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

医学 慢性疼痛 生物统计学 病历 人口 医疗保健 协议(科学) 编码(社会科学) 健康信息学 公共卫生 记录链接 人口健康 梅德林 慢性病 流行病学 初级保健 社会经济地位 鉴定(生物学) 算法 家庭医学 焦点小组 长期护理 公共卫生监督 慢性病 数据挖掘 电子健康档案 数据收集 物理疗法 接收机工作特性 诊断代码 医疗急救 定性研究 心理干预 腰痛 电子数据采集 止痛药 联动装置(软件) 卫生服务研究 决策支持系统
作者
Ian-Ju Liang,Nouf Abutheraa,Cassie Higgins,Paul Cameron,Gary Macfarlane,Fran Sullivan,Kathryn Martin,Peter Donnan,Sue Cole,Hussein Patwa,Blair H. Smith,Lesley; id_orcid 0000-0002-1563-8600 Colvin
出处
期刊:BMC Public Health [BioMed Central]
标识
DOI:10.1186/s12889-026-28262-8
摘要

Abstract 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 ).

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