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

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  • 1Department of Epidemiology and Biostatistics, Western University, London, Ontario, Canada.

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Summary
This summary is machine-generated.

This case study details a co-development project for a data-driven decision support tool in Community Health Centres (CHCs). The tool evolved to aid organizational planning for mental health care capacity, improving patient outcomes.

Keywords:
Artificial IntelligenceDecision Support ToolDescriptive EpidemiologyDiabetesElectronic Health RecordsMachine LearningMental HealthPrimary Health Care

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Area of Science:

  • Health Informatics
  • Primary Health Care Research
  • Health Services Research

Background:

  • The Alliance for Healthier Communities supports Ontario Community Health Centres (CHCs) in delivering team-based primary care.
  • This case study focuses on the co-development of a data-driven decision support tool within the Practice Based Learning Network (PBLN).

Purpose of the Study:

  • To describe the process and lessons learned from co-developing a decision support tool using electronic health record (EHR) data.
  • To inform future initiatives for developing EHR-based decision support tools in primary health care settings.

Main Methods:

  • An iterative approach involving discussion sessions and case studies with CHC EHR data.
  • Six stages of development: population-level study, team engagement, problem selection, and two sandbox case studies (individual and population-level predictions).

Main Results:

  • Initial population-level analysis highlighted client data representation issues in EHRs.
  • The project evolved from individual risk prediction for mental health decline post-diabetes diagnosis to organizational planning for mental health care capacity.

Conclusions:

  • The co-development process yielded insights for future EHR-based decision support tool initiatives.
  • The developed tool aims to support both diabetes and mental health care, and informs population-level capacity planning models.