Accepted for/Published in: JMIR Human Factors
Date Submitted: Dec 2, 2021
Date Accepted: Apr 22, 2022
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Implementation of a web-based computerised decision support system for Community Mental Health Services using national electronic health records
ABSTRACT
Background:
A high proportion of patients with severe mental illness relapse due to non-adherence to psychotropic medication. In this paper, we use Normalisation Process Theory (NPT) to describe the implementation of AI2 – a web-based clinical decision support system (CDSS) for Community Mental Health Services (CMHS). AI2 has two distinct functions: 1) overview of medication and treatment history to assist in reviewing patient adherence and 2) alerts for non-adherence to support early intervention.
Objective:
Our objective was to pilot test the software and to better understand the challenges of implementation in a CMHS.
Methods:
NPT and Participatory Action Framework were used to both explore and support implementation. Qualitative data were collected over the course of the 14-month implementation in which researchers were active participants. Data were analysed and coded using the NPT framework. Qualitative data included discussions, meetings, and work product including emails and documents.
Results:
This study explores the barriers and enablers of implementing a CDSS to support early intervention within CMHS using Medicare data from Australia’s national electronic record, My Health Record (MyHR). The implementation was a series of ongoing negotiations, which resulted in a staged implementation with compromises on both sides. Clinicians were initially hesitant about using a CDSS based on MyHR data and expressed concerns about the changes to their work practice required to support early intervention. Substantial workarounds were required to move the implementation forward. This pilot implementation allowed researchers to better understand the challenges of implementation and the resources and support required to implement and sustain a model of care based on automated alerts to support early intervention.
Conclusions:
The use of decision support based on electronic health records is growing, and while implementation is challenging, the potential benefits of early intervention to prevent relapse and hospitalisation and increased efficiency for the healthcare system are worth pursuing.
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