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. 2010 Nov-Dec;17(6):681-8.
doi: 10.1136/jamia.2009.002568.

Impact of generic substitution decision support on electronic prescribing behavior

Shane P Stenner  1 , Qingxia Chen, Kevin B Johnson
Affiliations

Impact of generic substitution decision support on electronic prescribing behavior

Shane P Stenner et al. J Am Med Inform Assoc. 2010 Nov-Dec.

Abstract

Objective: To evaluate the impact of generic substitution decision support on electronic (e-) prescribing of generic medications.

Design: The authors analyzed retrospective outpatient e-prescribing data from an academic medical center and affiliated network for July 1, 2005-September 30, 2008 using an interrupted time-series design to assess the rate of generic prescribing before and after implementing generic substitution decision support. To assess background secular trends, e-prescribing was compared with a concurrent random sample of hand-generated prescriptions.

Measurements: Proportion of generic medications prescribed before and after the intervention, evaluated over time, and compared with a sample of prescriptions generated without e-prescribing.

Results: The proportion of generic medication prescriptions increased from 32.1% to 54.2% after the intervention (22.1% increase, 95% CI 21.9% to 22.3%), with no diminution in magnitude of improvement post-intervention. In the concurrent control group, increases in proportion of generic prescriptions (29.3% to 31.4% to 37.4% in the pre-intervention, post-intervention, and end-of-study periods, respectively) were not commensurate with the intervention. There was a larger change in generic prescribing rates among authorized prescribers (24.6%) than nurses (18.5%; adjusted OR 1.38, 95% CI 1.17 to 1.63). Two years after the intervention, the proportion of generic prescribing remained significantly higher for e-prescriptions (58.1%; 95% CI 57.5% to 58.7%) than for hand-generated prescriptions ordered at the same time (37.4%; 95% CI 34.9% to 39.9%) (p<0.0001). Generic prescribing increased significantly in every specialty.

Conclusion: Implementation of generic substitution decision support was associated with dramatic and sustained improvements in the rate of outpatient generic e-prescribing across all specialties.

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Conflict of interest statement

Competing interests: None.

Figures

Figure 1
Figure 1
Ordering a medication using RxStar prior to implementation of e-prescribing decision support with generic medication suggestions. A user enters a name of a branded medication or part of a brand name as a search string and clicks the ‘search’ button. Only the brand name formulations that match the search string are displayed.
Figure 2
Figure 2
Ordering a medication using RxStar following implementation of e-prescribing decision support with generic medication suggestions. A user searches for a brand name medication. RxStar displays the names of all generic formulations with brand equivalents below each generic formulation. To the right of each result, RxStar displays the formulary status (approved, prior authorization required, etc).
Figure 3
Figure 3
Monthly proportion of generic prescriptions before and after implementation of generic substitution decision support. Use of the e-prescribing system and number of e-prescriptions (open circles) increased with time. Proportion of generic e-prescriptions increased 22.1% after the system implementation date (day 451). The proportion of non-e-prescribing generic prescriptions (crosses) remained consistently and significantly lower.
Figure 4
Figure 4
Monthly volume-adjusted proportion of statin prescriptions before and after implementation of generic substitution decision support. The proportion of commercially available generic statin equivalents increased dramatically after the system implementation date. A slight within-class effect was observed for the brand-only statin, Lipitor, without any intervention to directly recommend a within-class generic alternative.
See this image and copyright information in PMC

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