Evidence map›Paper›PMID 32370742›Full record

ArticleBMC nephrology2020

Developing quality indicators for Chronic Kidney Disease in primary care, extractable from the Electronic Medical Record. A Rand-modified Delphi method.

Steve A Van den Bulck, Patrik Vankrunkelsven, Geert Goderis, Gijs Van Pottelbergh, Jonathan Swerts, Karolien Panis, Rosella Hermens

Open access · goldAbstract read
In one paragraph

Article in BMC nephrology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 2 pooled it
1.8field-weighted citation impact, top 14% 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

18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 28 citations in OpenAlex.

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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

7 authors at 2 institutions in 2 countries.

Steve A Van den BulckAcademic Center for General Practice, Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 33 blok J, 3000, Leuven, Belgium. Steve.vandenbulck@kuleuven.be.ORCID 0000-0003-0744-3923
Patrik VankrunkelsvenAcademic Center for General Practice, Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 33 blok J, 3000, Leuven, Belgium.
Geert GoderisAcademic Center for General Practice, Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 33 blok J, 3000, Leuven, Belgium.
Gijs Van PottelberghAcademic Center for General Practice, Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 33 blok J, 3000, Leuven, Belgium.
Jonathan SwertsAcademic Center for General Practice, Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 33 blok J, 3000, Leuven, Belgium.
Karolien PanisAcademic Center for General Practice, Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 33 blok J, 3000, Leuven, Belgium.
Rosella HermensAcademic Center for General Practice, Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 33 blok J, 3000, Leuven, Belgium.
KU Leuven · BERadboud University Nijmegen · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic kidney disease (CKD) is a common chronic condition and a rising public health issue with increased morbidity and mortality, even at an early stage. Primary care has a pivotal role in the early detection and in the integrated management of CKD which should be of high quality. The quality of care for CKD can be assessed using quality indicators (QIs) and if these QIs are extractable from the electronic medical record (EMR) of the general physician, the number of patients whose quality of care can be evaluated, could increase vastly. Therefore the aim of this study is to develop QIs which are evidence based, EMR extractable and which can be used as a framework to automate quality assessment.

methodsWe used a Rand-modified Delphi method to develop QIs for CKD in primary care. A questionnaire was designed by extracting recommendations from international guidelines based on the SMART principle and the EMR extractability. A multidisciplinary expert panel, including patients, individually scored the recommendations for measuring high quality care on a 9-point Likert scale. The results were analyzed based on the median Likert score, prioritization and agreement. Subsequently, the recommendations were discussed in a consensus meeting for their in- or exclusion. After a final appraisal by the panel members this resulted in a core set of recommendations, which were then transformed into QIs.

resultsA questionnaire composed of 99 recommendations was extracted from 10 international guidelines. The consensus meeting resulted in a core set of 36 recommendations that were translated into 36 QIs. This final set consists of QIs concerning definition & classification, screening, diagnosis, management consisting of follow up, treatment & vaccination, medication & patient safety and referral to a specialist. It were mostly the patients participating in the panel who stressed the importance of the QIs concerning medication & patient safety and a timely referral to a specialist.

conclusionThis study provides a set of 36 EMR extractable QIs for measuring the quality of primary care for CKD. These QIs can be used as a framework to automate quality assessment for CKD in primary care.

Indexed as

Quality Indicators, Health CareConsensusDelphi TechniqueElectronic Health RecordsFemaleHumansMalePractice Guidelines as TopicPrimary Health CareReferral and ConsultationRenal InsufficiencySurveys and Questionnaires

Identifiers

PMID32370742
PMCPMC7201612
OpenAlexW3023125195

What Socratic 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 Socratic graph.