Krefting Johannes, Sen Partho, David-Rus Diana, Güldener Ulrich, Hawe Johann S, Cassese Salvatore, von Scheidt Moritz, Schunkert Heribert
Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany.
German Center for Cardiovascular Research e.V. (DZHK), Partner Site Munich Heart Alliance, Munich, Germany.
Front Artif Intell. 2023 May 3;6:1155404. doi: 10.3389/frai.2023.1155404. eCollection 2023.
Outcome research that supports guideline recommendations for primary and secondary preventions largely depends on the data obtained from clinical trials or selected hospital populations. The exponentially growing amount of real-world medical data could enable fundamental improvements in cardiovascular disease (CVD) prediction, prevention, and care. In this review we summarize how data from health insurance claims (HIC) may improve our understanding of current health provision and identify challenges of patient care by implementing the perspective of patients (providing data and contributing to society), physicians (identifying at-risk patients, optimizing diagnosis and therapy), health insurers (preventive education and economic aspects), and policy makers (data-driven legislation). HIC data has the potential to inform relevant aspects of the healthcare systems. Although HIC data inherit limitations, large sample sizes and long-term follow-up provides enormous predictive power. Herein, we highlight the benefits and limitations of HIC data and provide examples from the cardiovascular field, i.e. how HIC data is supporting healthcare, focusing on the demographical and epidemiological differences, pharmacotherapy, healthcare utilization, cost-effectiveness and outcomes of different treatments. As an outlook we discuss the potential of using HIC-based big data and modern artificial intelligence (AI) algorithms to guide patient education and care, which could lead to the development of a learning healthcare system and support a medically relevant legislation in the future.
支持一级和二级预防指南建议的结果研究很大程度上依赖于从临床试验或选定的医院人群中获得的数据。现实世界中呈指数级增长的医学数据能够从根本上改善心血管疾病（CVD）的预测、预防和护理。在本综述中，我们总结了健康保险理赔（HIC）数据如何增进我们对当前医疗服务的理解，并通过患者（提供数据并为社会做出贡献）、医生（识别高危患者、优化诊断和治疗）、健康保险公司（预防教育和经济方面）以及政策制定者（数据驱动的立法）的视角来识别患者护理方面的挑战。HIC数据有潜力为医疗系统的相关方面提供信息。尽管HIC数据存在局限性，但大样本量和长期随访提供了巨大的预测能力。在此，我们强调HIC数据的益处和局限性，并提供心血管领域的实例，即HIC数据如何支持医疗保健，重点关注不同治疗方法在人口统计学和流行病学差异、药物治疗、医疗保健利用、成本效益和结果方面的情况。作为展望，我们讨论了使用基于HIC的大数据和现代人工智能（AI）算法来指导患者教育和护理的潜力，这可能会促成学习型医疗系统的发展，并在未来支持与医学相关的立法。
