University of Lausanne Reports Findings in Medical Informatics (Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach): Information Technology – Medical Informatics

2022 APR 14 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- New research on Information Technology - Medical Informatics is the subject of a report. According to news reporting originating in Lausanne, Switzerland, by NewsRx journalists, research stated, “Although the trend of progressing morbidity is widely recognized, there are numerous challenges when studying multimorbidity and patient complexity. For multimorbid or complex patients, prone to fragmented care and high health care use, novel estimation approaches need to be developed.”

The news reporters obtained a quote from the research from the University of Lausanne, “This study aims to investigate the patient multimorbidity and complexity of Swiss residents aged 50 years using clustering methodology in claims data. We adopted a clustering methodology based on random forests and used 34 pharmacy-based cost groups as the only input feature for the procedure. To detect clusters, we applied hierarchical density-based spatial clustering of applications with noise. The reasonable hyperparameters were chosen based on various metrics embedded in the algorithms (out-of-bag misclassification error, normalized stress, and cluster persistence) and the clinical relevance of the obtained clusters. Based on cluster analysis output for 18,732 individuals, we identified an outlier group and 7 clusters: individuals without diseases, patients with only hypertension-related diseases, patients with only mental diseases, complex high-cost high-need patients, slightly complex patients with inexpensive low-severity pharmacy-based cost groups, patients with 1 costly disease, and older high-risk patients. Our study demonstrated that cluster analysis based on pharmacy-based cost group information from claims-based data is feasible and highlights clinically relevant clusters. Such an approach allows expanding the understanding of multimorbidity beyond simple disease counts and can identify the population profiles with increased health care use and costs.”

According to the news reporters, the research concluded: “This study may foster the development of integrated and coordinated care, which is high on the agenda in policy making, care planning, and delivery.”

For more information on this research see: Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach. JMIR Medical Informatics, 2022;10(4). JMIR Medical Informatics can be contacted at: Jmir Publications, Inc, 130 Queens Quay E, Ste 1102, Toronto, On M5A 0P6, Canada.

Our news correspondents report that additional information may be obtained by contacting Dan Assouline, Center for Primary Care and Public Health (Unisante), University of Lausanne, Lausanne, Switzerland. Additional authors for this research include Anna Nicolet, Marie-Annick Le Pogam, Clemence Perraudin, Christophe Bagnoud, Joel Wagner, Joachim Marti and Isabelle Peytremann-Bridevaux.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.2196/34274. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

The publisher of the journal JMIR Medical Informatics can be contacted at: Jmir Publications, Inc, 130 Queens Quay E, Ste 1102, Toronto, On M5A 0P6, Canada.

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