Evidence map›Paper›PMID 41062161›Full record

ArticleBMJ health & care informatics2025

Development of data-driven clinical pathways: the big data clinical evidence-based pathways.

Xin Cui, Mengyun Sui, Hua Xie, Wen Chen, Wenqi Tian, Peiwen Wang, Xiaohua Jiang, Chen Fu, Su Xu

Abstract readMulticenter Study
In one paragraph

Article in BMJ health & care informatics, 2025. 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

9 authors.

Xin Cui *Shanghai Municipal Health Commission, Shanghai, China.
Mengyun Sui *Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.ORCID http://orcid.org/0009-0003-8817-1153
Hua Xie *Shanghai Municipal Health Commission, Shanghai, China.
Wen ChenHealth Economics, Fudan University, Shanghai, China.
Wenqi TianShanghai Municipal Health Commission, Shanghai, China.
Peiwen WangShanghai Municipal Health Commission, Shanghai, China.
Xiaohua JiangShanghai Municipal Health Commission, Shanghai, China.
Chen FuShanghai Municipal Health Commission, Shanghai, China.
Su XuShanghai Municipal Health Commission, Shanghai, China drxusu@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study developed clinical evidence-based pathways (CEBPWs) to standardise treatment protocols, align diagnosis-reimbursement criteria, detect upcoding and enable early overtreatment warnings.

methodsThe CEBPWs were developed based on hospitalised patient-level data from 1 January 2022 to 31 June 2024 in 166 public hospitals in 16 administrative districts of Shanghai. It includes a total of 5 319 336 cases, involving 3 688 108 groups of 'diagnosis+therapy'. 2.61 billion records of hospitalisation charges and 876.45 million records of outpatient charges were collected. GROWTH algorithm was used to find the combination of frequently charged items for examination, treatment, drugs and devices in 'diagnosis+therapy' group.

resultsCEBPWs comprise five key elements: objective evidence identification, accurate classification, value weighting, frequency weighting and temporal sequencing of evidence. We applied CEBPWs to 224 diseases, detecting issues including upcoding, overtreatment and fragmented care episodes to enhance healthcare quality. CEBPWs achieve 100% coverage in diagnostics, therapy and consumables, with 81.81% drug coverage. The pilot evaluation showed that there were violations in 433 cases, with a frequency deviation of 8.64% and cost deviation of 10.82%, with 8.95% for diagnosis, 9.44% for therapy, 14.81% for drugs and 8.98% for consumables. DISCUSSION: We were developed CEBPWs, breaking the limitations of the clinical pathways is that the experience of clinical experts rather than objective criterion based on the characteristics of big data and lack of diagnostic and therapy standards integrated with payment standards.

conclusionThe results indicate that CEBPW is critical tool for hospital management and regulation, address many drawbacks of clinical pathways.

Indexed as

Big DataCritical PathwaysEvidence-Based MedicineAlgorithmsChinaHospital CostsHospitals, PublicHumansOvertreatmentPilot ProjectsEvidence-Based MedicineHospitalsInformation Technology

Identifiers

PMID41062161
PMCPMC12506050

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.