Designing a Management and Referral Tool for Patients with Multiple Chronic Illnesses in Primary Care Settings

Abstract

Some local health organizations in Ontario (e.g., Local Health Integration Network or LHINs) have put forward a strategic objective to identify patients with preventable high cost healthcare service usage (e.g., hospitalizations, emergency department [ED] visits). To attain this goal, primary care service providers, who are considered the entry point to the health system, need tools to help diagnose, treat and refer those patients identified as being potential high users of the health care system. The goal of this study was to develop a management and referral tool to identify, manage and refer patients living with multiple comorbidities to specialized care teams such as Health Links. Data used in this analysis were obtained from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) primary care data holdings. The dataset created for this study contained 14,004 patient records. Data analysis techniques included use of both statistical and predictive analytic tools. The base models included four data mining classification algorithms: Decision Tree, Naïve Bayes, Neural Network and Clustering. The predictive modeling approach was complemented by an association analysis. The one-way ANOVA analysis indicated that age and health status (number of conditions, and individual medical conditions) identified statistically significant differences in patient utilization of health services. Results from the predictive analytics showed that patient age and patient medical conditions, as well as number of medical conditions for each patient (5 or more) could be used as criteria to develop tools (e.g. searches, reminders). Specifically, Parkinson disease, dementia and epilepsy were found to be important predictors (i.e. most frequently associated with) the top 4 most prevalent conditions (hypertension, osteoarthritis, depression and diabetes) within the population of the study. The association analysis also revealed that chronic obstructive pulmonary disease (COPD) was closely associated with the top 4 most prevalent conditions. Based on the findings of this study, Parkinson Disease, dementia, epilepsy and COPD can be used to identify patients with complex medical needs who are likely to be high users of the healthcare system and to be considered for early, personalized intervention.