Artikel

Treatment level and store level analyses of healthcare data

The presented research discusses general approaches to analyze and model healthcare data at the treatment level and at the store level. The paper consists of two parts: (1) a general analysis method for store-level product sales of an organization and (2) a treatment-level analysis method of healthcare expenditures. In the first part, our goal is to develop a modeling framework to help understand the factors influencing the sales volume of stores maintained by a healthcare organization. In the second part of the paper, we demonstrate a treatment-level approach to modeling healthcare expenditures. In this part, we aim to improve the operational-level management of a healthcare provider by predicting the total cost of medical services. From this perspective, treatment-level analyses of medical expenditures may help provide a micro-level approach to predicting the total amount of expenditures for a healthcare provider. We present a model for analyzing a specific type of medical data, which may arise commonly in a healthcare provider's standardized database. We do this by using an extension of the frequency-severity approach to modeling insurance expenditures from the actuarial science literature.

Sprache
Englisch

Erschienen in
Journal: Risks ; ISSN: 2227-9091 ; Volume: 7 ; Year: 2019 ; Issue: 2 ; Pages: 1-22 ; Basel: MDPI

Klassifikation
Wirtschaft
Thema
medical data analysis
store sales analysis
predictivemodeling
generalized additivemodels

Ereignis
Geistige Schöpfung
(wer)
Wang, Kaiwen
Ding, Jiehui
Lidwell, Kristen R.
Manski, Scott
Lee, Gee
Esposito, Emilio Xavier
Ereignis
Veröffentlichung
(wer)
MDPI
(wo)
Basel
(wann)
2019

DOI
doi:10.3390/risks7020043
Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Wang, Kaiwen
  • Ding, Jiehui
  • Lidwell, Kristen R.
  • Manski, Scott
  • Lee, Gee
  • Esposito, Emilio Xavier
  • MDPI

Entstanden

  • 2019

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