Evidence map›Paper›PMID 41047261›Full record

ArticleBMJ open2025

Study protocol for an open-label, single-arm, mixed methods feasibility study of the MWIQ AI-powered decision support tool for diabetes management in GP practices.

Jane Dickson, Scott G Cunningham, Chris Sainsbury, Martin K Rutter, N Kanumilli, Ewan Pearson, Doogie Brodie, Marryat Stevens, Deborah J Wake, Nicholas Conway

Abstract readClinical Trial Protocol
In one paragraph

Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Jane DicksonMedical School, University of Dundee, Dundee, Angus, UK e.j.dickson@dundee.ac.uk.ORCID http://orcid.org/0000-0002-4106-599X
Scott G CunninghamMedical School, University of Dundee, Dundee, Angus, UK.
Chris SainsburyUniversity of Birmingham, Birmingham, UK.ORCID http://orcid.org/0000-0002-7984-5721
Martin K RutterDivision of Diabetes, Endocrinology and Gastroenterology, The University of Manchester, Manchester, UK.
N KanumilliNorthenden Group Practice, Northenden, UK.
Ewan PearsonMedical School, University of Dundee, Dundee, Angus, UK.
Doogie BrodieMy Way Digital Health LTD, Dundee, UK.ORCID http://orcid.org/0000-0002-3504-4519
Marryat StevensMy Way Digital Health LTD, Dundee, UK.ORCID http://orcid.org/0009-0005-7427-0497
Deborah J WakeUsher Institute, The University of Edinburgh, Edinburgh, UK.ORCID http://orcid.org/0000-0003-4376-6973
Nicholas ConwayNinewells Hospital and Medical School, Dundee, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDiabetes affects ~10% of the world's population and is rising. Treatment costs in the UK are ~15% of the NHS budget. Diabetes-related complications can be lowered through better evidence-based clinician management and patient self-management. MyWay intelligence quotient (MWIQ) is an electronic platform that will provide clinical decision support around the diagnosis and treatment of patients with diabetes. This study evaluates the safety and clinical performance (clinical appropriateness/applicability, clinical impact and clinical usability) of MWIQ. METHODS AND ANALYSIS: The system will be implemented in real time in four to seven general practitioner (GP) practices. Clinicians with diabetes expertise will be recruited as validators, who will inspect records to ensure system robustness before use, and up to 14 healthcare professionals will use and evaluate the system.Quantitative and qualitative analyses will be triangulated to assess the MWIQ system. Assessment of clinical outcomes will be made using pseudonymised routinely collected clinical data, including adherence to quality performance indicators, diabetes diagnosis, diabetes investigations (eg, genetic testing), HbA1c, blood pressure, body mass index, cholesterol and foot risk score for the diabetes population concerned. Clinical and validator participants will also submit a weekly questionnaire, and these, along with interviews, which are scheduled during the testing process, will be analysed to provide data on the utility, safety and usability of the system. ETHICS AND DISSEMINATION: This study was approved, 08/01/2024, by the North of Scotland Research Ethics Committee (REC), IRAS project ID: 305267, REC, reference 23/NS/0134. The study has gained confidentiality advisory group (CAG) support (reference: 24/CAG/0002), medicines and healthcare products regulatory agency (MHRA) and health research authority (27/08/2024) approvals.Findings will be reported to (1) The funding body, (2) The participating GP practices, (3) The study PPIE group, (4) The MHRA to support a submission for recognition as a class 2 CE/UKCA marked device, (5) Presented at local, national and international conferences and (6) Disseminated by peer-reviewed publications. TRIAL REGISTRATION NUMBER: ISRCTN17422256.

Indexed as

Artificial IntelligenceDecision Support Systems, ClinicalDiabetes MellitusGeneral PracticeFeasibility StudiesHumansUnited KingdomClinical Decision-MakingClinical ProtocolsDIABETES & ENDOCRINOLOGYDiabetes Mellitus, Type 2Digital TechnologyeHealth

Identifiers

PMID41047261
PMCPMC12506132

What Socratic holds

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LicenceCC BY
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Registered trials

None linked

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 Socratic graph.