Evidence map›Paper›PMID 39761318›Full record

ArticlePloS one2025

Research on quantitative evaluation of medical insurance fraud supervision policy based on 'Antecedents-Process-Outcomes' framework.

Zixiao Zhang, Shaoqun Ding, Zitao Yang, Huaxia Hu

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
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

4 authors.

Zixiao ZhangSchool of Public Administration, Southwestern University of Finance and Economics, Chengdu, Sichuan, China.ORCID 0000-0001-6020-1451
Shaoqun DingAging and Social Security Research Center, Southwestern University of Finance and Economics, Chengdu, Sichuan, China.
Zitao YangSchool of Management, Wuhan University of Technology, Wuhan, Hubei Province, China.
Huaxia HuSchool of Management, Wuhan University of Technology, Wuhan, Hubei Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fraud in medical insurance is a serious problem that threatens the safety and sustainability of medical insurance funds. The process of reducing or even eliminating the impact of fraud is related to maintaining the balance of payments for medical insurance funds and reforming the payment system based on total amount control. As a result of reviewing the policy background of medical insurance fraud in China, combined with the policy evaluation model in the area of public management, this paper develops a conceptual framework of 'Antecedents-Process-Outcomes' that emphasizes the fraud and governance of medical insurance funds. This paper uses grounded theory to look at 180 cases of medical insurance fraud and then uses the PMC index model to rate 18 policies. It then looks at the joint progressive analysis framework of medical insurance fraud and fraud supervision. In this paper, we analyze the policy similarities and differences of medical insurance fraud supervision in China from three perspectives: policy attributes, policy contents, and policy effects. The average PMC index of the 18 policies is 4.98, which is generally acceptable; however, there are some deficiencies in the policy field, policy supervision chain, policy orientation, and policy tools. Then, it puts forward suggestions for improving the four policy shortcomings in order to provide theoretical and practical enlightenment for the high-quality development of the medical security system and realize the new medical security in the process of Chinese-style modernization.

Indexed as

FraudInsurance, HealthChinaHealth PolicyHumans

Identifiers

PMID39761318
PMCPMC11703012

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Registered trials

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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.