a r ward. Under-utilized f a c i l i t i e s can be allocated to more patients. The control of resource u t i l i z a t i o n permits a balanced stable schedule thus allowing an even work flow so that idleness and overtime are minimized. From the point of view of smooth hospital scheduling, there might be an optimal number of patients waiting for admission. A longer waiting l i n e then necessary would imply that i t would be easy to maintain a stable schedule. However, i f the waiting time i s too long then many patients w i l l cancel. This causes excessive and some-times unsuccessful search for replacement patients, and added work to reschedule the o r i g i n a l patients. According to the long waiting times cause many doctors to request admittance of patients to more than one hospital at a time. This allows the doctor to choose the e a r l i e s t possible admittance date but adds to the confusion of the already d i f f i c u l t task of scheduling patients. Both cancellations and multiple admittance requests cause un-stable schedules. The hospital l i k e any other organization increases i n e f f i c i e n c y as the schedule increases in s t a b i l i t y . Yet a longer waiting l i n e , as we have seen, w i l l not guarantee t h i s s t a b i l i t y . In t h i s study, we w i l l simulate several changes in procedure which might be expected to reduce the length of the waiting l i s t , tending toward a more stable schedule. For f u l l understanding, we w i l l need to consider the composition of the waiting l i s t . The waiting l i s t for a hospital i s comprised of several waiting l i s t s , one f o r each physician. The diagram shows the way the schedul-ing s t a f f views i t . Each physician may have any number of patients waiting f o r service at any one time. There are several reasons why the number varies from physician to physician. If a physician i s to take an extended vacation or leave to study a specialty he may reduce his waiting l i s t to zero. He does this by transferring his patients to another physician and re f r a i n i n g from accepting any new patients. Some p r a c t i t i o n e r s , by the very nature of t h e i r s p e c i a l t i e s , treat E l e c t i v e E l e c t i v e • • • • * E l e c t i v e Elective E l e c t i v e E l e c t i v e E l e c t i v e Elective E l e c t i v e Urgent E l e c t i v e Elective E l e c t i v e Urgent E l e c t i v e Urgent Urgent Physician Number 1 2 3 j Number of Requests n, n. n_ n. 1 2 3 j FIGURE 1. Schematic view of Admission Waiting L i s t most patients in t h e i r o f f i c e and admit only a small percentage of t h e i r patients. Others have admitting privileges to more than one hospital and evenly d i s t r i b u t e t h e i r patients to a l l hospitals. A portion of some physicians' waiting l i s t s i s composed of urgent patients. The s t a f f continuously and a c t i v e l y seek to schedule these patients f o r service f i r s t . In both p r i o r i t i e s , urgent and e l e c t i v e , the ordering d i s c i p l i n e observed i s f i r s t - i n f i r s t - o u t or FIFO. The composition of the waiting l i s t must also be considered in terms of what services each patient w i l l require, such as operating theatre time and a p a r t i c u l a r ward that w i l l need to care for him. This partly depends on the physician who admits him, but not e n t i r e l y . Most physicians have a s p e c i a l t y and hence admit t h e i r patients primarily to one ward. For example a gynaecologist admits his patient to a gynaecology ward. However, he may also admit to other wards. If a gynaecologist's patient requires orthopedic care he w i l l admit her to an orthopedic ward because i t has the special support structures necessary for orthopedic care. Therefore, in general one cannot sum the number of patients waiting for orthopedic physicians to f i n d the t o t a l number of patients on the orthopedic waiting l i s t . Patients of non-orthopedic physicians usually occupy a substantial part of the waiting l i s t . Since the ward i s the basic unit of the h o s p i t a l , scheduling of admissions must take account of the ward's resources; so the waiting l i s t i s also broken down into a series of ward waiting l i s t s . 14 2.4 Daily Problems of Hospital Admissions Systems Hospital admission systems are employed for many tasks. Some of these tasks require forecasting and judgment and others require technical knowledge. The major tasks are: 1. maintaining bed occupancy, 2. scheduling operating theatres, 3. approximating how many patients to notify for entry, and 4. rapid replacement of patients who have cancelled. Only a certain percentage of the beds are to be kept continuously occupied because some must be reserved for emergencies. There are many reasons why this is not a simple task. The percentage reserved for emergencies may change from season to season. For instance, in autumn and winter more athletes may become orthopedic emergencies than in summer. Each ward may have a different patient mix for each season. Another complication is that hospitals recognize three p r i o r i t i e s : elective, urgent and emergent. The admitting staff must decide how many urgents and electives are to be admitted so as to leave enough beds for emergents, and, which of these can be allocated operating theatre time. These lead to the next problem. They must also decide how to allocate operating theatre time. Every ward in the hospital is competing for operating theatre time. Not a l l operating theatres have the same equipment. Some surgeries can only be done in certain operating rooms. Which surgeries are really urgent? Which can be delayed? These are questions that can 15 only be answered by judgment and experience. Moreover, they need to estimate how many cancellations and postponements are going to take place next week and how many patients are to be n o t i f i e d t h i s week for tentative entry next week. These are j u s t some of the problems that the hospital admission s t a f f must face every day. 2.5 General Flow of Patients There are three p r i o r i t i e s of patients in the hospital we investigated: emergent, urgent, and el e c t i v e . (This i s standard f o r most hospitals.) Emergent has the highest p r i o r i t y and e l e c t i v e the lowest. The urgent p r i o r i t y i s further c l a s s i f i e d as urgent medical and urgent s u r g i c a l . A closer look at each c l a s s i f i c a t i o n w i l l give a general idea of the flow of patients. The Emergent Patient The emergent patient requires immediate care. Without immediate care, death or permanent damage w i l l occur. Since emergencies can occur a t any time and cannot be scheduled, special f a c i l i t i e s are reserved for emergent patients. The Urgent Medical Patient The urgent medical patient i s defined as an urgent patient who does not require surgery. An example i s a patient experiencing back problems where movement produces pain. These patients are usually put in t r a c t i o n or special fixtures to rel i e v e pain and allow the problem area to recover, without possible further injury. The urgent 16 medical patient only competes for beds. They are given p r i o r i t y over electives and usually gain admittance in a week of l e s s . The flow of a typical urgent medical patient i s s i m i l a r to the following. The request form from the physician i s put on a special waiting l i s t separate from the elective waiting l i s t . Each day the bed scheduler checks the appropriate ward for an unoccupied bed. When a bed i s found the urgent medical patient's physician i s n o t i f i e d . The patient i s generally admitted that day or the following day. The Urgent Surgical Patient In general, a hospital has one of two constraints: operating theatre time or beds. If there i s no available bed or operating theatre time, the patient must wait. Whichever the s t a f f allocates to the patient f i r s t i s the constraint at that time. The request i s put on a l i s t on a f i r s t - i n f i r s t - o u t basis. The urgent surgical patient's request waits on a l i s t u n t i l both a bed and operating theatre time are available. The physician i s n o t i f i e d and the patient enters on the day p r i o r to operating day. The Electi v e Patient The e l e c t i v e patient has the lowest p r i o r i t y . His waiting time is anywhere from a few days to a few months with the average about four to f i v e weeks. The e l e c t i v e patient i s put on a waiting l i s t according to a first-come, f i r s t - s e r v e basis, unless a s p e c i f i c date that can be met by the hospital i s requested. As in the case of the urgent patient the e l e c t i v e must also wait for both beds and operating theatre time. 17 Surgery cases are usually admitted the previous day to control d i e t , amount of medicine, and also to perform simple standardized t e s t s . Usually the e l e c t i v e non-surgical patient i s given the lowest p r i o r i t y . Unless the physician personally recommends the patient's admittance, the wait i s so long that the patient usually forgoes admittance v o l u n t a r i l y . I f an elective non-surgical patient requires entry the name i s placed on the active l i s t along with the other e l e c t i v e requests that were received at the same time. When a l l urgents and surgical cases have been scheduled, a bed i s sought f o r the e l e c t i v e non-surgical. It i s easier to schedule a non-surgical as operating theatre scheduling i s not involved. However, as previously mentioned unless the physician i n s i s t s or the hospital has a surplus of f a c i l i t i e s the patient has a long wait. 18 C H A P T E R 3 tOCRIPTrCfN OF HOSPITAL USED IN SIMULATION 3.1 General The study i s mainly concentrated on one urban acute care hospital. Several hundred physicians admit to this hospital regularly. The physicians are supported by a large s t a f f . The hospital i s located in a r e s i d e n t i a l d i s t r i c t that i s mainly inhabited by professional people. Consequently the patients tend to be well educated and f i n a n c i a l l y stable. 3.2 Hospital Admission Flow Two types of admission are omitted from the discussion of this acute care h o s p i t a l : the emergent admission and the e l e c t i v e medical admission. Emergent patients are not considered as part of the normal admissions procedures. Emergencies are admitted and serviced on a r r i v a l . Special f a c i l i t i e s are set up to handle these cases, so there is no waiting or scheduling necessary. Many el e c t i v e medical patients do not require h o s p i t a l i z a t i o n . Obesity, f o r example, i s one type of elect i v e medical admission. These patients wish to use the hospital to help them maintain a diet. Most hospitals providing acute care have many patients with more pressing medical/surgical problems. 19 Thus, there remain three types of admissions to be discussed: 1. the urgent medical patient, 2. the urgent surgical patient, and 3. the e l e c t i v e surgical patient. A l l of these patients discuss with t h e i r doctors the necessity for h o s p i t a l i z a t i o n . The patient and the physician or his assistant decide on a tentative entry period. Some of the requests are submitted to the hospital by the physician during his regular v i s i t s , and some are mailed. A f t e r receiving the requests, the hospital sends out pre-r e g i s t r a t i on forms to e l e c t i v e surgical patients (and to maternity patients). Urgent patients are usually admitted before any forms could reach them so they are not sent forms. From here the procedure d i f f e r s for each type of admission u n t i l the patient enters the hospital. 1. The Urgent Medical Admission Urgent requests are usually submitted personally by the physician. Occasionally, they are sent to the hospital. The bed scheduler puts the urgent requests on an active l i s t . She then looks for a free bed in the requested ward any time in the next seven days. I f no beds are available she scans for a bed in a l l wards compatible with the patient ( i . e . female to female ward beds, children to pediatric beds, e t c . ) . If s t i l l no beds are found she t r i e s to f i n d a patient who i s soon to be discharged. She explains the s i t u a t i o n to the attending physician. Usually a patient i s discharged a l i t t l e e a r l i e r than average freeing a bed for the urgent patient. ( A l l of the early discharges are already in the recovery and observation stage. With a l i t t l e help they can 20 recover j u s t as quickly at home.) The morning a bed i s found, the patient i s c a l l e d and asked to enter that afternoon. 2. The Urgent Surgical Admission This type of request i s again usually carried to the hospital by the physician. This request is given to a surgery s l a t e r . She puts urgent requests in an active f i l e . A l l active requests must be scheduled f o r surgery within seven days. Because of the following reasons she i s very successful in finding s l o t s on the surgery s l a t e fo r urgent surgeries: 1. Urgent surgeries are given a higher p r i o r i t y than e l e c t i v e s ; 2. Emergent surgeries do not compete for the same f a c i l i t i e s as urgent or e l e c t i v e surgeries, since part of the operating theatre and of the ward are always kept for them. At t h i s h o s p i t a l , a certain portion of the upcoming surgery schedule i s reserved f o r urgent patients. This allows the physician to book an urgent patient as late as the day before surgery. If the physician's regular surgery day is close and the a l l o t t e d time already f i l l e d , the following sequence i s observed. The physician i s informed and asked i f the urgent patient can be postponed u n t i l his next operating day. If the urgent patient cannot be delayed, freetime on the physician's non-operating days is examined next. Usually the physician accepts this time. Under the occasional circumstance i n which the physician cannot accept the time, then an e l e c t i v e surgical patient on the schedule must be replaced by the urgent patient. 21 Usually the day before surgery (sometimes the morning of the surgery day), the bed scheduler i s requested to f i n d a bed for the patient. If a search of a l l appropriate wards does not y i e l d a free bed, an e l e c t i v e patient scheduled to enter that afternoon is removed from the schedule. However, very few patients have needed re-scheduling at ttie time of thi s study. The patient i s asked to enter as soon as a bed and a s l o t on the surgery sl a t e are found. In summary, the request of a typical urgent patient begins on a waiting l i s t - Every day the surgery scheduler attempts to schedule t h i s patient a t a time compatible to both physician and operating theatre f a c i l i t i e s . As soon as a time has been scheduled, the physician i s n o t i f i e d . In most cases the urgent surgical patient i s admitted the day p r i o r to surgery for diet and medication control as well as standardized t e s t s . After the operation, his recovery i s closely monitored u n t i l he i s recovering at a sat i s f a c t o r y rate. His release i s based on the physician's observations and judgment. 3 . The E l e c t i v e Surgical Admission In t h i s h o s p i t a l , surgery schedules or slates are prepared 10 days i n advance. There are additions and changes in the f i r s t seven or eight days but very few i n the l a s t two or three. When t h i s hospital receives the el e c t i v e surgical request, i t is sent to the surgery scheduler. She puts the request i n an inactive f i l e . Some patients prefer scheduling surgery far in the future so they can prepare f o r i t . Some patients must write for r e l a t i v e s to babysit; others have legal obligations. The hospital s t a f f under-22 stand these situations and try very hard to accommodate these patients. Because these patients plan f a r in advance and request a date f a r in the future the surgery scheduler i s very successful in scheduling these type of patients. The inpatients, urgent patients, and the patients who have planned in advance have been allocated operating theatre time. Next the scheduler f i l l s out the rest of the surgery sla t e with patients taken from the inactive f i l e . About seven days p r i o r to admission day for e l e c t i v e patients, the hospital n o t i f i e s the doctor, who contacts the patient with the tentative admission date. For urgent patients this period i s anywhere from one to seven days. Because the physician often needs to control the d i e t and type and amount of medication necessary p r i o r to surgery, the admission day i s usually the day before surgery. In occasional cases two or more days are required. But these cases are quite infrequent. When the patient's name comes up, his case i s placed on the surgery schedule i f s u f f i c i e n t time i s available. After he i s given operating theatre time and i s j u s t about to enter, a bed i s found f o r him. He then waits a day i n the hospital prior to his surgery. During the morning of admission, the bed scheduler checks for beds to accommodate the afternoon admissions. She checks f o r beds in the requested wards f i r s t . I f she needs more she checks other wards for free beds. Very r a r e l y does she have to reschedule a patient because a bed cannot be found. But, i f necessary, the rescheduled patient i s put on a l i s t which guarantees admittance within a week. 23 When a bed i s found,-the patient is phoned for admission that afternoon. 3.3 Hospital Data Bank This hospital participates i n the Professional A c t i v i t y Study (PAS). PAS i s part of a computerized medical record information system. P e r i o d i c a l l y each participating hospital sends an abstract of each discharged patient's record to Ann Arbor, Michigan. The information i s assimilated, rearranged, and stored i n a computer data f i l e . A concise printout of summary and comparative information i s returned to the hospital. The data i s used for p l o t t i n g national as well as regional trends and other studies. Part of the data used in this study i s from the PAS printout. The information gathered from the printout f o r each patient consisted of: patient hospital i d e n t i f i c a t i o n number, diagnosis number, surgery number, physician number, age, sex, length of stay, admission date and the Provincial Hospital Insurance Number. The patient I.D. number and the insurance number were used to trace further information such as the request date, the p r i o r i t y , operation length, and the room number which were on other forms. When the data was gathered the l a t e s t complete year of PAS tabulations was 1971. Our model w i l l simulate the operation for the hospital i n 1971. 