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. 2017:2017:9569348.
doi: 10.1155/2017/9569348. Epub 2017 Jul 10.

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

Irene Bellini  1 , Valentina Barletta  2 , Francesco Profili  2 , Alessandro Bussotti  3 , Irene Severi  4 , Maddalena Isoldi  4 , Maria Bimbi  4 , Paolo Francesconi  2
Affiliations

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

Irene Bellini et al. Biomed Res Int. 2017.

Abstract

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.

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Figures

Figure 1
Figure 1
Final results.
See this image and copyright information in PMC

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