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Tackling antibiotic resistance by scanning antibiotic prescription patterns in primary care and identifying targets for improvement


Voorschrijven van antibiotica in de huisartspraktijk en op de huisartsenpost: scannen, monitoren, verbeteren

Wanneer bacteriën ongevoelig raken voor antibiotica (resistentie) zijn bacteriële infecties in de toekomst moeilijker te bestrijden. Dit kan voorkomen worden door antibiotica voor te schrijven volgens de richtlijnen. Nederlandse huisartsen schrijven relatief weinig antibiotica voor in vergelijking met huisartsen in andere landen. Toch is er ook in Nederland ruimte voor verbetering. In dit project ontwikkelen we daarom een eerstelijns antibiotica-scan. Hiermee brengen we in kaart op welke punten het voorschrijven van antibiotica in de huisartspraktijk en op de huisartsenpost beter kan. We kijken naar het voorschrijven bij verschillende diagnoses, dosering en risicogroepen. Deze scan dient als basis voor verdere monitoring van het naleven van richtlijnen. We gebruiken hiervoor gegevens van huisartspraktijken en huisartsenposten die deelnemen aan Nivel Zorgregistraties Eerste Lijn.


Titel: Instrument om voorschrijven antibiotica te monitoren
Auteur: ZonMw
Magazine: Huisarts en Wetenschap


Samenvatting van de aanvraag

Background: According to the principles of a learning health system routinely recorded prescription data have great potential to support and improve health care and health outcomes. One area where these principles can be applied is antibiotic resistance (ABR), more specifically antibiotic prescribing behavior in general practice as well as out-of-hours primary care services (OOH-PCSs). Eighty to ninety percent of oral and topical antibiotics are prescribed in primary care, both during office hours and out-of-office hours. Although the Netherlands has a relatively low antibiotics prescribing rate compared to other countries, variation between general practitioners suggests room for improvement. Following the principles of a learning health system improved antibiotics prescribing can be achieved by monitoring antibiotics prescribing and use of routine health care data to provide feedback on antibiotics prescribing to health care professionals. Aim: We propose a comprehensive Antibiotics prescribing in Primary Care scan to identify targets for improved guideline adherence to reduce the ABR burden and to set a benchmark for future monitoring. So far, guideline adherence is mainly studied for the decision whether or not to prescribe and the type of antibiotic that is prescribed. In this study, we aim: 1. to refine existing indicators of guideline adherence and 2. to assess these for primary care during office hours, as well as for out-of-hours primary care. 3. to provide feedback on antibiotics prescribing to GPs and out-of-hours services 4. to enhance the findability, accessibility, interoperability and reuseability (FAIRness) of the used data set, to further stimulate its use for ABR research. This project will lead to ABR reduction, following the principles of a learning health system in which routinely recorded prescription data are used to support and improve antibiotic prescribing behavior in general practice as well as the GP out-of-hours primary care services (OOH-PCSs). To ensure meaningful results for GPs and out-of-hours primary care services, stakeholders will be involved from the start of the study. Furthermore, the project team includes two GPs who were also involved in the writing of this proposal. Method: We use routinely recorded electronic health records data from the Nivel Primary Care Database (Nivel-PCD). This unique database comprises data from 500 general practices with 1.7 million patients listed and 29 GP out of hours services covering a catchment area comprising a population of 11 million. We will extract disease episodes for which guidelines advise to prescribe antibiotics and for which guidelines advise not to prescribe antibiotics. For each disease episode in 2018/2019 we determine guideline adherence for antibiotic prescribing, type of antibiotic, and dosage and duration and specifically determine inappropriate antibiotic prescribing. Advice for specific patient groups (e.g. children) is studied as well. Practice variation will be determined, both for general practice and the out-of-hours setting and the results will be discussed with a panel of stakeholders in the context of barriers and facilitators of guideline adherence. Subsequently outcomes are visualized in a color map indicating urgency of action (red, high urgency; orange, medium; green, no action needed), showing how antibiotics prescribing can be improved. Individual GP and out-of-hours services receive feedback on their antibiotic prescribing in comparison to their peers. Last, FAIRness of the dataset will be improved by implementing the assignment of persistent identifiers to data and metadata, by making metadata available on the web and by providing a meta data protocol that complies to meta data standards. Dissemination and implementation: An implementation plan was developed describing actions that will take place during and after the project to ensure sustained rational antibiotics prescribing and a corresponding reduction in ABR. To achieve a learning healthcare system, results will be shared with existing regional networks working on prudent antibiotic use and GPs and OOH-PCSs participating in Nivel-PCD will receive feedback via an online portal. Specifications for generating feedback will become publicly available, so that feedback can be generated for GPs and OOH-PCSs nationally. The provided benchmark will be discussed with GPs and OOH-PCSs in regional meetings. Furthermore, results are disseminated in (inter)national journal, via conference visits, via the Dutch College of General Practitioners into guidelines and to GPs and via the Dutch Institute for Rational Use of Medicine for material for peer review meetings (upon update of the guidelines). We will report on the status of FAIRness of Nivel-PCD and process of improving FAIRness to inform other databases.



Looptijd: 100%
Looptijd: 100 %
Onderdeel van programma:
Gerelateerde subsidieronde:
Projectleider en penvoerder:
dr. K Hek
Verantwoordelijke organisatie: