From Interview #111
With Akifa Khattak
Artificial intelligence is rapidly transforming healthcare delivery, but its regulatory, legal, and economic implications remain uncertain. In this interview, Akifa Khattak, a healthcare attorney and biotech expert, explains how AI is reshaping governance, infrastructure, and reimbursement models across the industry. She outlines the emerging risks tied to data ownership, liability, and energy-intensive infrastructure, while highlighting the growing role of agentic AI in clinical workflows. As healthcare systems adopt AI-driven tools for decision support and automation, leaders must balance innovation with oversight. This discussion explores how policy, transparency, and shared accountability will determine whether AI improves patient outcomes or introduces new systemic risks.
From Interview #110
With Dr. Peter Brodeur
As clinical AI adoption accelerates, healthcare leaders face a critical question: what is truly ready for practice versus what remains experimental? In this conversation, Dr. Peter Brodeur, a physician-researcher at Beth Israel Deaconess Medical Center and contributor to the ARISE Network report on clinical AI, breaks down where the field stands today. Drawing from a comprehensive review of emerging evidence, he highlights both promising advances and persistent limitations in AI-driven clinical decision support, patient-facing tools, and workflow integration.
The discussion explores the concept of the “jagged frontier,” where AI systems demonstrate superhuman performance in controlled settings yet fail unpredictably in real-world scenarios. Dr. Brodeur also emphasizes the growing importance of safety evaluation, human-computer interaction design, and outcomes-based benchmarking. For healthcare executives, clinicians, and digital leaders, this interview provides a grounded, evidence-based perspective on how to responsibly implement AI while improving patient outcomes and operational efficiency.
From Interview #109
With Dr. Bernardo Perez-Villa and Dr. Peter Alperin
Why do so many AI startups fail in healthcare despite strong funding and promising innovation? In this interview, Dr. Bernardo Perez-Villa and Dr. Peter Alperin break down the structural, financial, and operational realities that determine whether digital health solutions succeed or fail. While AI offers unprecedented potential to augment clinical decision-making, real-world adoption depends less on technical performance and more on workflow integration, reimbursement strategy, and stakeholder alignment.
The discussion highlights a critical disconnect between clinical research success and practical implementation, emphasizing that healthcare systems are inherently risk-averse and resistant to disruption. From regulatory hurdles and CPT reimbursement challenges to product-market fit and go-to-market strategy, the conversation provides a clear-eyed view of what it takes to build viable healthcare AI solutions. For healthcare leaders, innovators, and investors, this interview offers actionable insights into navigating the complex healthcare innovation landscape.
From Interview #108
With Monique Lappas
The financial crisis in healthcare is deeper than most clinicians and executives realize. In this interview, Monique Lappas, founder and CEO of Qualify Health, explains how hospitals lose billions annually due to denied claims, underpayments, and patient affordability challenges. Drawing from her background in finance and healthcare consulting, she outlines how AI-driven automation can identify financial assistance opportunities, reduce bad debt, and improve revenue cycle performance.
Lappas reveals how her company uses intelligent workflows to match patients with copay programs, charitable foundations, and financial aid in real time. The result is a rare alignment of incentives: hospitals recover revenue while patients avoid overwhelming medical debt. This discussion also explores systemic issues such as Medicaid reimbursement gaps, payer dynamics, and the growing burden of administrative complexity. For healthcare leaders, the message is clear: leveraging AI in revenue cycle management is no longer optional—it is essential for financial sustainability and patient access.
