Riverbend Medical Center: AI-Enabled eVisit Billing

Dong-Gil Ko, Umberto Tachinardi, Eric Warm

Product #:W47109_P

Supplier:Ivey

Discipline:General Management, Information Systems

Setting:United States, 2024

Details:Case (PDF) • 9 pages • 2025-12-18

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This case presents Riverbend Medical Center’s dilemma in late 2024 over whether to adopt artificial intelligence (AI)-assisted billing for patient portal e-visits. Chief digital health officer Dr. Alex Reed must decide whether a model that flags billable secure message threads should be implemented. The issue was highlighted by Dr. Alisha Schwartz, who managed after-hours messages without pay. Despite the introduction of new Current Procedural Terminology (CPT) e-visit codes, fewer than 5 per cent of eligible threads were billed in 2023, leaving effort unrewarded and revenue uncollected. Riverbend’s AI model, using domain engagement and cognitive judgment, showed potential to capture more billable events without overcharging. Students must weigh the strategic, operational, and ethical implications of Reed’s 2025 decision.

Learning Objectives

This case is well-suited for graduate and executive business administration courses in digital transformation, health care management, health information technology, service operations, and technology strategy. It can also be used in courses focusing on change management or AI in business to illustrate how data analytics can inform managerial decisions. The level of technical detail provided is accessible to a general business audience while still offering depth for those interested in health tech or operations. The case prompts interdisciplinary discussion, touching on strategy (adoption of innovation), operations (workflow improvement, efficiency), organizational behaviour (physician burnout, change adoption), and ethics (patient satisfaction, fairness in billing). After analyzing the case and discussion questions, students will be able to do the following:

Assess integration challenges of new digital services.
Compare time-based and complexity-based service metrics.
Analyze stakeholder perspectives and ethical concerns.
Explore strategic decision-making around AI adoption.
Formulate an implementation strategy (or alternatives).