Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2026
Artificial Intelligence-Enabled Opportunities to Reduce Administrative Bloat in Gastroenterology Practices: Scoping Review.
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Wong Rachael S, Lopez Marlynn P, Albhaisi Somaya, Arjal Russ, El Kurdi Bara, Henderson Wendy A, Inamdar Sumant, Kratschmer Christina, McCutchen Aja S, Norman Joshua S, Samarasena Jason B, Sinha Sidhartha R, Soroush Ali, Tan Mimi C, Wadhwa Vaibhav, Mathews Simon C, Shah Eric D
Аннотация
BACKGROUND & AIMS: Health care administration accounts for approximately 25% of health care expenditures. We performed a scoping review of artificial intelligence-enabled administrative products in gastroenterology and hepatology, including widely deployed commercial platforms, examining administrative functions, validation practices, and alignment with high-burden workflows.
METHODS: A 2-component search strategy combining systematic literature search and a targeted environmental scan of commercial platforms identified 34 discrete artificial intelligence products and 2 narrative reviews. Each product was assessed using the 4-domain Framework for the Appropriate Implementation and Review of Artificial Intelligence rubric. Validation was categorized as unvalidated, internally validated, vendor-sponsored, or externally validated.
RESULTS: Products spanned 8 administrative function domains, including billing and coding, clinical documentation, patient engagement, and prior authorization. Twenty-six of 34 were general-platform products, and only 8 were gastroenterology-specific, of which 7 remained precommercialized. Four of 34 products reported no validation data, 2 relied on vendor-sponsored evidence, and 4 were externally validated. Framework for the Appropriate Implementation and Review of Artificial Intelligence scores were lowest for Fairness (1.59/5; standard deviation, 0.66), followed by Reliability (2.57/5; standard deviation, 0.97), Interpretability (2.72/5; standard deviation, 1.01), and Accountability (2.78/5; standard deviation, 0.64).
CONCLUSIONS: Commercialized products were broadly designed, least validated, and scored lowest on Framework for the Appropriate Implementation and Review of Artificial Intelligence metrics, particularly Fairness, whereas gastroenterology-specific products with greater methodological rigor remained largely precommercialized. Commercial general-platform products lacked gastroenterology-specific validation, and high-burden specialty workflows including hepatology care coordination and transplant documentation remained comparatively underserved. Realizing the potential of administrative artificial intelligence in gastroenterology requires moving toward empirically validated, specialty-specific, and equitable solutions, where success is measured by reduced administrative burden and time returned to patient care, not by revenue generation.
METHODS: A 2-component search strategy combining systematic literature search and a targeted environmental scan of commercial platforms identified 34 discrete artificial intelligence products and 2 narrative reviews. Each product was assessed using the 4-domain Framework for the Appropriate Implementation and Review of Artificial Intelligence rubric. Validation was categorized as unvalidated, internally validated, vendor-sponsored, or externally validated.
RESULTS: Products spanned 8 administrative function domains, including billing and coding, clinical documentation, patient engagement, and prior authorization. Twenty-six of 34 were general-platform products, and only 8 were gastroenterology-specific, of which 7 remained precommercialized. Four of 34 products reported no validation data, 2 relied on vendor-sponsored evidence, and 4 were externally validated. Framework for the Appropriate Implementation and Review of Artificial Intelligence scores were lowest for Fairness (1.59/5; standard deviation, 0.66), followed by Reliability (2.57/5; standard deviation, 0.97), Interpretability (2.72/5; standard deviation, 1.01), and Accountability (2.78/5; standard deviation, 0.64).
CONCLUSIONS: Commercialized products were broadly designed, least validated, and scored lowest on Framework for the Appropriate Implementation and Review of Artificial Intelligence metrics, particularly Fairness, whereas gastroenterology-specific products with greater methodological rigor remained largely precommercialized. Commercial general-platform products lacked gastroenterology-specific validation, and high-burden specialty workflows including hepatology care coordination and transplant documentation remained comparatively underserved. Realizing the potential of administrative artificial intelligence in gastroenterology requires moving toward empirically validated, specialty-specific, and equitable solutions, where success is measured by reduced administrative burden and time returned to patient care, not by revenue generation.