Evidence map›Paper›PMID 42433423›Full record

ArticleFrontiers in public health2026

The compatibility of diagnosis-intervention packet with the individualized treatment paradigm of traditional Chinese medicine: an analytical study.

Fan Ruoxi, Tu Lang, Li Shulei, Zhou Xiaoyuan

Abstract read
In one paragraph

Article in Frontiers in public health, 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
–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

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

4 authors.

Fan RuoxiWest China School of Public Health/West China Fourth Hospital, Sichuan University, Chengdu, China.
Tu LangWest China School of Public Health/West China Fourth Hospital, Sichuan University, Chengdu, China.
Li ShuleiWest China School of Public Health/West China Fourth Hospital, Sichuan University, Chengdu, China.
Zhou XiaoyuanWest China School of Public Health/West China Fourth Hospital, Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Traditional medicine (TM), including Traditional Chinese Medicine (TCM), serves as a vital component of global healthcare, yet lags behind modern medicine in standardized evaluation and payment model compatibility. In China, TCM's unique paradigm-centered on syndrome differentiation and non-surgical therapies-faces challenges under the Diagnosis-Intervention Packet (DIP) payment reform. However, systematic evidence on how DIP affects medical expenses across different levels of TCM service utilization remains scarce. This study aims to quantify DIP's effects on medical expenses between different groups, and to provide evidence for adjusting DIP policies based on TCM hospital characteristics. Method: We conducted a multiple-group interrupted time series analysis (MGITSA) on outcome variables reflecting hospitalization costs and TCM service structures, using a dataset containing 48,398 inpatient records from a TCM hospital in City C spanning from January 2018 to January 2021. The dataset included 4,343 records for the High-TCM group and 44,055 records for the Low-TCM group. Results: MGITSA indicated that after DIP implementation, the High-TCM group experienced significant increases in both hospitalization costs ( Conclusion: DIP exerts distinct short-term effects: the high-TCM group maintained cost stability, while the low-TCM group exhibited cost inflation. Policy adjustments, such as differential DIP scoring based on TCM treatment rates, are needed to enhance compatibility and support TCM's sustainable development.

Indexed as

Health ExpendituresHospitalizationMedicine, Chinese TraditionalChinaHumansInterrupted Time Series Analysiscompatibilitydiagnosis-intervention packetinterrupted time-series analysisreform effectivenessTCM

Identifiers

PMID42433423
PMCPMC13350343

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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.