ArticleBioMed research international2017
Identifying High-Cost, High-Risk Patients Using Administrative Databases in Tuscany, Italy.
Article in BioMed research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Letter in response to article in journal of infection: Impact of routine infant BCG vaccination on COVID-19.The Journal of infection · 2021Article
- [Clinical validation of 2 morbidity groups in the primary care setting].Atencion primaria · 2020Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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
designThe 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
findingsConsidering 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.
conclusionsThe final validated algorithm showed acceptable sensitivity and positive predictive value.
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