Walker, Lauren E 
ORCID: 0000-0002-3827-4387, Abuzour, Aseel S, Bollegala, Danushka ORCID: 0000-0003-4476-7003, Clegg, Andrew, Gabbay, Mark ORCID: 0000-0002-0126-8485, Griffiths, Alan, Kullu, Cecil, Leeming, Gary ORCID: 0000-0002-5554-5302, Mair, Frances S, 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-.
ISSN 2633-5565, 2633-5565
Abstract
<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: | multimorbidity, polypharmacy, frailty, mental health, artificial intelligence, medicines optimisation |
| Divisions: | Faculty of Health & Life Sciences Faculty of Health & Life Sciences > Clinical Directorate Faculty of Health & Life Sciences > Inst. Population Health Faculty of Health & Life Sciences > Inst. Systems, Molec & Integrative Biology > Inst. Systems, Molec & Integrative Biology Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 06 Jan 2023 10:05 |
| Last Modified: | 28 Feb 2026 11:42 |
| DOI: | 10.1177/26335565221145493 |
| Open Access URL: | https://doi.org/10.1177/26335565221145493 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3166807 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
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