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Introduction: Our goal was to investigate trends in computed tomography (CT) utilization in emergency departments (EDs) and its association with hospitalization. Methods: We conducted an analysis of an administrative claims database of U.S. privately insured and Medicare Advantage enrollees. We identified ED visits from 2005 through 2013 and assessed for CT use, associated factors, and hospitalization after CT, along with patient demographics. We used both descriptive methods and regression models adjusted for year, age, sex, race, geographic region, and Hwang comorbidity score to explore associations among CT use, year, demographic characteristics, and hospitalization. Results: We identified 33,144,233 ED visits; 5,901,603 (17.8%) involved CT. Over time, CT use during ED visits increased 59.9%. CT use increased in all age groups but decreased in children since 2010. In propensity-matching analysis, odds of hospitalization increased with age, comorbidities, male sex, and CT use (odds ratio, 2.38). Odds of hospitalization over time decreased more quickly for patients with CT. Conclusion: CT utilization in the ED has increased significantly from 2005 through 2013. For children, CT use after 2010 decreased, indicating caution about CT use. Male sex, older age, and higher number of comorbidities were predictors of CT in the ED. Over time, odds of hospitalization decreased more quickly for patients with CT.

BackgroundChronic 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. ObjectiveWe characterized individual episodes of chronic pain by annotating and analyzing EHR notes for a stratified cohort of adults with known chronic pain. MethodsWe used the Rochester Epidemiology Project infrastructure to screen all residents of Olmsted County, Minnesota, for evidence of chronic pain, between January 1, 2005, and September 30, 2015. Diagnosis codes were used to assemble a cohort of 6586 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 on quality of life, diagnostic procedures, medications, and other treatment modalities. ResultsA total of 94 chronic pain episodes from 62 distinct patients were identified by reviewing 3272 clinical notes. Documentation was written by clinicians across a wide spectrum of specialties. Most patients (40/62, 65%) had 1 pain episode during the study period. Interannotator agreement ranged from 0.78 to 1.00 across the annotated concepts. Some pain-related concepts (eg, body location) had 100% (94/94) coverage among all the episodes, while others had moderate coverage (eg, effects on quality of life) (55/94, 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 and occupational therapies were the most common nonpharmacological treatments. ConclusionsWe systematically annotated chronic pain episodes in clinical text. The rich content analysis results revealed complexity of the chronic pain episodes and of their management, as well as the challenges in extracting pertinent information, even for humans. Despite the pilot study 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 EHRs.

Background The impact of ongoing efforts to decrease opioid use on patients with cancer remains undefined. Our objective was to determine trends in new and additional opioid use in patients with and without cancer. Methods This retrospective cohort study used data from Surveillance, Epidemiology, and End Results program–Medicare for opioid-naive patients with solid tumor malignancies diagnosed from 2012 through 2017 and a random sample of patients without cancer. We identified 238 470 eligible patients with cancer and further focused on 4 clinical strata: patients without cancer, patients with metastatic cancer, patients with nonmetastatic cancer treated with surgery alone ("surgery alone"), and patients with nonmetastatic cancer treated with surgery plus chemotherapy or radiation therapy ("surgery+"). We identified new, early additional, and long-term additional opioid use and calculated the change in predicted probability of these outcomes from 2012 to 2017. Results New opioid use was higher in patients with cancer (46.4%) than in those without (6.9%) (P  < .001). From 2012 to 2017, the predicted probability of new opioid use was more stable in the cancer strata (relative declines: 0.1% surgery alone; 2.4% surgery+; 8.8% metastatic cancer), than in the noncancer stratum (20.0%) (P  < .001 for each cancer to noncancer comparison). Early additional use declined among surgery patients (‒14.9% and ‒17.5% for surgery alone and surgery+, respectively) but was stable among patients with metastatic disease (‒2.8%, P  = .50). Conclusions Opioid prescribing declined over time at a slower rate in patients with cancer than in patients without cancer. Our study suggests important but tempered effects of the changing opioid climate on patients with cancer. [ABSTRACT FROM AUTHOR]

