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Identifying High-Cost, High-Risk Patients Using Administrative Databases in Tuscany, Italy
المؤلفون المشاركون
Bellini, Irene
Barletta, Valentina
Profili, Francesco
Bussotti, Alessandro
Severi, Irene
Isoldi, Maddalena
Bimbi, Maria
Francesconi, Paolo
المصدر
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-07-10
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1139609
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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