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A visual approach to enhance coordination among diagnostic units using self-organizing map

Author

Listed:
  • R. K. Jha

    (Indian Institute of Management Raipur)

  • B. S. Sahay

    (Indian Institute of Management Raipur)

  • Manojit Chattopadhyay

    (Indian Institute of Management Raipur)

  • Yuvraj Gajpal

    (University of Manitoba)

Abstract

The 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.

Suggested Citation

  • R. K. Jha & B. S. Sahay & Manojit Chattopadhyay & Yuvraj Gajpal, 2018. "A visual approach to enhance coordination among diagnostic units using self-organizing map," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 45(1), pages 27-41, March.
    • Handle: RePEc:spr:decisn:v:45:y:2018:i:1:d:10.1007_s40622-017-0170-8
    DOI: 10.1007/s40622-017-0170-8
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    References listed on IDEAS

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    1. Manojit Chattopadhyay & Sourav Sengupta & B.S. Sahay, 2016. "Visual hierarchical clustering of supply chain using growing hierarchical self-organising map algorithm," International Journal of Production Research, Taylor & Francis Journals, vol. 54(9), pages 2552-2571, May.
    2. Mulhern, Francis J. & Caprara, Robert J., 1994. "A nearest neighbor model for forecasting market response," International Journal of Forecasting, Elsevier, vol. 10(2), pages 191-207, September.
    3. Isabella Cattinelli & Elena Bolzoni & Carlo Barbieri & Flavio Mari & José Martin-Guerrero & Emilio Soria-Olivas & José Martinez-Martinez & Juan Gomez-Sanchis & Claudia Amato & Andrea Stopper & Emanuel, 2012. "Use of Self-Organizing Maps for Balanced Scorecard analysis to monitor the performance of dialysis clinic chains," Health Care Management Science, Springer, vol. 15(1), pages 79-90, March.
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