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A data-driven approach to shared decision-making in a healthcare environment


This case study proposes a novel methodology for hospital management to plan and implement short to medium-term improvement initiatives by integrating data-driven decision-making with Multi-Criteria Decision-Making/Analysis (MCDM/A). Historical data on 165 patients operated upon in eye surgery department was first analysed (using Tableau software) to provide overall insights supported by process mining (using Celonis software) to identify the process bottlenecks that require immediate attention. The bottlenecks led to the identification of issues and their potential solutions. These potential solutions were taken as alternatives and run through Visual PROMETHEE software that incorporates the PROMETHEE II method, an MCDM/A method. By adopting a visual approach, the hospital management could arrive at a quick consensus regarding the actual situation and bottleneck, potential solutions to issues identified and their comparative ranking in an interactive environment. While insights from data analysis bring a consensus on the issues requiring a resolution, the solutions to these issue(s) can be compared and ranked by utilising PROMETHEE II. Hence, this paper proposes a unique methodology that facilitates both short-term and medium-term decision-making by utilising visual means for understanding current reality and developing/exploring potential solutions to identified issues.

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  1. Tableau: Tableau Desktop Application Software (

  2. Celonis: Celonis Process Mining Software (

  3. Visual PROMETHEE: Visual Promethee Software (


  1. Mardani, A., Hooker, R.E., Ozkul, S., Yifan, S., Nilashi, M., Sabzi, H.Z., Fei, G.C.: Application of decision making and fuzzy sets theory to evaluate the healthcare and medical problems: A review of three decades of research with recent developments. Expert Syst. Appl. (2019).

    Article  Google Scholar 

  2. Ghasemi, M., Amyot, D.: Process mining in healthcare: a systematised literature review. IJEH (2016).

    Article  Google Scholar 

  3. Glaize, A., Duenas, A., Di. Martinelly, C., Fagnot, I.: Healthcare decision-making applications using multi-criteria decision analysis: A scoping review. J Multi-Crit Decis Anal (2019).

    Article  Google Scholar 

  4. Tuzkaya, G., Sennaroglu, B., Kalender, Z.T., Mutlu, M.: Hospital service quality evaluation with IVIF-PROMETHEE and a case study. Socioecon. Plann. Sci. (2019).

    Article  Google Scholar 

  5. Stević, Ž, Pamučar, D., Puška, A., Chatterjee, P.: Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Comput. Ind. Eng. (2020).

    Article  Google Scholar 

  6. Farshidi, S., Jansen, S., de Jong, R., Brinkkemper, S.: A decision support system for software technology selection. J. Decis. Syst. (2018).

    Article  Google Scholar 

  7. Akter, S., Bandara, R., Hani, U., Fosso Wamba, S., Foropon, C., Papadopoulos, T.: Analytics-based decision-making for service systems: A qualitative study and agenda for future research. Int. J. Inf. Manage. (2019).

    Article  Google Scholar 

  8. Eom, S., Kim, E.: A survey of decision support system applications (1995–2001). J. Op. Res. Soc. (2006).

    Article  Google Scholar 

  9. Chen, J.Q., Lee, S.M.: An exploratory cognitive DSS for strategic decision making. Decis. Support Syst. (2003).

    Article  Google Scholar 

  10. Anabila, P., Kumi, D.K., Anome, J.: Patients’ perceptions of healthcare quality in Ghana. Int. J. Health Care QA (2019).

    Article  Google Scholar 

  11. Al-Qatawneh, L., Abdallah, A.A.A., Zalloum, S.S.Z.: Six sigma application in healthcare logistics: a framework and a case study. J. Healthcare Eng. (2019).

    Article  Google Scholar 

  12. Jans, M., Soffer, P., Jouck, T.: Building a valuable event log for process mining: an experimental exploration of a guided process. Enterp. Inf. Syst. (2019).

    Article  Google Scholar 

  13. Baier, T., Mendling, J., Weske, M.: Bridging abstraction layers in process mining. Inf. Syst. (2014).

    Article  Google Scholar 

  14. Ghobakhloo, M., Hong, T.S.: IT investments and business performance improvement: the mediating role of lean manufacturing implementation. Int. J. Prod. Res. (2014).

    Article  Google Scholar 

  15. Reijers, H.A., Vanderfeesten, I., van der Aalst, W.: The effectiveness of workflow management systems: A longitudinal study. Int. J. Inf. Manage. (2016).

    Article  Google Scholar 

  16. Ahmed, E.S., Ahmad, M.N., Othman, S.H.: Business process improvement methods in healthcare: a comparative study. Int. J. Health Care Qual. Assur. (2019).

    Article  Google Scholar 

  17. Jans, M., Alles, M., Vasarhelyi, M.: The case for process mining in auditing: Sources of value added and areas of application. Int. J. Account. Inf. Syst. (2013).

    Article  Google Scholar 

  18. Cook, J.E., Wolf, A.L.: Process discovery and validation through event-data analysis. Doctoral dissertation, University of Colorado (1996)

  19. Bolt, A., de Leoni, M.: van der Aalst, WMP: Scientific workflows for process mining: building blocks, scenarios, and implementation. Int J Softw Tools Technol Transfer (2016).

