Background: In October 2022, the Biden administration issued an executive order to the Center for Medicare and Medicaid Innovation (CMMI) to develop new health care payment and delivery models to lower prescription drug costs and promote access to innovative therapies. In response, the agency proposed 3 novel drug payment models for testing.
Objective: To understand the impact that CMMI demonstration projects can have on the prescription drug market.
Methods: We examined each of the models listed on the CMMI website and searched the Federal Register and news articles for additional models that contained interventions related to patient out-of-pocket drug costs, Medicare drug spending, or Medicaid drug spending. We excluded models with indirect effects on drug costs (for example, bundled payments). We comprehensively reviewed all previous cases in which CMMI has attempted models addressing prescription drug costs and spending and evaluated the circumstances, impact, and lessons learned that may aid policymakers in the design and implementation of new models.
Results: We identified 9 CMMI models containing direct interventions related to drug costs. Among prior models addressing drug prices, nearly half (44%, 4/9) were not implemented because of their scope, voluntary nature, and procedural challenges. No implemented models met the CMMI standard for expansion, although key elements of the Senior Savings model limiting monthly insulin costs to $35 were later incorporated into the Inflation Reduction Act.
Conclusions: In future CMMI implementation efforts, we suggest maximizing voluntary collaboration when selection bias concerns are minimal, using mandatory models when not, ensuring that the geographic scope is not overly ambitious, and adhering closely to statutory authority and established administrative procedure to minimize legal challenges and maximize model demonstration utility.
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http://dx.doi.org/10.18553/jmcp.2023.23208 | DOI Listing |
Res Social Adm Pharm
January 2025
Division of Social and Administrative Sciences & Sonderegger Research Center for Improved Medication Outcomes, University of Wisconsin-Madison School of Pharmacy, 777 Highland Avenue, Madison, WI, 53705, USA. Electronic address:
Introduction: Misuse of over-the-counter (OTC) medications by older adults (age 65+) can comprise Drug-Age, Drug-Drug, Drug-Disease, and Drug-Label types. Pharmacies in the United States are prevalent sources of OTCs and are an apt setting to address OTC misuse. Senior Safe™ is a pharmacy-system redesign for preventing older adult OTC misuse.
View Article and Find Full Text PDFBioinformatics
January 2025
School of Engineering, Westlake University, Hangzhou, 310024, China.
Motivation: Drug-target interaction (DTI) prediction is crucial for drug discovery, significantly reducing costs and time in experimental searches across vast drug compound spaces. While deep learning has advanced DTI prediction accuracy, challenges remain: (i) existing methods often lack generalizability, with performance dropping significantly on unseen proteins and cross-domain settings; (ii) current molecular relational learning often overlooks subpocket-level interactions, which are vital for a detailed understanding of binding sites.
Results: We introduce SP-DTI, a subpocket-informed transformer model designed to address these challenges through: (i) detailed subpocket analysis using the Cavity Identification and Analysis Routine (CAVIAR) for interaction modeling at both global and local levels, and (ii) integration of pre-trained language models into graph neural networks to encode drugs and proteins, enhancing generalizability to unlabeled data.
Clin Drug Investig
January 2025
Department of Public Health Sciences, University of Virginia, 560 Ray C Hunt Dr., Room 2107, Charlottesville, VA, USA.
Background And Objective: Cyclin-dependent kinase (CDK)4/6 inhibitors in combination with endocrine therapy (ET) significantly enhance progression-free survival and overall survival in patients diagnosed with HR+/HER2- metastatic breast cancer (MBC). However, they are highly expensive, and their economic impact has not been fully evaluated. This is a retrospective secondary analysis evaluating the cost effectiveness of these drugs, differentiating between medication-related and non-medication costs from a healthcare perspective.
View Article and Find Full Text PDFClin Transl Sci
January 2025
Global Biometrics and Data Management, Pfizer Research and Development, New York, New York, USA.
The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug-target interactions from big data, enabling more accurate predictions and novel hypothesis generation.
View Article and Find Full Text PDFTurk Kardiyol Dern Ars
January 2025
Department of Cardiology, Ankara University Faculty of Medicine, Ankara, Türkiye.
Objective: Atrial fibrillation (AF) is a common arrhythmia associated with a five-fold increased risk of stroke. Family physicians (FPs) serve as the primary contact point for patients seeking healthcare. While many surveys have assessed FPs' knowledge on AF across various countries, no such study has been conducted in Türkiye.
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