Examining clinician choice to follow-up (or not) on automated notifications of medication non-adherence by clinical decision support systems

Abstract

Background: Maintaining medication adherence can be challenging for people living with mental ill-health. Clinical decision support systems (CDSS) based on automated detection of problematic patterns in Electronic Health Records (EHRs) have the potential to enable early intervention into non-adherence events ("flags") through suggesting evidence-based courses of action. However, extant literature shows multiple barriers-perceived lack of benefit in following up low-risk cases, veracity of data, human-centric design concerns, etc.-to clinician follow-up in real-world settings. This study examined patterns in clinician decision making behaviour related to follow-up of non-adherence prompts within a community mental health clinic.

Methods: The prompts for follow-up, and the recording of clinician responses, were enabled by CDSS software (AI²). De-identified clinician notes recorded after reviewing a prompt were analysed using a thematic synthesis approach-starting with descriptions of clinician comments, then sorting into analytical themes related to design and, in parallel, a priori categories describing follow-up behaviours. Hypotheses derived from the literature about the follow-up categories' relationships with client and medication-subtype characteristics were tested.

Results: The majority of clients were Not Followed-up (n = 260; 78%; Followed-up: n = 71; 22%). The analytical themes emerging from the decision notes suggested contextual factors-the clients' environment, their clinical relationships, and medical needs-mediated how clinicians interacted with the CDSS flags. Significant differences were found between medication subtypes and follow-up, with Anti-depressants less likely to be followed up than Anti-Psychotics and Anxiolytics (χ² = 35.196, 44.825; p < 0.001; v = 0.389, 0.499); and between the time taken to action Followed-up₀ and Not-followed up₁ flags (M₀ = 31.78; M₁ = 45.55; U = 12,119; p < 0.001; η² = .05).

Conclusion: These analyses encourage actively incorporating the input of consumers and carers, non-EHR data streams, and better incorporation of data from parallel health systems and other clinicians into CDSS designs to encourage follow-up.

Keywords: Clinical decision support systems (CDSS); Digital psychiatry; Embedded mixed-methods study design; Interaction design; Proactive care.