24 CHAPTER 4 THE SIMULATION MODEL 4.1 Choosing A Hospital Subsystem This study is an attempt to improve an existing complex system, the hospital, by improving the hospital patient admission system, a particular subsystem. This subsystem was chosen because i t had been identified by a former researcher as a possible area that could produce benefits [7]. Having chosen a problem area, we now choose a method of study. Here again, there are many methods available. Some of the more popular techniques have been: linear programming, statistics, simulation, scheduling, inventory models, dynamic programming and many others. In each of these methods, assumptions are made, a model is built , and then data is gathered. Under certain conditions the complex system will to some degree behave as the model behaves. It is therefore essential to validate the model chosen. The method chosen in this paper is simulation. Simulation, an established technique, requires few assumptions than most of the other methods and can model situations that are too complex for other methods. There are many kinds of simulations in which some system is approximated by another more convenient system. 25 Simulation involves several steps: study d i r e c t i o n , system analysis, d e f i n i t i o n of objectives, model building, and experimentation. In this study, we have the following: Study Direction: System Analysis: D e f i n i t i o n of Objectives: Model Building: Experimentation: The hospital i s the system under consideration. The patient admission system w i l l be the sub-system singled out for detailed study. The admission system w i l l be studied in de t a i l to learn and understand constraints as well as to develop an i n t u i t i v e feel f o r problem areas. To improve the delivery of medical service to patients as well as improve u t i l i z a t i o n of resources. Developing a simulation model, gather data, building and debugging a computer model, and To change the model to show d i f f e r e n t schedules such as FIFO, shortest service f i r s t , and others. 4.2 Choosing An Admission Subsystem (The Ward) Before studying the hospital in d e t a i l , the admissions super-visor was consulted to explore possible areas f o r examination. Since i t was not possible to study the whole h o s p i t a l , some part or section had to be chosen. So that the model would be applicable to the hospital as a whole, the section must be typical in that i t had to be 26 in use a l l year round, and have a l l the normal hospital a c t i v i t i e s such as operating theatre and bed requirements associated with i t . After some discussion i t was decided to use a ward as the unit of study. Each ward has i t s own limitations and is a r e l a t i v e l y indepen-dent unit. The ward i s a fundamental subsystem of a hospital and whatever our study produced f o r one ward could probably be adapted by most other wards. The orthopedic ward was f i n a l l y selected. The main reasons were: 1. The ward has a l l p r i o r i t i e s of patients: emergents, urgents, and e l e c t i v e s ; 2. The ward has few major physicians, thereby reducing date c o l l e c t i o n problems; 3. The ward i s open a l l year and has a steady i n f l u x of patients; 4. The ward has less than 40 beds so i t i s of a manageable size for the computer storage available to us. 4.3 Date Gathering A preliminary model indicates in general how the constraints are to be modelled, what output to expect and therefore, what data i s needed. Some practitioners construct a total model before data c o l l e c t i o n . However this could be somewhat risky u n t i l the data has been c a r e f u l l y examined. In addition, this may r e s u l t in unforeseen problems. For example, even though data of the type necessary for the model i s 27 a v a i l a b l e , i t may not be available for the time span necessary f o r modelling. Another example i s the problem associated with handwritten data. Handwritten data i s harder to code and sometimes i l l e g i b l e . Handwritten data also tends to be omitted or abbreviated. For our applications, a preliminary model i s constructed before data c o l l e c t i o n and the actual mechanics or model structure i s f i n a l i z e d only a f t e r the data i s f u l l y examined. Factors such as the introduction of data into the workings of the model need not be considered at the preliminary stage. The important points to consider at this point are: what kind of data i s needed and whether i t i s available f o r the time span under consideration. 4.4 The Simulation Language Used The language used for this model was the General Purpose Simulation System (GPSS) developed by IBM. GPSS was chosen for three basic reasons: 1. It i s easy to learn and easy to use because of extensive syntax error messages. 2. The programmer was familiar with the language. 3. There i s a natural one-to-one relationship of transaction to patient, and the most patients move through the hospital in a sequential pattern which can e a s i l y be simulated by this language. GPSS is a good language to use for discrete simulations i f the following conditions are approximately s a t i s f i e d : 28 1. I-P' there: ar& discrete: objects: e i t h e r distinguishable cm" rrcct dtstti"ngirisiiab:Te, from each other.. 2. I f there are no more than 1,000 objects in the model at cfliy time'. 3. I f there are no more than 100. to 200 a c t i v i t i e s concurrent apt any one: i n s t a n t of simulated time. 4. If there are about 200 to 300 elementary subsystems in the parent system. These: are only very approximate guidelines which are gained more from e^p^rte^'ce than" from" computer manuals.. They are: more useful as orders of magnitude than as absolute numbers because unused e n t i t i e s can be reallocated to create needed e n t i t i e s . I f a model in the i n i t i a l stages exceeds some of the rough guide-Tines by an order of magnitude several steps can be taken: (a) A smaller part of the parent system can be chosen to simulate, f&j The o r i g i n a l parent subsystem can be partitioned—each smaller system can be modelled. The smaller models could be designed so that the output of one could be saved and fed as input into the next model, (c) 1. Each transaction can be made to count as a group of objects with common properties, (e.g. by assigning varying integral s i z e numbers to a parameter) 2. Inch transaction can be made to count as a group of a varying number of objects with d i f f e r e n t properties, (e.g. by looping the transaction checking parameters for d i f f e r e n t characteristics) 29 (d) Part of the system can be simulated i n another language f o r example FORTRAN and use the HELP block. (e) The model can be redesigned so the same set of blocks can be used with i n d i r e c t addressing. (f) The use of Simscript, GASP, SPURT, PL/1 or other languages can be considered. In GPSS the discrete unit used to simulate real world e n t i t i e s i s c a l l e d a transaction. The transaction, or XACT for short, is an a r t i f i c i a l contrivance used to simulate cars, ships, logs, patients, and many other discrete real world objects. In the model a XACT can be v i s u a l i z e d as a moving card with several uniform squares or parameters for retaining information carried by that XACT. In this p a r t i c u l a r program, transactions are used to mimic patients, patients' charts or request forms, surgery schedulers, and timing devices.(see Figure 2) A part of the hospital admission system w i l l now be described, as well as the corresponding model l o g i c , to point out one of the s a l i e n t features of the transaction. In the hospital the physician submits a patient's chart as a request for surgery. The chart is kept on f i l e u n t i l the surgery scheduler considers scheduling that patient for the operating theatre. When that patient i s scheduled his physician i s n o t i f i e d and in general the patient enters on the appropriate day. In the program we must use the a r t i f i c i a l i t y of the transaction ( i t s capacity to represent several d i f f e r e n t objects) to maintain a l o g i c a l meaning corresponding to real l i f e . In the program, transactions Transaction Number Necessary Language Information P P P • • * P P * • P P P P P Parameters F I G U R E 2. TRANSACTION SCHEMATIC 31 are created at approximately the same rate as the patients' requests entered the hospital. The attributes of the patients are deposited in the parameters. These transactions wait in l i n e to be scheduled for surgery. Up to this point, to sustain agreement with the hospital's admission system, the transactions must be considered as patient's charts. The transaction must continue to be interpreted as a patient's chart u n t i l the transaction takes a bed in the hospital model. At that time the same transaction must be considered a patient and the previous interpretation must be discarded. So the same transaction must be considered as two d i f f e r e n t objects at d i f f e r e n t points in the program. The a r t i f i c i a l i t y of the transaction introduces f l e x i b i l i t y into the modelling process which simplifies the task of simulating complex systems. 4.5 General Model Structure In the model, patients are created at the same rate as requests entered the h o s p i t a l . This i s done on a stochastic basis according to the d i s t r i b u t i o n encountered in the 1971 season under consideration. In the following discussion the patient and the patient's request f o r surgery can be considered the same. The patients' requests are introduced into the model randomly from a uniform d i s t r i b u t i o n of 100 ± 95 min (because in real l i f e , patients can check i n within a 3 hr period each day). As the patient enters the model, attributes such as p r i o r i t y , disease number, length of stay and physician number are assigned to the patient. About 3.4% 32 do not require any surgery and these patients are channeled to a special program section for urgent medical patients. Every day free beds are sought f o r the urgent medical patients. In any case, at the end of a week i f no free beds are found the urgent medical patient i s brought in and placed in a special set of beds. In this hospital beds have not been a constraint, such patients are always admitted within seven days. The patients are now divided into emergent urgent and e l e c t i v e categories, with each category going to a d i f f e r e n t segment of the program. The emergent surgeries were sent in immediately to check for beds. The model d i f f e r s from real l i f e here. The patients in the model check f o r free beds but in real l i f e the admissions clerk would be searching for a bed. If no bed is found, the emergent is brought in and placed in a special bed c a l l e d SPES where he remains u n t i l his departure. The patient then queues for a physician at high p r i o r i t y (i.e.,only i f a physician i s actually operating w i l l he have to wait and then he i s put to the front of the l i n e ) . When the emergency patient's length of stay has elapsed he leaves the ho s p i t a l . The urgent surgical patient goes to a special waiting l i s t . This waiting l i s t i s determined by the next operating day designated fo r the orthopedic ward. In real l i f e a certain part of the s l a t e i s saved for urgents. The scheduler t r i e s to save about 10 to 12% of the time for urgent surgeries. In the model about 11% of the weekly allocated time i s saved for urgent surgeries that occur. 33 The urgent surgeries t r y to f i t on the schedule: i f they f i t , they go on; i f not, they return to a waiting l i n e to be checked l a t e r on. In real l i f e i f the surgery scheduler can squeeze the urgent in he i s allowed in the schedule. The physician must then perform the operation at the scheduled time whether i t i s his surgery day or not. In the model a s i m i l a r s i t u a t i o n occurs. In general, at thi s h o s p i t a l , beds are not a constraint. The main constraint i s operating theatre time. This forces admission procedures f o r e l e c t i v e surgeries into one set pattern. The f i r s t step i s to f i n d and all o c a t e the patient operating theatre time. Only af t e r operating theatre time i s allocated can a bed be sought for a patient. In r e a l i t y the bed scheduler does not look f o r a bed u n t i l the morning of the entry of the patient. In the model, ten days prior to surgery day the patients check to see i f they can f i t on the surgery schedule. If they can they wait u n t i l the day before surgery day when they check f o r a bed. If a bed i s found they enter on the following day; i f not they return to the front of the waiting l i n e . This almost guarantees them entry. At t h i s hospital every physician has a part i c u l a r day on which he performs almost a l l of his e l e c t i v e surgeries. If an electiv e patient cannot be scheduled or has to be rescheduled the request is usually delayed f o r a maximum of one week. So in the rare case a bed cannot be found the s t a f f t r i e s to admit the patient one week l a t e r or i f possible even sooner. However admitting that e l e c t i v e patient sooner than one week requires much extra e f f o r t . The s t a f f must find 34 operating." room time: for the. physician on a day that is not one of his n!orma3': operating.- days?.. The; physician must perform an elective surgery oh'that" day. The: physician or an assistant surgeon must be found for that^elective: patient; This: could: be and is justified for urgent patients. Occasionally this is done for elective patients but the frequency does notjustify the extra programming effort needed for this small amount. After the: elective: enters: the hospital he waits for surgery day when he goes to: surgery and then waits for his length of stay to eTap"se~. After" this" he leaves the hospital. 4v6~ fetaiTetf Model Structure The simulation program is divided into seven segments: 1. The clock segment, 2. The patient generator, The attribute assignment segment, 4. The urgent surgical patient segment, 5. The urgent medical patient segment, 6. The emergent patient segment, 7. The" elective patient segment. The patient flow follows the direction indicated in the block diagram of Figure 3. Patients are created, assigned identifying characteristics, and then sent to the appropriate segment. Each segment is a module which functions independent of any other segment. 35 Patient Generator A t t r i Assic .bute jniaent / What \ / Kind o>f\ Patient i s \ This / The Urgent S u r g i c a l Segment The Urgent Medical Segment The Emergent Patient Segment The E l e c t i v e Patient Segment FIGURE 3. BLOCK DIAGRAM OF SIMULATION PROGRAM 35 The Clock Segment T M s segment controls the timing: o f the flow erf patients: in the model and simulates- the timing function of the surgery scheduler. A t t h i s hospital,• each orthopedic physician has an operating day on which he performs most of his e l e c t i v e and urgent surgeries. Because t h i s i s done on rather a regular basis, an event clock i n the program simulates the act of the surgery scheduler beginning to f i l l the surgery s l a t e f o r each physician. Pet Ore r i g h t time the event clock creates a transaction which retlloves -a patient's chart from the correct physician's waiting l i s t and sends i t to the e l e c t i v e scheduling segment. From there the e l e c t i v e scheduling segment takes control and completes the process. In r e a l l i f e , cases for the operating theatre are chosen every week from each physician's waiting l i s t . They are chosen according to c r i t e r i a explained in section 3.2. This i s a dynamic competing process with i t s outcome dependent on many variables. To approximate thi s process of successfully competing for operating theatre time, a varying amount of time i s allocated to each physician i n every simulated week. The amount of time i s s t o c h a s t i c a l l y chosen from a range o f times that the physician had successfully competed f o r in Some week of 1971. In the h o s p i t a l , the s t a f f r e a l i z e that urgent surgery requests can be submitted by any physician at any time. In an e f f o r t to simpl i f y scheduling urgent patients, approximately 10 to 12% of the t o t a l operating theatre i s reserved for urgent surgery. In the program the allocated time i s divided into two parts: the e l e c t i v e a l l o c a t i o n of 89% and the urgent a l l o c a t i o n of 11%. Every Wee]; 10 Days P r i o r t o Operating Day Create a Surgery Scheduling Xact Remove a Case (FIFO) from L i s t of C o r r e c t P h y s i c i a n , Route Case t o Scheduling Segment Return Next Week FIGURE 4. A TYPICAL CLOCK Each Week Create 4 Scheduling Transactions Zero out Last Weeks al l o c a t i o n of Surgery Time Stochastically Get a Maximum Amount of Time For Each Physician Allocate the Correct Amount of Time to Urgent and Elective Schedules Leave and Return Next Week FIGURE 5. THE WEEKLY SURGERY TIME ALLOTMENT CLOCK 39 Each Day Create 1 Transaction to Make Patients S t o c h a s t i c a l l y Create a Number of Patients and Send Them to Attri b u t e Assignment Segment Leave and Return Next Day FIGURE 6. THE PATIENT GENERATOR 40 The Patient Generator This i s a very simple segment. Each day anywhere from 0 to 9 patient requests were received for t h i s ward. A d i s t r i b u t i o n function was made from the 1971 data. As was previously mentioned, the transaction created should be considered a patient's chart or a 1 request for surgery rather than a patient. The Attribute Assignment Segment In order to simulate d i f f e r e n t patients each transaction i s assigned to carry d i f f e r e n t attributes or c h a r a c t e r i s t i c s . P r i o r i t y , operation number, length of stay, physician number and operation time are some of the main attributes of the patients. Some of the attributes require further examination. Certain r e s t r i c t i o n s and relationships are not immediately obvious in the program. P r i o r i t y would be assigned f i r s t . Depending on the p r i o r i t y the operation number would come from the