Open Collections

Hall, William

University of British Columbia

The task of providing high quality care has become increasingly difficult as pressure mounts on both the funding and demand side of the provision equation. Health leaders have simultaneously had to contend with a decreasing availability of resources along with rising expenditures in part due to an aging population and the development of new innovations in care delivery. Health system transformation and improvement are crucial to addressing these challenges. To spur such transformation, Learning Health Systems (LHS) have been posited as a framework to enable health organizations to generate and apply knowledge in a manner that delivers higher performance and greater value. Despite the opportunities afforded by LHS, published literature has identified several impediments to adoption including: data management, prioritization, and evaluation. This thesis built upon existing LHS theoretical models by incorporating health economic methods to address the aforementioned challenges and create a novel model for economic learning health systems (eLHS). Practically, the first steps of an eLHS model were carried out and documented including an auto-regressive integrated moving average (ARIMA) forecasting analysis, and simulation analysis using Markov modelling. These analyses were conducted in partnership with a local health organization within the context of providing home health services for seniors. Results from the analyses forecasted growing community and acute expenditure on care for senior home health patients, set target thresholds for innovations designed to reduce dependence on acute care, and found that two possible home health interventions were cost-effective using simulation with a decision analytic Markov model. Recommendations for future stages of the eLHS process in this context were made as well as applications of the eLHS frameworks in other domains of care.

Text

2022-10-20

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/82951

Population and Public Health

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/