Clusters of medical specialties around patients with multimorbidity – employing fuzzy c-means clustering to explore multidisciplinary collaboration

BMC Health Services Research

Research

Barbara C. van Munster¹  Liann I. Weil¹  Marlies Verhoeff²  Yolande Vermeeren³  Hung Chu⁴  Leslie R. Zwerwer⁵  Janke de Groot⁶  Jako S. Burgers⁷  Patrick P. T. Jeurissen⁸ 

[1] Department of Geriatric Medicine, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands;Department of Geriatric Medicine, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands;Knowledge Institute of the Federation of Medical Specialists, Utrecht, the Netherlands;Department of Internal Medicine, Gelre Hospitals, Apeldoorn/ Zutphen, the Netherlands;Donald Smits Center for Information and Technology, University of Groningen, Groningen, the Netherlands;Donald Smits Center for Information and Technology, University of Groningen, Groningen, the Netherlands;Department of Health Sciences, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands;Knowledge Institute of the Federation of Medical Specialists, Utrecht, the Netherlands;Maastricht University, Care and Public Health Research Institute (CAPHRI), Maastricht, the Netherlands;Scientific Center for Quality of Healthcare (IQ healthcare), Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands;

关键词: Multimorbidity;    Care coordination;    Machine learning;    Multidisciplinary collaboration;    Cluster analysis;    Electronic health records;   

DOI  :  10.1186/s12913-023-09961-z

 received in 2022-11-29, accepted in 2023-08-24,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundHospital care organization, structured around medical specialties and focused on the separate treatment of individual organ systems, is challenged by the increasing prevalence of multimorbidity. To support the hospitals’ realization of multidisciplinary care, we hypothesized that using machine learning on clinical data helps to identify groups of medical specialties who are simultaneously involved in hospital care for patients with multimorbidity.MethodsWe conducted a cross-sectional study of patients in a Dutch general hospital and used a fuzzy c-means clustering algorithm for the analysis. We explored the patients’ membership degrees in each cluster to identify subgroups of medical specialties that provide care to the same patients with multimorbidity. We used retrospectively collected electronic health record data from 2017. We extracted data from 22,133 patients aged ≥18 years who had received outpatient clinical care for two or more chronic and/ or oncological diagnoses.ResultsWe found six clusters of medical specialties and identified 22 subgroups. The clusters were labeled based on the specialties that most characterized them: 1. dermatology/ plastic surgery, 2. six specialties (gynecology/ rheumatology/ orthopedic surgery/ urology/ gastroenterology/ otorhinolaryngology), 3. pulmonology, 4. internal medicine/ cardiology/ geriatrics, 5. neurology/ physiatry (rehabilitation)/ anesthesiology, and 6. internal medicine. Most patients had a full or dominant membership to one of these clusters of medical specialties (11 subgroups), whereas fewer patients had a membership to two clusters. The prevalence of specific diagnosis groups, patient characteristics, and healthcare utilization differed between subgroups.ConclusionOur study shows that clusters and subgroups of medical specialties simultaneously involved in hospital care for patients with multimorbidity can be identified with fuzzy c-means cluster analysis using clinical data. Clusters and subgroups differed regarding the involved medical specialties, diagnoses, patient characteristics, and healthcare utilization. With this strategy, hospitals and medical specialists can further analyze which subgroups are target populations that might benefit from improved multidisciplinary collaboration.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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