emergent group or the non-emergent group. The operation number i s simply a way of i d e n t i f y i n g the kind of operation. For instance, bunionectomies would have one number, bone fusions another. After the operation number is assigned, i t i s used to determine an operation time or length of surgery time, and a length of stay. In an attempt to l o g i c a l l y aggregate the data, we chose the c l a s s i f i c a t i o n s put out by the Commission on Professional and Hospital A c t i v i t i e s (CPHA). [3] One or more surgery numbers are combined or used to form an operation number. For example, the three surgery 41 numbers for amputation of hand, forearm, or upper arm, are all refer-enced by one CPHA number. In the program, operation numbers are assigned to patient transactions. Each operation requires a distribution of times because each number refers to usually more than one surgery number. In fact there is even a distribution of times when the same -physician performs the same operation on different patients. This is because surgery times occasionally depend on age, which stage disease has progressed to and other factors. -Each operation number also requires a distribution for lengths of stay in a hospital for the same reasons. That is why we have used operation number to stochastically choose both length of stay and surgery time. Each physician works at a different pace. However from the data gathered we could not obtain distributions of different surgery times for each physician. Therefore we were forced to be satisfied with one all-inclusive set of surgery times for each operation number. Since the average length of time per surgery for each physician was different from the overall average, a correction factor or normalizing factor was used. The overall average time per surgery is 0.926 hr or about 55.6 min. It's clear that certain operations are emergent and not elective and vice versa. This is one of the main reasons why priority, that is emergent, urgent, and elective was used as the f i rst step in the delivery of attributes to the "patient transaction." As there was not sufficient data to have urgent operation numbers in its own init ial category, all urgent operation numbers were combined PHYSICIAN AVERAGE TIME CALCULATION CORRECTION NUMBER PER SURGERY FACTOR 0.8411 hours 0.8411 0.91 0.926 1.201 hours 1.201 1.3 0.926 0.9016 hours 0.9016 0.97 0.926 0.8012 hours 0.8012 0.87 0.926 TABLE 1. SURGERY PHYSICIAN TIME FACTOR 43 wfith" the e l e c t i v e operation numbers to form one category of operation mimtxers. The Urgent Medical Patient After the assignment segment the urgent patients that don't require surgery are sent to th i s segment to wait. Every day the bed scheduler checks f o r free beds. If none are found they are returned to the urgent medical waiting l i s t . The number of free beds determine the number of patients admitted to hospital. The charts are checked every day. If some patient has already wafted one week he i s brought in regardless. In real l i f e no urgent medical patient i s kept waiting for more than seven days. In the program i f a l l the beds are f u l l for more than seven days, the urgent patient i s entered into a special group of beds c a l l e d DEF because he d e f i n i t e l y must be brought i n . The Emergency Segment \ In the program, as well as real l i f e , the emergency patient i s never refused entry into this hospital. He waits f o r his physician at a very high p r i o r i t y . Only i f the physician i s operating at that time w i l l the patient wait. If a l l beds are f u l l a special set of beds c a l l e d SPES i s used to accommodate the emergency patient. The Urgent Surgical The urgent surgical patient's request i s put on an active l i s t . The patient is scheduled on the closest following orthopedic operating day. MED 1 Urgent Medical Patients Wait Here Every Day Creats A 'nurse' xact to check for Beds TEST SEGMENT Update patient chart as to How Long He Has Waited Yes Return to Medi. Cause No Beds Yet. ? . Enter Hospital Bed Def Wait U n t i l Length of Stay Elapses Leave Hosp. Bed Are Iny Beds JSmpty Yes F i l l Empty Beds With Urgent Medical Patients Are There iny Urgent JMed. Pat, Lef t i n .Line No Bring Their Charts To Test Seament Leave Return Tomorrow FIGURE 7. URGENT MEDICAL PATIENT FLOW IN MODEL 45 A certain portion of the surgery schedule i s reserved f o r urgents. These urgents are not necessarily unique to the physician operating that day. We believe that the rational behind this non-uniqueness i s that a l l physicians w i l l have urgents on an o f f operat-ing day and i t i s better to schedule a l l operations of one ward at the same time rather than f i t t i n g them in on a random basis. As was previously mentioned the amount reserved i s approximately 10 to 12%. The clock segment i s used to update the closest following Orthopedic surgery day. Urgent patients requiring surgery are removed from the active l i s t to see i f they f i t on the schedule. If there i s enough room on the surgery schedule they are scheduled. If not, they wait on the active l i s t u n t i l the next Orthopedic schedule. However, in real l i f e they are brought in within a week regardless of hospital bed u t i l i z a t i o n . The program also guarantees t h i s . This i s checked da i l y to guarantee a l l urgents a quick entry. On the day p r i o r to surgery day the urgent patients are admitted prior to the el e c t i v e patients to check for beds. Even i f a l l the beds are f u l l the urgents are guaranteed entry. In real l i f e , beds are not a constraint for urgent patients and emergent patients. In the program this rule is observed also. A special set of beds designated MUST insure the urgent surgical patient entry to the hospital once he i s on the surgery schedule. Enter Bed I Give This Patient High P r i o r i t y Wait Only i f Surgeon i s Operating + Go for Surgery Return Recuperate From Surgery Leave the System FIGURE 8. EMERGENT PATIENT FLOW IN MODEL A c t i v e L i s t Urgent S u r g i c a l P a t i e n t s Wait Here Surgery Sched. P a t i e n t s wait Here When Scheduled Are any Beds fre< E n t e r Bed I Wait One Day f o r D i e t and Me d i c a t i o n C o n t r o l Wait f o r A P h y s i c i a n Behind Emergents B But i n F r o n t o f E l e c t i v e s v Have the Righ t P h y s i c i a n p er-form Surgery Remain U n t i l Length of Stay E l a p s e s Leave the System Go To Surgery Schedule No Return t o A c t i v e L i s t FIGURE 9 . URGENT SURGICAL PATIENT FLOW IN MODEL 48 The Elective Segment The surgery scheduler estimated that 5% of the electives cancel and do not re-apply for admission. Two causes for cancellations are the admittance of the patient to another hospital, and patient fear of surgery. Cancellations are not recorded because only admitted patients are tabulated and processed. However, i t is clear that can-cellations affect the admission procedure so the program creates 5% more electives than recorded in PAS tabulations. After scheduling the patients, 5% are removed from the system prior to entry into hospital. In the program, physician 5 is a conglomeration of sixty or more non-orthopedic physicians. About 85% of these physicians admit on an average one or less patients a month. This makes i t impractical to gather scheduling data or simulate these physicians individually. Hence all non-orthopedic physicians were aggregated into one group. As was previously stated, these sixty or more physicians are not orthopedic physicians but do admit patients to the orthopedic ward when their patients require orthopedic care. In the program, the patients of physician 5 are immediately separated from the rest. Each patient waits a length of time randomly chosen from an empirical waiting time function. This function is the 1971 distribution of waiting times for orthopedic patients with non-orthopedic physicians. The waiting time approximates the wait due to scheduling for these patients. If after the wait an empty bed cannot be found, he is sent back to wait again to simulate reschedul-ing. Again 5% of these patients cancel out prior to entering the hospital. 49 5% of the E l e c -t i v e s Create Patients Because 5% Cancel Is 'This Patient's" Physician an irthopod Y e s J o i n The Correct Physician's Waiting L i s t No Is There Less fThan 30 Min. of Unsched. >f Time Remove Another Pat. Req. from L i s t Wait According to Waiting D i s t . of Non-Ortho. Phy. Patients Remove Another Pat.'s Req. from Waiting L i s t Wait For 9 Days Go To Hospital to Check For Beds Go to Hosp. to Check f o r Beds FIGURE 10 . ELECTIVE PATIENT FLOW IN MODEL As discussed in the description of the clock segment, every week an event clock stochastically allocates a certain amount of operating theatre time to each physician. Ten days prior to each physician's surgery day a clock removes a patient from the waiting l i s t and sends him to the scheduling segment to see i f his case can be completed in the allotted time. If his case can f i t in the allotted time he is scheduled. If his case can't f i t and thirty minutes or ;more remains unscheduled another patient is checked to see i f he fits on the schedule. Enough patients are scheduled to leave less than thirty minutes of time unscheduled. The patient then waits ten days. After this i f an empty bed can be found for him he enters. If a bed cannot be found he is returned to the front of the waiting line. 4.7 Model SoDhistication When model building has been completed there are s t i l l some procedures and occurrences in the parent system that have not been modelled. The standard approach is to omit these from the model and compensate for this in the output analysis. There are four reasons why these procedures and occurrences are not modelled. 1. The data is too difficult or is impossible to gather, 2. The analyst has not performed a thorough enough data analysis to investigate whether the feature could be included, 3. Modelling the feature would not change the output or add any constraint to the model, 51 4. The objectives of the study are unchanged by the modelling of this feature. We w i l l define the amount of sophistication of a model as the number of subtle or complex procedures in real l i f e that can be incor-, porated in the model. The sophistication of this model was limited by the data that could be obtained. One problem area that the s t a f f would l i k e to study i s the number or d i s t r i b u t i o n of people that stay a f t e r they have been signed out. The common reasons given are: 1. The patient's spouse or r e l a t i v e works un t i l 5:00 P.M. so cannot pick him up unt i l 6:00 P.M. 2. The patient needs someone at home to help him l i f t the wheelchair up and down the s t a i r s . Even though these patients are signed out before noon they occupy a bed for that day thus preventing the entry of an el e c t i v e patient to the hospital or at least to the correct ward. If a bed is occupied appreciably past 3:00 or 4:00 P.M. there w i l l be i n s u f f i c i e n t s t a f f to prepare the bed for a l a t e r a r r i v a l since the housekeeping s t a f f and most of the nurses go o f f duty at 3:00 P.M. Since this i s an occurrence in the parent or real l i f e s i t u a t i o n that i s not included i n the model, i t s introduction into the model more sophisticated. At present, the s t a f f has no data on the frequency with which this occurs. Consequently the only way to include this feature is to 52 check the d a i l y discharge sheets. It was not feasible to do so. This feature was not included in the model. It is possible to compen-sate for t h i s feature during model output analysis. One o f the objectives of this study i s to improve the admission system. If the phenomenon of the patients leaving in the evening could be modelled, the model would be more useful because late departures prevent the s t a f f from making the necessary admissions. Unfortunately, i t i s rather d i f f i c u l t to gather the data. The discharge sheets are held at nurse stations which have barely s i f f i c i e n t area and f a c i l i t i e s f o r normal operation. The discharge sheets are needed by other departments as well as the nurses so removal of the discharge sheets oven f o r short periods of time would hinder normal functioning. Consequently the data was not gathered and the feature was not modelled. This feature was accounted for in the model by compensating the output s t a t i s t i c s . 4.8 Physical Characteristics of the Model The simulation program is written i n the GPSS (General Purpose Simulation System) language developed by IBM. The model consists of 338 blocks, 71 functions, 16 variables, 9 storages and 1 macro. Because of the length of a simulated run, d i f f e r e n t random number generator seeds ( i n i t i a l values) were used. The seeds for random number generators 1, 2, and 3 were changed to 643,31 , and 6,352, re s p e c t i v e l y . A standard run requires about 20 sec to assemble and approxi-mately 627.2 sec of CPU, (Central Processing Unit) time on an IBM 360/67 computer, to execute. 4.9 Validation of the Model Before a model can be used for experiments, some standard must be chosen so i t can be used as a base for comparison purposes. Our base or standard model i s a simulation of the hospital as i t was operating in the 1971 winter season. The va l i d a t i o n w i l l be based on a comparison of the model output with the data gathered. There are three ways in which the model could be validated. [8] These are: 1. Data fed in should produce approximately the same results out. 2. Data generated by the model should agree with the data c o l l e c t e d or agree with the information provided by hospital s t a f f . 3. Data fed in should produce results that can be checked with other data collected for v a l i d a t i o n . Corresponding examples are: 1. If 20% of the patients are assigned to physician A by the model, then the output should have 20% of the patients treated by physician A, subject to the v a r i a b i l i t y introduced by the manner in which patients pass through the model. 2. If patients have to wait due to u n a v a i l a b i l i t y of resources, then the waiting time could be compared with the waiting time gathered from the data or experienced by the s t a f f . 54 3. The a v a i l a b i l i t y of the operating room time should r e s t r i c t the t o t a l number of patients that have operations per week. In this paper only one season was chosen f o r validation purposes. The v a l i d a t i o n s t a t i s t i c s are gathered at the end of eight runs of the season. Each run i s ten times the length of the actual season. The s t a t i s t i c s gathered were the t o t a l number of surgeries performed by each physician, the length of the waiting l i s t f o r orthopedics and the bed u t i l i z a t i o n . Because t h i s i s a stochastic simulation rather than a deterministic one the output should d i f f e r a l i t t l e from run to run. The average of the eight runs i s taken and this i s used to compare against the true value or the value that is extracted from data analysis. The results of a standard validation run are given in Table 2. Most of the results of Table 2 are very close to the actual values experienced in 1971. Unfortunately i t i s d i f f i c u l t to check the average number of requests or Queue Length in the hospital from the data because of some missing data. The average number of requests for 1971 i n the model was 74.4. The hospital s t a f f checked and found the average number of requests to be about eighty f o r the 1972 season. We believe the 74.4 value i s f a i r l y close to the actual value in the absence of 1971 data. The bed u t i l i z a t i o n must be explained. The model showed an average of 80.4%. The hospital's figures are much higher than this fo r several reasons. A bed i n the hospital i s considered "used" or "occupied" i f a patient states that he w i l l enter the following or the same afternoon. In the model, a bed i s not considered f u l l u n t i l 55 1 2 3 4 5 Queue Bed Length U t i l i z a t 11.3 16.5 28.3 32.0 77.7 74.6 82.1 10.4 20.9 31.1 28.6 75.6 79.1 81.9 13.1 18.9 31.5 28.1 78.7 64.0 80.0 11.3 19.1 28.5 26.9 74.8 59.7 79.5 10.5 18.1 23.1 26.2 77.7 44.2 77.3 11.2 16.8 29.9 27.6 77.8 77.8 80.3 12.1 21.4 26.1 30 .4 79.8 117.1 79.5 11.1 18.1 32.2 28.8 74.5 79.2 82.4 91.0 149.8 235.7 228.6 616.6 595.7 643.0 Averages 11.3 18.7 29.9 28.5 77.0 74.4 80.4 True number gathered from data 11.0 18.0 32.0 28.0 76.0 See See Discussion Discussion TABLE 2. RESULTS OF A STANDARD RUN 56-the patient enters the h o s p i t a l . If in real l i f e a patient cancels out or does not show up then the hospital has considered the bed occupied when in fact i t could have been empty. In another instance, the hospital considers an empty bed f u l l i n the case where a patient t i e s the bed up u n t i l 5:00 to 6:00 P.M. before he leaves. The bed may have been assigned to another patient or not allowed in the hospital at a l l . So i n r e a l i t y the bed i s empty for the night but the bed i s considered "occupied" i n the d a i l y s t a t i s t i c s . These two factors cause the hospital to report a higher occupancy rate than the ward a c t u a l l y accommodates. The nurses believe 85 to 90% i s the occupancy rate of t h i s ward but in f a c t due to the two factors mentioned, the rate i s c l o s e r to 80% than to the reported 85 to 90%. Based on these r e s u l t s , we conclude that our model simulates the hospital features that we are most concerned with. Moreover, we believe that the model reacts as the hospital would react to changes that a f f e c t the major features of the simulation model. Exp. Descrip- T o t a l Number of Surgeries Avg. Bed Avg. Avg. Comments No. t i o n per Physician Len. U t i . Que. Wait Of % Len. Time 1 2 3 4 5 Stay E l e c t (days) Surg 1 2 3 4 5 (days) -.Standard Run 11.3 18.7 29.9 28.5 77.0 11.7 80.4 74.4 31.4 Simulation of hosp. winter 1971 (Base f o r Comparisons) 1. Q u i c k c a l l s 7.5% 11.5 18.6 29.8 28.7 77.3 10.8 80.3 53.6 22.7 7.5% of e l e c t i v e pats, are Q u i c k c a l l 2. Constant Queue 12.0 19.3 29.8 28.7 79.9 11.1 82.3 64.0 25.9 The minimum Length constant no. of requests per ward i s 64.0 3. Minimum Queue 11.9 19.9 31.9 29.0 77.7 11.1 83.1 49.5 20.3 The minimum Length constant no. of requests per p h y s i c i a n 4. D i f f . Std. Sched. Longest Surgery 12.4 19.7 29.9 28.6 80.1 11.4 83.2 95.8 39.3 F i r s t Shortest Surg. 12.5 19.4 30.3 27.6 80.2 10.9 81.7 112.2 45.6 F i r s t 5. Excess Surgery 11.5 17.6 31.0 29.6 75.9 11.0 83.0 33.5 14.3 Time 6. Maui 13.5 22.9 34.5 33.5 90.0 10.2 80.3 1.8 .9 Bed constraint only TABLE 3.. . SUMMARY OF EXPERIMENTAL RESULTS 58 CHAPTER 5 EXPERIMENTAL RESULTS 5.1 General A series of experiments were performed to examine various policies. Since i t was shown in the previous chapter that the model was a vali d simulation of the parent hospital system, we infer that is the following simulated policies were incorporated, the parent system would change in a similar manner. The simulated experiments were: 1. Quickcalls, 2. Constant Queue Length, 3. Minimum Queue Length, 4. Different Standard Schedules, 5. Excess Allocation of Surgery Time, 6. A Maui System Under Bed Constraints. The results are summarized in Table 3. Also included in Table 3 for comparison purposes are the results of the standard run described in section 4.9. 