How often do prescribers include indications in drug orders? Analysis of 4 million outpatient prescriptions

Am J Health Syst Pharm. 2019 Jun 18;76(13):970-979. doi: 10.1093/ajhp/zxz082.

Abstract

Purpose: To examine the extent to which outpatient clinicians currently document drug indications in prescription instructions.

Methods: Free-text sigs were extracted from all outpatient prescriptions generated by the computerized prescriber order entry system of a major academic institution during a 5-year period. Natural language processing was used to identify drug indications. The data set was analyzed to determine the rates at which prescribers included indications. It was stratified by provider specialty, drug class, and specific medications, to determine how often these indications were in prescriptions for as-needed (PRN) versus non-PRN medications.

Results: During the study period, 4,356,086 prescriptions were ordered. Indications were included in 322,961 orders (7.41%). From these orders, 249,262 indications (77.18%) were written for PRN orders. Although internal medicine prescribers generated the highest number of medication orders, they included indications in only 6.26% of their prescriptions, whereas orthopedic surgery providers had the highest rate of documenting indications (33.41%). Pain was the most common indication, accounting for 30.35% of all documented indications. The drug class with the highest number of sigs-containing indications was narcotic analgesics. Non-PRN chronic medication prescriptions rarely included the indication.

Conclusion: Prescribers rarely included drug indications in electronic free-text prescription instructions, and, when they did, it was mostly for PRN uses such as pain.

Keywords: CPOE; drug indications; free-text sig; medication ordering; patient safety; prescription instructions.

MeSH terms

  • Ambulatory Care / standards
  • Ambulatory Care / statistics & numerical data*
  • Datasets as Topic
  • Drug Prescriptions / standards
  • Drug Prescriptions / statistics & numerical data*
  • Humans
  • Medical Order Entry Systems / standards
  • Medical Order Entry Systems / statistics & numerical data*
  • Medication Errors / prevention & control
  • Natural Language Processing