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Case study

Leveraging generative artificial intelligence to optimise a core international business process

Janine R. Kamath, Nathaniel R. Witte, Josie Dappah Colon and Christine Mitchell
Management in Healthcare: A Peer-Reviewed Journal, 10 (2), 126-137 (2025)
https://doi.org/10.69554/RDFR4298

Abstract

The application of artificial intelligence (AI), large language models and generative AI (GenAI) is accelerating rapidly across many industries worldwide. Innovative models and tools are being piloted to understand their business and societal value. Models such as ChatGPT (OpenAI), Gemini (Google) and Copilot (Microsoft) have become increasingly sophisticated and widely adopted. Understanding the advantages and drawbacks of GenAI models is crucial for organisations seeking to leverage GenAI to maximise their efficiency and expand their capabilities. This paper describes the strategic approach, implementation and findings of using GenAI in a health care business context. A GenAI model was used to analyse and summarise strategic and operational information submitted by external organisations seeking to collaborate with Mayo Clinic. The goal was to reduce process cycle time and manual resource intensity. In parallel, a comparative human review was conducted by functional experts. Both the AI-generated and human-reviewed results were evaluated using sentiment analysis (SA) and then categorised as positive, neutral or negative. Attention was paid to possible inaccuracies and biases associated with the use of the GenAI model. GenAI reduced the manual workload by approximately 20−25 per cent and presented information in a well-structured format. However, the SA showed the GenAI results as positively biased and the human-generated results as neutral. These results were most likely influenced by a positive bias in the input documents, the lack of contextual comparators/ standards in the GenAI model and an inherent bias within the model’s algorithm(s). Additionally, limitations in input size and language translation hindered the GenAI model’s effectiveness. The possibility of AI hallucinations/inaccuracies emphasised the importance of validation by a human expert. Furthermore, carefully formatting the input data enhanced the value and relevance of the GenAI model’s outputs. GenAI has the potential to increase efficiency and alleviate manual burden, but it must be thoughtfully implemented to address inaccuracies, biases and privacy concerns. This article is also included in The Business & Management Collection which can be accessed at https:// hstalks.com/business/.

Keywords: bias; due diligence; generative artificial intelligence; international; models; optimisation

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Author's Biography

Janine R. Kamath , MA, MBA, is Executive Director of Mayo Clinic International. She previously chaired the Department of Management Engineering and Consulting and currently serves as Assistant Professor of Health Care Systems Engineering and Health Care Administration at the Mayo Clinic College of Medicine and Science. Before joining Mayo Clinic, she was a manager at two large pharmaceutical companies. At Mayo Clinic, she has advanced and led multiple strategic initiatives on digital and platform environment; operations optimisation; international business growth; diversity, equity and inclusion; care model transformation; core process engineering; and quality, affordability and service. Janine is engaged with national and international leadership and governance committees and has a passion for entrepreneurship, diverse talent growth and driving results through empowered, high-performing teams. Janine is the Board Chair and President Emeritus of the Association for Internal Management Consultants. She serves as course director and presents at national and international conferences and educational programmes. She publishes in peer-reviewed journals and has authored book chapters and a book on the legacy of management engineering and consulting at Mayo Clinic. Janine actively engages with professional and community organisations and contributes to academic institutions through college and high school student education and mentorship programmes.

Nathaniel R. Witte , MS, is a senior strategist in the Strategy Department at Mayo Clinic. He specialises in data analytics, visualisations and process optimisation and has created data collection tools and visualisation dashboards for long-range planning, which provides insights for organisational leaders. Mr Witte has led data metric analysis and data stewardship discussions, implemented innovative models in artificial intelligence/machine learning and automation, and designed service line optimisation initiatives to ensure safe and efficient patient care. Before joining Mayo Clinic, Mr Witte was an associate health systems engineer and a graduate student intern in the Mayo Clinic Kern Center for the Science of Health Care Delivery. He earned a master’s degree in industrial engineering and a bachelor’s degree in industrial and systems engineering from the University of Minnesota. Mr Witte has published research on medical assessment practices and has presented at various conferences on topics such as artificial intelligence optimisation and health care delivery.

Josie Dappah Colon , MS, is a senior global project manager in the strategic programmes and integration team at Mayo Clinic International. In this role, she partners with clinical and operational experts to optimise processes, boost efficiency and deliver tailored solutions for strategic international partners. With more than 20 years of diverse experience spanning the health care and corporate finance sectors, she combines her extensive programme and project management expertise with a strong clinical background as a licensed physical therapist, bringing more than eight years of hands-on patient care experience in various health care settings. Prior to joining Mayo Clinic, Ms Colon held pivotal roles in business development, client engagement and programme and product management at Bank of America, Merrill Lynch and T. Rowe Price. Her clinical experience includes leadership roles at Mercy Medical Center, Mt. Washington Pediatric Hospital and Encompass Health, where she led cross-functional teams toward the successful implementation of data-driven clinical programmes. Ms Colon holds degrees from Morgan State University, Drexel University and Neumann University, and is a Six Sigma Green Belt and a certified Project Management Professional. Ms Colon delivers educational seminars to allied health students and staff working in resource-limited medical settings.

Christine Mitchell , MS, is a seasoned health care management professional with 25 years of experience in strategy consulting, health care operations and project management. As a senior global project manager at Mayo Clinic International, she spearheads large-scale strategic initiatives that drive innovation and excellence in health care delivery. Ms Mitchell’s expertise spans greenfield hospital projects, clinical service line development and improvement in operations and patient experience. She has a strong track record of connecting international and local changemakers, leading cross-functional teams and delivering innovative solutions. She is passionate about transforming international health care through strategic collaboration and capacity building. Before joining Mayo Clinic, Ms Mitchell held leadership roles at Brigham and Women’s Hospital, PricewaterhouseCoopers China, Jiahui Health and Parkway Health China. She earned her bachelor’s degree in human biology from Stanford University and her master’s degree in health policy and management from Harvard School of Public Health. Ms Mitchell is also a certified project management professional. She has published in peer-reviewed journals and continues to contribute to the advancement of health care management.

Citation

Kamath, Janine R., Witte, Nathaniel R., Colon, Josie Dappah and Mitchell, Christine (2025, December 1). Leveraging generative artificial intelligence to optimise a core international business process. In the Management in Healthcare: A Peer-Reviewed Journal, Volume 10, Issue 2. https://doi.org/10.69554/RDFR4298.

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cover image, Management in Healthcare: A Peer-Reviewed Journal
Management in Healthcare: A Peer-Reviewed Journal
Volume 10 / Issue 2
© Henry Stewart
Publications LLP

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