Provider Bias in prescribing opioid analgesics: a study of electronic medical Records at a Hospital Emergency Department

Lisa A. Keister, Chad Stecher, Brian Aronson, William McConnell, Joshua Hustedt, James W. Moody

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Background: Physicians do not prescribe opioid analgesics for pain treatment equally across groups, and such disparities may pose significant public health concerns. Although research suggests that institutional constraints and cultural stereotypes influence doctors’ treatment of pain, prior quantitative evidence is mixed. The objective of this secondary analysis is therefore to clarify which institutional constraints and patient demographics bias provider prescribing of opioid analgesics. Methods: We used electronic medical record data from an emergency department of a large U.S hospital during years 2008–2014. We ran multi-level logistic regression models to estimate factors associated with providing an opioid prescription during a given visit while controlling for ICD-9 diagnosis codes and between-patient heterogeneity. Results: A total of 180,829 patient visits for 63,513 unique patients were recorded during the period of analysis. Overall, providers were significantly less likely to prescribe opioids to the same individual patient when the visit occurred during higher rates of emergency department crowding, later times of day, earlier in the week, later years in our sample, and when the patient had received fewer previous opioid prescriptions. Across all patients, providers were significantly more likely to prescribe opioids to patients who were middle-aged, white, and married. We found no bias towards women and no interaction effects between race and crowding or between race and sex. Conclusions: Providers tend to prescribe fewer opioids during constrained diagnostic situations and undertreat pain for patients from high-risk and marginalized demographic groups. Potential harms resulting from previous treatment decisions may accumulate by informing future treatment decisions.

Original languageEnglish (US)
Article number1518
JournalBMC public health
Volume21
Issue number1
DOIs
StatePublished - Dec 2021

Keywords

  • Crowding
  • Electronic medical records
  • Emergency departments
  • Inequality
  • Opioids
  • Prescription bias
  • Undertreatment

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

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