The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Automated Decision Support For Community Mental Health Services Using National Electronic Health Records: Qualitative Implementation Case Study
Open accessPublished version of a preprint
Abstract
Background: A high proportion of patients with severe mental illness relapse due to nonadherence to psychotropic medication. In this paper, we use the normalization process theory (NPT) to describe the implementation of a web-based clinical decision support system (CDSS) for Community Mental Health Services (CMHS) called Actionable Intime Insights or AI2. AI2 has two distinct functions: (1) it provides an overview of medication and treatment history to assist in reviewing patient adherence and (2) gives alerts indicating nonadherence to support early intervention. Objective: Our objective is to evaluate the pilot implementation of the AI2 application to better understand the challenges of implementing a web-based CDSS to support medication adherence and early intervention in CMHS. Methods: The 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 analyzed and coded using the NPT framework. Qualitative data included discussions, meetings, and work products, 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 system, 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 us 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 hospitalization and ensure increased efficiency of the health care system are worth pursuing.
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Digital decision-support systems in broader mental health settings may help structure clinical reasoning, reduce uncertainty, and support more consistent decision-making.
“Such systems may help structure clinical reasoning, reduce uncertainty, and support more consistent decision-making [14,15].”
From A Digital Tool for Culturally Responsive Reflection and Case Conceptualization in First-Line Services to Support Children With a Refugee Background: Development and Usability Study (2026) · cites this paper as [15]
The Methods (Actionable Intime Insights Application) describe guideline-based alerts and a patient-history timeline, while the Results (Reflexive Monitoring: Reviewing) say clinicians saw the timeline as useful for care planning and that it was adopted to inform clinical-review decisions. This supports the possibility of better-informed, more structured decisions in community mental health care, but the paper does not establish reduced uncertainty or more consistent decision-making. (LLM, conf=0.82)
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The qualitative content analysis process
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“Transcripts and documents were uploaded to NVivo software and coded using a deductive thematic analysis approach using the following NPT framework constructs: coherence, cognitive participation, collective action, and reflexive monitoring (Table 3) [30,31].”
Organizing and the process of sensemaking
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“The decision rules for generating alerts are derived from best practice guidelines for the treatment of schizophrenia, bipolar disorder, and major depression [25,26].”
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“The decision rules for generating alerts are derived from best practice guidelines for the treatment of schizophrenia, bipolar disorder, and major depression [25,26].”
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“Underpinning the structure of NPT is the pragmatic need to understand the challenges of introducing CDSS into health care settings [22,23].”
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“NPT has emerged as a framework for understanding complex health care interventions and the implementation of health technologies and electronic health records [17-21].”
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Van Bogaert, C. A., Jame, B., Serneels, G. M., & Van Daele, T. (2026) · JMIR Formative Research · doi:10.2196/86809
Metadata
- Authors
- van Kasteren, Y., Strobel, J., Bastiampillai, T., Linedale, E., & Bidargaddi, N.
- Affiliations
- Flinders University (AU)
- South Australian Health and Medical Research Institute (AU)
- Corresponding author
- Bidargaddi, Niranjan
- Venue
- JMIR Human Factors
- Publisher
- JMIR Publications Inc.
- Published
- 2022-07-05
- Volume
- 9
- Issue
- 3
- Pages
- e35403
- ISSN
- 2292-9495 (ISSN-L)
- PMC
- PMC9297136
- OpenAlex
- W4226149114
- Cited by
- 6
- References
- 14
- Citations by year
- 2025: 3 · 2024: 2 · 2022: 1
- FWCI
- 0.75
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Human Factors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- clinical decision support systems, community mental health services, medication adherence, early intervention, severe mental illness, Normalization Process Theory, implementation barriers and facilitators, My Health Record, automated alerts, relapse prevention, clinician attitudes, workarounds, participatory action research
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