Putting a Population Health Lens to Multimorbidity in Ontario

Abstract

Almost all Ontarians die with multimorbidity, and most accumulate more than five conditions over their lifetime. Our health system is still largely focused on specialties and treating one disease at a time-an approach that is incompatible with the healthcare needs of patients with multiple and often complex chronic conditions. This burden requires a health system that recognizes that patients will more likely live and die with multiple chronic conditions than not (i.e., multimorbidity management versus specialized care). There are important and meaningful differences in the types and numbers of conditions that patients die with. In particular, increases in the most preventable conditions are greater among the most deprived members of our society. To address the worrying trends seen here, chronic disease prevention-not only management-must be a priority, with a strong focus on health equity. Chronic disease prevention and a strong focus on equity are signatures of a population health approach. This work echoes calls for a stronger emphasis on population health in the health system.

Exploring an Innovative Care Model and Telemonitoring for the Management of Patients With Complex Chronic Needs: Qualitative Description Study

“…This paper provides an overview of the needs of patients and clinicians in developing an integrated care model for patients with CCN and the needs of TM. Study findings revealed that significant gaps remain in meeting the needs of patients with CCN in current health care delivery practice [ 36 , 37 ]. Patients continue to navigate a fragmented care system [ 36 ] between primary care and siloed specialty care, creating challenges to timely access and care management.…”

“…An integrated NP-led model of care focused specifically on patients with CCN would align with sustained calls for a common trajectory of multimorbidity and chronic disease management that focuses on patients from a more holistic and needs-based perspective [ 9 , 37 ]. Our findings suggest that a colocated model with a primary clinical contact can address the challenges faced by patients and clinicians in ambulatory care.…”

Machine Learning Applied to Routinely Collected Health Administrative Data

“…This approach holds promise for many health system applications. Patients with chronic diseases drive the majority of healthcare spending (Tinetti et al 2012), yet our healthcare and public health systems often take siloed approaches to chronic disease management and prevention (Rosella and Kornas 2018). For example, patients with diabetes are at risk for a wide range of complications including cardiovascular events, kidney disease and retinopathy.…”

A study protocol for a predictive model to assess population-based avoidable hospitalization risk: Avoidable Hospitalization Population Risk Prediction Tool (AvHPoRT)

“…Given that survey data were not used, details on socio-demographics (e.g., immigration status, ethnicity), individual-level socioeconomic status (e.g., income, education), social support (e.g., living alone), or health behaviors (e.g., smoking, alcohol consumption, body weight) were not included. Therefore, there are opportunities to improve model performance by adding variables that are lacking in administrative data and creating a model that better informs population health approaches [ 39 ]. Furthermore, a model that allows for prediction at the population level contributes by allowing for accurate distribution of risk in the population, which can ensure that strategies for prevention are allocated to the populations that will most likely benefit from attention and outreach.…”