    Article  Google Scholar 

  20. De Medeiros, A.A., van Dongen, B.F., Van der Aalst, W.M., Weijters, A.J.M.M: Process mining: extending the α-algorithm to mine short loops (2004)

  21. Razmak, J., Aouni, B.: Decision support system and multi-criteria decision aid: a state of the art and perspectives. J. Multi-Crit. Decis. Anal. (2015).

    Article  Google Scholar 

  22. Behzadian, M., Kazemzadeh, R.B., Albadvi, A., Aghdasi, M.: PROMETHEE: A comprehensive literature review on methodologies and applications. CIPS Supply Management (2010).

    Article  Google Scholar 

  23. Albadvi, A., Chaharsooghi, S.K., Esfahanipour, A.: Decision making in stock trading: An application of PROMETHEE. CIPS Supply Manag. (2007).

    Article  Google Scholar 

  24. Abdelhadi, A.: Maintenance scheduling based on PROMETHEE method in conjunction with group technology philosophy. Int J Qual Reliability Mgmt (2018).

    Article  Google Scholar 

  25. Briggs, T., Kunsch, P.L., Mareschal, B.: Nuclear waste management: An application of the multi-criteria PROMETHEE methods. CIPS Supply Manag. (1990).

    Article  Google Scholar 

  26. Schwartz, M., Göthner, M.: A multidimensional evaluation of the effectiveness of business incubators: an application of the promethee outranking method. Environ Plann C Gov Policy (2009).

    Article  Google Scholar 

  27. Ishizaka, A., Resce, G., Mareschal, B.: Visual management of performance with PROMETHEE productivity analysis. Soft. Comput. (2018).

    Article  Google Scholar 

  28. Nassereddine, M., Azar, A., Rajabzadeh, A., Afsar, A.: Decision making application in collaborative emergency response: A new PROMETHEE preference function. Int. J. Disaster Risk Reduct. (2019).

    Article  Google Scholar 

  29. Singh, A., Gupta, A., Mehra, A.: Best criteria selection based PROMETHEE II method. Opsearch (2020).

    Article  Google Scholar 

  30. Amaral, T.M., Costa, A.P.: Improving decision-making and management of hospital resources: An application of the PROMETHEE II method in an emergency department. Op. Res. Health Care (2014).

    Article  Google Scholar 

  31. Ozsahin, D.U., Isa, N.A., Uzun, B., Ozsahin, I.: Effective analysis of image reconstruction algorithms in nuclear medicine using fuzzy PROMETHEE. In: 2018 Advances in Science and Engineering Technology International Conferences (ASET), pp. 1–5. IEEE (2018).

  32. van der Aalst, W.M.: Process mining software. In: van der Aalst, W. (ed.) Process Mining, pp. 325–352. Springer, Berlin Heidelberg (2016)

    Chapter  Google Scholar 

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We thank the hospital management [name withheld due to confidentiality issues] in Central India for permissions to conduct this study. The authors thank the anonymous reviewers for their valued propositions that have made the paper more systematic and instructive.

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Correspondence to Rakesh Verma.

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Adapted from [22]


gj(s), gj(t):

Values of the alternatives ‘s' and ‘t' on criteria ‘j'

dj(s, t):

Deviation between the values of alternative ‘s' and ‘t' on criteria ‘j'

Pj(s, t):

Preference of alternative ‘s' in-relation to alternative t on each criterion and is a function F(j) of dj(s, t)

w j :

Weight of criterion ‘j'

π(s, t):

Complete index of the preferences


Positive outranking flow for each alternative


Negative outranking flow for each alternative


Net outranking flow for each of the alternative

Stage1: Deviations are determined \(d_{j} \left( {s,t} \right) = ~g_{j} \left( s \right) - ~g_{j} \left( t \right)\)(1)
Stage2: Alternatives are assessed for their relative preferences \(P_{j} \left( {s,t} \right) = ~F_{j} \left[ {d_{j} \left( {s,t} \right)} \right]~,\;j = 1, \ldots ..k\)(2)
Stage3: Get the complete index for the preferences \( \forall \;s,t~ \in ~A~,~~\pi \left( {s,t} \right) = ~\mathop \sum \limits_{{j = 1}}^{k} P_{j} \left( {s,t} \right)w_{j}\)(3)
Where π(s,t) is taken as s over t (and with values 0 to 1) is the weighted sum of p(s,t) for each criterion
Stage4: Determine the partial rankings \(\varphi ^{ + } \left( s \right) = ~\frac{1}{{n - 1}}\mathop \sum \limits_{{xA}} \pi \left( {s,x} \right)~\)(4)
\(\varphi ^{ - } \left( s \right) = ~\frac{1}{{n - 1}}\mathop \sum \limits_{{xA}} \pi \left( {s,x} \right)~\) (5)
Stage5: Calculate the full rankings \(\varphi \left( s \right) = ~\varphi ^{ + } \left( s \right) - ~\varphi ^{ - } \left( s \right)\)(6)

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Singh, S., Verma, R. & Koul, S. A data-driven approach to shared decision-making in a healthcare environment. OPSEARCH 59, 732–746 (2022).

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  • Data-driven
  • Interactive
  • Decision support
  • Process-mining
  • Healthcare