Using big data and Population Health Management to assess care and costs for patients with severe mental disorders and move toward a value-based payment system

Background Mental health (MH) care often exhibits uneven quality and poor coordination of physical and MH needs, especially for patients with severe mental disorders. This study tests a Population Health Management (PHM) approach to identify patients with severe mental disorders using administrative health databases in Italy and evaluate, manage and monitor care pathways and costs. A second objective explores the feasibility of changing the payment system from fee-for-service to a value-based system (e.g., increased care integration, bundled payments) to introduce performance measures and guide improvement in outcomes. Methods Since diagnosis alone may poorly predict condition severity and needs, we conducted a retrospective observational study on a 9,019-patient cohort assessed in 2018 (30.5% of 29,570 patients with SMDs from three Italian regions) using the Mental Health Clustering Tool (MHCT), developed in the United Kingdom, to stratify patients according to severity and needs, providing a basis for payment for episode of care. Patients were linked (blinded) with retrospective (2014–2017) physical and MH databases to map resource use, care pathways, and assess costs globally and by cluster. Two regions (3,525 patients) provided data for generalized linear model regression to explore determinants of cost variation among clusters and regions. Results Substantial heterogeneity was observed in care organization, resource use and costs across and within 3 Italian regions and 20 clusters. Annual mean costs per patient across regions was €3,925, ranging from €3,101 to €6,501 in the three regions. Some 70% of total costs were for MH services and medications, 37% incurred in dedicated mental health facilities, 33% for MH services and medications noted in physical healthcare databases, and 30% for other conditions. Regression analysis showed comorbidities, resident psychiatric services, and consumption noted in physical health databases have considerable impact on total costs. Conclusions The current MH care system in Italy lacks evidence of coordination of physical and mental health and matching services to patient needs, with high variation between regions. Using available assessment tools and administrative data, implementation of an episodic approach to funding MH could account for differences in disease phase and physical health for patients with SMDs and introduce performance measurement to improve outcomes and provide oversight. Highlights Mental health (MH) care often exhibits uneven quality and poor coordination of physical and mental health needs, especially for patients with severe mental disorders. Diagnosis alone may poorly predict condition severity and resources necessary to treat this patient group, but instruments such as the Mental Health Clustering Tool (MHCT) have been developed to better assess disease severity and diverse health needs within MH diagnosis groups. A Population Health Management approach using administrative health databases, the Mental Health Clustering Tool, and diverse comorbidity measures was tested in three regions in Italy for patients with severe mental disorders to map delivery, consumption, and cost patterns, and explore determinants of variation in cost, aimed to better assess organization and quality of care. Considerable variation in consumption patterns and costs, after stratifying patients based on needs and disease severity (MHCT) versus mere diagnosis, highlighted areas to address in designing a performance- versus volume-based payment model to better serve and follow MH patients. Methodology tested here using big - and “small” (from clinicians/patients) - data can be implemented by other healthcare systems to compare care pathways and outcomes across geographical areas and tie performance to payments for MH services.