How can artificial intelligence optimize value-based contracting?

Author:

Poveda Jose LuisORCID,Bretón-Romero Rosa,Del Rio-Bermudez Carlos,Taberna Miren,Medrano Ignacio H.

Abstract

AbstractEfforts in the pharmaceutical market have been aimed at ensuring that the benefits obtained from the introduction of new therapies justify the associated costs. In recent years, drug payment models in healthcare have undergone a dramatic shift from focusing on volume (i.e., size of the target clinical population) to focusing on value (i.e., drug performance in real-world settings). In this context, value-based contracts (VBCs) were designed to align the payment of a drug to its clinical performance outside clinical trials by evaluating the effectiveness using real-word evidence (RWE). Despite their widespread implementation, different factors jeopardize the application of VBCs to most marketed drugs in a near future, including the need for easily measurable and relevant outcomes associated with clinical improvements, and access to a large patient population to assess said outcomes. Here, we argue that the extraction and analysis of massive amounts of RWE captured in patients’ electronic health records (EHRs) will circumvent these issues and optimize negotiations in VBCs. Particularly, the use of Natural Language Processing (NLP) has proven successful in the analysis of structured and unstructured clinical information in EHRs in multicenter research studies. Thus, the application of NLP to analyze patient-centered information in EHRs in the context of innovative contracting can be utterly beneficial as it enables the real-time evaluation of treatment response and financial impact in real-world settings.

Publisher

Springer Science and Business Media LLC

Subject

Pharmacy,Health Policy

Reference36 articles.

1. CatSalut. Guía para la definición de criterios de aplicación de esquemas de pago basados en resultados (EPR) en el ámbito farmacoterapéutico (acuerdos de riesgo compartido) Barcelona: Generalitat de Catalunya; 2014.

2. Braining G, Lynch M, Hayes K. Value-based agreements in healthcare: willingness versus ability. American Health & Drugs Benefits. 2019;12(5).

3. Kee A, Maio V. Value-based contracting: challenges and opportunities. Am J Med Qual. 2019;34(6):615–7.

4. Chatterjee A, Dougan C, Tevelow B, Zamani A. Innovative pharma contracts: When do value-based arrangements work? : McKinsey & Company; 2017.

5. Buyse M, Carter S, Sarnataro K. Factors influencing the implementation of value-based contracting between pharmaceutical manufacturers and payers. J Clin Pathways. 2018;4(4):27–30.

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3