Mining and evaluation of patients' diagnostic therapeutic paths through state sequences analysis
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The concept of care pathways is increasingly being used to enhance the quality of care and to optimize the use of resources for health care. Nevertheless, recommendations regarding the sequence of care are mostly based on consensus-based decisions as there is a lack of evidence on effective treatment sequences. In a real-world setting, classical statistical tools resulted to be insufficient to adequately consider a phenomenon with such high variability and has to be integrated with novel data mining techniques suitable of identifying patterns in complex data structures. Data-driven techniques can potentially support the empirical identification of effective care sequences by extracting them from data collected routinely. The purpose of this study is to perform sequence analysis to identify different patterns of treatment and to assess the most efficient in preventing adverse events. The clinical application that motivated the study of this method concerns the several problems frequently encountered in the quality of care provided in the mental health field. In particular, we analyzed administrative data provided by Regione Lombardia related to all the beneficiaries of the National Health Service with a diagnosis of schizophrenia from 2015 to 2018 resident in Lombardy, a region of northern Italy. This methodology considers the patient's therapeutic path as a conceptual unit, i.e., a sequence, composed of a succession of different states that can describe longitudinal patient's status. This kind of information, such as common patterns of care that allowed us to risk profile patients, can provide health policymakers an opportunity to plan optimum and individualized patient care by allocating appropriate resources, analyzing trends in the health status of a population, and finding the risk factors that can be leveraged to prevent the decline of mental health status at the population level.
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