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Record W3033070441 · doi:10.2196/18780

Medical Insurance Information Systems in China: Mixed Methods Study

2020· article· en· W3033070441 on OpenAlexvenueno aff
Yazi Li, Chunji Lu, Yang Liu

Bibliographic record

VenueJMIR Medical Informatics · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsInformatizationChinaBusinessStatus quoInformation systemComputer sciencePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Since the People's Republic of China (PRC), or China, established the basic medical insurance system (MIS) in 1998, the medical insurance information systems (MIISs) in China have effectively supported the operation of the MIS through several phases of development; the phases included a stand-alone version, the internet, and big data. In 2018, China's national medical security systems were integrated, while MIISs were facing reconstruction. We summarized China's experience in medical insurance informatization over the past 20 years, aiming to provide a reference for the building of a new basic MIS for China and for developing countries. OBJECTIVE: This paper aims to sort out medical insurance informatization policies throughout the years, use questionnaires to determine the status quo of provincial MIIS-building in China and the relevant policies, provide references and suggestions for the top-level design and implementation of the information systems in the transitional period of China's MIS reform, and provide a reference for the building of MIISs in developing countries. METHODS: We conducted policy analysis by collecting the laws, regulations, and policy documents-issued from 1998 to 2020-on China's medical insurance and its informatization; we also analyzed the US Health Insurance Portability and Accountability Act and other relevant policies. We conducted a questionnaire survey by sending out questionnaires to 31 Chinese, provincial, medical security bureaus to collect information about network links, system functions, data exchange, standards and specifications, and building modes, among other items. We conducted a literature review by searching for documents about relevant laws and policies, building methods, application results, and other documents related to MIISs; we conducted searches using PubMed, Elsevier, China National Knowledge Infrastructure, and other major literature databases. We conducted telephone interviews to verify the results of questionnaires and to understand the focus issues concerning the building of China's national MIISs during the period of integration and transition of China's MIS. RESULTS: In 74% (23/31) of the regions in China, MIISs were networked through dedicated fiber optic lines. In 65% (20/31) of the regions in China, MIISs supported identity recognition based on both ID cards and social security cards. In 55% (17/31) of the regions in China, MIISs at provincial and municipal levels were networked and have gathered basic medical insurance data, whereas MIISs were connected to health insurance companies in 35% (11/31) of the regions in China. China's MIISs are comprised of 11 basic functional modules, among which the modules of business operation, transregional referral, reimbursement, and monitoring systems are widely applied. MIISs in 83% (20/24) of Chinese provinces have stored data on coverage, payment, and settlement compensation of medical insurance. However, in terms of data security and privacy protection, pertinent policies are absent and data utilization is not in-depth enough. Respondents to telephone interviews universally reflected on the following issues and suggestions: in the period of integration and transition of MISs, close attention should be paid to the top-level design, and repeated investment should be avoided for the building of MIISs; MIISs should be adapted to the health care reform, and efforts should be made to strengthen the informatization support for the reform of payment methods; and MIISs should be adapted for the widespread application of mobile phones and should provide insured persons with more self-service functions. CONCLUSIONS: In the future, the building of China's basic MIISs should be deployed at the national, provincial, prefectural, and municipal levels on a unified basis. Efforts should be made to strengthen the development of standard codes, data exchange, and data utilization. Work should be done to formulate the rules and regulations for security and privacy protection and to balance the right to be informed with the mining and utilization of big data. Efforts should be made to intensify the interconnectivity between MISs and other health systems and to strengthen the application of medical insurance information in public health monitoring and early warning systems; this would ultimately improve the degree of trust from stakeholders, including individuals, medical service providers, and public health institutions, in the basic MIISs.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.330
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2020
Admission routes1
Has abstractyes

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