5.2 Quickcalls Quickcalls are defined as elective patients willing to enter the hospital on a very short notice, that is one day or less. In a questionnaire that was sent out to previous patients we found a large 59 portion of the patients were willing to enter on a twenty-four hour on-call basis, (see Appendix D.) That is to say, the admission date is not scheduled, but in some two week period the patient is called and expected to enter on or before the following day. In this way, i f anyone cancels, a Quickcall could be brought in fairly quickly. This eliminates a possible unfruitful search for a replacement. The incentive for a patient to become a Quickcall is to keep his waiting time to a maximum of, in this case, two weeks. The present average waiting time is approximately 4 1/2 weeks. The figure of 7.5% Was chosen as an arbitrary starting figure for the number of Quickcall patients entering the hospital. It is possible that 7.5% of the patients could not be guaranteed admission to the ward. This figure would actually depend on the cancellation rate of the ward. In the program, when a patient cancels, another patient will replace him from the Quickcall l i s t unless the l i s t is empty. In the model, the physician would now empty his waiting l i s t more often and therefore have less surgeries to put on schedule the following week. Since the patient inflow rate, the cancellation rate, and the operating theatre time allocation remain the same, the physician does not perform any more operations in the long term than in the normal non-replacement case, (see section 4 .8). The only effect is that the simulated line length decreased from 74 .4 to 53.6 requests. Such an efficient admission system does not occur at present. In actual practice, however, the physician would not limit his work in this way, but would probably use the increased time available in the operating theatre to perform 5% more surgeries. 60 A comparison of the output s t a t i s t i c s from this experiment with the standard run s t a t i s t i c s (see Table 3) indicates that a l l physicians are operating at the same level as expected, bed u t i l i z a t i o n and the average length of stay are very close: the only s t a t i s t i c s that d i f f e r s i g n i f i c a n t l y are the length of the queue and the waiting time in the queue. The number of patients waiting f o r surgery decreases from 74<4 to 53.6; and the waiting time decreases from 31.4 to 22.7 days. This i s quite a substantial saving in waiting time. However, i t depends on two assumptions. F i r s t patients must be found who are w i l l i n g to become Quickcalls and who actually show up when c a l l e d , and second the hospital must accommodate those Quickcalls who were not cal l e d during the two week period but have to be brought in because the maximum time l i m i t has elapsed. 5.3 Constant Queue In 1971 the physicians were allowed to submit to the hospital as many admittance requests as they received. Consequently, the hospital was storing and handling a large number of requests res u l t i n g in much paper work. In an e f f o r t to investigate the p o s s i b i l i t y of reducing the number of requests now handled by the s t a f f , the model was altered to control, by keeping constant, the number of requests allowed i n t o the hospital. The average l i n e length or average number of requests in the 1971 season, shown by the model i n a normal simulated run, is 74.4 requests. The altered model showed that i f the average number of 61 requests allowed f o r this ward were reduced to si x t y - f o u r , the physicians would s t i l l d e l i v e r the same level of service. A number of t r i a l runs were performed l i m i t i n g the t o t a l number of requests allowed to this ward. The t o t a l number was kept constant at 70, 65, 64, and 63. The purpose of choosing the d i f f e r e n t l i m i t s was to determine at what l i m i t the level of services f o r any physician f e l l below that of the standard run. However i f the number of requests were reduced appreciably below s i x t y - f o u r , at least one of the physicians would treat fewer patients. It i s necessary to explain what i s implied by the decrease in waiting time from 31.4 to 25.9 days. It would be reasonable to assume that the d i s t r i b u t i o n , and consequently the rate of requests, of c l i e n t e l e for medical services remained r e l a t i v e l y constant. If this rate were constant and the number of requests were decreased, then the patient's waiting time would not decrease because the request would now be f i l e d i n the physician's o f f i c e . The decrease shown i n the table i s apparent, not r e a l , because under th i s scheme the patient enters the hospital's records a f t e r he has already been waiting for some time. One solution to this problem is to provide extra operating theatre time and beds u n t i l the l i n e length decreased to an acceptable l e v e l . During th i s short period of time, the physician would be able to treat more patients. Since the rate of requests remains constant the l i n e length would soon decrease to an acceptable level (see Figure 11). 62 SCHEMATIC: Simplified Diagram Showing Method o f Reducing Line Length. Waiting l i s t of 20 patients 5 patients 1 2 • • • 20 5 patients requests admitted into the a r r i v i n g h o s p i t a l weekly weekly (a) Waiting l i s t of 10 patients 5 patients 1 2 • • • 10 15 patients requests admitted into the a r r i v i n g h o s p i t a l for t h i s weekly week (b) Result: Decrease i n length of waiting time 5 patients requests a r r i v i n g weekly Waiting l i s t of 10 patients 10 5 patients admitted into the hospital weekly FIGURE 11. Schematic diagram of explanation on reducing l i n e length. A physicians waiting l i s t f or: (a) f i r s t week (b) second week (c) t h i r d week 63 5.4 Minimum Queue (Constant Number of Requests per Physician) This experiment i s an extension of the previous experiment. To c l a r i f y , the hospital retains a l l present procedures of selecting surgical patients and a l l o c a t i n g beds. The only change i s that each physician submits a maximum number of el e c t i v e surgical requests, for example, s i x . Only when some of the six have either been admitted or have cancelled out can be submit more requests. This experiment i s more pr a c t i c a l than the preceding one. In this experiment each physician is allowed the number of requests he needs on f i l e to maintain his present level of service. Under the overall l i m i t f o r the ward, some physician might occasionally have an i n s u f f i c i e n t number to maintain his present level of service. The model was altered to control each physician's l i n e length. The number of el e c t i v e requests each physician was allowed to submit to the hospital was held constant. This was accomplished by having each physician check d a i l y and submit requests as soon as any patients cancelled or were admitted to hospital. Clearly because each physician has a d i f f e r e n t disease mix, patient age mix, and working pace the number of requests required for each physician need not be the same. In this experiment, a number of t r i a l runs were performed l i m i t i n g the total number of requests submitted to the hospital by each physician. Levels of 7, 6, 5, 4, and 3 requests per physician were used. From the f i v e runs the minimum f o r each physician to maintain his standard level of service was chosen; the results are shown in Table 3, experiment 3. The model showed that the minimum 64 queue length f o r physicians 1, 2, 3, 4, and 5: were 4, 4, 4, 5, and 18, respectively. That i s , i f the physicians are: to: d e l iver the same TewT of service, they must maintain at least" the: mentioned minimum number of requests i n the admittance o f f i c e . This minimum assumes the physicians would check d a i l y to see i f any patients have been scheduled, and would submit more requests i f necessary. However, i f the physician or his receptionist would not or could not check with the hospital d a i l y , then the number would have to \s& Increased. The increase would depend on the: number of times a week a check could be made. In the results f o r this experiment, in Table 2, the average queue length or average number of requests f o r the ward i s 49.5. This is much higher than the sum of the minimum number of e l e c t i v e requests f o r each physician. This is because urgent patients' requests must be submitted regardless of the number of e l e c t i v e cases waiting f o r surgery. In that way urgent patients can be given a higher p r i o r i t y and be admitted faster than e l e c t i v e s . As in the previous experiment the decrease in waiting time i s more apparent than real because now the request remains in the physician's o f f i c e rather than in the ho s p i t a l . (The previously mentioned solution must be implemented before the decrease i s realized.) 5.5 Different Standard Schedules A standard technique f o r reducing waiting time is to use d i f f e r e n t scheduling rules. The most common ones are F i r s t - I n First-Out (FIFO), shortest service f i r s t and longest service f i r s t . The standard 65 procedure now used at this hospital is b a s i c a l l y FIFO. The model was changed to incorporate shortest surgery f i r s t . The requests were i n order of increasing surgery time and requests were chosen from the front of the l i n e . We expected the l i n e length to decrease but instead i t increased. As was explained in section 4.9 the average of eight runs was used as the determining s t a t i s t i c f o r analysis. It was noted that at each of the eight i t e r a t i o n s the length o f the waiting time increased. When the surgery s l a t e was examined i t was found that using up a l l the short surgeries f i r s t l e f t much available operating theatre time unbooked. This i s because the next surgery was a long surgery and i n most cases j u s t barely f a i l e d to f i t on the s l a t e . Naturally i n real l i f e i f much operating room time i s l e f t unused the surgery scheduler would simply choose a request that could be scheduled, regardless of the order. I f she kept s t r i c t l y to the order of shortest service f i r s t , some time would have to be wasted. Consequently, in this case a s t r i c t use of shortest service f i r s t would not be optimal. At f i r s t glance, t h i s seems odd, because shortest service f i r s t works well i n most systems. The reason that i t works badly in this case i s that the average surgery time (the service to be scheduled) i s quite large i n comparison to the available time on the operating theatre schedule. A modified version of longest service f i r s t was also simulated. The longest operation was scheduled f i r s t , then the next longest, and so on. When long surgeries could not be scheduled the shorter ones were t r i e d . This scheduling procedure was much more e f f i c i e n t 66 than the shortest service f i r s t . However, i t was not good as FIFO. The waiting time for longest service f i r s t was 39.3 days as compared to the FIFO waiting time of 31.4 days. An increase of eight days waiting time was the net result. Because of the limitations of the availability of operating theatre time, i t appears that FIFO is the best rule to use i f just one rule iis to be used. However, we feel that a detailed analysis would show that a hybrid or multi-scheduling system would improve the efficiency of the surgery schedule. (The kind of problem that was encountered in maintaining a s t r i c t order of shortest service f i r s t would',, c l e a r l y , be met no matter what order was s t r i c t l y followed: toward the end of a block of time, some space will frequently be l e f t whichi does not match the next operation in the queue.) 5.6 Excess Surgery Time In this experiment, each physician was granted excess operating theatre time, more than double what he had had. (This simulates in part what might happen i f new operating theatres were built.) Again patients were created at the same rate. The results show that the waiting time decreased by about 54%. Numerically the waiting time decreases from 31.4 days to 14.3 days and the corresponding line length decrease was from 74.4 to 33.5 or a decrease of 55%. There are two reasons why the line is s t i l l substantial in length. The f i r s t is because the 36 beds in this ward become a constraint, because the number of patients entering the ward is 67 limited by the beds available. The second reason i s that e l e c t i v e patients are not admitted on Thursday, Friday or Saturday since none of the orthopedic physicians operate regularly on any of the days following those days. It would appear that the number of patients treated should increase due to the decrease in waiting l i n e length, 40.9 but this i s not the case. The patients get medical/surgical problems at the same rate so no permanent increase in the total number of patients served i s possible. In the program, when the surgery time becomes v i r t u a l l y unlimited the physician's waiting l i n e decreases to zero occasionally. There is s t i l l an average waiting l i n e because not a l l physicians waiting lines w i l l decrease to zero simultaneously. In actual practice, however, the physician might begin to submit requests at a much higher rate, perhaps because of taking on new patients or advising more elective surgery; i n that case the waiting time would probably return to what i t i s now. 5.7 The Maui Experiment The Maui system was implemented in the model under bed constraints. In e f f e c t the Maui system "enables an instant review to be made of those patients who remain in hospital longer than a pre-determined length of stay a l l o t t e d to a s p e c i f i c diagnosis." [2] The method i s b a s i c a l l y to assign to each patient an expected length of stay from a standard table. This expected length of stay can be modified by the physician in case the diagnosis or the patient's 68 Condition changes. Each day, all patients who had been scheduled to leave the prior day but have not left are reviewed. Information is Obtained from each overdue patient's chart, doctors and nurses. If necessary the discharge date is revised. One of the main advantages of the Maui system is the monitoring of all patients. In most hospitals without Maui, i f the physician has not pre-arranged for a patient to leave and is unavailable when a patient is due to leave, the patient remains. But under the Maui system, his doctor is consulted the next day about his readiness to leave. In hospitals that have incorporated the Maui systems, the length of stay is usually reduced. A simplified version of the Maui system was incorporated into the program. We have assumed that the length of stay is reduced by one day. The surgery constraint was also eliminated, to match the experiment done in St. Joseph's Hospital in Victoria. Our results were similar to theirs, where the line length decreased from eighty to zero. [2] Without surgery constraints and with the Maui system incorporated into the model, the line length was 1.8. This is a drastic reduction from the 74.4 in standard runs. 69 CHAPTER 6 CONCLUSIONS In general, we can conclude Experiments 1, 2, and 3 in Table 3 are e a s i l y applicable to any hospital in North America and the results of Experiments 4, 5, and 6 are s p e c i f i c to one hospital. A l l of the experimental results were compared to the standard run of the hospital as i t was operating in the 1971 winter season. The results of the standard run are given at the beginning of Table 3 for comparison purposes. The uniqueness i s due to the constraints in the ward we studied. Normally, the d i s c i p l i n e of s h o r t e s t - s e r v i c e - f i r s t e i t h e r increases the output (number of patients served), or decreases the waiting time or both. But in th i s ward the surgeries are f a i r l y lengthy and the allocated time for surgeries per week r e l a t i v e l y short. Experiment 5 gave the physicians excess surgery time. The results are a decrease in the number of admittance requests in the hospital and a decrease in the waiting time. Since operating theatre time i s the main constraint at this h o s p i t a l , this result i s quite s p e c i f i c . In other hospitals where bed a v a i l a b i l i t y i s the main con-s t r a i n t an excess a l l o c a t i o n of surgery time might produce no effects on the output. To t e s t the model against the results obtained in an actual hospital experiment [2], the model was altered to incorporate bed 70 constraints only. The results of the simulation are given in Experi-ment" 6 of Table 3 . The Maui system used in the model produced results similar to those experienced in actual practice where the average number of admittance requests decreased to an insignificant value. Based on the results of Table 3 , two significant concepts are obtained from this study: 1. The Quickcall, 2. The Minimum Queue Length or constant number of requests per physician. These are general results based on the f i r s t three experiments. The idea of a Quickcall is very practical. While a physician is discussing the need for surgery with the patient he could easily, at his option, have the patient f i l l out a short form concerning the minimum notice he is willing to accept before entry to the hospital. In this way the physician could screen out cases which he would prefer riot to handle without more advance planning. This would cost the physician very l i t t