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BioMed Research International
Volume 2017, Article ID 9569348, 12 pages
Research Article

Identifying High-Cost, High-Risk Patients Using Administrative Databases in Tuscany, Italy

1Medical School of Hygiene and Preventive Medicine, University of Florence, Florence, Italy
2Agenzia Regionale Sanità, Tuscany, Italy
3Health Care Continuity Unit, University Hospital of Careggi, Florence, Italy
4Local Health Authorities of Central Tuscany, Florence, Italy

Correspondence should be addressed to Irene Bellini; moc.liamg@03inillebeneri

Received 24 November 2016; Revised 16 April 2017; Accepted 24 May 2017; Published 10 July 2017

Academic Editor: Alberto Raggi

Copyright © 2017 Irene Bellini et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Objective. (1) Assessing the performance of the algorithm in terms of sensitivity and positive predictive value, considering General Practitioners’ (GPs) judgement as benchmark, and (2) describing adverse events (hospitalisation, death, and health services’ consumption) of complex patients compared to the general population. Data Sources. (i) Tuscany administrative database containing health data (2013-5); (ii) lists of complex patients indicated by GPs; and (iii) annual health registry of Tuscany. Study Design. The present study is a validation study. It compares a list of complex patients extracted through an administrative algorithm (criteria of high health consumption) to a gold standard list of patients indicated by GPs. GPs’ decision was subjective but fairly well reasoned. The study compares also adverse outcomes (Emergency Room visits, hospitalisation, and death) between identified complex patients and general population. Principal Findings. Considering GPs’ judgement, the algorithm showed a sensitivity of 72.8% and a positive predictive value of 64.4%. The complex cases presented here have higher incidence rates/100,000 (death 46.8; ER visits 223.2, hospitalisations 110.87, laboratory tests 1284.01, and specialist examinations 870.37) compared to the general population. Conclusions. The final validated algorithm showed acceptable sensitivity and positive predictive value.