BackgroundDeploying accurate computable phenotypes in pragmatic trials requires a trade-off between precise and clinically sensical variable selection. In particular, evaluating the medical encounter to assess a pattern leading to clinically significant impairment or distress indicative of disease is a difficult modeling challenge for the emergency department. ObjectiveThis study aimed to derive and validate an electronic health record–based computable phenotype to identify emergency department patients with opioid use disorder using physician chart review as a reference standard. MethodsA two-algorithm computable phenotype was developed and evaluated using structured clinical data across 13 emergency departments in two large health care systems. Algorithm 1 combined clinician and billing codes. Algorithm 2 used chief complaint structured data suggestive of opioid use disorder. To evaluate the algorithms in both internal and external validation phases, two emergency medicine physicians, with a third acting as adjudicator, reviewed a pragmatic sample of 231 charts: 125 internal validation (75 positive and 50 negative), 106 external validation (56 positive and 50 negative). ResultsCohen kappa, measuring agreement between reviewers, for the internal and external validation cohorts was 0.95 and 0.93, respectively. In the internal validation phase, Algorithm 1 had a positive predictive value (PPV) of 0.96 (95% CI 0.863-0.995) and a negative predictive value (NPV) of 0.98 (95% CI 0.893-0.999), and Algorithm 2 had a PPV of 0.8 (95% CI 0.593-0.932) and an NPV of 1.0 (one-sided 97.5% CI 0.863-1). In the external validation phase, the phenotype had a PPV of 0.95 (95% CI 0.851-0.989) and an NPV of 0.92 (95% CI 0.807-0.978). ConclusionsThis phenotype detected emergency department patients with opioid use disorder with high predictive values and reliability. Its algorithms were transportable across health care systems and have potential value for both clinical and research purposes.

Background: Opioid overdose deaths have disproportionately impacted Black and Hispanic populations, in part due to disparities in treatment access. Emergency departments (EDs) serve as a resource for patients with opioid use disorder (OUD), many of whom have difficulty accessing outpatient addiction programs. However, inequities in ED treatment for OUD remain poorly understood. Methods: This secondary analysis examined racial and ethnic differences in buprenorphine access using data from EMBED, a study of 21 EDs across five health care systems evaluating a clinical decision support system for initiating ED buprenorphine. The primary outcome was receipt of buprenorphine, ED administered or prescribed. Hospital type (academic vs. community) was evaluated as an effect modifier. Hierarchical models with cluster effects for site and clinician were used to assess buprenorphine receipt by race and ethnicity. Results: Black patients were less likely to receive buprenorphine (6.4% [51/801] vs. White patients 8.5% [268/3154], odds ratio [OR] 0.59, 95% confidence interval [CI] 0.45–0.78). This association persisted after adjusting for age, insurance, gender, clinician X‐waiver, hospital type, and urbanicity (adjusted OR [aOR] 0.64, 95% CI 0.48–0.84) but not when discharge diagnosis was included (aOR 0.75, 95% CI 0.56–1.02). Hispanic patients were more likely to receive buprenorphine (14.8% [122/822] vs. non‐Hispanic patients, 11.6% [475/4098]) in unadjusted (OR 1.57, 95% CI 1.09–1.83) and adjusted models (aOR 1.41, 95% CI 1.08–1.83) but not including discharge diagnosis (aOR 1.32, 95% CI 0.99–1.77). Odds of buprenorphine were similar in academic and community EDs by race (interaction p = 0.97) and ethnicity (interaction p = 0.64). Conclusions: Black patients with OUD were less likely to receive buprenorphine whereas Hispanic patients were more likely to receive buprenorphine in academic and community EDs. Differences were attenuated with discharge diagnosis, as fewer Black and non‐Hispanic patients were diagnosed with opioid withdrawal. Barriers to medication treatment are heterogenous among patients with OUD; research must continue to address the multiple drivers of health inequities at the patient, clinician, and community level. [ABSTRACT FROM AUTHOR]