医学
疼痛评估
人口
格拉斯哥昏迷指数
物理疗法
队列
疼痛管理
内科学
麻醉
环境卫生
作者
Rory Quinn,Siobhán Masterson,David W. Willis,David Hennelly,Conor Deasy,Colm P. O’Donnell
标识
DOI:10.1177/27536386231162609
摘要
An effective pain management strategy requires understanding of the epidemiology of pain in the population of interest and accurate measurement upon which to base quality improvement plans. The aims of this study were to estimate the incidence of pain in the prehospital setting and to explore features that impact on (1) documentation of pain; (2) severity of pain reported by patients. This retrospective cohort study included 212,401 care episodes attended by National Ambulance Service practitioners during 2020. Descriptive analysis of patient and care episode characteristics and regression analyses for the outcomes pain recorded and severity of pain were performed. We also used text pattern-matching of the notes field to estimate the proportion of patients in pain for whom a pain score assessment had not been documented. Sixty-five percent of all patients had a pain score documented and 29.5% were in pain (11% in severe pain). Likelihood of pain being recorded was most strongly associated with: Glasgow Coma Scale (GCS) Score, working diagnosis of the patient, location of the incident, and patient age. Likelihood of pain severity was most strongly associated with: transport status of patient, GCS score, and patient age. We treated missing data as a separate category and found consistent associations between the outcomes and missing data. We also found that pain was a symptom in approximately 15% of cases where no formal pain score assessment was documented. The data showed associations between routinely collected variables and the likelihood of pain recording and pain severity. Our findings also demonstrate the impact of missing data. To mitigate missing data impact, we suggest that EMS agencies consider making pain score assessment a mandatory requirement of their reporting for every patient. We also recommend that services report the extent and impact of missing data when measuring clinical performance.
科研通智能强力驱动
Strongly Powered by AbleSci AI