The DynAIRx Project Protocol: Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity.

Walker, Lauren E ORCID: 0000-0002-3827-4387, Abuzour, Aseel S, Bollegala, Danushka ORCID: 0000-0003-4476-7003, Clegg, Andrew, Gabbay, Mark, Griffiths, Alan, Kullu, Cecil, Leeming, Gary, Mair, Frances S ORCID: 0000-0001-9780-1135, Maskell, Simon ORCID: 0000-0003-1917-2913
et al (show 7 more authors) (2022) The DynAIRx Project Protocol: Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity. Journal of multimorbidity and comorbidity, 12. 26335565221145493-.

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<h4>Background</h4>Structured Medication Reviews (SMRs) are intended to help deliver the NHS Long Term Plan for medicines optimisation in people living with multiple long-term conditions and polypharmacy. It is challenging to gather the information needed for these reviews due to poor integration of health records across providers and there is little guidance on how to identify those patients most urgently requiring review.<h4>Objective</h4>To extract information from scattered clinical records on how health and medications change over time, apply interpretable artificial intelligence (AI) approaches to predict risks of poor outcomes and overlay this information on care records to inform SMRs. We will pilot this approach in primary care prescribing audit and feedback systems, and co-design future medicines optimisation decision support systems.<h4>Design</h4>DynAIRx will target potentially problematic polypharmacy in three key multimorbidity groups, namely, people with (a) mental and physical health problems, (b) four or more long-term conditions taking ten or more drugs and (c) older age and frailty. Structured clinical data will be drawn from integrated care records (general practice, hospital, and social care) covering an ∼11m population supplemented with Natural Language Processing (NLP) of unstructured clinical text. AI systems will be trained to identify patterns of conditions, medications, tests, and clinical contacts preceding adverse events in order to identify individuals who might benefit most from an SMR.<h4>Discussion</h4>By implementing and evaluating an AI-augmented visualisation of care records in an existing prescribing audit and feedback system we will create a learning system for medicines optimisation, co-designed throughout with end-users and patients.

Item Type: Article
Uncontrolled Keywords: artificial intelligence, frailty, medicines optimisation, mental health, multimorbidity, polypharmacy
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Clinical Directorate
Faculty of Health and Life Sciences > Institute of Population Health
Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 06 Jan 2023 10:05
Last Modified: 07 Jan 2023 03:42
DOI: 10.1177/26335565221145493
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