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Background <p>Chronic 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.</p> Methods <p>This 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.</p> Discussion <p>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.</p> Trial Registration <p>This study is registered with the UK’s Clinical Study Registry (ISRCTN reference number: 37628569; Registered on 27 February 2024; DOI: <a href="https://doi.org/10.1186/ISRCTN37628569">https://doi.org/10.1186/ISRCTN37628569</a>).</p>

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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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月11日

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  • Ian-Ju Liang,
  • Nouf Abutheraa,
  • Cassie Higgins,
  • Paul Cameron,
  • Gary Macfarlane,
  • Frank Sullivan,
  • Kathryn Martin,
  • Peter Donnan,
  • Sue Cole,
  • Hussein Patwa,
  • Blair H Smith,
  • Lesley Colvin

摘要

Background

Chronic 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.

Methods

This 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 Registration

This 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).