AI-driven Optimization in Healthcare: the Diagnostic Process

Keywords: artificial intelligence, process optimization, chatbot, healthcare, diagnosing


Purpose: Process optimization in healthcare using artificial intelligence (AI) is still in its infancy. In this study, we address the research question “To what extent can an AI-driven chatbot help to optimize the diagnostic process?”

Design / Method / Approach: First, we developed a mathematical model for the utility (i.e., total satisfaction received from consuming a good or service) resulting from the diagnostic process in primary healthcare. We calculated this model using MS Excel. Second, after identifying the main pain points for optimization (e.g., waiting time in the queue), we ran a small experiment (n = 25) in which we looked at time to diagnosis, average waiting time, and their standard deviations. In addition, we used a questionnaire to examine patient perceptions of the interaction with an AI-driven chatbot.

Findings: Our results show that scheduling is the main factor causing issues in a physician’s work. An AI-driven chatbot may help to optimize waiting time as well as provide data for faster and more accurate diagnosis. We found that patients trust AI-driven solutions primarily when a real (not virtual) physician is also involved in the diagnostic process.

Practical Implications: AI-driven chatbots may indeed help to optimize diagnostic processes. Nevertheless, physicians need to remain involved in the process in order to establish patient trust in the diagnosis.

Originality / Value: We analyze the utility to physicians and patients of a diagnostic process and show that, while scheduling may reduce the overall process utility, AI-based solutions may increase the overall process utility.

Research Limitations / Future Research: First, our simulation includes a number of assumptions with regard to the distribution of mean times for encounter and treatment. Second, the data we used for our model were obtained from different papers, and thus from different healthcare systems. Third, our experimental study has a very small sample size and only one test-physician.

Paper type: Empirical 


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How to Cite
Lyon, J. Y., Bogodistov, Y., & Moormann, J. (2021). AI-driven Optimization in Healthcare: the Diagnostic Process. European Journal of Management Issues, 29(4), 218-231.