l e extra time and effort and offers him greater f l e x i b i l i t y in choice of cases to perform i f a patient cancelled. The Quickcall system could result in a significant saving of hospital resources by eliminating unnecessary searching for substitute patients for cancellations. In particular, this concept could be readily incorporated into the increasingly popular Maui system. The simulation showed that the Quickcall system reduced the waiting time by approximately 8 1/2 days. Since one of the main reasons for cancellations is the long waiting time, the Quickcall 71 concept would tend to reduce the number of cancellations. The reduc-tion of cancellations would bring more stability to the schedule and thus allow the staff to utilize the hospital resources more efficiently. Physicians might not allow the hospital to restrict the number of elective surgery requests a week. It might be necessary to convince them of the usefulness of limiting the number of requests. Since there is a limit to the number of cases a physician can submit under this concept, he would automatically screen and arrange them in order of preference. At present he must rely on the hospital to give priority to the elective patients whose need is greatest. Another advantage is that the physician can arrange his next few weeks of surgery or patients' lengths of stay to suit his schedule. Another advantage of the minimum queue length concept is that the hospital would need to store fewer requests. This would result in a saving of space and a reduction in administrative work. The model has also provided insight into the fact that block scheduling for surgery would not be acceptable i f the block of time is too small, (see section 5.5) Block scheduling would probably be most effective where operating theatre time is not a major constraint. The model could be extended to include more sophisticated features. Some of the surgeries performed by the physicians are referrals. Quite often the assistant surgeon is the referring doctor. In some cases, patients or even the referring doctor will not allow surgery unless the referring doctor is assisting (e.g. gynaecology), i f the scheduling system in the model included a provision for 72 scheduling the time of two physicians, perhaps t r i a l solutions or the s e n s i t i v i t y of t h i s to scheduling factors could be tested. This requires more data. Another possible extension would be to more f u l l y u t i l i z e operating theatre time. Perhaps opening a theatre one night a week might allow physicians who have admitting privileges but who have moved to use t h i s time to serve some of t h e i r former patients that l i v e close to the old ho s p i t a l . These are a few suggestions that could be investigated by simulation experiments. Some patients need to be admitted sooner than others. For example, someone whose work i s made d i f f i c u l t by his medical condition c l e a r l y should be admitted before someone who merely needs cosmetic surgery i f both are e l e c t i v e cases. Factors that determine need have to be chosen and scales made to measure the factors. This would require further data and study. When scales are available then patients of the same p r i o r i t y could be admitted in order of decreasing need or u t i l i t y . This experiment could be incorporated into the previously described model. In conclusion, the structure of the simulation model enables a wide variety of experiments to be run on the admission system with-out the high cost and r i s k of actual p i l o t studies in the hospital. The simulation model of the admissions process described in this paper has accomplished the following: 1. It has been demonstrated that a v a l i d model of the admissions process can be obtained. It was used with confidence f o r a variety of experiments. These experiments showed the usefulness of the Quickcall l i s t and fixed length l i s t s of patient admission requests. It i s recommended that these concepts be considered in the design or redesign of actual hospital admissions systems. It could be extended to test further changes in the hospital admission system and to evaluate t h e i r e f f i c i e n c y and effectiveness. 74 BIBLIOGRAPHY 1. BARNOON, S. and WOLFE, HARVEY. "Scheduling a Multiple Operating Room System: A Simulation Approach." Health Services  Research, Winter 1968, pp. 272-285. 2. BILLING-MEYER, W. and STEPHENS. "An Experiment in Bed U t i l i z -ation." B r i t i s h Columbia Medical Journal, Vol. 13, No. 5, May 1971. 3. COMMISSION ON PROFESSIONAL AND HOSPITAL ACTIVITIES (CPHA). Length of Stay in PAS Hospitals Canada 1970. Ann Arbor, Michigan, October 1971. 4. GOLDMAN and KNAPPENBERGER. "How to Determine the Optimal Number of Operating Rooms." Modern Hospital, 111 (September), p. 114. 5. KENNEY, J.J . and MURRAY, G.R. "Computer Models Evaluate Altern-ative Methods." Industrial Engineering, Vol. 3, No. 12, December, 1971. 6. MICHIGAN UNIVERSITY. The School of Public Health "Abstracts of Hospital Management Studies." The Co-operative Information  Centre f o r Hospital Management Studies, The University. 7. MILSUM, J.H., TURBAN, E., VERTINSKY, I. "Hospital Admission Systems: Their Evaluation and Management." Working Paper 103 Faculty of Commerce and Business Administration, The University of B r i t i s h Columbia 8. NAYLOR, T.H. and FINGER, J.M. " V e r i f i c a t i o n of Computer Simulation Models." Management Science, Vol. 14, October, 1967. 9. STIMSON, DAVID H. Operations Research in Hospitals: Diagnosis and Prognosis. Hospital Research and Educational Trust. 10. THOMPSON, J.B. et al_. "How Queueing Theory Works f o r the Hospital." Modern Hospital, Vol. 44, March 1960, pp. 75-78. 11. WHITSTON, C.W. "An Analysis of the Problems of Scheduling Surgery." Hospital Management, A p r i l , 1965, p. 58, May 1965, pp. 44-45. 75 BIBLIOGRAPHY (Not S p e c i f i c a l l y Referenced) COLLINS, G.L. "Cost Analysis and E f f i c i e n c e Measures for Hospitals." Inquiry, Vol. V, No. 2, p. 50. COMMISSION ON PROFESSIONAL AND HOSPITAL ACTIVITIES H-I CDA. Hospital Adaptation of the International C l a s s i f i c a t i o n  of Diseases. CROSS ROBERTS and FROAR. "Electronics Processing of Hospital Records." Computers in the Service of Medicine, Vol. 1, 1969, McLachlan and Shegog Editors, pp. 23-29, London: Oxford University Press. DONABEDEAIN, AVIDIS. "Evaluating the Quality of Medical Care." Mil bank Memorial Fund Quarterly, Vol. 44, July, 1966. GOLDMAN J . , KNAPPENBERGER, H.A., ELLER, J.C. "Evaluating Bed A l l o c a t i o n Policy with Computer Simulation." Health Services  Research, Summer, 1968, p. 119. KAVEL J . , and THOMPSON, J.D. "Computers Can T e l l You What Will Happen Before i t Happens." Modern Hospital, Vol. 109, 1967. RIKLI and ALLEN, 1968. "Federal Survey Examines Computerized Admission Systems." Modern Hospital, Vol. 3111, October, pp. 99-102. ROBINSON, G.H., WING, P. and DAVIS, L.G. "Computer Simulation of Hospital Patient Scheduling Systems." Health Services Research, Summer, 1968, p. 130. SARKAR, D.A. "Schedulling for an X-Ray Department." Industrial  Engineering, Vol. 4, No. 12, December, 1972. THOMPSON and FETTER. "Research Helps Calculate OLB. Bed Needs." Modern  Hospital, Vol. 102, January, pp. 98-101. THOMPSON et al_. "Predicting Requirements for Maternity F a c i l i t i e s . " Hospitals J.A.H.A., Vol. 37, February 16, p. 45. WEEKWERTH, V.E. "Determining Bed Needs from Occupancy and Census Figures." Hospitals J.A.H.A., Vol. 40, January-April, 1966, pp. 52-54. WOOD, CT . and LAMONTAGNE, A. "Computer Assists Advances Bed Bookings." Hospitals J.A.H.A., March 1969. APPENDIX A Computer Program P r i n t o u t y / • t I * * * ' G P S S V - M T S V E F S I 0 N « * *** IBM PR OCR AM PRODUCT F7?i-XS2 (VIM?) *** 1 FOR UP-TO-DATE ! MfW. NATION P FGAR DING *GPS5V 'SLIST NEWStGPSSV STAU'-'Sf'T REALLOCATE X\Z , 10O0. -MC, 35 3, FAX, 20, STO, 20. QUE ,3 CLOG, 100 I REALLOCATE J43 , 2 . FUN .1 00 ,VA* ,1 OO.BVP.,0 ,FMS ,0,HMS ,0 2 REALLOCATE c 3V , 503 , US V , 1 CO ,C , 60, GRP, 0,C O'-l, 96000 • BLOCK REALLOCATE L SV , 0, L'-fS , 0 ,'4 AC , 3 <t STATfMFMT • NUMBER • LOC OPESATIPrj A, P.,C,D, E, F.G.H, I COMMENTS " SI-ULATE K * ft rt ft ftft ftft ft ft ft ftftft ^  ft ft* ftft ft* ftftftftftftftftftftrt Art ft 4 ft ft ftftft ftft ftft ftiftftftftftftftftftft*ftftftftftftfttfft A * USE THIS ONE 7 * * * 0 9 10 ft ft ftft »?ft. ft ft VV.* * ft ft* ftft ftft ftft ftft ftft ft ftft ft ftftftft ftft ft** ftftftft rt ftft ftft ftft ft* 11 « * 12 * * 1?. ft ft IV * LGH SIMULATION NOCEL RUN FOR 1971 * * * 15 16 i ; » * i 0 ftftftftftftftftftftx-ftw ft* ftrtftftftftftft« ftft ftft rtftftaftftftft ft ftftftftftftftft* ftft ftftftftftftftftftftftftft ft ftftftft ft ftftftftftftftftftftftftft ic. * " 2 0 ft 21. ft 2? * 2i ft 24 ft ». 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J fM - eg • fM rvj CJ (M rg o- *- rr m » in •c • m f- ^ * O * O V o 4 O * o o o O * * rH « O rH # 0 • • rH J 78 f- r- r- i*- r-r tt ft * » i it > :i. '^ - ./ K o c c o c .» « • * ct ct (i* a: O C Ci U » rvj n *t * < _j _J _J _* ft »~ It- *- h-f". it/1 I/} (/) ft O •« O < * Z fr 2" < * — * — i * > & > ^ r < IK -s < ft- to |K I/) o. * i/i * 75 + a: >- t— o UJ ^ -a — >_ t— ^  >; U J a; V x n. z: x: •• LL CJ C X. ft ! Ji <T >- ft >- o <r ». V <J tA c * C '*' ^> * O on 7 iv i •IJ o r> * ri L:- X * >: i - is f- < H M ^ O ft * ft *c ft - H m |.f ir x •ft«-##tt-Bft#ft««ft«ft#ftft r <i !*• « ft <r j« n ft ft <: 'ft < *&.;*»&. ft !ft ft * * ft ft * ( 1 FUNCTICN RN1.02 0,30/1,30 2 FUNCTION RNl.m 0,1 5/. 046, 15 /. 319, 30/. 5 92, GIVES OPTIME FOR 326 GIVES UPTIME FOR 445 60/. 63 8,75/. 7 74 ,90/. 91 0,120/1.0,150 8 1 > 90 91 9 2 9** 94 I 3 FUNCTION RN1.06 GIVES OPTIME FOR 446 f 0,1 5/. 041, 15/. 6 3 3, 30/. 816, 45.93 , 0/1.0,90 95 S 4 FUNCTION 3NI.37 GIVES UPTIME FPP 447 0,15/. 122, L5/.7 32. 30/. 7 56, 45/. 902, 60/. 9 2 6, 75/1. 0,9 0 Of; 5 FUNCTION PN1.0H GIVES OPTICS FCP 443 . 100 0,30/.057, 30/.171,45/.4.35 * 6 FUNCTION ' P. N1 ,D6 60/. 5 ?1, 73/. 557 ,9 0/. 94 3,12 0/1. 0,150 GIVES OPTIME FRR 450 10! 1 07 3 03 0,15/.l 3 2, 15 /.454, 30/. 726 45/.903,60/1.0,150 I 04 1 05 1 0'. 7 • C:JNCT!CN R,'U,03 GIVES OPT!'"E FOR 451 j •" 0,30/i04, 3.)/. 160,4 5/. 6 4, 60 /. 76,7 5/» 8 3, 90/. <J 2 ,120/1. 0,180 * « CHNOflfN 1*1,98 0IV6S llPTJMd FOR 45? ""0,30/1, 3 0 * ' ...... — " "' » " * 9 FUNCTION RN1.02 GIVES DPT IME FOR 453 ...... 107 1 0* # 1 0'' ^  ^ _ 3 1). ? 12 0,30/1. 30 * io I--UN:TI-CN :<NI,O') GIVES OPTIME FOR 454 3 1 : 1 14 ) 15 0,15/. 1 0, 15/. 5 3 3, 30/. 6 33 ,4 5/. 8 3 3, 60/. 366, 7 5/. 932 , 90/. 96ft , 120/1. 0 ,1 50* 11 FUNCTION RN1.06 GIVES UPTIME FOR 456 117 13 8 0,60/. 10 3, •>>/. 13 3, 7 5 / . 443, * 12 FUNCTION RN1.D3 90/.897,120/1.0 ,150 GIVES OPTIME FOR 457 ! 19 1 20 1 2) 0,30/.905, 33/1.0,45 * 13 FUNCTION RN1.05 GIVES OPTIME FOP 45 3 3 22 1 23 3 24 0,15/. 2 5,15/ . 75 , 7.0/. 33 ,60/1. 0,150 «i 14 FUNCTION RN1.D12 GIVES OPTIME FOR 459 1 ?' 327 0,15/. 01-M 5 / . 3 02, 3 0/. 378, .718,163 . 944 ,130/. )6 3 ,71 C/l. C240 45/. 39 7,90/. 510,1 20/. 5 4 8,135/. 699,150 3 29 3 30 * 15 FUNCTION PM1.08 0,15 /. 02 3, 15 /. 56 3, 30/. 746, GIVES OPTIME FOR 460 45/. 316,60/. e 2 0, 7 5/. 971 ,° 0/1. 0,1 50 I 2). 1 32 * 16 FUNCTION *N1,D8 0,30/.027, 30/. 135,60/. 351 , GIVES OPTIME FOR 461 90/. 675,12 0/. 72 9,135/. 972.1? 041. 0,180 135 136 * 17 FUNCTION R.N1.C7 0,15/.I 0 5, l " i / . 5 2 5, 30/. 6 33, GIVES OPT I ME FOP. 462 t 45 /.041.60/.946,75/1.0,10 ! .-• ' 13b 1 18 FUNCTION RN'1,03 0,15 /. 34 i, 15/1.0,45 GIVES 0"TIME FOP. 46? 140 141 3 4? * 19 FUNCTION RU1.07 0.15 /.50 0, 15/.727, 30/. 773, GIVES OPTIME FOR 464 • 45/.819,60/.955,90/1.0,13 5 i 4 J 14 * 20 FUNCTIf N RN1.D6 0, 15/. 93 2, 13/. 612, 3 0/. 906, GIVES OPTIME FOR 465 . 45/.967,60/J.0,90 1 4r 3 47 ; 4P y .954 ,44 640/. 9 69, '.6080/. 9 84,51840/1.0,12 0960 * « 44S = REPAIR AND PL A ST IC OPERATIdN CM BONE ( P 1 .0-B1. 3 ; 01. 5, e 1. 7-81. «> * 36 FUSION RN1.C31 GIVES LSTAY FOR *?0 I .289,. 686,. 853,. 927,.992 I 0.14 40/. 2S ), 1440/. 40 5, 2P 80/. 479 ,4 320/. 545 ,5760/. 59 5. 72 00/. 621', b460 2 01 219 2!.l 212. 212 21'-. J . f t 4 ? , l ; ) 0 1 ) .686 ,11520/. 719, 12960/.736 ,14400/. 761, 17280/. 7^4,18720/. 811 ,20160 . ??6 ,21'>.)>_ _ _ _ ". 8 5? , 2 3.14 8 70,2' 4 3 C / . H 87 ,2 5920/ .8 95 ,5 6 2 4 0 / • 90; , ? j'MSO/".' V l 1, 3 2T5 0 .919 , 36000 _. 9 27_. 4 )??J/. 944, 42 2 00/. 9 52 . 6 C4 30/. 96 0 ,619 20/. 0 6* . 792 00/. "76 , B 3520 .9"34 ,"j/)20 . 932 ,11 :'.C:J 1/ 1.0,1413 20 2) ; 216 i ! 7 = 19 '20 ."21 ; 22 223 37 FUNCTION -";M1,033 GIVES LSTAY FOR A li j . 04 - , . 47d , . 71 « , . H3 9 , 0.14 40/. 04', .1443/. ))S,2330/.152.4;20/.2el,576C/.3?f,72..10/.J!iO,94H.U . 424 , I'K'u) .«16 ,. -'=3 . 47 b, 115 20/. 500, 12'1 5 0/. 5 33, 14400/. 56 7, 1 58 40/. 62 0 , 1 72 60/. * 74, 2 016 0 .6=16 ,21603 . 71S,23 )40/. 740, 244 80/. 762 ,2 592 0/. 73 4,2 72 60/. 79c- ,31.100/. 8 06 ,2024 0_ .528 ,3 3 1 20 . 639 , 4 J? 2 )/. 3 50,4 W60/. 361 ,4 3200/. 8 7 2,460 80/. BW3 ,47520/. 13 94,5 616 0 . 905,57oO) ; 2 7 2 20 2:1 ,«16 . 6 6 2 4 1/. 927 , 705 6 0/ .9 33 ,7 2000/. 94 9, 792 00/. "fll. ?0'j4 0/. ^71 ,9 5040 . 932 , 11 3 TAJ ,91?,ldl.MJ/l.'l,216000 _ _ * . " ' " " ' «' *5 l-« 0»EN REDUCTION OF FRACTURE EXCEPT JAW At*> FACE < 8 2 . 2-P. 2 . 3 : e 2 , 5 > 2 31. •; 2 37 2 2" 33 ctj-jcriCW W\7Wt GIVES LSTAY FO  4f? 0,1440/. 100, 1440/. 500, 5760/, 750 , 1 2 96 0/. 90 , 2 Oi 60/. 95 , 2 «,<, - 0/1 . 0 , 50400 * "'39 F ' JNCMCN RNl,03 GIVES ' L STAY FOP 4i.~ 0,1 443/. 05,1 4 40/. 100,2 f 80/. 5 00, 3460/. 750, 144 00/. 900,22 04') / ,''50,3 0240 1,0,87010 2 40 i41 242 :. 4r 40 FUNCTION It >l 1,1)7 GIVES LSTAY FOR 454 O.2R3J/.10J,2;130/.50 0, 72 00/. 750 , 1296 0/. 900 , 2 1 6 00/.. 9 50, 2 f 300 /1. 0,_8064 0 ' « ' ' ' ~ " 41 FUNCTION RN1,329 GIVES LSTAY FOR 45H (. 012,. 767, . 726, . P88,1. 0) O',*7{>0/. 01 3, i 760/. 024, 72 00/. 047 , 0460/. 059 , 1 00.10/. 1 1 7 , I I 60/. ]. 7, ) 2960 ?46 2 47 24',  5T7.V77 y.'-4"uTl /. 3 47,1 3".TW. 471 ,1 7S20 /. 52 9 ,3. F. 7 20 /. 6 22 ,2 01 60 /. f'E . 21.600 ,6->0.23)43 . 7 36 ,2 44 20/. 74d,2 512 C/.760 ,2 7-1 0/. 79 5 , 2'» 9 00/. 8 IK , 3 02^ 0/. " c 3 , 31 6-3 0 "":'376 ; 3 i n o ' ' " * ~ . 83 3 , 374 4 0/. 9C0.3P33 0/.9 23 ,4 0320/.9 35,4176O/. 953,4?,2C)n/. 9B1 ,489.'.0 . 993 .5 )4 00 2', ' "2 60 ' 2 61 :• 6 2 ~ T 7 ~ 2 64 1.0, :>/6l J : * « 456= EXCISICN OF INTERVERTEBRAL CA RT ! IA GF (82.41 42 CU\'CTICN PN1.014 GIVES LSTAY FOP 457 (. 045,.9541 0,2?. 3 )/, J4 5. 2 830/. 30 6 , 4320/, 612 , 5760/. 791 ,7200/. °] 0, 8413/.932, IOC 80 09 O ~T^r7iT*n . 954 ,1 4-'- 01/. 151 , 15 = 4 0/ .5 76 ,2 016 0/. 93 3, 2 -040/. 990 , 2 7 360/1 .0.43 200-*~-4?7=- ixcnTr!TT3"r~sr«tLWA'ir'"c"A,vnc.v'cr 'nF-rCn-c~riiTPTr 43 FJNC; IOH RN1.07 GIVES L S7 A Y FOR 45". 26 rr 8 1 CJ C 1 r-* IV f v r~ r- r- r-, f\i t\t r--l- t r- a- c- o r- r- ;r~ r- cc >\J iv cv;t\i ,v rv. a; a <*-l \ . Iw IV,' K \ (Vj Ovi o C r\i -j- ro • -o v. -O O r--4J r-O * Cil OJ a * c U- O O r- cc rvj — ~ rv o •— vl- IT O -v. f l <"V O ' ~t <r - . » in i >- j-J r->» -.-'in %*• » a: a.* -i ~i ~ iv n, r. „• c o r; o i ir-- C ,v/i cj {> o i a t (V U- o 7 m IT* O 7) f> ,t2 r-« i i o o >- c ±1 R -> -1 . V i> * O in O -4-.i— -o r-- >-;• c • • (V* . I\ vt J • •> j-0 o t> o t< a--4--4-\, i \ rs, »vj ix i C m cu * in rvj C x : .813,15*40 .6 39 ,2 0160/. 392 ,21 60 C/. 918 ,2 5920/. 94 4,30240/. 9 70,40320/1 .0,76320 A 53 F UNCI I CM SNJ.,029 CIVES L STAY FOR 9999=MISC 0 P ( . 1 5 7 , . 471, . 729, . 91 5) 0. 1^ 4 0/. 15 7, 144 0/. 271, 26 SO/. 314,4320/. 3 71 , 576 0/. 400 , 72 00/.41 4 , 8460 . 4 5 7,10033 2 2'"' 331 3 32 -i ^ _J 1 71 , u 5 20/. 4S5 ,12 9oC/.514,14-,00/. 600,1 5P40/.M4 , 1 72 30/. 557 ,1 h72 0 .700,20160 . 7 2 9 , 3 3 0 4 0 / . 7 4 3 , 2j4 4 >3 0 / . 7 fl 6 , ? 5 9 2 0 / . 81 5 , 2 R8 0 0 / . P 44j3 024 3/JJJ58 .33120 S72 , i i > » o " " " .915 , ((.0 JO/. 92) , 3 7440/. 94 3, 4032 0/. 95 7, 4 1760/. 9 71 ,5). l!<-0/. 98 5,66240 1.0,74-1) 3 26 " ? 7 " 2 3 «~ •> ?.'/ ? 4Q C>- ! LY F'.INCT !C"M P. N 6, 09 .129,3/.355,1/.5C0.?/.64 5,3/.e3 6,4/.925,5/. 96 7,6/,983, 7/1.0,9 3 4,3 :• 4? "-.'<, 4" 60 F'lN'Crif.N RVI. 024 0EC70-JAN PR12 FCN F CP GIVING 110 ». 011. 64 6, 9 07 , Oil ..»/. l.M, 3/. 151 ,4/. 226, * / . 248 ,6/.2hO,7/. 302 ,1 0 / . 42 0 ,11 / . 624 , 12 .63 5,1i . 64c .14/.75- ,15 /.776 ,1 6/.7e7 ,1 7/. 793 ,1 8/. 309, 1 9/ .R4]. , 21/. 86 2.22 . F d 5 , .2 3 _ .907 ,2-,/. UH, 25 /.9 29, 2c/. 9 73 ,27/1. 0, 23 * " * _61 F'J'ICriOM 3,N1,013_ _0EC70-JA*' PR 3 F*>l FOR GIVING OP (.019,.R6PI .01 9', 17 .'13 1, v>7 .' A"3 476 / ."604, 7 / . 6 2776/ ."717711 /TI To, T277T3 0, I"4/. 349,19 ' . 36« ,22 / ..» H .2S/.Q06 ,27/1. 0,26 # '"63 F:'-4CT IC*" " IN'2,05 01 V F S 'P itY S I C I AN /( F 01-'"'UL C 70-JAN' PV<3'FC"("6 J ' " .101 ,1/. ?. }2. 2/. 355, 3 /. 440, 4/1. 0,5 3 ^  34'J 3 50' 3 5 7 .15 F T : J ; T ITM R':7,05 GIVES PHYSICIAN Ii Ft,"> OEC70-JAN °M2 FCN62 . 1/. 26 0. 2/. 4 30, 3/. 6 3 8, 4/1.0,5 04 JUNCTION RN3.C2 .778,0/1.3,51-3 65 FlIV.TICM ^V.rg ALLOTS" TfiTAT WEEKLY SURGERY DEC-JAN 56 ALLOTS TOTAL WEEKLY SU°GE»Y DEC-JAN 18 r22T7377TT7m/.' ,.45, 12 0/. 5 5t ,130/. 6i 7, 2 i 0/. 77a , 2 15/. ii .:, 740/1. 0, 540 66 Firj-rir-i RN3,07 A L L O T S TOTAL W E E K L Y S T G E R Y D E C - J A M 26 . 22 3 ,0/. 33 4, 1 95/. 5 56 , 210/. 66 7,24 0/.7 7° , 2 7 0/. F<!9, 300/1. 0, 33 J 67"" 'F'J-iCT IOM" ' RN3, O R " ' " A L L O T S T O T A L VEEKIY"SURr.rP.Y"'EiFC-J'Atf-?*' . 11 1 .)/. 22 2, 30/ . 3 33 , 75 / . 444, 160/. 555 , 195/. 66f ,25 5/. ,270/1.0 ,300 3 6:i • 6 1 3 62 363 TO" CTTTi Frsr3T"07T" "TDTTf S PAT ACC'WINC ' 0 "OCX AO'-ilT ilIST xnoc . 30 7 , 0/. 6? 2, 1/. ii 3 2, 2/. 96 3, 2/. 99 5, 4/1.0, 5 NPENT l=!l\'CT!C'J RN5.07 CtEATfS RHO PAT THAT OON • T GET IU RIGHT AWAY ,737 ,0/. .J-,"r,"l/.-art72 /. 933, 3/.949747.""9P.:'757!.077, XWAIT FinCTIFM RM7,!314 WAITING TI".E FOR DOCX , 050 ,1440/. 1 50, 2?30/. 2 00,4 320/. 2 50.5 7.' 0/. 400,7200/. 550 ,8640 rt"!^7T0J IV. 770.1440 0/. 7U0, i 72F0/.3C 0, 21'.00/. b50.43200 . 900 ,6 71 2 1/. 9 5 J , 1°00 °0 / I .0 ,2 1 744 0 I -)I TI AL LS50/XH1 00 , 31 / X1 E-! , 1 9°/XI 99 , £nn MUST STiKlG-2 " 2 0 " " UiM'iLMT 'MED '-'.UST' A DMI TTc'O"'RrFTTP [ T-VTFFK"" 2 66 367 -3T-F-7 69 3 70 • 71 372 3 7": ~ 1 V -375 3 76 "TT7-37E DEE ' >. \GE 3 0 '•IE 01 C \ L PATItT' •OEF'INITELY GET IM OO to tikHlrf ONi: XFt. K ELAPSES US' Cfct.'T SUROKPY PATIKNTS DON'T HAVE TO ~WM T "'F0"?.'~A " f('E!5"_  THESE 36 Ol-Ti-ifl :3E0S URHEO STUACE •3HO S'ORIGE 2 <!4 3R'i 36 ' r T T ' ' R 7 ' F.f-f 1 4 VARUrtLE 1 5 0 » P 5 USEO F O R GETTING A ? A V E VALUE 1 5 1 - 1 6 6 . F O R PRiQPMULT, 4 4 - -* 4 5 0 VARMbLE 1 6 0 » P 5 USED F O R OET. A FSV 1 6 1 - 1 6 6 F O R AFTER MULT 4 6 1 « 4 b~' •xxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxx.xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 4 <:•; < * X ; 5 4 J f * X ^ r r 1 * X 4 H O S P S T A . ^ T M A C R O 4 5 7 ' ~ X 4 ' . -+ X 4 5 S * X 4 r. 0 ENTER * A E N T E R EITHER B<=!