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

Mental health (MH) care is often characterized by uneven quality, high service use, problems with equitable access, and difficulties in coordinating care and addressing physical as well as mental health issues, especially for patients with severe mental disorders [1‐4]. Population health management (PHM) is a patient-centred, data-driven approach to optimising the health of populations over individual life spans that begins with the identification of the population, assessment of their needs and risk stratification, and moves on to defining organizational and tailored interventions in a person-centered model [5, 6]. PHM interprets care as the ability to manage the continuum of health services through different phases of disease (i.e., prevention, diagnosis, treatment, follow-up, end-of-life care) to address the health needs of defined group of people at all points, stressing disease prevention, integration of social and health care, innovation in care delivery, and continual monitoring of the status of at-risk patients [5, 6]. By focusing on the health needs of a specified population, PHM can integrate and pursue both the Triple Aim (i.e., simultaneously improve population health and outcomes, and reduce costs) [7] and value-based care, that is, maximizing outcomes while minimizing costs (e.g., through integrating care, measuring outcomes and costs, moving to bundled payments) [8]. PHM often builds on efforts to integrate care to guide implementation and has been employed for various chronic conditions such as diabetes and congestive heart failure, notably in the United Kingdom (UK) [9, 10] and the United States [11‐13]; however, its application in MH has been modest [4, 5, 7].

The aim of this study was to test the applicability of a PHM framework for MH using administrative health information systems to identify problems, evaluate care pathways and factors influencing costs in order to improve care, access and outcomes in a universal healthcare system (Italy) characterized by planning and funding allocation at the national level and decentralized care organization and delivery at the regional and local levels. We also tested use of the Mental Health Clustering Tool (MHCT), a needs assessment tool developed in the United Kingdom (UK) to better stratify patients according to severity and changing MH needs over time [14, 15]. Employing a holistic and multidisciplinary approach, independent of the diagnosis, the MHCT assigns patients that present similar characteristics to homogeneous severity groups, where patient needs of varying intensity and impact can be combined and analysed as episodes of care [14, 15]. Combined with physical health needs as evidenced in administrative health databases (AHDs), the study further aimed to provide a first step in exploring the feasibility of a bundled payment system to replace the current fee for service (FFS) reimbursement system in Italy to promote better outcomes and integration of care along the continuum for MH patients, especially those affected by severe mental disorders (SMDs).

Italy makes an interesting case study for its extensive system of health data collection (for physical and mental health) and its PHM approach to chronic disease management under the 2016 National Chronicity Plan [16]. A pioneer in the shift from institutionalism to community-based care through a major 1978 health reform, Italy has been a harbinger of radical change in MH care systems in Europe and the United States [17‐19]. The resulting Italian organization of MH services into Mental Health Departments (MHDs) led to the development of intra-organizational (MH services) and extra-organizational (social and healthcare) collaboration, but also resulted in increased variation among MHDs and subsequently weakened integration of services and limited managerial models for performance evaluation/reporting [19, 20].

This study has explored the use of big data within a PHM approach, using mental health and administrative healthcare databases from four mental health departments in three regions in Italy, to assess its potential for planning and evaluating MH care services, costs, pathways and interventions, with an underlying goal to explore a move toward a value-based, prospective payment system.

As largely discussed in the literature,[24, 25] AHDs from national or regional health systems constitute a fundamental source of readily available and relatively inexpensive, large quantities of data, reliable for their population coverage and collection over time, especially in countries with a NHS (e.g., Italy, the United Kingdom, Canada, Finland) or with extensive systems of electronic health records and claims data (such as the United States and Germany). Such databases typically contain information regarding healthcare organizations (e.g., hospitals, ambulatory care, emergency care, long-term care) and patients (including demographics and clinical diagnosis and procedure codes) as these pertain to specific clinical encounters that might be useful in monitoring quality of care, targeting disparities in access, and understanding consumption and cost patterns throughout the care pathway (community vs. hospital settings, professionals involved, key phases in the episode of care, service use). The routine collection of data and evaluation of the quality of the care provided in its various facets (appropriateness, continuity, safety and effectiveness) is important for patients with SMDs, particularly since they are associated with greater clinical and psychosocial needs and make up about two-thirds of adult MH service users in Italy [21]. Where evaluations of existing delivery models reveal problems for all or specific targets of patients, new delivery models can be proposed in an informed fashion and analysed to address questions of equity in access and outcomes. The use of big data and PHM approaches can additionally offer significant opportunities to develop new payment models for MH care, including bundled payments to allow for a shift from volume-based to value-based payment models [26].

As highlighted by a realist literature review which underlies this study, [27] developing episodic or bundled payments to replace fee-for-service systems requires providers and commissioners to cover at least three elements. First, develop an understanding of the active caseload being served by the providers involved, defining for any financial year the number of services provided to the patients in each cluster, the case mix of services provided, and the relative costs. Second, determine the episode of care for each cluster and the delivery model for the specific target population in different contexts to explain possible underlying reasons for cost variability. Third, risk adjustment to adapt the bundled payment to patient characteristics (e.g. age, sex, comorbidities) and regional or local variation in healthcare services organization and financing. The analysis presented in this paper aimed to test this model, by identifying the population, analysing the consumption patterns and costs per cluster and the factors affecting cost variations among regions and among clusters within the same region. Within a value-based perspective, a further step would be to determine how quality and outcome measures (including also patient preferences and experience) should be linked to payment. These aspects should be further explored and monitored using appropriate metrics [28].

