Leadership and Project Management for AI-Enabled Metabolic Care
Keywords:
artificial intelligence, project governance, technology adoption, value-based care, metabolic careAbstract
Healthcare leaders are under pressure to adopt artificial intelligence (AI) while protecting patients, controlling costs, and demonstrating measurable value. This paper develops a leadership and project-management framework for evaluating and scaling AI-enabled metabolic care, using Twin Health as a bounded case study rather than a presumed market winner. The analysis establishes the national metabolic burden, compares technology-enabled programs that may complement or reduce reliance on GLP-1 therapy, and applies transparent selection criteria to the focal case. It then organizes adoption around five governance gates: clinical value and safety; economic value; equity and access; data and AI governance; and change leadership and accountability. The paper does not report a national savings forecast or net ROI because the population, adoption, intervention-cost, implementation-cost, effect-persistence, and population-overlap assumptions required for a reproducible estimate must be established within the adopting organization. Instead, it provides a transparent measurement architecture for payer- or employer-specific pilots, including medical and productivity outcomes. The principal contribution is managerial: executive sponsorship, cross-functional governance, stage gates, performance measures, and stop-or-scale decisions for translating promising AI evidence into safe, equitable, and accountable value.
References
Athinarayanan, S. J., Adams, R. N., Hallberg, S. J., McKenzie, A. L., Bhanpuri, N. H., Campbell, W. W., Volek, J. S., Phinney, S. D., & McCarter, J. P. (2019). Long-term effects of a novel continuous remote care intervention including nutritional ketosis for the management of type 2 diabetes: A 2-year non-randomized clinical trial. Frontiers in Endocrinology, 10, 348. https://doi.org/10.3389/fendo.2019.00348
Barthold, D., Chiguluri, V., Gumpina, R., Castro Sweet, C., Pieratt, J., Cordier, T., Matanich, R., Rogstad, T. L., & Prewitt, T. (2020). Health care utilization and medical cost outcomes from a digital diabetes prevention program in a Medicare Advantage population. Population Health Management, 23(6), 414–421. https://doi.org/10.1089/pop.2019.0184
Bollyky, J. B., Bravata, D., Yang, J., Williamson, M., & Schneider, J. (2018). Remote lifestyle coaching plus a connected glucose meter with certified diabetes educator support improves glucose and weight loss for people with type 2 diabetes. Journal of Diabetes Research, 2018, 3961730. https://doi.org/10.1155/2018/3961730
Castro Sweet, C., Bradner Jasik, C., Diebold, A., DuPuis, A., & Jendretzke, B. (2020). Cost savings and reduced health care utilization associated with participation in a digital diabetes prevention program in an adult workforce population. Journal of Health Economics and Outcomes Research, 7(2), 139–147. https://doi.org/10.36469/jheor.2020.14529
Centers for Disease Control and Prevention. (2024a). Behavioral Risk Factor Surveillance System (BRFSS). https://www.cdc.gov/brfss/
Centers for Disease Control and Prevention. (2024b). Adult obesity facts. https://www.cdc.gov/obesity/adult-obesity-facts/index.html
Centers for Disease Control and Prevention. (2026). National diabetes statistics report (2023 data, last reviewed January 2026). https://www.cdc.gov/diabetes/php/data-research/index.html
Damschroder, L. J., Aron, D. C., Keith, R. E., Kirsh, S. R., Alexander, J. A., & Lowery, J. C. (2009). Fostering implementation of health services research findings into practice: A consolidated framework for advancing implementation science. Implementation Science, 4, 50. https://doi.org/10.1186/1748-5908-4-50
Downing, J., Bollyky, J., & Schneider, J. (2017). Use of a connected glucose meter and certified diabetes educator coaching to decrease the likelihood of abnormal blood glucose excursions: The Livongo for Diabetes Program. Journal of Medical Internet Research, 19(7), e234. https://doi.org/10.2196/jmir.6659
Emmerich, S. D., Fryar, C. D., Stierman, B., & Ogden, C. L. (2024). Obesity and severe obesity prevalence in adults: United States, August 2021–August 2023 (NCHS Data Brief No. 508). National Center for Health Statistics. https://dx.doi.org/10.15620/cdc/159281
Finkelstein, E. A., Trogdon, J. G., Cohen, J. W., & Dietz, W. (2009). Annual medical spending attributable to obesity: Payer- and service-specific estimates. Health Affairs, 28(5), w822–w831. https://doi.org/10.1377/hlthaff.28.5.w822
Graham, S. A., Pitter, V., Hori, J. H., Stein, N., & Branch, O. H. (2022). Weight loss in a digital app-based diabetes prevention program powered by artificial intelligence. Digital Health, 8, 20552076221130619. https://doi.org/10.1177/20552076221130619
Hallberg, S. J., McKenzie, A. L., Williams, P. T., Bhanpuri, N. H., Peters, A. L., Campbell, W. W., Hazbun, T. L., Volk, B. M., McCarter, J. P., Phinney, S. D., & Volek, J. S. (2018). Effectiveness and safety of a novel care model for the management of type 2 diabetes at 1 year: An open-label, non-randomized, controlled study. Diabetes Therapy, 9(2), 583–612. https://doi.org/10.1007/s13300-018-0373-9
Hswen, Y., Rafaï, I., Lacombe, A., Davin-Casalena, B., Dubois, D., Blayac, T., & Ventelou, B. (2025). Does improving diagnostic accuracy increase artificial intelligence adoption? A public acceptance survey using randomized scenarios of diagnostic methods. AI in Health, 2(1), 114–120. https://doi.org/10.36922/aih.3561