> OR EX RED X 4 ( . ) * 4 6 2 1 ' W < 1 2 STORE TIME OF ENTf< Y I N T O DEO IN 0 ' R A M F TE R 1 2 X 4 6 2 f . . . I „ . x 4 6 4 1 E N T E 1 AOPRO GO TO AOMISSIONS PROCEOURE 4 6 5 « 4 l-1 OEP-UT 1 5 L E A V E THE SUPGEPY C U E ' I F X 4 6 7 * X 4 i - S ADVANCE _ 1 5 M I N T O F I L L I N FOP MS W A L K T O FLOUR X 4 l,t THEN T U B E D A M 0 GET INTO P . J . ' S - - - j j — 4 7 0 * X 4 7 1 5 NT Ed NUFSE N U R S E W I L L CHrOi: BLOODPRESS T E M P FTC. X 4 7 ; « X 4 7-: L E A V E A O P P Q L E A V E T H E ADMISSICN PROCEDURE X 4 7 4 * X 4 7 6 ' A O V A N C E 1 0 , 2 N U R S E M O W C H E C K S W E I G H T E T C . x " ' " 4 7 6 * X 4 7 7 LEAVE N U 3 S E THE NURSE I S F P E E F O P . OTHER OUTlf* X 4 7 " , « X 4 7 , O N E J = V 3 4 f . n * X 4 (.1 GATS L S V ! ' " " T H I S " " G T T E IS'OPE.NED B Y A C L O C K O H T H E " X ' PHYSICIAN'S SURGERY O A Y X i. fi V 3 >v. « X 4 t : • OUE'JE 1 7 THESE PATIENTS A P E WAITING F U R O P CP', S'JRGEPY D A Y X 4 E 7 * X ' , o -\ P K E M P T VI 4 * 9 * 4 9 0 S E I 2 E P 5 Go T O SEIZE THE RIGHT S U R G E O N X A ? i * 4 4) 2 R E T.H.N VI 4 9 2 * X " . .. L<.u  O E ° « T 1 7 GOT A SURGEON OPERATION STARTS N O W X 4 9 C . * X 4 9 . ' . A O v ' A N C ' J 3 4 S U K U t F . Y T l " t X 4 y ; ft X 4 ) ^ RELEASE P 5 RELEASE T u n APPROPRIATE SURGEON X 4 * X '•: 0 0 LOGIC '< V I W H E N A L L T H A T H A V E H E E N SCHEDULE/) F O R . A X 5 0 ! * .PARTICULAR SURGERY D A Y H A V E B E E N OPERATED O N X 5 , ) ; ft X 6 0 . -*• 5 0 4 TEST 0 P 3 , M P 1 2 *0 T H I S MAKES SUPE THAT M O NEGATIVE REMAINING 5 0 -« T I M ; i s c . A L c u i ATrn 5 0 L -* -. J 7 V X 5 u -oo 8k r\ -i' t~ f rr T: •( r - U r l l \ u> U • -ll - 1 U'l tf\ < — O -I > c >• a. r < UJ — c O r-— cc — T' < U. to JT ? zz z> r c K * * * ! » *t tr1 * ^ r~ ( f 85 '-• O r- L\J f. j ^ r~ r~ t~ r- > a*. if* tTi •/> o: ^ CM; rsi -1 .1 i/i ir ir I 1< < LO <3 3^  C — r. •) S O O O O O i . C .f r > o o c o — 1 -H UJ 1 f 43 44 V 45 A CV AN C = LOGICS T ? V U N A T 6 " 1 11 0 THAT 'S A L L FOf- NOW. F E TURN NEXT WEEK 6?9 6 30 '. 31 6 32 6 35 6;4 \ / 46 GENERATE 10030, ,1 3720, , , 0 SURGERY DAY EPt IP E M ' G AT E 6-.5 6 3 6 (.37 \ 47 ' UNLINK '34.SEE1.ALL TRY TO FIT ALL Uf.GENTS IN NEXT Si ATE 6 3 1 ( , 1 1 t ' O 43 L O O K S 12 49 AC'MNCC I 64) 6 4.2 t 43 50 LC3ICR 12 1-.44 t 4 5 51 TER3I NATE 0 THAT 'S AIL FOR N O W . F E TURN NEXT WEEK 1,46 * 647 * 1 52 GENERATE 3260 , ,3360, , . 0 SUROEFY DAY FO" DOCX OPEN GATE 65 0 -.5 1 '. 5 2 53 54 * LOGICS ACVVNCE 1 3 1 r o ' £ 54 6 55 • L oo i: ;< 13 , TERN I NATE 0 THAT 'S ALL FOR NOW. RETURN NEXT WEEK ,-. 5 -. 57 * GENER-UF 10030,,, , , 5 EVEDY WEEK. CPS ATT A XAC T TO. AL I FIT WEEKLY SURGE0Y TIME-t. 5-6 ) 661 5S 59 * SW2VALUE S \V : V MUE 199*,1 USED 193*,1 USED T O UPDATE COUNTER FROM 300-400 TO UPDATE CriJMTfR F POM 2 00-200 t 6-f 67 (.6 4 60 61 6 7 SWEVALUE S.W2V41UE SPLIT :<199,.*4 S A V E P H Y 4 A L L O T E D T H E X15P.X12 S A V E PHY4 ACTUAL T I M E 3,NEXT1,5 CREATE 3 MOPF XACT, ONE FOR EACH PHY 51CIAN 66 5 ! 66 6 6 7 63 NEXT1 SAVEVALUE V I ,0 Z E R O OUT PREVIOUS WEEK'S ACTUAL SURGERY Tl MF S - - r - 7 , - T - -- 6 ) l. 7.) • 64 $.W = \/.\LuE Vl ,0,H ZER'S O U T T H E ALLOCATED URGENT SUf.C T I H E i: 1,. 6 72 6 72. 45 ASSIGN 4.VS4LL0T 66 SAVEV ALOE •>5 , C N*4 THIS PUTS THE WEEKLY S'IFGFFY TIMES THE TOTAL ALLOCATED FOR H A T H PHY I N X1-X5 6 7 4 - --6 75 ! T 67 S «.•:'/ALOE P5,VtURGN, H THIS PUTS THE FRACTION G F SURGERY TIME ALLOCATED TO UKCENTS FOP EACH PHY IN XIII -XH6 6 / / 6 7.-. t 79 : - SWEV \LUE P5,VtELECT THIS TAKES A FRACTION O F WHAT IS IN THE ELECTIVE F - A C T A NO STORES IT IN XI-X THE 5 SAME t. H J 4 01 t "• ? tW -ePIACTS T H r T H " I.NAL IOTAL WITH ELECTIVE FRACTION i HI. < • :i i 6 J.4 OH* 69" • TS-HIN'.rE 0 ~ " " ' THAT'S ALL E 02 THIS l-'E'K £ 3 7 6 4R 7J GENEVA TE 1440 , , , , ,0 l$r CAUSE OCT. T OF X ADMITS UNL Y 1 PAT 00 CT) 87 1 Cf C- C> r~ -j- ^  • •C O «i. xiJ A1 ^ , r- r- r - !» r-- r- _< r-l ., r- r- r- if* *- r-X t- t- a _j s a *r c X IL £ < —« j LO U.J •— X J. 1 . h-LO _J ~ < C O ci i z AI c m >- —• > »- _* < J u ac-<: r- > _J (NI LL, <t ** X CL. rr <=, >- LJ J> O —J 7. O •— LO ? O 2 l l •LJ i> « » « * « LO V, 7" 'JJ < LO c >• r- r- f- r- f-C LL, UJ LO ui; i — LL -T , <./> IP . -• ' i _ l U! i Cl. i — o; LO LO is; r\; ,\, o-r~ r- h- r- r*-i- >• . M f>- !^ f. r- i-f—- 1^— I— I LJ C y- > m <r t-" —• —• LO a * P. >—I < y i ( 92 * SATE LS TRANSFER 70.NCM0R ,CCNT2 74<; 7 50 751 7 52 93 NOM OR ft TEST 0 P5.1,FINIS 753 754 / f 94 ft ft CON 72 ASSIGN 4,^*1 ASSIGN THE OP TIME 7 55-756 T 7 s 95 « ft SAV2V ALUE V14*,P4 USEO FOR ADOING THE PRE-MtlLT OPTIMES IN 151-156 7 5<"-7' 0 96 97 ft ASSIGN ASSIGN 13.VI UILL OSE THIS TO GET CORRECT FACTOR F OK UP MOLT 4, VI? MULT THE OPTIME FOR EACH 0||Y 761 762 7;. 3 98 ft ft SAVSV ALOE V15*,P4 USEO FOR AOOING THE POST MOLT OPTIMES 7r.4 7 6:'. 7 6 99 100 ft TEST £ TRA 1.". PER :>= 18 , 0,P PR 10 SEND THE SKEO PA T TO SK.IO ONLY .087 , . .".HOP fl.7* REOUIRE NO SUROCPY 7'. 7 7 t r. 101 ft ft TEST NE P2.4C00.EMER IF THIS IS AN EMERGENCY GO TO t"CR IF NUT 00 ON ' 7 70' 7 71 772 102 ft ft TEST NE P2.2CO0.tjr.OPS IF THIS If AN URGENT GO TO UGOP Z 7 7S 774 77' " 10? 104 ft UGO " 3 TR'ANS EE'V TIE JE .WAIT 15 77/. - — 7 77 77* 105 *< ft 3JE JE °5 !T< 7ri J 7P! 106 ft UGO" * L INK URCP.FIFC URG WAIT HERE TO BE SCHIU 1 i / ?»": 7 84 -107 10? N4H ft R p P. I 0 L IN< ASSI-.N XHlCO.FtPfl u"; GFNTS WAIT h F ? E IN ? l , 5 2 , 3 i , " 4 . A C C W !>! Hi 2,0 / i -. 71.6 787 109 ft ft TRA NS I- ER ,WMT }•:.•> T'l 7 9 0 ft TO WHAT IS THL NfcXl CLUStSI SLATE I'-'. 7i : 2 110 SEE1 TEST 3 RtBEO.O. PtO ARE THERE ANY BEOS IN BIGHT WAHJ FOR UPG PAT 70: U l ft ft ENTER 3C0 YES THERE IS FOR THIS PAT /->'••• — 796 79' 112 _.11.3 114 * * OEPA< r OS0 \1 T ACVANCE 15 P5 1440 DAY BEFORE SU!-GEPY MOST ELAPSE 111 7 5 3. -7 9-P F 00 : F01 fO-— — 115 ft ft PRIORITY 1 r J ' 8 04 ~ l i T " " ONE /: ' 18 WAIT FOR SUPGt'UN U7 P1EE'-ic>r v l h )•": 00 oo 1 89 O r> O r-. ,\, .•**• icy jO V "_J U3 90 f .1 _ , 0 si r- - a r ••• N L r-r- *- f- r~ r-r~ r- -r a c. m rr a: a a" o~ - "*• c c- *- (- - - c? c e r-» O .-. ~l r— r-. ix nj •'X (-•] 'V IM i - - •X) oJ a_ cr •V c u. L- u- t •JL J." X LT L* C C" : cr j c <r i CO 1 >- LU >- j X . X r JL LL, - 2". ! < 1— _J > < ~z • r* - h- 2. _, C c 'Z <J _ J h-- X zz> a: 1— ^ . ; 0 r. LL' z: M 1 (" iL _J zc X fi. ' zz U J o < •J.I LT 37. ; LO o J I _) O CO -J a. — I'- U O > < X ic ' < cc "^ TJ UJ C — L L -I _ i LL u LO 3Z •j- l ~ V (jj T C- O zz cc <3 ZX UJ > t— or. c. O IJJ O of o > X < J. cr. i: 1 U_' X zw LO < 3 ; .u u_ 17 r~ _J x I- — >-O <1 - <I t- LL 3T a; . 0 7" t- n _ LO Zz r. ~ LL- a LLJ L J X •X. < LU c /I < C > CO x _J LU 0 > LL LL CL < U 2 : X < > SL 1 ~r c to CC K a >- X o O cr* L J X L: O — y • X- Q ^: X LL. O a u- a. - t— c > O ' o ti _o _ J Lf.! a: cc LO - 3 CO LL LL UJ LL ti z X — •4 >. a •c LO al c r.' X j: Jt 53. X UJ U J CO UJ CO _J 0 x 5- Ui liJ —J ZL~ X CL a c LU r- CC Ll X 0 — c D a: X rt Li X 1_) 1— 1— 1— LL ?T L O u X *Z LO > "ut o m l - L^  K- 4^ X h- — •— X u: u. C (X zzz 7T> u ~ X *— _J if r> ZC zzz 1^ 'JZ az et U< U J JJ JJ LU > ' c :s> > u < 1- / l ZZZ kl. > c ^ : •x " ? < — r- r- P-< < — LJ X ZD •3 r- LL' X <* L" LL' a' <r V— X < UJ <: LL ZZ u. Q ZX CO _J CO LO X LO _J - J f—• c 7* Ci* a: zx CD ~ zz ci co -J r- 2* rO 0 : >• r- X. zzz Q c - J c L'J _1 0 <i J . X < »-0 • to >. ->. CJ » X fM - 0 c' INI f>: u- . (_) U J JJ 0 <r~ V— L - LU u. 0 IL L J CO Ut CU r- ^ O 0 : O X .c -T _> a- C cr; O a Ut *-•• + UJ sT D —* u- T) tn cr. in tr ILi »A 0 - J" - 4 rv > c > -1 A- o > > O r-* 0 > an rr ° ^ or c n ** Ui IU L— 1— cf IM •— UJ U J Ut < c- 111 *T ut O —• n 7* I— LJ LA t*l LJ O 7^ 0 r* _J li. l l ! L " CC •*r tu IL' 7- ^: LO *™1 C J > *-> If <T 1— V> u; v~ £ > _ J CT* Ml 77 _ J O* _j co _ J Ct* LO 'O rC -z> or 111 • J I'J rr> • ; UJ •u L • (jj 'U I O UJ r/ Cv n LO cr C tc - I r- T-* 0 - J O X 1— x ^ I— X 1- "* 1-r-i - ^ t— O. K— O cr. i ~ y, ZZs L-- a UJ u V-« * * c * * * * * * « « * » « « * ft « r- CC Cr o (Xl -j 0 ca 0 tr a 0 - 0 Cl ro •r vf •t _ ^ If. m Lf. ir. in «o •c _ 175 GOT TO =NT=* OE F THIS URGENT HAS DEFINITELY GO TO COME IN 92'. 9 20 9 ' 1 N 17A V 177 DEPART DEPART 15 P5 9.; -. 93? 9 3'- .' we ADVANCE P3 WAIT LENGTH OF STAY s ? : > '13 7 \ 179 LEA'/E OE F U2 GENT MED PAT IS FINISHED. •; jt. 9 35 9*0 183 TER MNAT- 0 LEAVE THE HOSP * URGENT 5 MME HERE TO 3E SC HEDUL E D FOR SURGERY 5'.' 9 '. 2 9<o 131 "US'JR ASSIGN 20*, 1 THIS COUNTS HO',j MANY TIMES AN UPGF NT HAS TRIED TO FIT ON THE SLATE O A ? 9','. 13.3 183 TEST L SAVEVALUE P 2C, 7, MUS VIPi ,H IF THIS PAT HAS UEEN SCHEDULED MORE THAN 7 TIMES HE MUST CO MF. IN (GO TO MUS I THIS SAVES T1 If AMOUNT HF UI-'OENT D.R. TIME •'. 4 ". •<5 0 9 5 1 9r; THAT IS REDOES TED 'iY EACH r»'iY 9 5'' 9' '• 184 . ASSIGN :s.vi _ _ XVI WON'T WORK SH ASSIGN VI TO "lb 135 TEST G XH*1 6, XM»5,NAH AND USE XP! t VP. X"l<. TEST IF THE A'-'OUNT H E SURGICAL TI f - ' E * EXCEEDS 5 r'. 5 5 7 " 5 ' THE ALLOTTED TIME REMOVE FROM SCHPO IF NOT GO TU NAH AND WAIT THERE 95 91. 0 '5 fc) V b V v«.-4u'. IPs S AVEV ALUE V1-.P4,H THIS lVGENT COULDN'T FIT ON SURGERY SLATE SURTRACT HIS TIME FROM THE SLATE 1ST ' 188 ADVANCE TXANS F ER 1 , 11 GOP WAIT UNTll An OTH.-RS AF." T R t c O RETURN TO URGENT ACTIVE LIST 9' 7 139 v.US ENTER MUST THIS URGENT Mil ST RE ADMITTED AS NO UR GEN TS 9 70 190 191 DEPART DiPAR T IS "5 9 II 972 47? 192 SEI ZE 95 97? 97' 193 ADVANCE ?>4 S / ' 9 7 { -l c 4 RELEASE or, Q 7 ' . WAITS MOSE THAN 1 WEEK REGARDLESS 4 "ij 5 f. ] ?2 1* = l « o " ACVVJCE LEAVE •' ) •'UST LENGTH OF STAY MUST ELSPSl LEAVE RED q.o/. >' r TERMINATE 0 LEAVE HOSPITAL c ,i. :• 1 9 - '•' ID y 193 N0T1 UNLINK NF. D, XUED, S *EXR ED OTHER PAT "UST CHECK FOR HFDS ALSO a 19Q UNLINK MED,TEST1,ALL ALL THE REST OF THE URGE NTS MUST BE CHECKED * 200 TERMINATE 0 RETURN AGAIN TOMOFPOW SF.V 5 90 9 5'I 9r2 992 ' 3 ^ 4 9 9 3 i 201 SEE2 * ENTER 3E0 CO TO CORRECT P ED r c.f 5 97 1 202 | 03 "ART .15 ' 9 •'• ! 20' DEPART !>8 1 1 00 1 1 i 204 ft « ADVANCE P 3 LENGTH OF STAY MUST EI.APSE 10 0) 1002 100 3 205 * LEAVE 3cC LEAVE THF CORRECT BED FREE FOR SOMEONE ELSE 1 1 0'-1C0-" 10 0'. ; 20h # TER'-II NATE 0 L P A V E THF HOSPITAL 1. 3 07 : O P Jl-O'. 20T L 203 XOED ft DEPART EXREO 15 THERE ARE-SOME EXTRA. 3EDS E OR THESE PAT'S U10 1 01! 103 2 20° _._ ft * DEPART P5 10)3 }•)} -1'!) '. • 210 ft * AOVAMCE P3 LENGTH OF STAY MUST ELAPSE " " ion. '' 1017 ! 0 ' -211 LEAVE EX3EE FKtL BED E OR OTHEF P AT 1 ) ? '' ]-J2:i 132! 212 TERMINATE 0 LEAVE THE HOSPITAL ft « THIS ft IS THE EME RGENCY SEGMENT OF THE MODEL X X 1 3 : •-: oi -10 2 4 « X *xxxyxxxxxxxxxxx>xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx ft • lol': 13 2': 1. (.' 2 7 213 ft EMFR TEST G RSPEC, 0, 5?E IF ORTHO REDS ARE FULL, GO TO SPECIAL KEDS 1.0 25 102-5 13 20 ft *xxxxxxxxxxxxxxx>>.xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx * X 1 , • • ! 0 1 '•) 2 1 REGS ERO< ORTHO. WARD ~". ft X • XXXXXXXXXXXXXXX>XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXX XXX 10:4 1 0 3 10 j-! 214 ft ft 2NTE3 31 D AN EMERGENCY CASE WHL ENTER A BED IMMEDIATELY UPON ENTRY INTO THE HOSPITAL !. IJ / 10 3* 1 0 V-~ '21"5" 21C-ft MARK PRIORITY "14 3 !'•)<'! 104; . l-)*2 217 ft ft DUE JE 15 0PEM=OPERATIONS ON EMERGENCY CASES  •.: 4 • 104 1 1 0 4 5 ?IS • ft ft PREEMPT v l " OOEUC UP FOE OPERATION ( WAITING TI'-'F Will. . 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X X X X T X X X X X X •* » « »!«-«• * • J ' c <r ; UJ IT-n c: ; 2 3 * 2 3 9 I TEST fl A O n N C E V 2 , 0 , G C N 1 FREE V2 L E N G T H OF STAY IN DEO E L A P S E S 1 1 Ov 11.10 1 ) 1 1 1 1 1 3 2 ^ 0 GON 1 LEAVE S P E S L E A V E THE P.ED !! !<• i 241 T6R-1IN4TE 0 T H I S EMERGENCY C A S E L E A V E S THE H O S P I T A L 11 I 11 16 1 1 1 7 \ 2 4 2 WAI r O'JEJE 15 T H I S I S THE OllfiiJE THAT P A T I E N T S WAIT 11 1 f' I 1 ! 0 I I 2 0 2 4 3 TRANSFER • 0 5 0 , , C A N C 5 5 C A N C E L AND NEVER RETURN l l >:. 1 1 22 1) 23 2 4 4 T.34^SFER ' ,'S'TAT ! i. 112 5 2 4 5 CANC SPL IT 1 » S T A R T , 1 8 PF C R E A T E C A N C E L L A T I O N S THAT NEVER P E T 1 1 2 ' 2 4 6 STAT O U i IE " 5 U S C C COUNT EACH I ' H Y ' S P A T + S T A T G A T H E R I N G 1 1 2 7 1! ?.<••• ! 1 2 9 2 4 7 TEST NE ? 5 , 5 , A D V IF T H I S IS A ML'N ORTHO SURGEON GO TO AliV I 1. ?•! 1 1 3 1 I I 32 1 2 4 3 2 4 9 TR.ANSFE* T R ^ S F E * , J C I N • C 7 5 , J O ! 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(.•: ) ! 4 7 1 1 •'. - J ( 2t5 ^ 2 6 6 * * * ft A S S I G N I" E S T G 1 6 , V I X * 1 6 , X * 5 , 0 F T 1 S I N C E . X V I K I L L I'.'OT WORK M U S T A S S I G N VI TO 9 1 6 4 N 0 U S E X P 1 6 I F T H E T O T A L (IP T I M E I S L E S S T H A U 1 ) 6 5 \ 1 1 7 0 1 1 7 1 1 1 7 2 1 ' . 7 3 1 ' 7 4 j 2 6 7 * • T E S T L V I L E F T » 3 0 , C E T 2 A L L O T E D T I M E G E T A N O T H E R P A T I E N T I F T H I S P A T I f N T O V E R F I L L S T H E A l L O T T E D 1 ) 7 5 S 1 1 7 6 1 ! 7 7 "ft * T I M E ' J U T M O P E T H A N 3 0 > I M U T E S " i S " F R E E ' I F T I M E I S UIIE C U E D U L ED G E T A NOT HE R P A T l W f c U 7 9 1 1 .'- 1 2 6 3 * A S S I G N 2 * , 1 A D D 1 T O T H E P P I O R I T Y S O T H I S P A T I E N T G O E S B A C K TO T H E F R O N T 0 = H I S C L A S S ) ) 8 1 I 1 5 2 ) 1 c •' 2 6 9 "ft * S A V E V A L U E V l - . P A T H I S P A T I E N T O V E R F I L L ! 0 S C H E D U L E R E M O V E H I S T I M E F R O M S C H E D U L E i : <S 1 ! fi •": ! ': 8 ' 2 7 0 ft A 0 7 A N C E 1 W A I T U N T I L O T H F R S H A V E B E E N S C H E D U L E D 1 j 1 ! 2 7 1 ft * T R A J S E ER~ ~ " . J O I N " G l . l ' 3 A C K T O W A ! T I N G L I N E " U P S C H L U F U L L " " 1 \ * 1 ) 9 ! 1 ; 2 " C E T 2 ft O N E INK. P S . N S X T i . U B A C K T H I S P A T I E N T N V r F . F I L L l 1) S C H E D U L E L U T L E F T M O R E T H A N 3 0 M I N S L A C K l i 5 • 1 • 5 . 1 ! 1 1 9 7 ! .! i't 2 7 3 -* « * * A S S I G N ~ " 2 » , 1 ~ * A D O 1 T O T H E P R I O R I T Y S O T l i ! S P A T I E N T G O E S B A C K T O T H E F R O N T (IF H I S C L A S S 2 7 4 * * S W 2 V A L U E " A D V A N C E V l - , » 4 1 ' S I N C E T H I S f ' A T i r . ' J T w I L L N O T F I T I N T O S C H F . f M J L F R E M O V E H I S O P E R A T I O N T I M E F R O M T H E S t H E D U t E W A I T U ^ T R O T H E R S H A V E B E E N S C H E D U L E D 1 !. "' •/ 1 2 0 0 1 2 0 ! 1 2 0 2 1 2 0'-. 1 2 H -2 7 6 T R A N S F E R , . I C I N ft * GE T 1 ' . . 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NO, CHECK TRANSFER , LfCK OR A Fl flEl1AR T LOOK A^nNCJL P5 0 HOSP M1C30 3E0, INI.O'.ITl .EXIT! 2 37 .- ! :- ' 2 3" 2 AO ?',') 2.4!) 2 AO 1 9 0 " 29! TN I 5NT-JT 1AR< RED 12 T?rr 240 ; '••) 2 40 292 ""293-294 . ENTE* "DEPART ' ADVANCE AOPRO T r — 15,5 2 •') ;:'•') -"-(!" ?'-0 2 40 295 295 "297-29e E.NTER NURSE L E A VE AD PRO ~ C VANCE LEAVE 10,2 " NURSE T4TT 2 4 0 2 4.') 2 40 2 40 299 300 301 OUE'JE GATE LS V3 - v r 2 40 2^0 2"4"V" 2 40 2 4 0 202 DE'1 A3 T QUEUE _V3_ 17 3 03 "ro"4-305 PRE EN PT "SEa= RETURN VI "P5~ VI 240 2 40 ; 49 2 4 0 2 4 I j 2 40 2 '.3 ; 40 2 40 306 "307 303 DEPART ~ "ADVANCE" OUT 1 RELiASE 17 "P47 P5 7T~r 2 4 J 2 40 r •• 3 "• ." 4 0 2 4.0 L O cn 3 0= LOGIC R VI TEST G P3 ,»1F12, EX IT1 / \ 311 312 ADVANCE EXIT1 LEAVE V2 3E0 12.41) 12 40 1240 12 40 12 40 1 2 40 •\ ( 213 TERMINATE 0 1240 1240 12 4 0 \ * 314 CHECK T EST 3 RSEX3E0, OtNOPE CHECK FOR FREE HEOS IN OTHFR WAFDS 12*V> — 1241 12'.2 • HDSP MACRO EX1E0,INII,OUT2,EXIT2 1 2 44 12 44 315 INI I ENTER EX3E0 !244 ••• 1244 12 44 316 MARX 12 12 44 V> 4 4 1244 317 319 ENTER l> = »WT \r.PRO 15 • " 12 44 ••' 1244 1244 31° ADVANCE 15,5 12 4' 1 t 44 320 ' E,\T:R NURSE # . 12 44 J ?44 12 44 321 . . . . 5 " . .... LEAVE ACVANCE ADPF 0 10,2 . • . 12 4., 1 244 12 44 1244 12 44 1 44 323 L E AVE N'JRSE 324 0.)= J= V3 J 244 U 4 1244 325 GATE LS VI 326 0 -"ART V3 * " '1 2 44 12 44 1244 327 WzJ- 17 1 i. 4 -12 4 4 32S 32" PREEMPT" SEI V I P5 1 2 44. - • 12 44 1244 330 RET.JR N VI 12 44 12 4 4 331 0E° SR T 17 ' ' 1 - 4 4 -1 T- /. 4 337 ACVANCE P4 i V .'. rf. 333 OUT 2 RELEASE R5 1 .' 4', 1 2 44 1 /4-3 34 Ln.HC R VI 1 .. 4 <-V 1 < 4 > 98 y ( S T A R T 1 R E S E T I V ! T l A L L S 9 0 / X H 1 0 0 , 3 1 / X I 9 8 , 1 9 9 / X I 9 9 , 2 9 9 3 4 1 C L O C K G E N E * A T ; 3 4 9 6 0 0 M U L T I P L E D E E I N I T I C ) O r S Y M 3 0 L I N A B O V E S T A T E M E N T S T A R T 1 1 2 8 6 • 1 2 H 7 1 2 . 8 8 J . 2 H 9 i ; « ' ] { E NO 1 2 9 ! . 1 ! • I O L O C R O S S - P E F E k F N C E "\ B L O C K S S Y " 3 0 l N U ' - l i E R R E F E R E N C E S f M J V _T> n r i e E G N 76 C A H C 2 ^ ' 5 _ 1_13 2 C M C X 2 3 6 1 2 2 " ) C H E C K 3 1 4 1 2 3 1 C L O C K 3 4 1 I C O N 12 9'V V7\ I E » E R 2 1 3 ' 7 7 1 ! E X 1 T 1 3 1 ? 1 2 4 0 f E x i T S 3 3 7 . 1 2 4 4 C A S T 2 3 1 1 1 4 3 F J N J J 1 3 6 7 5 3 F l . N S H Z-,0 1 2 1 S F 1 N 1 2 5 1 1 2 2 - 3 G E T 1 2 7 7 1 1 7 4 _ - j . E Y 2 " - 2 7 ? . " 1 1 7 7" G O " : I 2 4 0 1 1 0 9 GOT JO 1 7_5 9 2 5 C O i j f l 2"3 7 1 1 4 1 G R - ' 3 2 53 6-7 A H E R E )3 7 1 . 3 7 2 0 I " H U R " ' 2 5 0 " T 1 3 5" I N I 2 9 3 I I N I I 3_15 i J O I N 2^ 3 mi rrrs 1T90 ITO"? j L O O K 2 3 9 1 2 3 2 I ME P P 1 5 0 ' 7 6 9 _ I V E ' O l i ' s '2 9 2 3 ! V ' J S 1 3 7 9 4 8 _ _ S_UR 1_31_ T O O N A M 1 0 " 9 5 3 N E X T 1 ' 6 3 6 6 7 N E X T 2 2 * 4 5 3 4 5 4 1 5 4 9 5 6 0 1 1 9 3 1 2 0 7 " N C - O R ' 9.V - 7 4 9 N O " E 3 ) 9 . 1 2 4 2 N O T 1 6 7 9 0 4 ' •  NTTT1 i"T3" ST5 ? F F • 2 2 S 1 0 6 2 O l . ' T T l 1 2 1 3 2 0 " ' • " " ' 0 U T T 2 1"'.2 8 5 4 — — G U T T 3 153 8 8 7 O U T 1 3 33 C U T 2 . I ° R O 1 2 7 7 9 3 i P R 0 2 1 4 4 8 2 3 . C U T K - ?R3 12 IT : * R P R 1 0 1 )3 7 6 7 S E F l U ) 3 9 6 6 1 7 . 6 2 6 6 3 8 1 1 5 5 STT3_ 2 7 1 v T T mn i S = F 225 1 0 3 0 I S T A R T _ 14 _ 7 1 3 1 1 2 6 I " S T A T " " 2 4 6 ' ~ 1 1 2 4 " 1 2 4 8 : " : : I TE S T I 1 7 2 9 9 2 I. T F S 1 1 1 4 7  UGO'" U G O ° S WAIT XBSD 136 104 2 V2 2 )7 •357 7 7 4 7 7 6 7 8 9 9 1 7 9 9 0 C P . C l S S - P . t r E F ENCE S T O R A G E S SYMBOL N U ' . B E R R E F E R E N C E S I 14 0 715 ~A0P~RO~-BED ~CE = OCCX - w -NURSE S ' E S LR BED 3 ° 4 3 3 8 1 2 4 Q 3 4 ? ' 3.