From 3M Health Information Systems
A Stratified Population – New book on population health management
Having the right information is key in population health decision making. Finding the appropriate granularity within a health dataset and generating comprehensive forecasts to determine the clinical needs of a specific population is the focus of a new book published in Italy called Popolazione a Strati (A Stratified Population) (Rubbettino Editore, Società e scienze sociali, Problemi e processi sociali, ISBN: 9788849861266) . The author, Gennaro Sosto, together with other authoritative health policy decision makers and academic contributors, discusses the need for a demand-driven health care governance program instead of the current supply-driven model stressed by the National Chronic Care Plan. To achieve this goal, methodologies that stratify the population like 3M™ Clinical Risk Groups (CRGs) form the experimental basis for identifying real clinical needs for accurately applying and monitoring health care interventions, reducing the variability of care and increasing quality in all its different aspects (clinical, economical, management, etc.) while allocating resources among different settings and enhancing the digital transformation of the Italian health system .
The book was published in February, before COVID-19 turned into a pandemic emergency in Italy and the rest of the world. It describes the population stratifying experience with innovative integrated processes and tools. Modern health care governance operates in a constantly changing environment: complex, dynamic, determined by an explosion of pathologies that had no precise clinical paths a decade ago. The availability of the patient information flow that describes his or her burden of illness represents an important keystone for Italian health care, currently reflected in the context of the COVID-19 pandemic. Two chapters of the book focus on the observational study which is based on the 3M™ CRGs and on the population of the Molise Region.
The author concludes that “the observational study identifies the prevalence of chronic diseases, particularly those that meet well-defined diagnostic criteria, quickly and reliably, while also studying the association with other chronic diseases, with the distribution of health status and severity levels. While prevalence estimates of administrative hospitalization databases provide information on the acute aspects of individual disorders, this innovative system manages to reliably account for chronic conditions, even if they do not require a specific hospitalization of the patient.” (n.n. our translation). 
The results reveal that 4 out of 10 enrollees (40 percent of the Molisian population) have at least one minor chronic condition up to dominant chronic pathologies in 3 or more organs.
The author concludes: “the application of this clinical categorical model produces sophisticated data aggregation on administrative data and the results transformation allows us to obtain accurate results up to geo-localization of the risk at a regional, local health unit (ASL) or district or municipal level. This model, especially during this historical moment, can support the identification of multimorbid cohorts more susceptible to [COVID-19] and to hospitalizations,  as well as for the purposes which it was originally designed: management of the patient’s clinical complexity and needs.”
Alessandra Di Maio, is a product specialist for Grouping and Performance Systems, 3M Italy.
- Sosto, G. (2020). (Book) Una popolazione a strati. ISBN 9788849861266. https://www.store.rubbettinoeditore.it/una-popolazione-a-strati.html – Accessed 28 May 2020
- Hughes, John S., et al. “Clinical Risk Groups (CRGs): A Classification System for Risk-Adjusted Capitation-Based Payment and Health Care Management.” Medical Care, vol. 42, no. 1, 2004, pp. 81–90. JSTOR, jstor.org/stable/4640693 – Accessed 28 May 2020.
- “Una popolazione a strati” – Quotidiano Sanità – Accessed 28 May 2020, from http://www.quotidianosanita.it/lettere-al-direttore/articolo.php?articolo_id=85546.