EconPapers    
Economics at your fingertips  
 

Assessing sustainable effectiveness of the adjustment mechanism of a ubiquitous clinic recommendation system

Min-Chi Chiu () and Tin-Chih Toly Chen ()
Additional contact information
Min-Chi Chiu: National Chin-Yi University of Technology
Tin-Chih Toly Chen: National Chiao Tung University

Health Care Management Science, 2020, vol. 23, issue 2, No 6, 239-248

Abstract: Abstract Advances in computer and communication technologies have engendered opportunities for developing an improved ubiquitous health care environment. One of the crucial applications is a ubiquitous clinic recommendation system, which entails recommending a suitable clinic to a mobile patient based on his/her location, hospital department, and preferences. However, patients may not be willing or able to express their preferences. To overcome this problem, some ubiquitous clinic recommendation systems mine the historical data of patients to learn their preferences, and they apply an algorithm to adjust the recommendation algorithm after receiving more patient data. Such an adjustment mechanism may operate for several periods; however, this raises a question regarding the sustainability (i.e., long-term effectiveness) of such an adjustment mechanism. To address this question, this study modeled the improvement in the successful recommendation rate of a ubiquitous clinic recommendation system that adopts an adjustment mechanism as a learning process. Both the asymptotic value and learning speed of the learning process provide valuable information regarding the long-term effectiveness of the adjustment mechanism. The proposed methodology was applied in a regional study to a ubiquitous clinic recommendation system that adjusts the recommendation mechanism by solving an integer nonlinear programming problem on a rolling basis. The experimental results revealed that the proposed method exhibited a considerably higher level of accuracy in forecasting the successful recommendation rate compared with several existing methods. Although the adjustment mechanism exhibits long-term effectiveness, the learning speed requires improvement.

Keywords: Ubiquitous clinic recommendation; Successful recommendation rate; Learning; Sustainable (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://link.springer.com/10.1007/s10729-019-09473-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:hcarem:v:23:y:2020:i:2:d:10.1007_s10729-019-09473-5

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10729

DOI: 10.1007/s10729-019-09473-5

Access Statistics for this article

Health Care Management Science is currently edited by Yasar Ozcan

More articles in Health Care Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2022-05-12
Handle: RePEc:kap:hcarem:v:23:y:2020:i:2:d:10.1007_s10729-019-09473-5