Evidence map›Paper›PMID 36717855›Full record

ArticleBMC medical informatics and decision making2023

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

Dan Thorpe, Jörg Strobel, Niranjan Bidargaddi

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Dan ThorpeDigital Health Research Lab, College of Medicine and Public Health, Flinders University, Adelaide, SA, 5042, Australia.
Jörg StrobelDigital Health Research Lab, College of Medicine and Public Health, Flinders University, Adelaide, SA, 5042, Australia.
Niranjan BidargaddiDigital Health Research Lab, College of Medicine and Public Health, Flinders University, Adelaide, SA, 5042, Australia. niranjan.bidargaddi@flinders.edu.au.ORCID 0000-0003-2868-9260

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMaintaining 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.

methodsThe prompts for follow-up, and the recording of clinician responses, were enabled by CDSS software (AI

resultsThe 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 (χ

conclusionThese 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.

Indexed as

Decision Support Systems, ClinicalElectronic Health RecordsFollow-Up StudiesHumansClinical decision support systems (CDSS)Digital psychiatryEmbedded mixed-methods study designInteraction designProactive care

Identifiers

PMID36717855
PMCPMC9887874

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.