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Enregistrement W3033070441 · doi:10.2196/18780

Medical Insurance Information Systems in China: Mixed Methods Study

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

Notice bibliographique

RevueJMIR Medical Informatics · 2020
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealthcare Systems and Reforms
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésInformatizationChinaBusinessStatus quoInformation systemComputer sciencePolitical science

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,432
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,036
Tête enseignante GPT0,330
Écart entre enseignants0,294 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations14
Publié2020
Routes d'admission1
Résumé présentoui

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