Evidence map›Paper›PMID 36138390›Full record

ArticleBMC health services research2022

The challenge of healthcare big data to China's commercial health insurance industry: evaluation and recommendations.

Jun Wu, Jiajun Qiao, Stephen Nicholas, Yunqiao Liu, Elizabeth Maitland

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
15.3field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 36 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 4 institutions in 3 countries.

Jun WuDong Fureng Institute of Economic and Social Development, Wuhan University, Wuhan, China.
Jiajun QiaoDong Fureng Institute of Economic and Social Development, Wuhan University, Wuhan, China. 530111680@qq.com.
Stephen NicholasNational Institute of Management and Commerce, Eveleigh, Australia.
Yunqiao LiuDong Fureng Institute of Economic and Social Development, Wuhan University, Wuhan, China.
Elizabeth MaitlandUniversity of Liverpool, Liverpool, UK.
Wuhan University · CNUniversity of Liverpool · GBUniversity of Newcastle Australia · AUWuhan Donghu University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChina'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.

methodsBy 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.

resultsWe 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.

conclusionsHealthcare 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.

Indexed as

Big DataInsurance, HealthChinaDelivery of Health CareHumansReproducibility of ResultsCommercial health insuranceDRGsHealthcare big dataPBM

Identifiers

PMID36138390
PMCPMC9494838
OpenAlexW4296613854

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.