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Modeling public health care expenditure using patient level data: Empirical evidence from Italy

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Abstract

In this work we present some results obtained with a unique database of patient level data collected through GPs. The availability of such data opens new scenarios and paradigms for the planning and management of the health care system and for policy impact evaluation studies. The dataset, representative of the Italian population, contains detailed information on prescribed drugs, laboratory tests, outpatient visits and hospitalizations of more than 2 millions patients, managed by 900 GPs overtime. This pool of registers has produced a stock of information on about 25 millions of medical diagnosis, 100 millions of laboratory and diagnostic tests, 10 millions of blood pressure measurements and 50 millions of drug prescriptions. Using this novel dataset we analyze the expenditures of the Italian NHS over time, across age and geographical areas for the period from 2004 to 2011.

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  • Vincenzo Atella & Federico Belotti & Valentina Conti & Claudio Cricelli & Joanna Kopinska & Andrea Piano Mortari, 2016. "Modeling public health care expenditure using patient level data: Empirical evidence from Italy," CEIS Research Paper 367, Tor Vergata University, CEIS, revised 10 Feb 2016.
    • Handle: RePEc:rtv:ceisrp:367
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    More about this item

    Keywords

    cost analysis; big data; disease burden; Electronic Medical Records; primary care; cost sharing;
    All these keywords.

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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