Please use this identifier to cite or link to this item: https://ir.iimcal.ac.in:8443/jspui/handle/123456789/3231
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dc.contributor.authorJha, R. K.
dc.contributor.authorSahay, B. S.
dc.contributor.authorChattopadhyay, Manojit
dc.contributor.authorGajpal, Yuvraj
dc.date.accessioned2021-08-27T08:55:52Z
dc.date.available2021-08-27T08:55:52Z
dc.date.issued2018-03
dc.identifier.issn0304-0941 (print version) ; 2197-1722 (electronic version)
dc.identifier.urihttps://doi.org/10.1007/s40622-017-0170-8
dc.identifier.urihttps://ir.iimcal.ac.in:8443/jspui/handle/123456789/3231
dc.descriptionR. K. Jha, B. S. Sahay & Manojit Chattopadhyay, Indian Institute of Management Raipur, GEC Campus, Raipur, 492015, India; Yuvraj Gajpal, Asper School of Business, University of Manitoba, Winnipeg, MB, R3T5V4, Canada
dc.descriptionp.27-41
dc.descriptionIssue Editor – R. Rajesh Babu
dc.description.abstractThe paper presents a modified visual clustering method for patients visiting diagnostic units (DUs) using self-organizing map approach. The clustering of patients in homogenous groups helps healthcare managers in efficient scheduling of patients in each homogenous group such that their waiting time can be minimized. The grouping of patients would also help in enhancing coordination among diagnostic units (DUs). Modified Kohonen’s self-organizing map (SOM) was used to solve the visual clustering problem. Two distinct cases for patients visiting diagnostic units of clustering problems were solved in this paper. In the first case, patients are allowed to visit DU’s in any order of sequence. In the second case, patients are allowed to visit DUs based on a predefined sequence. Numerical experiments were conducted using randomly generated data sets. Finally, performance of modified visual SOM approach was measured using grouping efficiency for the first case and group technology efficiency for the second case.
dc.publisherIndian Institute of Management Calcutta, Kolkata
dc.relation.ispartofseriesVol.45;No.1
dc.subjectSelf-organizing map
dc.subjectVisual clustering
dc.subjectPatient-diagnostic cluster unit
dc.subjectGrouping efficiency
dc.subjectGroup technology efficiency
dc.titleA visual approach to enhance coordination among diagnostic units using self-organizing map
dc.typeArticle
Appears in Collections:Issue 1, March 2018

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