This framework shall be useful for any organization attempting the identification and resolution of root issues, in their journey towards excellence.
Authors
Saroj Koul, Professor, Jindal Global Business School, O.P. Jindal Global University, Sonipat, Haryana, India.
Sudhanshu Singh, National Institute of Industrial Engineering, Powai, Mumbai, Maharashtra.
Rakesh Verma, National Institute of Industrial Engineering, Powai, Mumbai, Maharashtra.
Summary
Decisions are guided by data and concerns (issues) that may recur as per the situation. While there is a growing literature on data-driven decision-making, the linkages of data to issues are not explicit, especially in the healthcare environment. Based on the established root cause analysis models, the attempt here is to develop an integrated framework for the visual, hierarchical and quantitative representation of the different causal relationships.
A framework so designed demonstrates the interplay of data and issue(s). The issue(s) identified from the literature and actual issue(s) raised in the healthcare environment are exhibited as an Effect-Why diagram with probabilities analyzed using Markov chain in Lindo18 software. Last, of all, the network diagram (and its associated table) show a comprehensive inter-relationships and provide details on the Effect-Why diagram for understanding decision-making.
The Markov chain illustrates the issues that are expected to be seen in the long term. This framework shall be useful for any organization attempting the identification and resolution of root issues, in their journey towards excellence.
Published in: International Journal of Operations and Quantitative Management
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