# Data-Driven Decision Support Tool Co-Development with a Primary Health Care Practice Based Learning Network

## TL;DR

This case study details the co-development of a decision support tool with primary health care centers to improve diabetes and mental health care using electronic health records.

## Contribution

The study presents a collaborative process for creating a data-driven decision support tool tailored to primary health care needs.

## Key findings

- An iterative approach was used to develop a decision support tool based on electronic health records.
- Three key decision support areas were identified: risk prediction, triaging referrals, and identifying care access needs.
- The tool's focus shifted from individual risk prediction to organizational planning for mental health services.

## Abstract

The Alliance for Healthier Communities is a learning health system that supports Community Health Centres (CHCs) across Ontario, Canada to provide team-based primary health care to people who otherwise experience barriers to care. This case study describes the ongoing process and lessons learned from the first Alliance for Healthier Communities’ Practice Based Learning Network (PBLN) data-driven decision support tool co-development project.

We employ an iterative approach to problem identification and methods development for the decision support tool, moving between discussion sessions and case studies with CHC electronic health record (EHR) data. We summarize our work to date in terms of six stages: population-level descriptive-exploratory study, PBLN team engagement, decision support tool problem selection, sandbox case study 1: individual-level risk predictions, sandbox case study 2: population-level planning predictions, project recap and next steps decision.

The population-level study provided an initial point of engagement to consider how clients are (not) represented in EHR data and to inform problem selection and methodological decisions thereafter. We identified three initial meaningful types of decision support, with target application areas: risk prediction/screening, triaging specialized program referrals, and identifying care access needs. Based on feasibility and expected impact, we started with the goal to support earlier identification of mental health decline after diabetes diagnosis. As discussions deepened around clinical use cases associated with example prediction task set ups, the target problem evolved towards supporting the upstream task of organizational planning and advocacy for adequate mental health care service capacity to meet incoming needs.

This case study contributes towards a tool to support diabetes and mental health care, as well as lays groundwork for future CHC EHR-based decision support tool initiatives. We share lessons learned and reflections from our process that other primary health care organizations may use to inform their own co-development initiatives.