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Abstract

The problem of unaffordable prescription medications in the United States is complex and can result in poor patient adherence to therapy, worse clinical outcomes, and high costs to the healthcare system. While providers are aware of the financial burden of healthcare for patients, there is a lack of actionable price transparency at the point of prescribing. Real-time prescription benefit (RTPB) tools are new electronic clinical decision support tools that retrieve patient- and medication-specific out-of-pocket cost information and display it to clinicians at the point of prescribing. The rise in US healthcare costs has been a major driver for efforts to increase medication price transparency, and mandates from the Centers for Medicare & Medicaid Services for Medicare Part D sponsors to adopt RTPB tools may spur integration of such tools into electronic health records. Although multiple factors affect the implementation of RTPB tools, there is limited evidence on outcomes. Further research will be needed to understand the impact of RTPB tools on end results such as prescribing behavior, out-of-pocket medication costs for patients, and adherence to pharmacologic treatment. We review the terminology and concepts essential in understanding the landscape of RTPB tools, implementation considerations, barriers to adoption, and directions for future research that will be important to patients, prescribers, health systems, and insurers.

Details

Title
Where Do Real-Time Prescription Benefit Tools Fit in the Landscape of High US Prescription Medication Costs? A Narrative Review
Author
Wong, Rachel 1   VIAFID ORCID Logo  ; Mehta, Tanvi 2 ; Very, Bradley 3 ; Luo, Jing 3 ; Feterik, Kristian 3 ; Crotty, Bradley H. 4 ; Epstein, Jeremy A. 5 ; Fliotsos, Michael J. 6 ; Kashyap, Nitu 7 ; Smith, Erika 4 ; Woreta, Fasika A. 8 ; Schwartz, Jeremy I. 9 

 Renaissance School of Medicine at Stony Brook, Department of Biomedical Informatics, Stony Brook, USA 
 Duke University School of Medicine, Durham, USA (GRID:grid.26009.3d) (ISNI:0000 0004 1936 7961) 
 University of Pittsburgh School of Medicine, Department of Medicine, Pittsburgh, USA (GRID:grid.21925.3d) (ISNI:0000 0004 1936 9000) 
 Froedtert & the Medical College of Wisconsin Health Network, Milwaukee, USA (GRID:grid.415100.1) (ISNI:0000 0004 0426 576X) 
 Johns Hopkins University School of Medicine, Department of Medicine, Baltimore, USA (GRID:grid.21107.35) (ISNI:0000 0001 2171 9311) 
 Yale School of Medicine, Department of Ophthalmology and Visual Science, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710) 
 Yale New Haven Hospital, Joint Data Analytics Team, New Haven, USA (GRID:grid.417307.6); Yale New Haven Health and Yale School of Medicine, Internal Medicine and Information Technology, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710) 
 Johns Hopkins University School of Medicine, Wilmer Eye Institute, Baltimore, USA (GRID:grid.21107.35) (ISNI:0000 0001 2171 9311) 
 Yale University School of Medicine, Section of General Internal Medicine, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710) 
Pages
1038-1045
Publication year
2023
Publication date
Mar 2023
Publisher
Springer Nature B.V.
ISSN
08848734
e-ISSN
15251497
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2790218935
Copyright
© The Author(s), under exclusive licence to Society of General Internal Medicine 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.