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

Abstract

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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Notes

  1. Tableau: Tableau Desktop Application Software (www.tableau.com).

  2. Celonis: Celonis Process Mining Software (https://www.celonis.com).

  3. Visual PROMETHEE: Visual Promethee Software (http://www.promethee-gaia.net/index.html).

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Acknowledgements

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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Appendices

Appendix

Adapted from [22]

Abbreviations

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

φ+(s):

Positive outranking flow for each alternative

φ(s):

Negative outranking flow for each alternative

φ(s):

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). https://doi.org/10.1007/s12597-021-00543-3

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Keywords

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