-10 39 12.40 7 93 1 2 4 0 - o j f j -1244 P 2 4 1 2 4 4 9 0 4 9 1 1 9 9 6 1 0 0 5 1 0 3 0 103.3 1 0 7 2 l ' . ? l ll°o III 9 1 5 9 9 0 1 0 1 0 ) 0 1 9 124 7 1 2 4 4 ) J 4 4 2 3 0 9 6 9 3 9 2 12 4 0 3 9 6 1 0 8 7 3 P 5 8 5 2 12 4 0 1 1 1 4 E 9 1 " 1244 124 4 C P O S S - R E F E P E N C F . V A P I A B L E S S Y M B O L NU'« 3E R R E F E R E N C E S ALLOT E L E C T L E F T 7 4 - 0 42 2 4 1 2 6 7 3 6 8 1 1 1 7 7 " 8 2 7 " ' 6 54 " ' ' 8 5 6 8 8 9 L E F T 2 " 1 6 "' 42 7 " 8 2 0 " " " H87 '." STAY 5 4 1 7 7 4 0 3 42 5 6 73 C P O S S - R E F E F ENCF U S E R - C H A I NS ' N W l S V UcFWMes" S Y M B O L C A L L " M E D U R O * 1 1 3 9 " B 9 9 " 7 0 0 1 1 4 7 " " 9 0 4 ™ 7 3 3 ~9-07 T5TI 9 1 T - ~TTt 9"7Tj 997" C R O S S - R E F E R E N C E F U N C T I O N S S Y « AOL NUM31R REFER F.MCES CHNGE D A I L Y 3 3 .53 B2 34 2 7 3 5 7 1 8 — ooc Nnp-(T O R [ , 3 59 70 29 8 6 .37 3 7 d T 4 5 7 2 0 7 3 3 s XDOC X H 1 I T 59 71 371 694 275 1216 r **** ASSEMBLY TIME = .43 MINUTES ***•» O S I MUL AT E U S E T H I S O N E L OH S I M U L A T I O N M O J E l R U N F O R 1 9 7 1 OA Y P H Y S I C I A N * P H Y S I C I A N 1 " C N ' O A Y 1 D R . G E L P K F " 2 ' T U E S D A Y ~ " _ ~ D R . nARTLCTT" 3 K E E S O A Y 3 D R . C O U S I N S 4 T H U R S D A Y 4 D R . B U R G E S S S A V E V A L U E * U S E O F O R S A V I N G 1 W E E K L Y S U R G E R Y T I M E A L L O T T E D T O G E L p K E O N M O N D A Y 2 W E E K L Y S U R G E R Y T I M E A L L O T T E D T O B A R T L E T T ( j ' l T U E S D A Y 3 •<£ 3 K L Y S U F . G E R Y T I M E A I. L OT T EO T O C O U S I N S O N VI S O ^ P S H A Y WE E \ L Y S U R G E R Y T I M E A L L O T T E D T O B U R G E S S O N T H U R O A Y 5 - 8 LE F t 3 L A N K ' " i F MORE T H A N 8 M.O. CHANCE V I 9 S T O R E S T H I S W E E K S A C T U A L S U K G F R Y T I M E F O R G E L P K F 1 0 S T O R E S T H I S W E E K S A C T U A L S U R O E K Y TI'-'E FOP. I U P T L E T T " 1 1 S T O R E S ' T H I S « £ E s S A C T U A L S u R l ' . i ' P Y TIME F O R C O U ' H J S 1 2 S T O R E S T H I S W E E K S A C T U A L S U I - G E F Y T I M E F O R B U R G E S S P A R A M E T E R * U S E D F O R S A V I N G * P A R A M E T E R " U S E D F O R S A V I N G * 1 P R I M A R Y O P E R A T I O N * * 2 P R I O R I T Y : F M E R G E NT = 4 0 0 0 U R G E N T = 2 0 ' 0 0 F L E C T 1 V E =0 " » 3 LE NGT H O c S T A Y " « A P R I M A R Y O P E R A T I O N T I M E » 5 P R I M A R Y P H Y S I C I A N • x x x x x m x x x x x x x x m x x x x m x x s r i K o T x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x ' * L G H S I M U L A T I O N F O R J A N U A R Y A M I ) F E S U A R Y 1 9 7 1 * " i n » I T V F 11NE T ! ON ' ' ^ E L E C T I V E I .? 0 0 0 - H R f. 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OP 4 0 0 0 6 1 ) F O P A 6 1 G I V E N P R I O R I T Y / t « * ooc 50 W I L L G E T TH F U N C T I O N E A P P R O P R I A T E 3 2 . 0 3 O O C * F U N C T I O N (/ ( 6 2 OR 6 3 F O R A G I V E N ' P \ 0 6 2 2 0 0 0 6 2 4 0 0 0 6 3 1 F U N C T ION 7N 1 , 0 2 0 • 2 0 3 0 F U N C T I O N 1 5 * M 1 , 0 3 . 0 4 6 3 0 1 5 . 3 1 9 3 0 . 6 9 2 . 9 ] 0 A 6 0 1 2 0 Voj'3 1 . 1 7 5 1 5 0 . 7 74 9 0 •> 0 • S 1 6 F U N C T I O N 1 5 4 5 R N 1 , 0 6 . 0 4 1 . 9 5 3 1 5 6 0 . 6 3 2 1 . 0 3 0 9 0 A 0 F U N C T I O N l 5 3 N 1 , 0 7 . 1 7 2 1 5 . 7 3 2 3 0 . 9 5 6 1 . 0 A 4 5 9 0 . 0 0 2 6 0 . 9 2 6 7 5 3 0 . 4 3 5 F U N C T I O N 3 0 6 0 R N 1 , 0 3 . 0 5 7 . 5 7 1 3 0 7 5 . 1 7 1 . 3 5 7 4 5 9 0 . 9 4 3 1 2 0 1 . 3 ISO *, F U N C T I O N V J 1 . 0 5 0 .12i> * 4 ^ " . lo'2"~ ~ . 7 0 3 " 1 5 6 0 . 4 5 4 1 . 0 3 0 1 5 0 7 0 . 6 4 ' F U N C T I O N 3 0 6 0 RN 1 »'J 3 . 04 . 7 6 3 0 7 5 . 1 5 0 . 8 9 4 5 9 0 . 9 2 1 2 0" 1 . 0 1 3 0 -9 F U N C T I O N R N 1 . 0 2 * 0 A 3 0 1 3 0 9 F U N C T I O N ? 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R A C K C H A I N S O R E M O V E F R O M B A C K T O F R O N T 2 7 3 A D V A N C E 1 ' 2 7 9 - " " " T R A N S F E R - " , 2 . 3 2 2 5 0 A D V A N C E FN 7 1 2 8 1 T R A N S F E R . 3 5 0 , 2 3 2 , 2 6 0 2 8 2 D E P A R T 3 5 2 0 ? : T E S T NE , ' 5 , 5 , 2 ^ 6 " ^ A D V A N C E 1 T 9 6 0 * 2 8 5 T R A N S F E R . 3 5 0 , 2 3 6 , 2 6 1 2 8 6 T E S T G 3 4 , 0 , 3 1 4 '•• • * 2 8 7 ! , T R A N S F E R ,2 31 | 2 3 3 J? 1 2 8 9 A D V A V C E 0 1 " ' • " i I 2 9 0 E N T E R -f 291 292 2«3 294 295 2 "A -"29" 2°3 299 "300" 301 304 305 Tos" 307 30P "30-r 310 311 "3 12 313 MARK E N T E R 0EPA1T ADVANCE ENTER _L E AVE ADVANCE LEAVE O u E U F '--.tfl'LT' 0 E 3 A R T IF PRTE^T" S E 12 E RETURN "DF PART-ADVANCE -mm1-TEST 0 ADVANCE 'VE'A'VE T E R K I N \ T = 12 7 15 15 ,5 6 7 " T o T T 5 V3 VI V3 J5 VI 1 T P5 " v l J3,N''12,312 •</.: TT4 TEST' fi" "35,0,339 41 315 31 T' 317 213 T " 32 0 321 322 323 324 ""325"" 32.5 32 7 "72 3'-32 > 33 0 ~rn~ 3 32 ?' 3 "3 3 4" 335 33D E N T E R —<SK<~— E N T E R O E ^ / P T "~Vo/iN.;T E N T E R L E AV r A D V A N C E " L E A V E O U E U E "TTAffrrr O E D A R T O ' J E O E " - - • . ? : ' • ' - - - T S E ! 7 E R E I OR N -75-riSTtf-T-ADVANCE RELEASE LOG ICR" TEST 0 ADVANCE "TT7~ 333 T'EAVT T E R M I N A T E " i r I 15 15,5 5 7 10,2 5 V3 V I V3 17 " V l " "9 V I ~T7 ,).'. ->5 "VI P3.MP12.337 V2 33 V" ASSIGN" 2*. 2 TRANSFER ,246 LINE 3-,! 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Ji} -rt • J-T j W ' 31 R E S E T I N 1 T I AL L S 9 0 / X H 1 0 0 , 3 1 / X 1 9 3 . 1 9 9 / X 1 9 9 . 2 9 9 T ^ I C E N E K A T E T^o-^ TTJ!-S T A R T 1 r RELATIVE CLOCK 349600 ABSOLUTE CLOCK 4278240 s BLOCK COUNTS BLOCK CURRENT TOTAL BLOCK CURRENT TOTAL BLOCK CURRENT TOTAL BLOCK CURRENT TOTAL BLOCK CUPP EOT TOTAL 1 0 2 11 0 8 5 21 0 84 31 0 84 4} 0 84 2 0 2 12 0 ' 85 2 p 0 84 3? 0 64 42 0 B4 \ 3 0 2 I 3 0 84 2 3 0 P4 23 0 84 43 0 84 t 0 .34 14 0 84 24 0 8 4 34 0 8 4 £ 6 0 5 0 •34 15 0 64 2^ 0 ' 84 35 0 (' 4 4 6 . 0 84 6 0 34 15 0 8 5 26 0 84 36 0 84 4 6 0 54 7 0 35 17 0 65 7 7 0 34 27 0 5 4 47 0 64 • s 0 .3 5 13 0 85 28 0 84 38 0 84 4 3 0 64 9 0 '35 19 0 84 ?9 0 •84 39 0 B4 49 0 34 • 10 0 85 20 0 8.4 30 0 64 40 0 8 U 5 0 0 34 BLOCK CUR-;E NT TO T AL BLOCK CURRENT TOTAL BLOCK CUR P-C NT TOT AI 81 OCK CUE "ENT TOTAL 3.LOC K C u n NT TE TAL 51 .)' 5 4* 61 0 84 71 0 59 0 l-i 0 5 / 0 0 ; - 0 "" 1576 52 0 253 0 336 7 7 0 5°0 S2 0 0 93 0 418 5 5 0 253 67 0 3 36 73 0 6 90 u~i 0 19'', 9 7 9 ; . o c ; 54 0 2 53 64 0 336 74 0 55 0 64 0 1 97 0 94 0 1 8 3 i 55 0 253 55 0 336 75 0 590 85 0 1 970 9 3 0 • 1 i 3 5 5r> 0 253 66 0 336 76 0 0 86 0 167 0 0' 0 ! EES 57 0 34 67 0 336 77 0 0 .37 0 ! 9 70 97" 0 ~ T?~f. 5 0 0 84 OS 0 336 78 0 590 88 0 1970 96 0 ! 8 3 5 59 0 84 69 0 336 79 0 2 '. C 5 •89 0 197 0 9 r , 0 1 8 3 5 oO 0 84 70 0 5 9 0 HO 0 ?4] 90 0 197 9 1 ",!) •> .14;.) BLOCK CURRENT TOTAL BLOCK CURRENT TOTAL BLOCK CURRENT T0TA1 BLOCK CUBBRNT TOTAL ''IOC '.II' A'.'.'T T'.TAL 10 1 . 0 1537 111 0 2 2 1 2! 0 2? nr " ' 0 " 3 !'. I" " " ' 9 ' 3 " 107 0 F50 112 0 22 122 0 22 132 c ! 4 7 0 '* 10 3 0 728 ! 13 0 22 12 7 0 7 ? 1 2 3 0 3 ! 4 3 0 3 104 0 122 1 14 0 22 124 0 n 134 0 3 ,6 0 105 0 122 1 15 0 22 125 0 22 125 0 3 1 4 5 0 0 10.5 0 732 J 16 0 22 126 0 157 126 0 3 ! 4 0 0 0 10 7 25 117 0 ' "22 127 0 137 0' ' T'4 7" " 3 — 0 103 0 i 74 118 0 128 0 3 1 7P 0 1 4 8 0 n 10 9 0 3 74 1 13 0 22 1 2 9 0 129 0 3 149 0 0 110 0 25 120 0 22 120 0 •> 140 0 i 5 .) 0 J BLOCK CURRENT TOTAL 3L0CK CURRENT TOTAL ELC'CK CURRENT TOTAL Rt OTK CUE PENT TOTAL P! OCK CU?^ENT TOTAL " 151 0 '" 0 ~ ~ 1 6 1 " : o ' - "1 24 17! ~ 0 ' 4 1 AT" ~ " 0 77 2 • • 1 n 1 "" 0" ' "— "9 7 ' 152 0 0 162 1 0 125 ! 77 0 J 1 82 0 737 1 9? 0 °7 1 53 0 0 ! 5 3 0 590 1 73 3 1 1 82 0 67 5 . 19 3 0 9 7 15 4 0 0 ! 64 0 5^0 I 7 4 0 L li'A 0 tj : 6 I 6-4 0 '-• 1 155 0 J 165 0 533 1 75 0 0 ! 85 0 52 5 19 5 3 4 7 156 0 . 0 166 0 583 1 76 0 0 ! i'5 0 6! 0 19 4 0 96 137" - J — "0" TS7 0 7 177 0 0 1 8 1 0 oi 0 -T97- 0 5 3 • 153 0 0 163 1 o 7 1 78 0 0 If A. 0 6' 0 193 0 3 159 0 0 16? 0 7 1 79 • 0 0 1 89 0 97 19? 0 l:>0 0 124 1 70 o 4 1 HO 0 U I '-0 U 9 7 .'. JJ u '• PLO.: "2: 202 JJ ? _2"04" 205 2 J 6 207 203 K CURRENT 1 0"' 0 0 TOT Al. 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' c if- —t r - c-j O O ^ rv: .•vj <"j m i n r \ m a, «o O m C • O IT C N v- i M . - . —« »-J O l Xi —t Pvl i f J st O M P O r l M cvi PJ rn <n m PI if* st O O sO o C PT x* -O st rvl r-i .-« *M •J- o •-» r - rn 0-r-- <_> i_» rv PJ m IM PI (•"• . i O -fj f\j o rj> in if> vO O;^H < i-ji'1"1 r^ i NT 'f' m sn —* * pj'rvi rv p.jjrvj rvj rvi PJ PJ Px, PJ 1- C PJ <: p- p- O O .-H , py rvj - n m P"I f r° C vn p.- cc "VJ i<> t- tf: n p>, rn m P - r i -O pj co st „ p-' p . p* sj tr ifi • m m m f<-i rp. p-. C . o * -O p- p- >t rvi -t) r.i —' I Pvl: Pj PJ i n — i r - m o -O ^ Pi r vt If If. p*. m rn m J3 O J3 -0 O *n -C sj PJ PJ st N-rv rv P: TVJ PI —I I PI fjv vf\ i r.'vt st • m rn ir, p-. O sC" O -I r-» Cvj xt o - n P: r - P- JC P . p". m 2 so sC m sj t.j m r -m o - m —* •p P x m p .^ n m fM T vt c sC *n r-- orr m P"I * n i m sC O O l O i so r-i !xj-PJ <-« -p.) ^- P- r v c r J:- xf. tr- p-: m m p"< E E !> R E L A T IVE C L O C K 3 4 9 6 0 0 ABSOLUTE CLOCK 5 1 2 7 8 4 0 SLOCK COUNTS SLOCK CURRENT TOTAL BLOCK CURRENT TOTAL BLOCK CURRENT TOTAL BLOCK CLRPENT TOTAL "LOCK CURO ENT TOTAL 1 0 2 11 0 8 4 2 1 0 .3 5 31 0 p 5 4? 0 ' 4 2 0 2 1 2 0 8 4 2 2 0 8 5 3 2 0 8 5 4 7 0 3 4 3 0 2 1 3 0 8 5 2 3 0 8 4 0 8 5 4 7 0 s A 0 .34 i 4 0 3 5 2 4 0 8 4 7 4 0 u t 4 4 0 •' •'• 5 . 0 34 1 5 0 8 5 2 5 0 8 5 3 5 0 fc t 4 5 0 ?4 5 0 3 4 1 6 0 8 4 2 6 0 8 5 34 c 8 5 4 S 0 n 4 7 0 ? 4 1 7 0 8 v 2 7 0 " 5 3 7 0 A T 0 • ' Z S 0 8 4 1 3 0 8 4 2 8 0 8 5 3 8 0 8 4 A f . 0 8 4 a 0 .14 1 9 0 .3 5 2 " 0 .3 5 3 9 0 > 4 A 0 1 0 0 5 4 2 0 0 8 5 • 3 0 0 8 5 4 0 0 8 4 4 0 0 2 4 BLOCK CURRENT TOT M BLOCK CURRENT TOTAL BLOCK CURRENT TOTAL BLOCK C L'R R r N T TO T A t ' a o r . " n/R'"E',T TOT.'.! 5 1 0 8 4 6 1 0 8 4 7). 0 T O O 8 1 0 5 5 0 " ~ 0 T f i ' • 5 2 0 2 5 3 6 2 0 3 3 6 7 2 0 50 0 82. 0 0 0 •> 0 5 3 0 2 5 3 6 3 0 3 3 4 71 0 5 9 0 8 3 0 : 5 ] 2 ri-> 0 : 07; 5 4 0 2 5 3 6 4 0 3 3 1 7 4 0 5 9 0 3 ' ' 0 -J 4 0 ] r i 0 55 0 2 5 3 ' 6 5 0 3 3 6 7 5 0 5 9 0 8 6 0 I O f I r, c 0 1 f I 0 i. 5 o 0 2 5 3 6 6 0 7 3 6 7 c 0 0 OA 0 1 0 4 1 9 4 0 1 E 1 0 5 7 0 3 4 6 7 0 3 . 3 6 7 7 0 0 « 7 0 1 ) 11 0 : r - 1 0 5 3 0 .34 6 3 0 3 3 6 7 B 0 5 9 0 .33 0 lu/. 1 3 4 0 U ' u 5 9 0 3 4 6 9 0 2 3 6 7 9 0 2 1 2 0 8 9 0 . ; 9> 1 9 " . 0 1 8 ? 0 t o 0 3 4 7 0 0 f r '0 3 0 0 9 6 2 9 0 0 1^6} j 0 0 0 ! ' 2 1 BLOCK; CURRENT . TOTAL B L O C K CURRENT TOTAL BLOCK CURRENT TOTAL BLOCK CURRENT TOTAL RI PC* CURRENT TF-TJl . 1 0 1 0 . 1 2 9 3 1 11 0 I t 1 21 0 1 6 1 3 1 0 4 T"4T" " 0 " 4 " U ' 2 0 7 7 3 1 1 3 0 1 6 1 2 7 0 « l>* 0 t. 1 4 ? 0 4 1 0 3 0 6 6 6 1 1 3 0 1 6 1 2 3 0 1 6 1 3 3 0 *T 1 <~ 1 0 4 t 1 0 4 0 1 1 2 i 1 4 0 . . . . u . " ' 1 2 4 0 1 2 1 ' 4 0 4 1 4 ^ , 0 0 • 1 0 5 0 1 1 2 1 1 5 0 1 6 1 2 5 0 1 6 1 3 i 0 4 1 4 3 0 0 1 3 6 0 6 7 3 1 1 6 0 1 6 1 2 6 0 1 6 7 1 :•>>• 0 A 1 4 4 0 0 • " " 1 0 7 " 0 """""' ' ~ 2 0 1 1 7 0 1 6 - — - - ) • • ! 7 d " " ~ - --4- • • 1 3 7 - f l — 4 • ' • 1 4 7 " ' f l ' _ . . f i . . . 1 0 3 0 3 8 9 n s 0 1 6 1 2 8 0 4 1 3 8 0 4 1 4 3 0 0 1 0 - ) 0 3 3 9 ! 1 9 0 1 6 1,29 0 4 1 3 9 0 4 149 0 0 1 1 0 0 2 0 1 2 0 0 J 3 0 0 4 1 4 f l 0' 4 ) 5 0 0 u BLOCK CURRENT TOTAL BLOCK C U i i R C N T TOTAL R Li" CK CURRENT T U T A l HL'T.K CUE P.RUT TOTAL 01 '" ' ,K ! q F f T TOTS! 1 5 1 0 0 1 5 1 0 '" "TiTT TTT 0 3 ! F! 0 4 A J 1 •- r "0"""""" 1 5 2 z 0 1 4 2 0 1 2 8 1 7 2 0 0 1 8 2 0 6 ° 0 1 9 2 0 1 5 3 0 0 1 6 3 0 5"0 1 77 0 0 1 8 ? 0 5-:6 1 9 1 0 f* /. 13*4 0 0 1 6 4 0 5 9 0 1 7 4 3 0 i l ) 4 0 5 " 1 1 <i + 0 1 5 5 0 0 l t > 5 •0 5 8 7 1 7 5 0 0 1 8 6 0 = 31. 9 5 0 44 1 5 6 0 0 1 6 6 0 5 8 7 1 7 6 0 0 1 3 6 0 5 4 6 1 r . t 0 4 7 i'5"7" o 0 ' " 1<>7' .J "T 1 77 0 0 1 0 5 6 6 " 1 9 7 ' 0"" - 9 7 ' 1 5 8 0 0 1 6 3 0 3 1 7 3 0 0 ; MH 0 5 ' 0 1 9 7 0 r» 1 5 0 0 0 1 6 9 0 3 1 7 9 0 0 1 8,9 0 9 4 1 99 0 0 1 5 0 0 1 2 8 . 1 70 0 .3 i 8 0 0 0 J 9 0 0 ' . 4 £•) i 0 'J H L . K K CURRENT TOTAL 0 LOCK CURRENT TOTAL CiLOCK CUE FT NT TOTAL CLOCK r i iRI ; f .6'T T' • T A L '••I - . c c u » ; - r ' : 7 T 'lT.U 2 3 1 TO ' " 1 2 4 — 2 i r "0" 3 ?'" 1 0 ' 4 /fc 2 3 i 0 JU <•.'«! ~o • - ' 3 4 2 0 2 0 1 2 4 2 1 2 0 3 2 2 2 0 <-76 2 3 2 0 ? 9 2 4 2 0 1 0 5 5 2 0 3 0 1 2 4 2 1 3 0 5 1 5 2 2 3 0 A 7 6 2 33 0 2 4 3 0 1 0 S 5 " 2 0 - 1 1 2 4 2 1 4 0 4 7 6 l- c.u 0 4 C6 u VJ .' ' » ! 7 0 5 0 1 7 5 7 1 5 0 4 7 6 7 2" 9 4 7 4 7 0 5 0 3 4 2 ' . 4 0 5 4 1 2 0 6 0 1 2 5 2 1 6 0 4 7 6 ? 7 ' 0 4 8 2 2 36 0 0 1 0 6 -r o r 0 \ ' 2 1 7 0 ^ 7 6 2 2 7 o • " ' " ' 4 8 2 3 2 7 0 ' .'. 7 0 ; rv 5 " i 2 0 3 0 3 2 in 0 4 7 6 2 7 8 0 " 9 2 2 ? 0 -.0 0 e " t j 2 0 9 0 3 2 1 9 0 4 7 6 2 2 9 J 7 9 2 3 9 0 3 9 0 0 ) { 2 1 0 0 3 2 2 0 0 4 7 6 2 3 0 0 3 9 ? 4 0 0 3 9 2 ^ 0 0 0 1 3 L 0 C K C U R R 2 5 1 2 5 2 2 5 1 E N T 0 0 0 T O T A L 0 0 0 B L O C K 2 51 2 6 2 2 6 3 C U R R E N T 0 0 0 T O T A L B L E C K C U R R E N T 5 5 2 7 ! 0 5 5 2 7 2 0 1 0 0 2 2 7 ? 0 T O T A L B L O C K 2 1 6 2 0 ! ! E 0 2 8 2 1 5 0 2 3 3 C U R R E N T 0 0 0 T O T A L 4 4 5 ! 0 7 ? 1 0 7 2 ' . L O C K C'|R7 2 ? 1 ? 1 7 2 37 ( \J 0 0 0 TE T.AL 5 2 ? 2 5 4 2 5 5 2 5 5 0 0 0 5 9 0 5 9 0 5 9 0 2 6 4 2 6 5 2 6 6 0 0 0 1 0 1 4 2 74 0 1 0 1 4 2 7 5 0 1 0 1 4 2 7 6 0 1 3 0 2 5 4 1 5 0 ? 3 f . 1 5 0 2 8 6 1 0 0 0 •V*. 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Of' CD cc. f 210 0 9 220 0 456 230 0 33 240 0 3 3 250 0 0 > BLOCK CURRENT TOTAL SLOCK CURRENT TOTAL BLOCK CURRENT TOTAL BLOCK CURRENT TOTAL 3LIT" TOTAL 251 0 0 261 0 66 2 71 0 17] 2 81 0 4'0 2 91 0 L 2 2 252 0 0 262 0 66 2 72 0 12 3 2 82 0 1075 2';2 0 4 2 3 25 3 0 0 263 0 962 2 7-> 0 173 2 83 0 i 07 5 2 93 0 ' ? ? } ? 254 0 5 90 264 0 "35 2 74 0 123 2 8*- 4 44] 2 44 0 522 A 25 5 0 5 90 265 0 °35 2 75 0 123 2 85 0 64 7 2 95 0 52 3 255 0 5 90 266 0 935 2 76 0 123 2 86 0 104) 2 96 0 92 2 257 0 0 2 47 0 294 2 77 0 641 2 37 0 92 3 2.9 7 6" 4 2 -25 3 0 0 263 0 17! 2 78 0 64 1 2 88 0 0 2 9" 0 . 92 2 25 9 0 0 269 0 171 2 79 0 64 1 2 39 •0 9 ' 3 2 "•• "> 1 O > 1 2;:0 0 24 2 70 0 171 2 80 24 476 2 9 0 0 92 s i:y.) 0 c / 4 BLOCK CURRENT TO T AL BLOCK CURRENT TOT AL BL NCK CURRENT TOTAI 8I0CK CURRENT TOTAL 81 l i C K CUR r-E NT 7 MAI. 30! 0 ~ '24" "'311' "16" 8 5 0 32! 0 102. 3 3 1 ' " ™0~ I'O.y T A T " 0" 1 302 0 92A 312 0 924 322 0 103 3 32 c 102 242 . 0 1 30 3 0 ,926 313 0 924 3 2 3 0 10 3 2 3 3 0 I 02 i 104 0 i 24 214 0 118 2 24 1 1 03 2:<<- 0 ! o 2 ! j 305 0 9 2 6 315 0 103 325 0 102 3 35 0 102 30 6 0 . 924 315 0 103 326 0 102 3 36 0 9 6 307 0 : " 126 ' '" 317 o """"103 227 _ . f f . 1.02" " " "3 3 7 " """ Y5 "- 1 02 303 0 926 318 0 103 320 0 102 3 33 0 102 339 0 924 319 0 103 729 0 192 3.19 0 1. 5 310 0 '"'V2S " 0 103 530 0 102 3*0 0 1 3 * * * ft ft ft ft *ft ft* ft ft^ftft* rtftftftrtrt ft* * *ft ft * f t i r * * * * ft* *ft * i * JSER CHAH.'S * * ft * * f t * f t * f t * f t * 4 r t * f t ft V. * * * * * * ft* * * f t ft * ft * * * * v. ( t t * ( * 1 USER CHAIN TOTAL AVERAGE CURRENT AVFRAGL MA X I MUM ENTRIES TIME/TRANS CONTENTS CUNT rN T5 CUNTI NT S 1 132 43044.792 6 . 5 3 ' 21B 14692.921 14 3. 770 17 3 302 14118.347 ? 0 5.018 15 4 313 " ' ! l"0s8 ' . " 5'9"3 6 4,085"" 1.4 UROP 5 J 6 1421.737 .9 80 5 _„______ "150 137 1331.562 .714 3 31 10 5040.000 .059 ') 32 2 2880.000 .006 1 37 2 2 880. 000 .006 1 34 3 ' 2S8"0.002j .010 1 4*4*444 4**444 4* 4444* 4 * * * * * 4 4 * 4 * * * i i * * * * FACILITIES « 4 * * * * * * * * ft* ft*. * * * * * * 4 ft * * * * * * * * * * * * ft* * * * * * * * -AVERAGE UT I L 17 AT1TN OIIRIIMG " f * " c ' l I" T r ' v '•' 'NlU'i IEK """AVERAGE TOTAL " AVAIL. ' UNAVAIL." """ C'JF RENT - p F B - r c r n — - • • T i.A ' - r & t T r . -nj-'trr'-; :NT7IES TI VE/T RAM T I'-'E T I ME T l v r STATUS AVA ILA M11 I T Y S c ! 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ANS E X C L U D I N G ZERO E N T R I E S ft ft ft ftftft * ft* « w * * ft* ft* ft ft ft* ft 4 ft* * * ft ftftftftft ft* ft. * t ft * r u i l W U P D S A V E V A L U E S » f * * * * * * * * * * * * f t * * * * * * * * * * * * * * * * * * * * * * * * * * * N U M 3 E R - C O N T E N T S N U M B E R - C O N T E N T S N U M B E R - C O N T E N T S N U ' 3 3 E R - C O N T E N T S - C O N T E N T S N U ^ E f . - C O N T E N T S 2 1 0 6 3 2 9 3 4 2 6 6 1 0 3P- 1 5 ' 6 0 6 9 3 1 rZ 9 7 0 5 0 1 5 ? 16 0 7 7 0 1 5 4 1 5 0 5 1 0 1 5 5 4 1 9 7 3 0 l6 l 3 4 6 9 5 1 6 2 1 2 4 6 E 6 1 6 3 1 6 4 1 3 0 4 4 2 1 6 5 4 1 9 7 3 0 1 9 S 7 6 4 1 9 9 3 * 4 2 0 0 2 6 0 2 0 ! 2 0 3 2 0 3 1 3 0 2 0 5 1 5 6 2 0 6 2 6 0 2 0 7 2 2 1 2 O f . 1 5 5 2 0 9 23<-2 1 0 1 6 9 2 1 2 2 4 7 21 3 2 0 3 2 1 4 2 O R 2 1 5 2 6 0 ''. 1 6 2 ; < -2! . -? 2 2 1 2 2 0 2 34 2 2 1 1 4 3 2 2 2 1 5 6 2 2 3 2 6 0 7 2 4 2 3 4 2 2 S 1 5 h 2 2 ? 2 6 2 3 0 1 5 6 2 12 ^ o 2 5 4 6 2 • " 3 5 7->c 2 2-6 5 2 2 3 7 2 34 2 3 8 2 34 2 3 c 2 i 4 2 4 0 2 t . O 2 4 < \ 5 2 4 2 1 5 6 2 4 3 1 6 0 2 4 4 2 2 1 2 4 5 1 6 9 7 4 6 5 2 - 4 7 2 2 ? 2 4 0 2 3 4 2 5 1 2 2 1 2 5 2 1 5 6 2 5 3 2 0 3 2 5 ' - 2 6 6 2 3 4 2 5 6 2 4 7 i s a i s r 2 5 9 2 3 f 2 6 1 2 6 0 2 6 2 ~ r~2 t " 2 6 2 6 4 1 4 3 2 6 5 1 5 6 2 5 6 1 5 5 2 6 7 5 2 2 6 ? 5 2 2 7 0 5 2 2 7 ! 2 6 0 2 7 2 2 2 1 2 7 2 I 5 6 2 7 4 1 5 6 ' 2 7 5 5 2 2 7 6 2 4 7 2 7 H 5 I 2 7 4 2 6 2 6 1 1 5 9 2 6 2 1 3 0 2ft ! 2 4 7 " f 4 J y > 2 3 5 2 6 7 3 6 ( 5 2 A 7 . 1 3 2 2 9 0 2 2 1 2 9 1 1 6 2 ° 7 1 6 9 2 9 2 2 3 4 2 9 4 9 1 2 9 5 6 5 2 9 7 1 4 2 7 O R 2 2 1 2 e 9 52 3 0 0 J f c i 2 01 2 4 0 302 " 2 6 3 0 3 i •' 0 3 0 5 1 v ? • • 0 6 2 6' 3 0 7 2 4 J 3 C 8 • 1 7 ? 3 0 9 2 4 0 3 1 0 1 72 3 1 1 2 i 2 2 6 < 3 1 3 226 ? 1 4 2 2 o 3 1 5 2 6 6 31 f. 2 6 2 1 7 2 r. 3 1 3 2 4 0 31 > 2 ' . 0 3 2 0 2 4 0 3 2 1 1 6 0 1 7 3 i 2 "'- -'. 613 ' ? 4 i*, e-3 2 5 26 3 26 2 4 0 3 2 R 1 7 3 3 2 9 26 3 3 0 1 7 3 ? : - 7 6 0 t s t o I I 1 I f I C T ) 1 6 3 164 APPENDIX B USER'S MANUAL Procedure For Admission Study (Manual Method) Assuming that the hospital has been selected, the f i r s t step is to estimate how much manpower is needed to complete the study in the desired time period. A discussion with university health researchers should produce a workable estimate. If the manpower i s s u f f i c i e n t then permission must be obtained from the administration of the hospital. During the discussion, determine what data i f any cannot be collec t e d . Ask i f any problem areas are p a r t i c u l a r l y outstanding in the area you are studying. Find out i f any major change i s forthcoming in the near future. Ask the same questions to the s t a f f personnel working on the admission system. Explain what your study hopes to accomplish. Discuss with the s t a f f what section or ward to study. Then obtain permission to watch the f a c i l i t i e s both the ward and the admission system u n t i l you learn the d a i l y routine. You w i l l save time and gain valuable information from the more experienced nurses on the s t a f f . The data that must be gathered i s the total operating time each week of the season under consideration, that each physician has been a l l o t e d or i f not available how much each physician has used. The next piece of information needed i s the average amount of cancellations for that physician or ward. Using the data gathered in the given algorithm produces an estimate of how many patients that the physician requires to maintain the same level of service f o r each season. A certain percentage of these must become q u i c k c a l l s . This w i l l be mainly determined by the number of cancellations. Both the numbers from the algorithm and the percentage of quickcalls must be used as an i n i t i a l solution. Without a computer, judgment, along with t r i a l and error, must be used. T r i a l and error w i l l compensate for the inte r a c t i o n of urgent and emergent patients on the e l e c t i v e patients. The manipulation of l i n e length per physic-ian and percentage of Quickcalls w i l l soon produce the stable desired system. ALGORITHM 1. For each week in the season under consideration sum the total operating room time used or "allocated" to physician A. 2. Divide each weekly sum by the total number of operations performed that week. This produces the average operation time per week for each week in the season. 