The role of big data in health care decision making: An Italian experience

Abstract

Objectives: The need to use real-world data to support Health Care decision was the main driver for the Italian Inter-University Consortium (CINECA) to set-up a population-based patient centric database (ARNO Observatory) which integrates big administrative data from National/ Regional Information Systems to monitor health economics, patient's outcomes and measure health performance in the real world. Methods: On a population of almost 12 million, since 1987, ARNO Observatory routinely collects and integrates NHS administrative data for each single patient with high quality and complete information. ARNO provides comprehensive data referred to patient as: demographics, outpatient drug prescriptions, inpatient hospital discharges, imaging and lab tests prescriptions. From ARNO Observatory we carried out analyses of population or on specific cohorts to evaluate prevalence of chronic disease, co-morbidities and cost of total burden of illness. Results: From a cohort of 2,5 million of ARNO population, one third subjects has at least one chronic disease during 2012 and this proportion rises with ages (84% in elderly over 80 years). The most common chronic disease is hypertension (23%), followed by COPD/asthma (13%), dyslipidemia (10%) and diabetes (6,3%). Integration of different data flows led to the evaluation of cost of illness which varies from 2.000 for hypertension or 2.800 for diabetes to 12.000 for Acute Coronary Syndrome. Most part of this cost is due to hospitalization (49% vs 40% for drugs and 12% for diagnostic examination and lab tests). Cost of illness is strictly correlated to age and presence of co-morbidities, actually a considerable number of patients has more than one disease (17%), in elderly this percentage rises up to 50%. Conclusions: A big data infrastructure is very important to integrate administrative and clinical data for real world analyses and it is a valid instrument to support clinical governance and clinical research decision making.