Evidence map›Paper›PMID 30485302›Full record

ArticlePloS one2018

Standardization of medical service indicators: A useful technique for hospital administration.

Li Wu, Conghua Ji, Hanti Lu, Xuewen Hong, Shan Liu, Ying Zhang, Qiushuang Li, Sijia Huang, Penglei Zhou, Jiong Yao and 1 more

Abstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

11 authors.

Li WuCenter of Clinical Evaluation, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang, China.
Conghua JiCenter of Clinical Evaluation, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang, China.ORCID 0000-0002-8329-8927
Hanti LuDepartment of Information, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang, China.
Xuewen HongDepartment of Information, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang, China.
Shan LiuCenter of Clinical Evaluation, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang, China.
Ying ZhangCenter of Clinical Evaluation, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang, China.
Qiushuang LiCenter of Clinical Evaluation, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang, China.
Sijia HuangDepartment of Information, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang, China.
Penglei ZhouDepartment of Information, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang, China.
Jiong YaoMedical Records and Statistics Room, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, China.
Yuxiu HuMedical Records and Statistics Room, the Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMany comparability problems appear in the process of the performance assessment of medical service. When comparing medical evaluation indicators across hospitals, or even within the same hospital, over time, the differences in the population composition such as types of diseases, comorbidities, demographic characteristics should be taken into account. This study aims to introduce a standardization technique for medical service indicators and provide a new insight on the comparability of medical data.

methodsThe medical records of 142592 inpatient from three hospitals in 2017 were included in this study. Chi-square and Kruskal-Wallis tests were used to explore the compositions of confounding factors among populations. The procedure of stratified standardization technique was applied to compare the differences of the average length of stay and the average hospitalization expense among three hospitals.

resultsAge, gender, comorbidity, and principal diagnoses category were considered as confounding factors. After correcting all factors, the average length of stay of hospital A and C were increased by 0.21 and 1.20 days, respectively, while that of hospital B was reduced by 1.54 days. The average hospitalization expenses of hospital A and C were increased by 1494 and 660 Yuan, whilst that of hospital B was decreased by 810 Yuan.

conclusionsStandardization method will be helpful to improve the comparability of medical service indicators in hospital administration. It could be a practical technique and worthy of promotion.

Indexed as

AdultAgedChinaFemaleHealth ServicesHospital AdministrationHospital CostsHospitalizationHumansLength of StayMaleMedical RecordsMiddle Aged

Identifiers

PMID30485302
PMCPMC6261548

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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.