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Where Do Real-Time Prescription Benefit Tools Fit in the Landscape of High US Prescription Medication Costs? A Narrative Review.
Wong, Rachel; Mehta, Tanvi; Very, Bradley; Luo, Jing; Feterik, Kristian; Crotty, Bradley H; Epstein, Jeremy A; Fliotsos, Michael J; Kashyap, Nitu; Smith, Erika; Woreta, Fasika A; Schwartz, Jeremy I.
Afiliação
  • Wong R; Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook, Stony Brook, USA. rachel.wong@stonybrookmedicine.edu.
  • Mehta T; Duke University School of Medicine, Durham, USA.
  • Very B; Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, USA.
  • Luo J; Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, USA.
  • Feterik K; Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, USA.
  • Crotty BH; Froedtert & the Medical College of Wisconsin Health Network, Milwaukee, WI, USA.
  • Epstein JA; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Fliotsos MJ; Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT, USA.
  • Kashyap N; Joint Data Analytics Team, Yale New Haven Hospital, New Haven, CT, USA.
  • Smith E; Internal Medicine and Information Technology, Yale New Haven Health and Yale School of Medicine, New Haven, CT, USA.
  • Woreta FA; Froedtert & the Medical College of Wisconsin Health Network, Milwaukee, WI, USA.
  • Schwartz JI; Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
J Gen Intern Med ; 38(4): 1038-1045, 2023 03.
Article em En | MEDLINE | ID: mdl-36441366
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Medicare Part D / Medicamentos sob Prescrição Tipo de estudo: Health_economic_evaluation / Prognostic_studies Limite: Aged / Humans País como assunto: America do norte Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Medicare Part D / Medicamentos sob Prescrição Tipo de estudo: Health_economic_evaluation / Prognostic_studies Limite: Aged / Humans País como assunto: America do norte Idioma: En Ano de publicação: 2023 Tipo de documento: Article