Evidence map›Paper›PMID 42178857›Full record

ArticleHealth policy and planning2026

An unintended complementarity: how the outpatient pooling policy increases inpatient services in China.

Jianhui Wang, Mengyao Zhang, Miao Yu

Abstract read
In one paragraph

Article in Health policy and planning, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Jianhui WangSchool of Public Policy and Management, Tsinghua University, Beijing 100084, China.ORCID 0009-0003-1777-2007
Mengyao ZhangTsinghua Shenzhen International Graduate School, Tsinghua University, Guangdong 518055, China.ORCID 0009-0000-5739-0757
Miao YuSchool of Healthcare Management, Tsinghua Medicine, Tsinghua University, Beijing 100084, China.ORCID 0000-0002-5659-0527

Funding

China National Health Development Research CenterNational Healthcare Security Administration Research ProjectNational Social Science Fund of China 17ZDA121Tsinghua University Graduate Education and Teaching Reform Project 202504J027
6 · The paper itself

Abstract

This study investigates the relationship between outpatient and inpatient services within China's tiered healthcare system following the implementation of outpatient pooling reform. Data were sourced from official administrative healthcare service records across counties in Sichuan Province (2010-20) and individual survey data from the China Health and Retirement Longitudinal Study (2011-18). Using a staggered difference-in-differences design with an imputation model, we identified an unexpected increase in hospital admissions post-reform, which was contrary to the policy's intent to optimize healthcare resource allocation. We found that increased use of outpatient services was positively associated with demand for inpatient care, demonstrating a complementary effect. Mechanism analysis revealed that the rising hospital admissions were partly attributable to two factors: (i) expansion of outpatient services in primary care institutions generating an induced hospitalization effect that contributed to the rise in inpatient volume and (ii) hospitals improving inpatient service efficiency by shifting selected diagnostic and treatment tests to the outpatient account. These findings confirm the complex interaction between outpatient and inpatient services and underscore the need for a more effective design of the medical insurance payment system to enhance the efficiency of China's healthcare services.

Indexed as

Ambulatory CareHealth Care ReformHealth PolicyHospitalizationInpatientsAgedChinaFemaleHumansLongitudinal StudiesMaleMiddle AgedOutpatientsChinacomplementary effectinpatient servicesoutpatient pooling reformtiered diagnosis and treatment

Identifiers

PMID42178857
PMCPMC13481781

What OpenQuestion holds

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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.