Evidence map›Paper›PMID 35144618›Full record

ArticleBMC medical informatics and decision making2022

A configurable method for clinical quality measurement through electronic health records based on openEHR and CQL.

Mengyang Li, Hailing Cai, Yunlong Zhi, Zehai Fu, Huilong Duan, Xudong Lu

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. 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

6 authors.

Mengyang LiCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road 38, Hangzhou, 310027, China.
Hailing CaiCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road 38, Hangzhou, 310027, China.
Yunlong ZhiCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road 38, Hangzhou, 310027, China.
Zehai FuCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road 38, Hangzhou, 310027, China.
Huilong DuanCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road 38, Hangzhou, 310027, China.
Xudong LuCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road 38, Hangzhou, 310027, China. lvxd@zju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOne of the primary obstacles to measure clinical quality is the lack of configurable solutions to make computers understand and compute clinical quality indicators. The paper presents a solution that can help clinical staff develop clinical quality measurement more easily and generate the corresponding data reports and visualization by a configurable method based on openEHR and Clinical Quality Language (CQL).

methodsFirst, expression logic adopted from CQL was combined with openEHR to express clinical quality indicators. Archetype binding provides the clinical information models used in expression logic, terminology binding makes the medical concepts consistent used in clinical quality artifacts and metadata is regarded as the essential component for sharing and management. Then, a systematic approach was put forward to facilitate the development of clinical quality indicators and the generation of corresponding data reports and visualization. Finally, clinical physicians were invited to test our system and give their opinions.

resultsWith the combination of openEHR and CQL, 64 indicators from Centers for Medicare & Medicaid Services (CMS) were expressed for verification and a complicated indicator was shown as an example. 68 indicators from 17 different scenes in the local environment were also expressed and computed in our system. A platform was built to support the development of indicators in a unified way. Also, an execution engine can parse and compute these indicators. Based on a clinical data repository (CDR), indicators were used to generate data reports and visualization and shown in a dashboard.

conclusionOur method is capable of expressing clinical quality indicators formally. With the computer-interpretable indicators, a systematic approach can make it more easily to define clinical indicators and generate medical data reports and visualization, and facilitate the adoption of clinical quality measurements.

Indexed as

Electronic Health RecordsLanguageAgedHumansMedicareUnited StatesClinical quality indicatorsClinical quality measureData reportsHealthcare quality improvementVisualization

Identifiers

PMID35144618
PMCPMC8830083

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Registered trials

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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.