Quality and Variability of Patient Directions in Electronic Prescriptions in the Ambulatory Care Setting

J Manag Care Spec Pharm. 2018 Jul;24(7):691-699. doi: 10.18553/jmcp.2018.17404. Epub 2018 Jan 18.


Background: The prescriber's directions to the patient (Sig) are one of the most quality-sensitive components of a prescription order. Owing to their free-text format, the Sig data that are transmitted in electronic prescriptions (e-prescriptions) have the potential to produce interpretation challenges at receiving pharmacies that may threaten patient safety and also negatively affect medication labeling and patient counseling. Ensuring that all data transmitted in the e-prescription are complete and unambiguous is essential for minimizing disruptions in workflow at prescribers' offices and receiving pharmacies and optimizing the safety and effectiveness of patient care.

Objectives: To (a) assess the quality and variability of free-text Sig strings in ambulatory e-prescriptions and (b) propose best-practice recommendations to improve the use of this quality-sensitive field.

Methods: A retrospective qualitative analysis was performed on a nationally representative sample of 25,000 e-prescriptions issued by 22,152 community-based prescribers across the United States using 501 electronic health records (EHRs) or e-prescribing software applications. The content of Sig text strings in e-prescriptions was classified according to a Sig classification scheme developed with guidance from an expert advisory panel. The Sig text strings were also analyzed for quality-related events (QREs). For purposes of this analysis, QREs were defined as Sig text content that could impair accurate and unambiguous interpretation by staff at receiving pharmacies.

Results: A total of 3,797 unique Sig concepts were identified in the 25,000 Sig text strings analyzed; more than 50% of all Sigs could be categorized into 25 unique Sig concepts. Even Sig strings that expressed apparently simple and straightforward concepts displayed substantial variability; for example, the sample contained 832 permutations of words and phrases used to convey the Sig concept of "Take 1 tablet by mouth once daily." Approximately 10% of Sigs contained QREs that could pose patient safety risks or workflow disruptions that could necessitate pharmacist callbacks to prescribers for clarification or other manual interventions.

Conclusions: The quality of free-text patient directions in e-prescriptions can vary dramatically. However, more than half of all patient directions sent in the ambulatory setting can be categorized into only 25 Sig concepts. This suggests an immediate, practical opportunity to improve patient safety and workflow efficiency for both prescribers and pharmacies. Recommendations include implementing enhancements to Sig creation tools in e-prescribing and EHR software applications, adoption of the Structured and Codified Sig format supported by the current national e-prescribing standard, and improved usability testing and end-user training for generating complete and unambiguous patient directions. Such quality improvements are essential for optimizing the safety and effectiveness of patient care as well as for minimizing workflow disruptions to both prescribers and pharmacies.

Disclosures: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Yang, Ward-Charlerie, Dhavle, and Green are employed by Surescripts. Rupp reported receiving consulting fees from Surescripts during the conduct of this study. No other disclosures were reported. The content in this article is solely the responsibility of the authors and does not necessarily represent the official views of Surescripts and Midwestern University or any of the affiliated institutions of the authors. Study concept and design were contributed by all the authors. Yang and Ward-Charlerie collected the data, and data interpretion was performed by Yang, Ward-Charlerie and Dhavle. The manuscript was primarily written by Yang, along with Dhavle and Green, and revised by Yang, Dhavle, Rupp, and Green.

MeSH terms

  • Ambulatory Care / organization & administration
  • Ambulatory Care / statistics & numerical data*
  • Drug Labeling
  • Drug Prescriptions / statistics & numerical data*
  • Electronic Health Records / statistics & numerical data
  • Electronic Prescribing / statistics & numerical data*
  • Medication Errors / prevention & control
  • Medication Errors / statistics & numerical data
  • Patient Safety
  • Pharmacies / organization & administration*
  • Pharmacies / statistics & numerical data
  • Qualitative Research
  • Quality Improvement*
  • Retrospective Studies
  • United States