Evidence map›Paper›PMID 27769288›Full record

ArticleBMC health services research2016

Comparing routine administrative data with registry data for assessing quality of hospital care in patients with myocardial infarction using deterministic record linkage.

Birga Maier, Katrin Wagner, Steffen Behrens, Leonhard Bruch, Reinhard Busse, Dagmar Schmidt, Helmut Schühlen, Roland Thieme, Heinz Theres

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
6.0field-weighted citation impact, top 4% 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, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.

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  18. Individual Data Linkage of Survey Data with Claims Data in Germany-An Overview Based on a Cohort Study.International journal of environmental research and public health · 2017
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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

9 authors at 6 institutions in 1 country.

Birga MaierBerlin Myocardial Infarction Registry, Technische Universität, Berlin, Germany. birga.maier@tu-berlin.de.
Katrin WagnerBerlin Myocardial Infarction Registry, Technische Universität, Berlin, Germany.
Steffen BehrensDepartment of Cardiology, Vivantes Humboldt Klinikum, Berlin, Germany.
Leonhard BruchDepartment of Cardiology, Unfallkrankenhaus Berlin, Berlin, Germany.
Reinhard BusseDepartment of Health Care Management, Technische Universitaet, Berlin, Germany.
Dagmar SchmidtDepartment Hospital Affairs, AOK Nordost, Berlin, Germany.
Helmut SchühlenDepartment of Cardiology, Vivantes Auguste-Viktoria-Klinikum, Berlin, Germany.
Roland ThiemeDepartment of Cardiology, Jüdisches Krankenhaus, Berlin, Germany.
Heinz TheresDepartment of Cardiology, Medical Park Humboldt Muehle, Berlin, Germany.
Technische Universität Berlin · DEAuguste-Viktoria-Klinik · DEHumboldt-Universität zu Berlin · DEJüdisches Krankenhaus Berlin · DEUnfallkrankenhaus Berlin · DEVivantes Klinikum · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAssessment of quality of care in patients with myocardial infarction (MI) should be based on data that effectively enable determination of quality. With the need to simplify measurement techniques, the question arises whether routine data can be used for this purpose. We therefore compared data from a German sickness fund (AOK) with data from the Berlin Myocardial Infarction Registry (BMIR).

methodsWe included patients hospitalised for treatment of MI in Berlin from 2009-2011. We matched 2305 patients from AOK and BMIR by using deterministic record linkage with indirect identifiers. For matched patients we compared the frequency in documentation between AOK and BMIR for quality assurance variables and calculated the kappa coefficient (KC) as a measure of agreement.

resultsThere was almost perfect agreement in documentation between AOK and BMIR data for matched patients for: catheter laboratory (KC: 0.874), ST elevation MI (KC: 0.826), diabetes (KC: 0.818), percutaneous coronary intervention (KC: 0.860) and hospital mortality (KC: 0.952). The remaining variables compared showed moderate or less than moderate agreement (KC < 0.6), and were grouped in Category II with less frequent documentation in AOK for risk factors and aspects of patients' history; in Category III with more frequent documentation in AOK for comorbidities; and in Category IV for medication at and after hospital discharge.

conclusionsRoutine data are primarily collected and defined for reimbursement purposes. Quality assurance represents merely a secondary use. This explains why only a limited number of variables showed almost perfect agreement in documentation between AOK and BMIR. If routine data are to be used for quality assessment, they must be constantly monitored and further developed for this new application. Furthermore, routine data should be complemented with registry data by well-established methods of record linkage to realistically reflect the situation - also for those quality-associated variables not collected in routine data.

Indexed as

AgedComorbidityDocumentationFemaleGermanyHospitalizationHospital MortalityHumansMaleMyocardial InfarctionPatient DischargePercutaneous Coronary InterventionQuality of Health CareRegistriesRisk FactorsHealth services researchHospital performanceMyocardial infarctionQuality of careRoutine administrative data

Identifiers

PMID27769288
PMCPMC5073420
OpenAlexW2537569812

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LicenceCC BY
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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.