Chronic pain affects more than 20% of adults in the United States and is associated with substantial physical, mental, and social burden. Clinical text contains rich information about chronic pain, but no systematic appraisal has been performed to assess the electronic health record (EHR) narratives for these patients. A formal content analysis of the unstructured EHR data can inform clinical practice and research in chronic pain.
We characterized individual episodes of chronic pain by annotating and analyzing EHR notes for a stratified cohort of adults with known chronic pain.
We used the Rochester Epidemiology Project (REP) infrastructure to screen all residents of Olmsted County, Minnesota for evidence of chronic pain, between 1/1/2005 and 9/30/2015. Diagnosis codes were used to assemble a cohort of 6,586 chronic pain patients; people with cancer were excluded. The records of an age- and sex-stratified random sample of 62 patients from the cohort were annotated using an iteratively developed guideline. The annotated concepts included date, location, severity, causes, effects to life, diagnostic procedures, medications, and other treatment modalities.
A total of 94 chronic pain episodes from 62 distinct patients were identified by reviewing 3,272 clinical notes. Documentation was written by clinicians across a wide spectrum of specialties. Most patients (65%; n=40) had one pain episode during the study period. Inter-annotator agreement ranged from 0.78 to 1.00 across the annotated concepts. Some pain-related concepts (e.g., body location) had 100% coverage among all the episodes, while others had moderate coverage (e.g., effects to life, 59%). Back pain and leg pain were the most common types of chronic pain in the annotated cohort. Musculoskeletal issues like arthritis were annotated as the most common causes. Opioids were the most commonly captured medication, while physical/occupational therapies were the most common non-pharmaceutical treatments.
We have systematically annotated chronic pain episodes in clinical text. The rich content analysis results revealed complexity of the chronic pain episodes and of the management, as well as the challenges in extracting pertinent information even for humans. Despite the pilot nature of the work, the annotation guideline and corpus should be able to serve as informative references for other institutions with shared interest in chronic pain research using the EHR.