Open Access Research article

Effect of electronic prescribing with formulary decision support on medication tier, copayments, and adherence

Joshua M Pevnick12*, Ning Li3, Steven M Asch45, Cynthia A Jackevicius106789 and Douglas S Bell112

Author Affiliations

1 Division of General Internal Medicine, Department of Medicine, Cedars-Sinai Health System, 8700 Beverly Blvd, PACT 400.8G, Los Angeles, CA 90048, USA

2 UCLA David Geffen School of Medicine, Los Angeles, CA, USA

3 Department of Biomathematics, UCLA David Geffen School of Medicine, Los Angeles, CA, USA

4 Stanford School of Medicine, Palo Alto, CA, USA

5 Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA

6 Institute for Clinical Evaluative Sciences, Toronto, Canada

7 University Health Network, Toronto, Canada

8 Department of Health Policy, Management and Evaluation, Faculty of Medicine, University of Toronto, Toronto, Canada

9 Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA

10 Department of Pharmacy Practice and Administration, College of Pharmacy, Western University of Health Sciences, Pomona, CA, USA

11 RAND Health, Santa Monica, CA, USA

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BMC Medical Informatics and Decision Making 2014, 14:79  doi:10.1186/1472-6947-14-79

Published: 28 August 2014

Abstract

Background

Medication non-adherence is prevalent. We assessed the effect of electronic prescribing (e-prescribing) with formulary decision support on preferred formulary tier usage, copayment, and concomitant adherence.

Methods

We retrospectively analyzed 14,682 initial pharmaceutical claims for angiotensin receptor blocker and inhaled steroid medications among 14,410 patients of 2189 primary care physicians (PCPs) who were offered e-prescribing with formulary decision support, including 297 PCPs who adopted it. Formulary decision support was initially non-interruptive, such that formulary tier symbols were displayed adjacent to medication names. Subsequently, interruptive formulary decision support alerts also interrupted e-prescribing when preferred-tier alternatives were available. A difference in differences design was used to compare the pre-post differences in medication tier for each new prescription attributed to non-adopters, low user (<30% usage rate), and high user PCPs (>30% usage rate). Second, we modeled the effect of formulary tier on prescription copayment. Last, we modeled the effect of copayment on adherence (proportion of days covered) to each new medication.

Results

Compared with non-adopters, high users of e-prescribing were more likely to prescribe preferred-tier medications (vs. non-preferred tier) when both non-interruptive and interruptive formulary decision support were in place (OR 1.9 [95% CI 1.0-3.4], p = 0.04), but no more likely to prescribe preferred-tier when only non-interruptive formulary decision support was in place (p = 0.90). Preferred-tier claims had only slightly lower mean monthly copayments than non-preferred tier claims (angiotensin receptor blocker: $10.60 versus $11.81, inhaled steroid: $14.86 versus $16.42, p < 0.0001). Medication possession ratio was 8% lower for each $1.00 increase in monthly copayment to the one quarter power (p < 0.0001). However, we detected no significant direct association between formulary decision support usage and adherence.

Conclusion

Interruptive formulary decision support shifted prescribing toward preferred tiers, but these medications were only minimally less expensive in the studied patient population. In this context, formulary decision support did not significantly increase adherence. To impact cost-related non-adherence, formulary decision support will likely need to be paired with complementary drug benefit design. Formulary decision support should be studied further, with particular attention to its effect on adherence in the setting of different benefit designs.

Keywords:
Clinical decision support; Electronic prescribing; Medication adherence