Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Observational Study
. 2023 Sep 7;23(1):960.
doi: 10.1186/s12913-023-09655-6.

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

Valeria D Tozzi  1 , Helen Banks  1 , Lucia Ferrara  2 , Angelo Barbato  3 , Giovanni Corrao  4   5 , Barbara D'avanzo  3 , Teresa Di Fiandra  6 , Andrea Gaddini  7 , Matteo Monzio Compagnoni  4   5 , Michele Sanza  8 , Alessio Saponaro  9 , Salvatore Scondotto  10 , Antonio Lora  11
Affiliations
Observational Study

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

Valeria D Tozzi et al. BMC Health Serv Res. .

Abstract

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.

Keywords: Big data; Health information interoperability; Healthcare delivery; Medical record linkage; Mental health; Mental health clustering tool; Population health; Value-based healthcare.

PubMed Disclaimer

Conflict of interest statement

GC received research support from the European Community (EC), the Italian Agency of Drug (AIFA), and the Italian Ministry for University and Research (MIUR). He took part to a variety of projects that were funded by pharmaceutical companies (i.e., Novartis, GSK, Roche, AMGEN and BMS). He also received honoraria as member of Advisory Board from Roche. No other potential conflicts of interest relevant to this article were disclosed.

Figures

Fig. 1
Fig. 1
Patient distribution by Mental Health Clustering Tool cluster (numbered) and region (% of total patients), and combined average over all regions
Fig. 2
Fig. 2
Mean costs (reimbursement tariffs, in Euros) per patient by Mental Health Clustering Tool cluster (numbered) and region, Year 2016
See this image and copyright information in PMC

References

    1. Ronaldson A, Elton L, Jayakumar S, Jieman A, Halvorsrud K, Bhui K. Severe mental illness and health service utilisation for nonpsychiatric medical disorders: a systematic review and meta-analysis. PLOS Med. 2020;17:e1003284. doi: 10.1371/journal.pmed.1003284. - DOI - PMC - PubMed
    1. Lawrence D, Kisely S. Inequalities in healthcare provision for people with severe mental illness. J Psychopharmacol Oxf Engl. 2010;24 4supplement:61–8. doi: 10.1177/1359786810382058. - DOI - PMC - PubMed
    1. Institute of Medicine (US) Committee on Crossing the Quality Chasm . Adaptation to Mental Health and Addictive Disorders. Improving the quality of Health Care for Mental and Substance-Use Conditions: Quality Chasm Series. Washington (DC): National Academies Press (US); 2006. - PubMed
    1. Sieck CJ, Wickizer T, Geist L. Population health management in integrated physical and mental health care. Adv Health Care Manag. 2014;16:139–50. doi: 10.1108/S1474-823120140000016006. - DOI - PubMed
    1. Struijs JN, Drewes HW, Heijink R, Baan CA. How to evaluate population management? Transforming the Care Continuum Alliance population health guide toward a broadly applicable analytical framework. Health Policy. 2015;119:522–9. doi: 10.1016/j.healthpol.2014.12.003. - DOI - PubMed

Publication types