3. Choose the minimum average operation time per week from step (2). 4. Choose the maximum or largest "allocated" time from step (1). 5. Divide the largest allocated time from (4) by the minimum average weekly operation time from (3). 6. Estimate the total number of people that cancel out on physician A or the percentage of people that cancel out before admission day for the ward physician A is a member of. 7. Add the number in (6) to the number in (5). This gives the working line length for physician A. For example: From chart 1 we see that steps 1 and 2 have been done for physicians A and B. Step 3 Physician A is 36.7 Step 4 Physician A is 260 Step 5 Physician A is -^j = 7.09 Step 6 Physician A from experience we know that 10% of the people cancel out of this ward. 7.09 x 10% + .709 = .71 Step 7 7.09 + .71 = 7.8 Now the working line length for physician A is eight patients. So he will be able to have eight requests for surgery in the office at any given time. For Physician B: Step 3 is 31.0 Step 4 is 310.0 Step 5 is ^ £ = 10 Step 6 Say 10% is the cancellation probability 10 x 10% = 1 Step 7 10+1 = 11 patients. X T T o t a l Weekly Surgery Time = X Min. 1 NO. O f Operations performed =x, Avg.Op.Time Per Week X /X 1 2 ] : i cn . [•otal Weekly ;urgery Time l i n . No. of Operations Performed Avg. Op. Time Per Week 1 230 5 46 215 5 43 2 255 6 42.5 0 Med. Confer. 0 -3 220 6 36.7 220 5 44 4. 0 on vacation 0 - 205 4 51.3 5 215 4, 53.8 220 3 73.3 6 260 7 37.1 0 sick 0 -7 225 5 45.0 310 10 31.0 8 210 5 42.0 215 6 35.8 Totals 1615 38 1365 33 CHART 1 168 APPENDIX C USER'S MANUAL (WITH COMPUTER FACILITIES) Procedure to do Admission Study Assuming the hospital has been selected, the f i r s t step is to estimate how much manpower is needed to complete the study in the desired time period. A discussion with programmers and university health researchers should produce a workable estimate. If the manpower i s s u f f i c i e n t then permission must be obtained from the administration of the hospital. During the discussion, determine what data i f any cannot be col l e c t e d . Ask i f any problem areas are p a r t i c u l a r l y outstanding, in the area you are studying. Find but i f any major change i s forthcoming in the near future. Ask the same questions to the s t a f f personnel working on the admission system. Explain what your study hopes to accomplish. Discuss with the s t a f f what section or ward to study. Gather as much contiguous data as possible. Do not overlook the use of a card sorter to manually sort data. Use s t a t i s t i c programs to analyze the data to help i n the model building stage. Build your model. Analyze each procedure you w i l l simulate and determine i t s relevance in terms of output and objectives. Use f u l l comments explaining why each l i n e i n the model i s necessary and what assumptions were made to use i t . Build the model as modular as possible. While building model t r y to minimize the use of handwritten data. 169 R e t u r n t o o b s e r v e system and t h i s time g a t h e r needed d a t a t h a t has n o t been c o l l e c t e d p r e v i o u s l y by the h o s p i t a l . Review the program w i t h t h e s t a f f . Ask i f e x p e r i m e n t s you have p l a n n e d a r e r e a s o n a b l e and i f they can be i n c o r p o r a t e d . Run t h e e x p e r i m e n t s and e x p l a i n r e s u l t s t o s t a f f . I n s t r u c t i o n s f o r Use o f t h i s Program In each o f t h e f o l l o w i n g s e c t i o n s d a t a must be c o l l e c t e d and i n s e r t e d a t t h e c o r r e c t p o i n t . A good w o r k i n g knowledge o f GPSS i s u n d e r s t o o d . The word f u n c t i o n i s a b b r e v i a t e d f e n . 1. The d i s t r i b u t i o n o f e l e c t i v e s , u r g e n t s and emergents r e q u e s t i n g e n t r y t o the ward i s t o be i n s e r t e d i n f e n PRIO. 2. The d i s t r i b u t i o n o f l e n g t h o f o p e r a t i o n times i s t o be p l a c e d i n f e n ' s 1-27 f o r CPHA c l a s s i f i c a t i o n s and i n f e n 28 f o r m i s c e l l a n e o u s o p e r a t i o n t i m e s . 3. The d i s t r i b u t i o n o f l e n g t h o f s t a y f o r CPHA c l a s s i f i c a t i o n s i s s e q u e n t i a l l y p l a c e d i n fens 31-57. T h i s o r d e r must c o r r e s p o n d t o t h e o r d e r f o r t h e o p e r a t i o n times i n f e n s 1-27. Fen 58 i s m i s c e l l a n e o u s l e n g t h o f s t a y . 4. The f e n DAILY i s t h e d i s t r i b u t i o n o f the number o f e n t r i e s per day. F o r i n s t a n c e the p r o b a b i l i t y t h a t 0 e n t e r e d i s .05 1 e n t e r e d i s .24 2 e n t e r e d i s .12 These are to be the patients that were in fact admitted in the time period under consideration. 5. Fen 60 gives the d i s t r i b u t i o n of disease numbers or diagnosis numbers that have been converted to CPHA numbers f o r e l e c t i v e and urgent surgeries. 6. Fen 61 gives the same information as fen 60 except that this i s f o r emergents only. 7. Fen 62 gives physician number for e l e c t i v e and urgent. This i s the physician number d i s t r i b u t i o n . 8. Fen 63 gives physician number f o r emergents. 9. Fens 64-67 each give the d i s t r i b u t i o n , of allocated surgery time per week, for a d i f f e r e n t physician. 10. XWAIT Fen the d i s t r i b u t i o n of waiting times f o r patients whose physicians do not normally admit to the ward. 11. Noent Fen. This i s the same as the DAILY fen except this number has requested entry but have not been admitted in the time period under consideration. 12. MUST Storage Enough beds must be allocated to MUST to handle overflow urgent patients that could not be scheduled for surgery in less than one week. 13. DEF Storage Enough beds must be allocated to DEF to handle the overflow of urgent medical patients. 14. URBED Storage. Enough beds must be allocated to URBED to handle urgent surgical patients that have been scheduled for surgery but cannot f i n d beds. 171 15. BED Storage. This i s the number of beds in the ward under consideration. 16. EXPED Storage. This is the maximum number of beds that have been used outside the ward for elective and urgent patients. 17. SPES Storage. Enough beds must be allocated to SPES so no emergency has to wait. 18. ELEC Fvariable. What percent of the.slate used f o r elect i v e s ? 19. URGN Fvariable. What percent of the slate i s reserved f o r urgents. 20. Fvariable 9 " 10 Operation factors f o r the four physicians 11 12 21. Block numbers 4, 7, 10, 13 are generate blocks. The o f f s e t time i n t e r v a l must, correspond to the amount of days in advance that the surgery scheduler starts to prepare the surgery schedule. 22. Block 280 i s the standard waiting time for an e l e c t i v e before he i s c a l l e d to enter the hospital (in our case 9 days). 23. Block number 96 what percent of the patients require no surgery. 24. Block numbers 277,281 must include the percent of patients that cancel out and never enter the hospital. 172 APPENDIX D QUESTIONNAIRE A questionnaire (Appendix) was developed to determine the schedule preference of previous patients at the test hospital. Previous patients were chosen so that i t would be possible to obtain patient viewpoints on problems they faced in the admissions process at that h o s p i t a l . The questionnaire was b r i e f and to the point to increase the return rate. Patients were asked for t h e i r schedule preferences among the following: 1. a d e f i n i t e date 1 to 6 months in advance, 2. a tentative date 1 to 4 weeks in advance, 3. an unscheduled entry with a guarantee of entry within two weeks on 24-hour notice. An attempt was made to relate these preferences to demographic data about the patient, but the sample size proved to be too small. Four hundred questionnaires were mailed out and 126 (31% were returned. Of the 108 that showed a schedule preference, 25% preferred alternative (1) above; 22%, a l t e r n a t i v e 2; and 36%,alternative 3. This l a s t r e s u l t was quite surprising and caused greater emphasis to be placed on this form of schedule in the experiments with the simulation model. It should be noted that i t would be impossible, due to administrative problems, for any hospital to allow 36% of i t s patients to come in on such short notice. The actual percentage would be much lower, primarily to f i l l beds that become empty due to cancellations. However, the fa c t that so many would be w i l l i n g to come on short notice implies that the maintenance of a l i s t of such patients, which we label quick-call patients, would be very simple for scheduling purposes. 175 Please place an [X] in the brackets opposite correct response. 1. 4. 5. 6 . 7. Number of months since you were Less than 1 admitted to Lion's Gate Hospital 2 - 4 [ h 5 - 8 [ ] 3 9 - 12 [ b more [ b 2. Sex 3. Age Male [ ]i Female [ ]2 Under 14 [ l i 15 - 24 [ k 25 - 34 [ b 35 - 44 [ b 45 - 54 I b 55 & over [ b Elease state your occupation specifically or i f you f a l l into one of the following categories, [X] the appropriate one. [ ]i Occupation (for example, i f a teacher state elementary, secondary, college, etc.. i f a clerk state off i c e , store, stock clerk, etc. Please be as specific as you can.) [ ]z Housewife [ ]3 Retired [ b Unemployed t b Student Number of people l i v i n g with you -include family, friends but do not count yourself. None [ l i 1 t ] 2 2 [ ]3 3 t b 4 [ ]5 5 [ ] 6 Over 5 [ ] 7 What percent of the total b i l l (waiting for operation, operation, hospitalization, recovery period) was covered by medical or other insurance? Lis t those things you had to pay for yourself (e.g., private nurse, 1. drugs, transportation, etc.) Use back of sheet i f necessary 2. Less than 50% 50% - 75% • 76% - 90% 91% - 100% h b b b 3. 176 3. Please recall the last entry or admission to the hospital when you were not an emergency or did not require urgent or immediate care. Please choose one of the following three and [x] the brackets opposite your choice. 8. The date of admission is scheduled 4 weeks to 6 months in advance. This date is chosen by you and your physician and is not to be changed by the hospital except under unusual circumstances. t 1 9. The date of admission is scheduled by the hospital from 1 to 4 weeks in advance. However due to heavy workload or a high number of emergencies you may be rescheduled by the hospital. If you are re-scheduled you are guaranteed admission within one week of original date. As many as 1 patient in every 5 may have to be rescheduled. [ ] 10. The date of admission is not scheduled. About 3 or 4 days after you and your physician have discussed the need for hospitalization, the hospital will contact you. You are guaranteed admittance within 2 weeks of this contact date. However you must be willing to enter on a 24 hour notice. [ 1 Please [x] MORE than 1 bracket i f more than 1 bracket applies: 11. Would you choose 10. i f transportation were provided FREE OF CHARGE? [ ] Yes t ] No 12. During the period of time between the scheduling of your operation and the actual date of the operation, did you: a) take time off work - with pay [ ] i - without pay [ ]z b) have your spouse care for you - spouse works and had to take time off . - with pay [ ] 3 - without pay [ K - spouse does not work [ ]s c) spouse did not have to care for me [ ]6 d) none of the above [ ]7 13. During this period of time between the scheduling of the operation and the actual operation, that is the waiting period, did you: a) feel any fear of loss of job? none [ ]j a l i t t l e [ J 2 a lot [ ] 3 b) feel any fear of disability? none [ ] i a l i t t l e [ ]z a lot [ ] 3 c) feel any anxiety? none [ ]1 a l i t t l e [ ]z a lot [ ] 3 d) feel any annoyance none [ ]1 a l i t t l e [ ]z a lot [ ] 3 e) could you think of any measure or way to reduce anxiety in the waiting period. 178 Please [x] MORE than 1 bracket i f more than 1 bracket applies: 14. It is preferable for the administration of the hospital that patients arrive and leave the hospital promptly at the times specified. That is between 11 A.M. to 3 P.M. Is this acceptable to you: a) yes [ ] x b) yes, i f child care i s provided [ ] 2 c) yes, i f transportation is provided [ ] 3 d) no If this i s not acceptable to you, please state what times are preferable. admission discharge Please state why 15. What i s the minimum advance notice you could be given of the date of your admission to the hospital? days 16. It would be preferable i f some patients could be scheduled for admission to the hospital on a 24-48 hour advance notice. Would this be acceptable to you? a) yes [ h b) yes, i f child care i s provided [ ] 2 c) yes, i f transportation is provided [ ] 3 d) yes, but only during certain times of year [ ]^ When? e) no [ J 5 17. If you feel we have not covered a l l the alternatives, or i f you feel there are other things that need to be changed regarding the areas we have covered, we would appreciate you l i s t i n g them here. Use back of sheet i f necessary. Thank you for your cooperation. Please return the completed questionnaire in the attached, stamped, addressed envelope. APPENDIX E Experimental Numerical Output RESULTS OF STANDARD RUN P h y s i c i a n Numbers Q# Wai t Bed ALS Time Ut l . (min) 1 2 3 4 5 11.3 16.5 28.3 32.0 77.7 74.6 45106 82.1 16337 10. 4 20.9 31.1 28.6 75.6 79.1 46519 81.9 16439 13.1 18.9 31.5 28.1 78.7 64.0 3 76 09 80. 0 15320 11.3 19.1 2 8.5 26.9 74.8 59. 7 37522 79. 5 15891 10.5 18.1 28.1 26.2 77.7 44.2 28524 77.3 15181 11.2 16. 8 29.9 27.6 77.8 77. 8 47787 80.3 16009 12.1 21.li 26.1 30. 4 79. 8 117.1 70104 79. 5 15526 11,1 18,1 32,2 28,8 74.5 79.2 48070 82,4 16105 91.0 149.8 235.7 228.6 616.6 595. 7 361241 643. 0 126809 The i average r e s u l t s are ' respect I v e l y : 11.3 18.7 29.9 28.5 77.0 74.4 45155 80.4 15851 QUICKCALL EXPERIMENT (With 7.5% Q u i c k c a l l s ) P h y s i c i a n Numbers 1 2 3 13.1 17.9 31.7 12.0 17.6 29.U 10.3 19.6 31.0 11.5 17.5 30.5 12.3 17.7 27.9 10.0 20.8 31.1* 12.3 17.8 29.9 10,9 20,1* 27,1 92.U 11*9. 3 238.9 The average r e s u l t s are 11.5 18.6 29.8 Q# I* 5 29.2 76.3 1*6.2 28.3 80.3 U8. 0 3 0.7 80. I* 1*6. 0 26.3 76.9 1*7.8 32.1 71*.9 1*9.1* 26. I* 75.7 73.3 28.1 78.7 51.6 28,9 75,8 67,2 230.0 619.0 1*29. 5 respe c t i vel y: 28.7 77.3 53.7 Wal t Bed ALS Time Ut l • (min) 27939 79. 2 11*891 30299 83. 3 16151 27750 82. 0 15523 29078 79. 1 15597 29Ui*i* 78. 2 15109 1*1*687 80. 3 15708 31069 80. 9 15502 1*1062 79, 7 15525 261327 61*2. 7 121*006 32666 80. 3 15501 THE EXPERIMENT WITH SHORTEST SURGERY FIRST P h y s i c i a n Numbers 1 2 3 13.7 17.2 30.3 9.9 21.0 32.0 14.8 20.1 31.4 12.4 19.5 28.5 12.4 18.1 29.3 11.7 20,2 30,2 74.9 116.1 181.7 The average r e s u l t s are 12.5 19.4 30.3 a* 4 5 29.2 82.5 75.1 27.3 79.5 88.5 28.5 81.5 113.3 27.0 79.1 126. 7 26.4 78.4 121.2 27,3 80,4 148.1 165.7 481.4 672.9 r e s p e c t i v e l y : 27.6 80.2 112.2 Wa i t Bed ALS Time Ut i (min) 44486 82. 8 15449 51564 83. 1 16188 64362 82. 6 15880 74990 81. 9 16173 71718 79. 1 15414 87095 80, 5 15324 394215 490. 0 94428 65703 81. 7 15738 THE EXPERIMENT OF CONSTANT QUEUE LENGTH ( t h e minimum f o r t h i s ward was 64 ) P h y s t c l a n s 1 2 3 13.8 15.1 28.tr 11.0 21.2 31.3 14.6 18. 4 34.0 11.6 22.1 29.3 13.3 19.0 27. 8 10. 4 19.1 31.1 10.3 20.1 25.9 11,2 19, 4 30,2 96.2 15k.k 238.0 The a v e r a g e r e s u l t s a r e 12.0 19.3 29.8 Q# 4 5 31.5 80.0 64. 0 28.9 82. 0 61*. 0 30.3 82.3 63. 9 25 .7 82.3 6k. 0 27.2 78. 7 6k. 0 27.0 79.7 6k. 2 29. 4 80.I* 6k. 0 29,If 78, 7 64, 0 229 .4 639.1 512. 1 r e s p e c t i v e l y : 28 .7 79.9 64 .0 Wai t Bed ALS T i m e U t i . (m in ) 37481 81. 0 15597 36302 84.4 16171 34258 85.1 16004 37218 84. 1 16023 37926 82.3 15807 39696 79. 0 15825 37644 83.8 16832 37948 80,9 15533 298473 660.6 127792 37309 82.3 15974 THE EXPERIMENT WITH LONGEST SURGERY FIRST PhysJ c l a n s 1 2 3 13.6 16.4 29.2 10.6 22.6 29.8 14.3 19.6 33.0 12.4 20.7 27.8 13.1 19.3 29.3 11.0 18.1 31.2 12.8 23.6 27.2 U.5 17,4 32,0 99.1 157.7 239.5 The average r e s u l t s a re 12.4 19.7 29.9 Q# 4 5 32.5 80. 8 99. 8 28.3 80.5 84. 9 29.7 82.1 69. 6 25.0 80.6 80. 8 26.7 76.5 85. 4 25.0 83.2 113. 1 32.4 80. 8 146. 5 29,3 76,4 86, 3 228.9 640.9 766. 4 r e s p e c t ! v e l y : 28.6 80.1 95.8 Wal t Bed ALS Time Ut i . (min) 58182 83. 7 15851 50354 84. 0 16554 41399 82.9 15911 49530 83.7 16660 51595 81.0 16451 66883 84.3 16839 84398 84.2 16611 52218 81,7 16005 454559 665.5 130882 56814 83.2 16360 THE EXPERIMENT FOR MINIMUM QUEUE LENGTH (below min: 3/3/3/4 f o r 1/2/3/4 resp.) P h y s i c i a n s 1 2 3 4 5 7.6 15.2 26.6 26.9 79.9 7.7 18.1 28.4 25.3 76.2 10.9 16.8 27.5 29.6 78. 3 8.3 19.2 25.9 24. 7 77.6 10.1 18.1 29.2 24.6 78.1 8.7 15.9 28.0 25.5 81.4 8.0 17.7 26.6 27.0 73.6 9,2 19,7 29,3 25,6 80, If 70.5 140.7 221.5 209.2 625.5 The average r e s u l t s are r e s p e c t i v e l y : 8.8 17.6 27.7 26.2 78.2 Q# Wai t Bed ALS Time Ut i . (min) 40. 0 25963 80. 3 16122 40.6 26214 81. 5 16652 41.0 25895 81.9 16168 40.1 27150 78.1 15310 40. 7 26325 78.3 15471 39.6 25988 80.5 15596 40.5 26930 78.5 16182 1*0,5 25926 80,0 15372 323.0 210391 639.1 126873 40.4 26299 79.9 15859 co THE EXPERIMENT WHERE PHYSICIANS ARE ALLOCATED EXCESS OPERATING THEATRE TIME PhysIc fans 1 2 3 4 5 13.4 16.0 31.2 32.1 74.8 10.8 16.7 32. 8 27.7 78.4 10.4 17.3 32.6 30.9 77.8 11.2 18.3 28.8 28.3 76.9 12.1 17.2 29.7 31.1 74.3 10.2 18.6 31.2 29.1 73.5 12,5 19,5 30,9 28.2 76,2 80.6 123.6 217.2 207.4 531.9 The average r e s u l t s are r e s p e c t l v e l y : 11.5 17.6 31.0 29.6 75,9 Q# Wal t Bed ALS Time Ut l . (min) 34.6 20955 82.0 15285 33.9 21096 83.2 15930 34. 7 20705 81.9 15565 32.9 20747 85.1 16334 32.4 19867 82.6 15680 32.0 19885 83.4 16223 33,9 20840 83,0 15749 234.4 144095 83.0 110766 33.5 20585 83.0 15824 CO VJ1 

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