Quality of care for MH is often characterized by large geographic variability, both because of significant underinvestment in terms of attention and resources and because of the lack of standardization among organizations, mandates and care pathways [3, 29‐31]. Analysis of the data here revealed a diversity of consumption models and costs for patients with severe mental disorders across MHDs and regions, which provide information on care provision and coordination that would be difficult to discern from clinical data or administrative data alone. Adding in the MHCT, based on clinical assessments, to stratify patients in the database helps to address the potentially poor predictive value of the diagnosis in defining the severity of the condition of MH patients and the resources necessary for care [32]. The MHCT was borrowed from the UK experiment in performance-based financing and is well-known in the UK among the community of psychiatrists and MH professionals [14, 33]. The segmentation of patients into 21 clusters according to the presenting problem severity and needs for care could provide a reliable proxy for patient needs and resource use instead of diagnoses since different levels of clinical and psychosocial involvement can be associated with the same diagnosis [33]. As such, the additional information based on the stage and severity of the condition provides insight into how critical differences in the mix and coordination of care could be addressed without compromising the level of severity of the disease (as performance measures).

For example, Emilia-Romagna appears to provide more care through ambulatory MH services as opposed to residential care, with lesser associated costs. As national policies emphasize territorial over residential care for MH patients [17‐20, 29, 30], care organization in Sicily and Lazio (and other regions) might be improved in this area, and regular administration of the MHCT would allow for comparisons of which model better serves patients in better managing severity and improving outcomes. The variation in care pathways and costs also highlights problems across MHDs in coordinating care, which has been shown to worsen outcomes, including mortality, as observed in a French study [34]. While it is difficult to determine which combinations of care lead to the best outcomes, tools such as the continuity of care index used in that study and the MHCT used here could be incorporated into analysis of MH care in Italy. We recommend that future research make further use of the types of services consumed in the SISM territorial services database, linked to outcomes where possible, to help identify best practices and determine where care in residential facilities might best be replaced by territorial services.

Additionally, problems noted in the UK regarding assigning clusters accurately, defining episodes, appropriateness for different settings of care, and physician discretion in coding within a prospective payment system [28, 33, 35], along with the costs of training personnel and ongoing assessment in implementing the MHCT (or a similar tool) in Italy and elsewhere, would need to be considered. The methodology used here included translating the MHCT for use in Italy and comprehensive training of healthcare professionals, which makes it suitable for expansion across Italy; however, difficulties noted in correctly assigning clusters would need to be addressed and accounted for.

Mental health has been shown to significantly and negatively impact physical health (and vice versa), with implications for access to care [1, 2]. Our results go beyond the MHCT, showing the impact of the mix of services for physical and mental health on overall resource use and costs, providing evidence that health systems should use data to incorporate both aspects to better allocate resources and coordinate care to address issues surrounding poorer physical health outcomes for MH patients [1]. Moreover, though drivers of diversity in service utilization and outcomes across regions and countries have been studied in other diseases [36], few have explored these areas in MH, a gap our study addresses. The impact on costs registered here for comorbidities shows the importance of addressing and following chronic conditions in MH patients. As such, the techniques applied here coincide with a pillar of the PHM model within an integrated care model framework: the definition and implementation of tailored patient-centred interventions targeting specific patient groups [2, 5, 6, 37].

Finally, the Italian case is significant for its pioneering shift from institutional to community-based care, extensive availability and use of data, and population health framework, which has made it possible to track developments and difficulties over time [16, 19, 20]. Problems related to quality of care, access and uneven application of the reforms over time have led to projects such as that described here to expand the use of linkable information to provide a more comprehensive analysis of care and delivery, especially for patients with SMDs, and explore the feasibility of structured means to drive improvement, such as results-based funding (e.g., bundled payments) in the future.

This study using mental and physical health administrative information systems uncovered considerable differences in consumption patterns and drivers of costs for patients with SMDs stratified using the MHCT in Italy, raising questions regarding the role of planning, providing and monitoring MH services across regions and the level of integration and coordination between services within and among regions. Analysis using big data and the PHM approach here showed the importance of considering both physical and MH, where regression analysis revealed the considerable impact on costs exerted by comorbidities, residential psychiatric care and physical healthcare consumption, providing new perspectives regarding system sustainability. Any move toward new payment schemes, e.g., bundled payments, should use the techniques tested here to focus on the most appropriate care delivered within a care episode or care program, either population or individually defined, at the lowest cost. PHM can promote innovative improvement interventions by building policies, delivery models, and system governance on the basis of precise data and information on consumption patterns and population, supported through continuous experimentation and maintenance.

The PHM approach tested here can be implemented by other healthcare systems at the national, regional or local level. How, and how completely, the components are implemented will depend largely on the specific characteristics of healthcare practices or organizations; the resources available to support the effort; the breadth and depth of existing information systems and privacy concerns; the level of skill and acceptance of information system management among policy makers, administrators and clinicians; and the collaborations and partnerships that exist within the matrix of the healthcare system.