Identifying High-Cost, High-Risk Patients Using Administrative Databases in Tuscany, Italy
Joint Authors
Bellini, Irene
Barletta, Valentina
Profili, Francesco
Bussotti, Alessandro
Severi, Irene
Isoldi, Maddalena
Bimbi, Maria
Francesconi, Paolo
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-10
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Bellini, Irene& Barletta, Valentina& Profili, Francesco& Bussotti, Alessandro& Severi, Irene& Isoldi, Maddalena…[et al.]. 2017. Identifying High-Cost, High-Risk Patients Using Administrative Databases in Tuscany, Italy. BioMed Research International،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1139609
Modern Language Association (MLA)
Bellini, Irene…[et al.]. Identifying High-Cost, High-Risk Patients Using Administrative Databases in Tuscany, Italy. BioMed Research International No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1139609
American Medical Association (AMA)
Bellini, Irene& Barletta, Valentina& Profili, Francesco& Bussotti, Alessandro& Severi, Irene& Isoldi, Maddalena…[et al.]. Identifying High-Cost, High-Risk Patients Using Administrative Databases in Tuscany, Italy. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1139609
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139609