Evidence map›Paper›PMID 30661117›Full record

ArticleJournal of medical systems2019

Optimization of Diagnosis and Treatment of Chronic Diseases Based on Association Analysis Under the Background of Regional Integration.

Kaijian Xia, Xiaowei Zhong, Li Zhang, Jianqiang Wang

Abstract read
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In one paragraph

Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
3.0field-weighted citation impact, top 9% of its field
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.

  1. Pooled it
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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 at 2 institutions in 1 country.

Kaijian XiaChangshu No.1 People's Hospital, Jiangsu, 215500, Changshu, China. xiakaijian@163.com.
Xiaowei ZhongChangshu City Health and Family Planning Commission, Jiangsu, 215500, Changshu, China.
Li ZhangChangshu No.1 People's Hospital, Jiangsu, 215500, Changshu, China.
Jianqiang WangChangshu No.1 People's Hospital, Jiangsu, 215500, Changshu, China.
Changshu No.1 People's Hospital · CNShanghai Municipal Commission of Health and Family Planning · CN

Funding

funded by the Jiangsu Committee of Health (No. H2018071). H2018071
6 · The paper itself

Abstract

In order to improve medical quality, shorten hospital stays, and reduce redundant treatment, an optimization of diagnosis and treatment of chronic diseases based on association analysis under the background of regional integration in the paper was proposed, which was to expand the scope of application of the clinical pathway standard diagnosis and treatment program in the context of regional medical integration and mass medical data, so that it had a larger group of patients within the region. In the context of regional medical integration, owing to the types of medical data were diverse, the preprocessing requirements and process specifications for diagnosis and treatment data were firstly proposed. At the stage of diagnosis and treatment unit optimization, the correlation between clinical behaviors was analyzed by using association rules of the FP-growth and Apriori algorithm. Through the optimization and combination of diagnosis and treatment units, the optimized clinical pathway was finally achieved. Experiments showed that after the optimization strategy by the paper proposed, the clinical path diagnosis and treatment achieved obvious improvement in medical quality under the condition that the medical cost was basically flat.

Indexed as

AlgorithmsBig DataChronic DiseaseClinical Decision-MakingCritical PathwaysData MiningEfficiency, OrganizationalElectronic Health RecordsHumansPopulation HealthQuality ImprovementChronic diseasesDiagnosis and treatmentFP-growth algorithmMedical qualityRegional medical integration

Identifiers

PMID30661117
OpenAlexW2910182844

What OpenQuestion holds

Textmetadata
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.