Automated Decision Support For Community Mental Health Services Using National Electronic Health Records: Qualitative...

Area of Science:

• Health Informatics
• Mental Health Services Research
• Implementation Science

Background:

• High rates of relapse in severe mental illness are linked to psychotropic medication nonadherence.
• A web-based clinical decision support system (CDSS), Actionable Intime Insights (AI²), was developed for Community Mental Health Services (CMHS).
• AI² aims to improve medication adherence and enable early intervention through historical data review and nonadherence alerts.

Purpose of the Study:

• To evaluate the pilot implementation of the AI² web-based CDSS within CMHS.
• To understand the challenges and facilitators of implementing a CDSS for medication adherence and early intervention.
• To identify necessary resources and support for sustaining such a system.

Main Methods:

• Employed the Normalization Process Theory (NPT) and a participatory action framework.
• Collected qualitative data over 14 months, with researchers as active participants.
• Analyzed data from discussions, meetings, emails, and documents using the NPT framework.

Main Results:

• Implementation involved negotiations, leading to a staged rollout with clinician hesitancy and workarounds.
• Clinicians expressed concerns regarding changes to work practices and reliance on My Health Record (MyHR) data.
• Pilot implementation highlighted implementation challenges, required resources, and support needs for automated alert systems.

Conclusions:

• The adoption of electronic health record-based decision support is increasing.
• Despite implementation challenges, the potential benefits for early intervention, relapse prevention, and healthcare efficiency are significant.
• Continued pursuit of these systems is warranted to improve patient outcomes and system efficiency.