Evidence map›Paper›PMID 34923989›Full record

ArticleBMC medical informatics and decision making2021

Process mining framework with time perspective for understanding acute care: a case study of AIS in hospitals.

Jianfei Pang, Haifeng Xu, Jun Ren, Jun Yang, Mei Li, Dan Lu, Dongsheng Zhao

Open access · goldAbstract read
In one paragraph

Article in BMC medical informatics and decision making, 2021. 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
0.9field-weighted citation impact, top 21% 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

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. Article
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

7 authors at 1 institution in 1 country.

Jianfei Pang *Information Center, Academy of Military Medical Sciences, Beijing, China.
Haifeng Xu *Information Center, Academy of Military Medical Sciences, Beijing, China.
Jun RenMedical Service Department, General Hospital of Xinjiang Military Command, Urumchi, China.
Jun YangMedical Service Department, General Hospital of Xinjiang Military Command, Urumchi, China.
Mei LiChina Stroke Data Center, Beijing, China.
Dan LuJiangsu 707 Natural Pharmaceutical Co., Ltd., Zhenjiang, China.
Dongsheng ZhaoInformation Center, Academy of Military Medical Sciences, Beijing, China. dszhao@bmi.ac.cn.
Academy of Military Medical Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAcute care for critical illness requires very strict treatment timeliness. However, healthcare providers usually cannot accurately figure out the causes of low efficiency in acute care process due to the lack of effective tools. Besides, it is difficult to compare or conformance processes from different patient groups.

methodsTo solve these problems, we proposed a novel process mining framework with time perspective, which integrates four steps: standard activity construction, data extraction and filtering, iterative model discovery, and performance analysis.

resultsIt can visualize the execution of actual clinical activities hierarchically, evaluate the timeliness and identify bottlenecks in the treatment process. We take the acute ischemic stroke as a case study, and retrospectively reviewed 420 patients' data from a large hospital. Then we discovered process models with timelines, and identified the main reasons for in-hospital delay.

conclusionsExperiment results demonstrate that the framework proposed could be a new way of drawing insights about hospitals' clinical process, to help clinical institutions increase work efficiency and improve medical service.

Indexed as

Brain IschemiaStrokeHospitalsHumansRetrospective StudiesAcute careIschemic strokeProcess miningQuality improvementVisual analysis

Identifiers

PMID34923989
PMCPMC8684667
OpenAlexW4200320907

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.