Adoption and use of stand-alone electronic prescribing in a health plan-sponsored initiative

Am J Manag Care. 2010 Mar;16(3):182-9.

Abstract

Objectives: To quantify rates of stand-alone e-prescribing (SEP) adoption and use among primary care physicians (PCPs) participating in a SEP initiative and to determine which physician and patient characteristics were associated with higher rates of each.

Study design: Using records from an insurer-led SEP initiative, we compared the characteristics of 297 PCPs who adopted SEP through the initiative with the characteristics of 1892 eligible PCPs who did not. Among 297 adopters, we studied the extent of SEP use.

Methods: Dependent variables included each physician's adoption of SEP and his or her e-prescribing use ratio (the ratio of electronic prescriptions to pharmacy claims in the same period). Independent variables included characteristics of PCPs (specialty, practice size, and prescribing volume) and their patients (patient age, sex, race/ethnicity, and household income).

Results: Solo practitioners, pediatricians, and physicians with more patients from predominantly African American zip codes were underrepresented among SEP adopters. The mean (SD) e-prescribing use ratio among adopters was 0.23 (0.28). Twenty percent of physicians maintained e-prescribing use ratios above 0.50. Available physician characteristics explained little of the variance in use, but physicians in smaller practices had greater use (P = .02).

Conclusions: Certain categories of physicians may need more tailored incentives to adopt SEP. On average, adopters used the SEP system for only about one-quarter of their prescriptions. Some adopters achieved high levels of SEP use, and further research is needed to elucidate the factors that enabled this.

Publication types

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

MeSH terms

  • Adult
  • Attitude to Computers
  • Diffusion of Innovation*
  • Drug Prescriptions*
  • Electronic Prescribing / statistics & numerical data*
  • Female
  • Humans
  • Insurance, Health
  • Insurance, Health, Reimbursement / statistics & numerical data
  • Logistic Models
  • Male
  • Medical Order Entry Systems / statistics & numerical data*
  • Middle Aged
  • New Jersey
  • Physicians
  • Practice Patterns, Physicians' / statistics & numerical data*
  • Primary Health Care
  • Retrospective Studies