Prescribers' interactions with medication alerts at the point of prescribing: A multi-method, in situ investigation of the human-computer interaction

Int J Med Inform. 2012 Apr;81(4):232-43. doi: 10.1016/j.ijmedinf.2012.01.002. Epub 2012 Jan 31.


Purpose: Few studies have examined prescribers' interactions with medication alerts at the point of prescribing. We conducted an in situ, human factors investigation of outpatient prescribing to uncover factors that influence the prescriber-alert interaction and identify strategies to improve alert design.

Methods: Field observations and interviews were conducted with outpatient prescribers at a major Veterans Affairs Medical Center. Physicians, clinical pharmacists, and nurse practitioners were recruited across five primary care clinics and eight specialty clinics. Prescribers were observed in situ as they ordered medications for patients and resolved alerts. Researchers collected 351 pages of typed notes across 102 hours of observations and interviews. An interdisciplinary team identified emergent themes via inductive qualitative analysis.

Results: Altogether, 320 alerts were observed among 30 prescribers and their interactions with 146 patients. Qualitative analysis uncovered 44 emergent themes and 9 overarching factors, which were organized into a framework that describes the prescriber-alert interaction. Prescribers' ability to act on alerts was impeded by the alert interface, which did not adequately support all prescriber types.

Conclusions: This empiric study produced a novel framework for understanding the prescriber-alert interaction. Results revealed key components of the alert interface that influence prescribers and indicate a need for more universal design. Actionable design recommendations are presented and may be used to enhance alert design and patient safety.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Adult
  • Aged
  • Attitude of Health Personnel
  • Decision Making*
  • Drug Interactions
  • Drug Therapy, Computer-Assisted / statistics & numerical data*
  • Female
  • Humans
  • Medical Order Entry Systems / statistics & numerical data*
  • Medication Errors / prevention & control*
  • Middle Aged
  • Patient Care Management
  • Reminder Systems*
  • User-Computer Interface