Comparison of observational methods to identify and characterize post-COVID syndrome in the Netherlands using electronic health records and questionnaires.

Background: Some of those infected with SARS-CoV-2 suffer from post-COVID syndrome (PCS). However, an uniform definition of PCS is lacking, causing uncertainty about the prevalence and nature of this syndrome. We aimed to improve understanding of PCS by operationalizing different classifications and to explore clinical subtypes.

Patterns of psychotropic drug prescriptions and general practice consultations among community-dwelling older people with dementia during the first two years of the COVID-19 pandemic.

Background: The COVID-19 pandemic and subsequent lockdown measures had serious implications for community-dwelling older people with dementia. While the short-term impacts of the pandemic on this population have been well studied, there is limited research on its long-term impacts. Quantifying the long-term impacts may provide insights into whether healthcare adaptations are needed after the acute phase of the pandemic to balance infection prevention measures with healthcare provision.

Dutch GP healthcare consumption in COVID-19 heterogeneous regions: an interregional time-series approach in 2020-2021.

Background: Many countries observed a sharp decline in the use of general practice services after the outbreak of the COVID-19 pandemic. However, research has not yet considered how changes in healthcare consumption varied among regions with the same restrictive measures but different COVID-19 prevalence.

Aim: To investigate how the COVID-19 pandemic affected healthcare consumption in Dutch general practice during 2020 and 2021, among regions with known heterogeneity in COVID-19 prevalence, from a pre-pandemic baseline in 2019.

A Natural Language Processing Model for COVID-19 Detection Based on Dutch General Practice Electronic Health Records by Using Bidirectional Encoder Representations From Transformers: Development and Validation Study.

Background: Natural language processing (NLP) models such as bidirectional encoder representations from transformers (BERT) hold promise in revolutionizing disease identification from electronic health records (EHRs) by potentially enhancing efficiency and accuracy. However, their practical application in practice settings demands a comprehensive and multidisciplinary approach to development and validation. The COVID-19 pandemic highlighted challenges in disease identification due to limited testing availability and challenges in handling unstructured data.

What can we learn from experiences in general practice during the COVID-19 pandemic? A qualitative study.

Background: Experiences with organizational changes in daytime general practices and out-of-hours (OOH) services during the COVID-19 pandemic may help to address the challenges in general practice care that were already a concern before the crisis. This study aimed to describe these experiences and the potential usefulness of the organizational changes for future general practice care and any future pandemics.

Methods: Semi-structured interviews were performed among 11 directors of OOH services, and 19 (locum) general practitioners (GPs) or practice managers, who were purposively sampled.