A data mining approach for estimating patient demand for health services
Ionuţ Ţăranu ()
Additional contact information
Ionuţ Ţăranu: University of Economic Studies, Bucharest, Romania
Database Systems Journal, 2016, vol. 7, issue 1, 28-34
The ability to better forecast demand for health services is a critical element to maintaining a stable quality of care. Knowing how certain events can impact requirements, health-care service supplier can better assign available resources to more effectively treat patients' needs. The embodiment of data mining analytics can support available data to identify cyclical patterns through relevant variables, and these patterns provide actionable information to adequate decision markers at health-care structures. The request for health-care services can be subject to change from time of year (seasonality) and economic factors. This paper exemplifies the efficacy of data mining analytics in identifying seasonality and economic factors as measured by time that affect patient demand for health-care services. It incorporates a neural network analytic method that is applied over a readily available dataset. The results indicate that day of week, month of year, and a yearly trend significantly impact the demand for patient services.
Keywords: Data mining; neuronal networks; decision support systems; healthcare IT (search for similar items in EconPapers)
References: View complete reference list from CitEc
Citations Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:aes:dbjour:v:7:y:2016:i:1:p:28-34
Access Statistics for this article
Database Systems Journal is currently edited by Ion Lungu
More articles in Database Systems Journal from Academy of Economic Studies - Bucharest, Romania Contact information at EDIRC.
Series data maintained by Adela Bara ().