A data mining approach for grouping and analyzing trajectories of care using claim data: the example of breast cancer

Nicolas Jay (), Gilles Nuemi (), Maryse Gadreau () and Catherine Quantin
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Nicolas Jay: ORPAILLEUR - Knowledge representation, reasonning - Centre Inria de l'Université de Lorraine - Inria - Institut National de Recherche en Informatique et en Automatique - LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery - LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications - Inria - Institut National de Recherche en Informatique et en Automatique - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique
Gilles Nuemi: DIM - Service Biostatistiques et Informatique Médicale (CHU de Dijon) - CHU Dijon - Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand
Maryse Gadreau: LNC - Lipides - Nutrition - Cancer (U866) - UB - Université de Bourgogne - INSERM - Institut National de la Santé et de la Recherche Médicale - AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement - ENSBANA - Ecole Nationale Supérieure de Biologie Appliquée à la Nutrition et à l'Alimentation de Dijon, LEG - Laboratoire d'Economie et de Gestion - UB - Université de Bourgogne - CNRS - Centre National de la Recherche Scientifique
Catherine Quantin: DIM - Service Biostatistiques et Informatique Médicale (CHU de Dijon) - CHU Dijon - Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand, LNC - Lipides - Nutrition - Cancer (U866) - UB - Université de Bourgogne - INSERM - Institut National de la Santé et de la Recherche Médicale - AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement - ENSBANA - Ecole Nationale Supérieure de Biologie Appliquée à la Nutrition et à l'Alimentation de Dijon

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Abstract: BACKGROUND: With the increasing burden of chronic diseases, analyzing and understanding trajectories of care is essential for efficient planning and fair allocation of resources. We propose an approach based on mining claim data to support the exploration of trajectories of care. METHODS: A clustering of trajectories of care for breast cancer was performed with Formal Concept Analysis. We exported Data from the French national casemix system, covering all inpatient admissions in the country. Patients admitted for breast cancer surgery in 2009 were selected and their trajectory of care was recomposed with all hospitalizations occuring within one year after surgery. The main diagnoses of hospitalizations were used to produce morbidity profiles. Cumulative hospital costs were computed for each profile. RESULTS: 57,552 patients were automatically grouped into 19 classes. The resulting profiles were clinically meaningful and economically relevant. The mean cost per trajectory was 9,600¿. Severe conditions were generally associated with higher costs. The lowest costs (6,957¿) were observed for patients with in situ carcinoma of the breast, the highest for patients hospitalized for palliative care (26,139¿). CONCLUSIONS: Formal Concept Analysis can be applied on claim data to produce an automatic classification of care trajectories. This flexible approach takes advantages of routinely collected data and can be used to setup cost-of-illness studies.

Keywords: Data mining; Formal concept analysis; Claim data; Trajectory of care; Cancer (search for similar items in EconPapers)
Date: 2013-11-30
Note: View the original document on HAL open archive server: https://inserm.hal.science/inserm-00917359v1
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Citations: View citations in EconPapers (3)

Published in BMC Medical Informatics and Decision Making, 2013, 13 (1), pp.130. ⟨10.1186/1472-6947-13-130⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:inserm-00917359

DOI: 10.1186/1472-6947-13-130

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