Assessing sustainable effectiveness of the adjustment mechanism of a ubiquitous clinic recommendation system.
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...
| Publicado en: | Health Care Management Science Vol. 23; no. 2; pp. 239 - 249 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143699622&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143699622 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13869620 BSE jtl: Health Care Management Science issn: 13869620 maglogo: N pubinfo: dt: Jun2020 vid: 23 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143699622 143699622 NLM30771053 143699622 10.1007/s10729-019-09473-5 NLM30771053 143699622 ppf: 239 ppct: 10 formats: tig: atl: Assessing sustainable effectiveness of the adjustment mechanism of a ubiquitous clinic recommendation system. aug: au: Chiu, Min-Chi Chen, Tin-Chih Toly affil: Department of Industrial Engineering and Management, National Chin-Yi University of Technology, 57, Sec. 2, Zhongshan Rd., 411, Taichung City, Taiping, Taiwan sug: subj: Patient Satisfaction Algorithms Ambulatory Care Facilities Administration Travel Male Female Taiwan Mobile Applications Human Male Female ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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