Joshi, S., Shamanna, P., Dharmalingam, M., Vadavi, A., Keshavamurthy, A., Shah, L., & Mechanick, J. I. (2023). Digital twin-enabled personalized nutrition improves metabolic dysfunction-associated fatty liver disease in type 2 diabetes: Results of a 1-year randomized controlled study. Endocrine Practice, 29(12), 960–970. https://doi.org/10.1016/j.eprac.2023.08.016
Nagi, M. A., Ahmed, H., Rezq, M. A. A., Sangroongruangsri, S., Chaikledkaew, U., Almalki, Z., & Thavorncharoensap, M. (2024). Economic costs of obesity: A systematic review. International Journal of Obesity, 48(1), 33–43. https://doi.org/10.1038/s41366-023-01398-y
National Center for Health Statistics. (2024). National Health and Nutrition Examination Survey (NHANES). https://wwwn.cdc.gov/nchs/nhanes/Default.aspx
National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). https://doi.org/10.6028/NIST.AI.100-1
Pantalone, K. M., Xiao, H., Bena, J., Morrison, S., Downie, S., Boyd, A. M., Shah, L., Willis, B., Beharry-Diaz, J., Milinovich, A., Joshi, S., Kaufman, F. R., & Mechanick, J. I. (2025). Type 2 diabetes pharmacotherapy de-escalation through AI-enabled lifestyle modifications: A randomized clinical trial. NEJM Catalyst Innovations in Care Delivery, 6(9), CAT.25.0016. https://doi.org/10.1056/CAT.25.0016
Parker, E. D., Lin, J., Mahoney, T., Ume, N., Yang, G., Gabbay, R. A., ElSayed, N. A., & Bannuru, R. R. (2024). Economic costs of diabetes in the U.S. in 2022. Diabetes Care, 47(1), 26–43. https://doi.org/10.2337/dci23-0085
Project Management Institute. (2021). A guide to the project management body of knowledge (PMBOK guide) and the standard for project management (7th ed.). Project Management Institute.
Sepah, S. C., Jiang, L., & Peters, A. L. (2014). Translating the Diabetes Prevention Program into an online social network: Validation against CDC standards. The Diabetes Educator, 40(4), 435–443. https://doi.org/10.1177/0145721714531339
Shi, Q., Nong, K., Vandvik, P. O., Guyatt, G. H., Schnell, O., Rydén, L., Marx, N., Brosius, F. C., Mustafa, R. A., Agarwal, A., Zou, X., Mao, Y., Asadollahifar, A.,
Chowdhury, S. R., Zhai, C., Gupta, S., Gao, Y., Lima, J. P., Numata, K., … Li, S. (2023). Benefits and harms of drug treatment for type 2 diabetes: Systematic review and network meta-analysis of randomised controlled trials. BMJ, 381, e074068. https://doi.org/10.1136/bmj-2022-074068
Stein, N., & Brooks, K. (2017). A fully automated conversational artificial intelligence for weight loss and behavior change: Longitudinal observational study among overweight and obese adults. JMIR Diabetes, 2(2), e28. https://doi.org/10.2196/diabetes.8590
Sweis, N. J. (2024). The economic burden of obesity in 2024: A cost analysis using the value of statistical life. Critical Public Health, 34(1), 1–13. https://doi.org/10.1080/09581596.2024.2333407
Tremmel, M., Gerdtham, U.-G., Nilsson, P. M., & Saha, S. (2017). Economic burden of obesity: A systematic literature review. International Journal of Environmental Research and Public Health, 14(4), 435. https://doi.org/10.3390/ijerph14040435
U.S. Bureau of Economic Analysis. (2024). National income and product accounts tables. https://www.bea.gov
U.S. Census Bureau. (2024a). American Community Survey (ACS). https://www.census.gov/programs-surveys/acs
U.S. Census Bureau. (2024b). Small Area Health Insurance Estimates (SAHIE). https://www.census.gov/programs-surveys/sahie.html
U.S. Census Bureau & U.S. Bureau of Labor Statistics. (2024). Current Population Survey (CPS). https://www.census.gov/programs-surveys/cps.html
Wang, Y., & Chen, X. (2016). Epidemiology of obesity and diabetes and their cardiovascular complications. Circulation Research, 118(11), 1723–1735. https://doi.org/10.1161/CIRCRESAHA.115.306825
Wilson, L., Malone, D., Potukuchi, P., Han-Burgess, E. J., Lee, K. C. S., Edwards, A.,
Yu, X., Thingalaya, N., Lee, F., & Brixner, D. (2025). Effects of a digital health intervention for adults with type 2 diabetes mellitus on health care resource use and health care charges in the United States: Retrospective cohort study. Journal of Medical Internet Research, 27, e67320. https://doi.org/10.2196/67320
Zare, H., Spooner, K. K., & Thorpe, R. J., Jr. (2026). Obesity and structural barriers: A county-level Oaxaca–Blinder decomposition of inequality, social vulnerability, healthy food access, and minimum wage policies. Economies, 14(6), 203. https://doi.org/10.3390/economies14060203
Zimmermann, G., Venkatesan, A., Rawlings, K., & Scahill, M. D. (2021). Improved glycemic control with a digital health intervention in adults with type 2 diabetes: Retrospective study. JMIR Diabetes, 6(2), e28033. https://doi.org/10.2196/28033
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Farah Farahati, Ph.D. (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright of their published works. Articles published in the *Proceedings of the Global Symposium on Leadership and Project Management* are distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license permits others to share, reproduce, distribute, and adapt published material, including for commercial purposes, provided appropriate credit is given to the original author(s), a link to the license is provided, and any changes are indicated.
License details: https://creativecommons.org/licenses/by/4.0/