Evidence map›Paper›PMID 38307535›Full record

ArticleBMJ open2024

Perceptions on artificial intelligence-based decision-making for coexisting multiple long-term health conditions: protocol for a qualitative study with patients and healthcare professionals.

Niluka Jeewanthi Gunathilaka, Tiffany E Gooden, Jennifer Cooper, Sarah Flanagan, Tom Marshall, Shamil Haroon, Alexander D'Elia, Francesca Crowe, Thomas Jackson, Krishnarajah Nirantharakumar and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
5.4field-weighted citation impact, top 4% of its field
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

8 citing papers in PubMed, 15 citations in OpenAlex.

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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

11 authors at 2 institutions in 2 countries.

Niluka Jeewanthi GunathilakaMinistry of Health Sri Lanka, Colombo, Sri Lanka.ORCID 0000-0001-6887-8790
Tiffany E GoodenInstitute of Applied Health Research, University of Birmingham, Birmingham, West Midlands, UK.ORCID 0000-0002-3905-5477
Jennifer CooperInstitute of Applied Health Research, University of Birmingham, Birmingham, West Midlands, UK.
Sarah FlanaganInstitute of Applied Health Research, University of Birmingham, Birmingham, West Midlands, UK.
Tom MarshallInstitute of Applied Health Research, University of Birmingham, Birmingham, West Midlands, UK.
Shamil HaroonInstitute of Applied Health Research, University of Birmingham, Birmingham, West Midlands, UK.ORCID 0000-0002-0096-1413
Alexander D'EliaInstitute of Applied Health Research, University of Birmingham, Birmingham, West Midlands, UK.
Francesca CroweInstitute of Applied Health Research, University of Birmingham, Birmingham, West Midlands, UK F.Crowe@bham.ac.uk.ORCID 0000-0003-4026-1726
Thomas JacksonInstitute of Applied Health Research, University of Birmingham, Birmingham, West Midlands, UK.
Krishnarajah NirantharakumarInstitute of Applied Health Research, University of Birmingham, Birmingham, West Midlands, UK.ORCID 0000-0002-6816-1279
Sheila GreenfieldInstitute of Applied Health Research, University of Birmingham, Birmingham, West Midlands, UK.ORCID 0000-0002-8796-4114
University of Birmingham · GBMinistry of Health, Nutrition and Indigenous Medicine · LK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionCoexisting multiple health conditions is common among older people, a population that is increasing globally. The potential for polypharmacy, adverse events, drug interactions and development of additional health conditions complicates prescribing decisions for these patients. Artificial intelligence (AI)-generated decision-making tools may help guide clinical decisions in the context of multiple health conditions, by determining which of the multiple medication options is best. This study aims to explore the perceptions of healthcare professionals (HCPs) and patients on the use of AI in the management of multiple health conditions. METHODS AND ANALYSIS: A qualitative study will be conducted using semistructured interviews. Adults (≥18 years) with multiple health conditions living in the West Midlands of England and HCPs with experience in caring for patients with multiple health conditions will be eligible and purposively sampled. Patients will be identified from Clinical Practice Research Datalink (CPRD) Aurum; CPRD will contact general practitioners who will in turn, send a letter to patients inviting them to take part. Eligible HCPs will be recruited through British HCP bodies and known contacts. Up to 30 patients and 30 HCPs will be recruited, until data saturation is achieved. Interviews will be in-person or virtual, audio recorded and transcribed verbatim. The topic guide is designed to explore participants' attitudes towards AI-informed clinical decision-making to augment clinician-directed decision-making, the perceived advantages and disadvantages of both methods and attitudes towards risk management. Case vignettes comprising a common decision pathway for patients with multiple health conditions will be presented during each interview to invite participants' opinions on how their experiences compare. Data will be analysed thematically using the Framework Method. ETHICS AND DISSEMINATION: This study has been approved by the National Health Service Research Ethics Committee (Reference: 22/SC/0210). Written informed consent or verbal consent will be obtained prior to each interview. The findings from this study will be disseminated through peer-reviewed publications, conferences and lay summaries.

Indexed as

Artificial IntelligenceState MedicineAdultAgedCross-Sectional StudiesHumansMultimorbidityPolypharmacyQualitative Researchprimary health carepublic healthqualitative research

Identifiers

PMID38307535
PMCPMC10836375
OpenAlexW4391481303

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 OpenQuestion graph.