Evidence map›Paper›PMID 29986668›Full record

ArticleBMC family practice2018

Reduction of inappropriate medication in older populations by electronic decision support (the PRIMA-eDS study): a qualitative study of practical implementation in primary care.

Anja Rieckert, Christina Sommerauer, Anja Krumeich, Andreas Sönnichsen

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in BMC family practice, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03901820 (Evaluation of a Drug Interactions Software to Limit Polypharmacy), which is not on this map. Cited by 23 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 3 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.

NCT03901820 nacompletednot on this mapstarted 2019, after this paper: background citation

Evaluation of a Drug Interactions Software to Limit Polypharmacy (Evaluación de un Programa de Interacciones Medicamentosas Para Limitar la Polifarmacia)

TypeinterventionalSponsorUniversity of Buenos AiresRan2019 to 2021Enrolled1,053ConditionsPolypharmacy, Elderly, Inpatient, Drug InteractionArmsMedication Interaction System of Dr App (SIMDA)
3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 3 syntheses or guidelines pooled it, 45 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

4 authors at 2 institutions in 3 countries.

Anja RieckertInstitute of General Practice and Family Medicine, Faculty of Health, Witten/Herdecke University, Alfred-Herrhausen-Str. 50, 58448, Witten, Germany. Anja.Rieckert@uni-wh.de.ORCID 0000-0002-9735-378X
Christina SommerauerInstitute of General Practice and Family Medicine, Faculty of Health, Witten/Herdecke University, Alfred-Herrhausen-Str. 50, 58448, Witten, Germany.
Anja KrumeichDepartment of Health, Ethics, and Society, Faculty of Health, Medicine, and Lifesciences, Maastricht University, Debyeplein 1, 6229 HA, Maastricht, The Netherlands.
Andreas SönnichsenInstitute of General Practice and Family Medicine, Faculty of Health, Witten/Herdecke University, Alfred-Herrhausen-Str. 50, 58448, Witten, Germany.
Witten/Herdecke University · DEMaastricht University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWithin the EU-funded project PRIMA-eDS (Polypharmacy in chronic diseases: Reduction of Inappropriate Medication and Adverse drug events in older populations by electronic Decision Support) an electronic decision support tool (the "PRIMA-eDS-tool") was developed for general practitioners (GPs) to reduce inappropriate medication in their older polypharmacy patients. After entering patient data relevant to prescribing in an electronic case report form the physician received a comprehensive medication review (CMR) on his/her screen displaying recommendations regarding missing indications, necessary laboratory tests, evidence-base of current medication, dose adjustments for renal malfunction, potentially harmful drug-drug interactions, contra-indications, and possible adverse drug events. We set out to explore the usage of the PRIMA-eDS tool and the adoption of the recommendations provided by the CMR to optimise the tool and prepare it for its future implementation.

methodsIn a qualitative study carried out in North Rhine-Westphalia, Germany, 21 GPs using the PRIMA-eDS tool within the PRIMA-eDS study were interviewed. Interviews encompassed the GPs' attitudes regarding use of the electronic case report form and the CMR, their response to the recommendations, and the implementation of the tool into daily practice routine. The collected data were analysed applying thematic qualitative text analysis.

resultsGPs found the patient data entry into the electronic case report form to be inconvenient and time-consuming. The CMR was conducted often outside practice hours and without the patient present. GPs found that the PRIMA-eDS CMR provided relevant information for and had several positive effects on the caring process. However, they encountered several barriers when wanting to change medication.

conclusionsIt is unlikely that the PRIMA-eDS CMR will be used in the future as it is now as patient data entry is too time-consuming. Several barriers towards deprescribing medications were found which are common in deprescribing studies. Given the positive attitude towards the CMR, a new way of entering patient data into the PRIMA-eDS tool to create the CMR needs to be developed.

Indexed as

Attitude of Health PersonnelDecision Support Systems, ClinicalGeneral PractitionersPotentially Inappropriate Medication ListPrimary Health CareAdultAgedFemaleGermanyHumansImplementation ScienceMaleMiddle AgedQualitative ResearchAgedComputerized clinical decision support systemDeprescribingEvidence-based medicineGeneral practitionerPerceptions

Identifiers

PMID29986668
PMCPMC6038343
OpenAlexW2826834708

What OpenQuestion holds

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Registered trials

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.