Using algorithms to initiate needs-based interventions for people on antipsychotic medication: implementation protocol.

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dc.contributor.authorOakey-Neate, L.
dc.contributor.authorSchrader, G.
dc.contributor.authorStrobel, J.
dc.contributor.authorBastiampillai, T.
dc.contributor.authorvan Kasteren, Y.
dc.contributor.authorBidargaddi, N.
dc.date.issued2020
dc.description.abstractIntroduction Non-adherence to antipsychotic medications for individuals with serious mental illness increases risk of relapse and hospitalisation. Real time monitoring of adherence would allow for early intervention. AI2 is a both a personal nudging system and a clinical decision support tool that applies machine learning on Medicare prescription and benefits data to raise alerts when patients have discontinued antipsychotic medications without supervision, or when essential routine health checks have not been performed. Methods and analysis We outline two intervention models using AI2. In the first use-case, the personal nudging system, patients receive text messages when an alert of a missed medication or routine health check is detected by AI2. In the second use-case, as a clinical decision support tool, AI2 generated alerts are presented as flags through a dashboard to the community mental health professionals. Implementation protocols for different scenarios of AI2, along with a mixed-methods evaluation, are planned to identify pragmatic issues necessary to inform a larger randomised control trial, as well as improve the application. Ethics and dissemination This study protocol has been approved by The Southern Adelaide Clinical Human Research Ethics Committee. The dissemination of this trial will serve to inform further implementation of the AI2 into daily personal and clinical practice.
dc.description.statementofresponsibilityLydia Oakey-Neate, Geoff Schrader, Jörg Strobel, Tarun Bastiampillai, Yasmin van Kasteren, Niranjan Bidargaddi
dc.identifier.citationBMJ Health & Care Informatics, 2020; 27(1):1-6
dc.identifier.doi10.1136/bmjhci-2019-100084
dc.identifier.issn2632-1009
dc.identifier.issn2632-1009
dc.identifier.orcidSchrader, G. [0000-0002-2504-8102]
dc.identifier.orcidBidargaddi, N. [0000-0003-2868-9260]
dc.identifier.urihttps://hdl.handle.net/2440/133315
dc.language.isoen
dc.publisherPublished by BMJ.
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/Medical Research Future Fund
dc.rights© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions.
dc.source.urihttps://doi.org/10.1136/bmjhci-2019-100084
dc.subjectBMJ Health Informatics
dc.subjecthealthcare
dc.subjectmedical informatics
dc.subjectpatient care
dc.subjectrecord systems
dc.subject.meshHumans
dc.subject.meshAntipsychotic Agents
dc.subject.meshMental Disorders
dc.subject.meshAlgorithms
dc.subject.meshArtificial Intelligence
dc.subject.meshMedicare
dc.subject.meshUnited States
dc.subject.meshMedication Adherence
dc.titleUsing algorithms to initiate needs-based interventions for people on antipsychotic medication: implementation protocol.
dc.typeJournal article
pubs.publication-statusPublished

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