Evidence map›Paper›PMID 42436406›Full record

ArticleBMC public health2026

Chronic Pain Identification Through Electronic Records (C-PICTURE): development and validation of an algorithm to identify people with chronic pain using primary care records: study protocol.

Ian-Ju Liang, Nouf Abutheraa, Cassie Higgins, Paul Cameron, Gary Macfarlane, Frank Sullivan, Kathryn Martin, Peter Donnan, Sue Cole, Hussein Patwa and 2 more

Abstract readValidation Study
In one paragraph

Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

12 authors.

Ian-Ju LiangDivision of Population Health and Genomics, School of Medicine, University of Dundee, Level 7 (204) Corridor E, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK. iliang001@dundee.ac.uk.
Nouf AbutheraaDivision of Population Health and Genomics, School of Medicine, University of Dundee, Level 7 (204) Corridor E, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK.
Cassie HigginsDivision of Population Health and Genomics, School of Medicine, University of Dundee, Level 7 (204) Corridor E, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK.
Paul CameronNHS Forth Valley, Larbert, UK.
Gary MacfarlaneAberdeen Centre for Arthritis and Musculoskeletal Health (Epidemiology Group), University of Aberdeen, Aberdeen, UK.
Frank SullivanDivision of Population and Behavioural Science, Medical School, University of St Andrews, St Andrews, UK.
Kathryn MartinInstitute of Applied Health Sciences, School of Medicine, University of Aberdeen, Aberdeen, UK.
Peter DonnanDivision of Population Health and Genomics, School of Medicine, University of Dundee, Level 7 (204) Corridor E, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK.
Sue Cole *Patient Partners, Dundee, UK.
Hussein Patwa *Patient Partners, Dundee, UK.
Blair H SmithDivision of Population Health and Genomics, School of Medicine, University of Dundee, Level 7 (204) Corridor E, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK.
Lesley ColvinDivision of Population Health and Genomics, School of Medicine, University of Dundee, Level 7 (204) Corridor E, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK. l.a.colvin@dundee.ac.uk.

Funding

Chief Scientist Office HIPS/22/18
6 · The paper itself

Abstract

backgroundChronic pain is a highly prevalent and disabling condition, yet its true population burden remains poorly characterised. This is partly due to the lack of validated case-finding methods within routinely collected electronic health records that adequately reflect people's lived experiences. Existing algorithms for identifying chronic pain are limited by poor validation, insufficient involvement of people with chronic pain, and low diagnostic accuracy. The C-PICTURE study aims to develop, refine, and validate an algorithm capable of accurately identifying individuals living with chronic pain within primary care electronic health records.

methodsThis mixed-methods study comprises four phases, preceded by a pilot algorithm. Six diverse GP practices are purposively selected across Scotland to reflect variation in geography, population demographics, and socioeconomic context. Phase A involves review of 1,200 electronic medical records across the six practices to create a reference dataset for algorithm comparison. Phase B includes a patient-reported outcomes survey sent to approximately 6,200 adults, collecting data on chronic pain presence, severity, impact, and management strategies. Phase C consists of semi-structured interviews and focus groups with people with chronic pain and healthcare providers to explore discrepancies between coding-based and self-reported chronic pain, understand coding practices, and examine how people use healthcare services to manage needs. Phase D pilots the algorithm across the six practices using the Primary Care Intelligence Service, comparing algorithm performance with medical record review and patient-reported outcomes data. Sensitivity, specificity, predictive values, and Area Under the Receiver Operating Characteristic Curve are estimated, with cross-validation to assess internal validity. Data linkage across phases enables refinement and validation of the final algorithm. DISCUSSION: The C-PICTURE study will address evidence gaps by producing the first validated population-based method for identifying chronic pain using Scottish primary care data. Integrating clinical records, patient-reported data, and qualitative insights ensures the algorithm reflects both biomedical and lived experiences of chronic pain. Findings will have implications for policy, practice, and research, including highlighting limitations in current coding practices, supporting surveillance, and informing national planning for pain management services. The validated algorithm could also be adapted for use across the UK and internationally, supporting epidemiological monitoring and enabling future chronic pain research.

trial registrationThis study is registered with the UK's Clinical Study Registry (ISRCTN reference number: 37628569; Registered on 27 February 2024; DOI: https://doi.org/10.1186/ISRCTN37628569 ).

Indexed as

AlgorithmsChronic PainElectronic Health RecordsPrimary Health CareAdultFemaleFocus GroupsHumansInterviews as TopicScotlandCase-finding algorithmChronic painElectronic health recordsHealth informaticsMixed-methodsPatient-reported outcomesPrimary careRead codes

Identifiers

PMID42436406
PMCPMC13640092

What OpenQuestion holds

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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.