What happens to orders written for older primary care patients?

Fam Med. 2012 Apr;44(4):252-8.

Abstract

Background and objectives: Data are limited on order completion errors in primary care. The objective of this study was to determine the incidence and nature of order completion errors among community-dwelling older adults.

Methods: This prospective, cross-sectional exploratory study was conducted at a suburban family medicine clinical teaching site. Patients ?70 years old who received ?one order at the study enrollment visit were eligible for inclusion. Errors in completion of orders for prescriptions, laboratory tests, imaging studies or screening procedures, and specialist referrals were assessed. Logistic regression was used to identify the independent variables associated with non-system-based errors.

Results: A total of 322 orders were written for 93 enrolled patients. An order error was identified in 59 (18.3%) orders written for 39 (41.9%) patients (mean 1.5, range 1--4, SD=0.85): 10 were system-based and 49 were non-system-based errors. Non-system-based errors included unfilled prescriptions (9.0%), uncompleted orders for imaging studies and screening procedures (13.0%), and uncompleted specialist referrals (17.4%). All laboratory orders were completed. In a logistic regression model, females were four times more likely to experience a non-system-based error than males (OR=4.02, 95% CI=1.43, 11.23).

Conclusions: Order completion errors were common in this sample of community-dwelling older adults, with non-system-based errors for prescriptions, imaging studies or screening procedures, and specialist referrals occurring more frequently than system-based errors, particularly among females. Providers should not assume that patients will complete orders as intended; rather, longitudinal management requires regular patient follow-up and review to ensure order completion.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Chronic Disease / therapy
  • Cross-Sectional Studies
  • Female
  • Humans
  • Logistic Models
  • Male
  • Medical Errors / statistics & numerical data*
  • Patient Compliance / statistics & numerical data*
  • Patients
  • Primary Health Care / statistics & numerical data*
  • Prospective Studies
  • Sex Factors