Whole-System Patient Flow Modelling for Strategic Planning in Healthcare

Health systems are under pressure to deliver high quality care to improve outcomes, access and financial sustainability. In the past decade, Ontario pursued such improvements through a series of transformation initiatives aimed at moving care provision to the community settings and integrating care around patient pathways through system. In this dissertation we present operational research (OR) approaches to facilitate whole-system strategic planning for health systems, with a focus on patient flow among care sectors to address transformation challenges in Ontarioâ s health care system and its regional health authorities. Firstly, we expand on Soft OR methods to collaboratively develop a qualitative model to capture the boundary and transitional view of patient flows across major care sectors in a health region. The model is not scoped around a specific question, but is meant to be a broad platform for exploring the patient flow relationships among multiple care domains. Secondly, we build on the findings of the qualitative model, and leverage the administrative datasets across these major care sectors to develop a high fidelity simulation model to evaluate the effects of policy interventions and their effects on system-wide patient flows. Methodologically this simulation builds on the structural simplicity of system dynamics with comprehensive, patient-level data to achieve a highly flexible simulation to model flows for a broad and modifiable range of patient cohorts and interventions. Finally, we implement both models in the analysis of whole-system care policies. The qualitative model is used for the analysis of slow stream rehabilitation policy options and is utilized to identify conflicts of this initiative with existing patient flow interventions. The simulation model is used to assess the cross-sector patient flow impacts of implementing stroke best practices. The results highlight the importance of community care investments and cross-sector referral patterns in realizing the greatest benefits from this policy.Ph.D

Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation

Breast cancer recurrence is an important outcome for patients and healthcare systems, but it is not routinely reported in cancer registries. We developed an algorithm to identify patients who experienced recurrence or a second case of primary breast cancer (combined as a “second breast cancer event”) using administrative data from the population of Ontario, Canada. A retrospective cohort study design was used including patients diagnosed with stage 0-III breast cancer in the Ontario Cancer Registry between 1 January 2009 and 31 December 2012 and alive six months post-diagnosis. We applied the algorithm to healthcare utilization data from six months post-diagnosis until death or 31 December 2013, whichever came first. We validated the algorithm’s diagnostic accuracy against a manual patient record review (n = 2245 patients). The algorithm had a sensitivity of 85%, a specificity of 94%, a positive predictive value of 67%, a negative predictive value of 98%, an accuracy of 93%, a kappa value of 71%, and a prevalence-adjusted bias-adjusted kappa value of 85%. The second breast cancer event rate was 16.5% according to the algorithm and 13.0% according to manual review. Our algorithm’s performance was comparable to previously published algorithms and is sufficient for healthcare system monitoring. Administrative data from a population can, therefore, be interpreted using new methods to identify new outcome measures