HeyJaredOur Thought Leaders
- Indexed articles, last 90 days
- 81
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- Sep 30, 2026
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- Jul 7, 2026
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How Virtual Care and AI Unlock Outcomes-Based Autism Therapy (opens the original)
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For years, autism therapy has forced healthcare purchasers into a false choice: expand access or control costs. But as diagnoses in the U.S. have climbed to one in every 31 children, demand for care has outpaced the supply of Board Certified Behavior Analysts (BCBAs), the profession’s most highly trained clinicians. Providers are responding by prescribing more therapy hours delivered by larger teams of lesser-trained clinicians, while payors continue to reimburse them for every additional hour.
Healthcare AI Governance: Moving from Data-Sensitivity Tiers to Reversibility Controls in Agentic Systems (opens the original)
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Ashok Benial is right. His argument in these pages last week — that most hospitals are buying AI faster than they can govern it, that “the tools go live while the guardrails are still on a slide deck” — is the most useful thing anyone has said about healthcare AI governance this quarter. His three controls are the correct ones: validate locally against your own population, monitor for drift after go-live, and fund the human review layer as a control system rather than overhead. Every health syst
Healthcare’s Semantic AI Blind Spot: Why LLMs Cannot Replace Deterministic Data Infrastructure (opens the original)
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Healthcare AI has reached an inflection point. AI is rapidly moving from isolated demonstrations to production systems embedded in clinical, operational, and research workflows. A new generation of AI applications is emerging to support nearly every aspect of healthcare delivery, operations, and research. As organizations deploy these capabilities at scale, they are exposing an invisible semantic challenge that has quietly existed for decades. Clinical information is translated repeatedly as it
Healthcare AI’s Decision Intelligence Mandate: Turning Predictive Analytics into Timely Clinical and Operational Action (opens the original)
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For years, healthcare leaders have asked if artificial intelligence can predict what will happen next, such as who might be readmitted, which patients could get worse, where resources are needed, and which interventions could help. Today, we can answer many of these questions, but a tougher question is what a healthcare organization should actually do with these predictions. This is where much of healthcare AI still falls short. The industry has invested heavily in electronic health records, clo
Why Calibrated Uncertainty and Deliberate Abstention Drive True Clinical AI Adoption (opens the original)
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Most teams building AI systems treat confidence as a solved problem. The model produces a probability, the interface displays it, and the reliability requirement gets checked off. Within weeks, the number becomes furniture. Anyone who has watched a clinician work through a queue of suggested codes, each stamped with a confidence percentage, knows the pattern: when the scores cluster near the top of the range, a 92 gives the reviewer little more reason to act than an 89, and they fall back on the
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