- Title
- The challenge of healthcare big data to China’s commercial health insurance industry: evaluation and recommendations
- Creator
- Wu, Jun; Qiao, Jiajun; Nicholas, Stephen; Liu, Yunqiao; Maitland, Elizabeth
- Relation
- BMC Health Services Research Vol. 22, no. 1189
- Publisher Link
- http://dx.doi.org/10.1186/s12913-022-08574-2
- Publisher
- BioMed Central Ltd.
- Resource Type
- journal article
- Date
- 2022
- Description
- Background: China’s social medical insurance system faces challenges in financing, product coverage, patient health responsibility sharing and data security, which commercial health insurance companies can help address. Confronting accelerated population aging, the rapid increase of patients with chronic diseases and the maternal and child healthcare needs created by the three-child policy, the Chinese government has encouraged the development of commercial health insurance. But China's commercial health insurance companies face financial sustainability problems, limited product ranges and high operating costs. At the same time, the informatization level of China's healthcare industry, and the value of healthcare big data, is increasing. We analyze and describe the potential application of healthcare big data in the life cycle of China's commercial health insurance system and provide specific action plans for Chinese commercial health insurance companies; identify the challenges to commercial health insurers; and make recommendations for the application of big health data by commercial health insurers. Our recommendations inform healthcare policy makers on the development of commercial health insurance and the improvement of the healthcare financing system. We not only verify the value of healthcare big data, but also identify specific ways that healthcare big data plays in the development of commercial health insurance. Based on the research results, we recommend new policies for government and new uses of healthcare big data for commercial health insurance institutions. The benign development of commercial health insurance will improve the level of health services in China. Methods: By interviewing health insurance managers (including actuaries, product managers, business executives, information technology medical workers, and commercial health insurance personnel) and by accessing research papers, industry reports, news reports and public information disclosure documents about commercial health insurance, we describe the impact of healthcare big data on the life cycle of commercial health insurance products and processes. Results: We identify the issues and challenges of commercial health insurers in the use of healthcare big data, and advance specific strategies to expand the use of healthcare big data. In the life cycle of commercial health insurance products, healthcare big data can improve premium income, control medical costs and increase operational efficiency. First, healthcare big data can increase premiums, products and services by attenuating moral hazard and adverse selection problems, where high quality clients over-pay and high-risk clients underpay for health insurance. Second, healthcare big data can reduce medical expenses compensation pay-outs by promoting the establishment of a management medical system. Finally, the use of healthcare big data improves operational efficiency by increasing payment speeds, identifying fraud and increasing claim verification processes through automating payments and reducing offline processes. We discuss the obstacles to obtain healthcare big data confronting commercial health insurance companies. The sharing and data mining of healthcare big data brings privacy risks to the insured and there are significant differences in data standards and quality of healthcare big data that limit the application of healthcare big data in commercial health insurance. We recommend that national, regional and local government departments coordinate policies to facilitate the cooperation between commercial health insurance companies and regional healthcare big data platforms. In terms of technology, we recommend the establishment of data sharing platforms and data exchange mechanism across institutions and regions according to nation-wide standards and specifications. Government management departments should establish healthcare big data standards and specification system, promote the construction of healthcare big data and ensure the integrity, authenticity and reliability of health data. We recommend data quality continuous improvement and management mechanisms that combine technology and management. Government regulation should oversee commercial health insurance institutions and establish data security management systems to monitor and supervise the privacy of personal data. Conclusions: Healthcare big data can play an important role in the development of China's commercial health insurance industry. Healthcare big data can increase commercial health insurers’ financial viability while providing improved, and cost-effective, products and services. By providing more and better information to insurers, healthcare big data attenuates the asymmetric information problem, addressing moral hazard and adverse selection problems. By combining hospital and medical organization management information systems with insurers’ data management, healthcare big data can help insurers set sustainable premiums, control medical costs and promote operational efficiency. At present, the informatization degree of China's healthcare industry remains limited. To improve the performances, products and services of commercial health insurers, we recommend government reforms in healthcare big data, such as expanding medical industry cooperation; further developing the processes of applying healthcare big data; augmenting data sharing; addressing privacy risks; setting data standards; and improving data quality.
- Subject
- commercial health insurance; healthcare big data; PBM; DRGs; SDG 1; SDG 3; SDG 17; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1490434
- Identifier
- uon:52903
- Identifier
- ISSN:1472-6963
- Language
- eng
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