The U.S. healthcare and life sciences (HCLS) sector is entering a period of historic disruption. Policy upheaval, budget cuts, and aggressive regulatory changes—some embedded in the Senate-passed “Big Beautiful Bill”—are colliding with ongoing innovation in tech, care models, and data strategy.

We’re not just being asked to build smarter systems. We’re being asked to build them in a rapidly shifting—and often contradictory—environment. Eligibility systems are being pushed into surveillance territory. AI is driving opaque denial algorithms. Privacy frameworks are eroding just as new therapies and delivery models require more nuanced consent and record-sharing structures.

As a long-time consultant in this space, I’ve watched integrators, vendors, and health systems struggle to keep pace. But I’ve also seen glimmers of hope—low-code tools deployed quickly, ethical stances taken quietly, and modular designs that allow for faster adaptation. There are ways to navigate this. But they require not just new tech, but a new mindset.

✅ Design for uncertainty.
✅ Build modular.
✅ Align with real-world needs, not just margins.

This post is part call to action, part personal reflection. And while I don’t claim to have all the answers, I do know this: what we build now will shape how patients experience care, how clinicians work, and how public trust is won—or lost.

📖 Read the full piece on Substack:
Bridging the Innovation Gap: Preparing Healthcare IT for an Unstable Future

Agentic AI—systems that act on data rather than just analyze it—is being hailed as a cure-all for the healthcare industry’s inefficiencies. Payers, providers, and pharma firms are investing fast. But how much of what’s being promised is actually feasible today, and how much is branding-driven hype?

In a new white paper, I explore the advertised, actual, and emerging uses of agentic AI in healthcare. From Salesforce’s acquisition of Informatica to UK-based “AI” firms exposed for running smoke-and-mirrors operations, it’s clear that the field needs clarity—and accountability.

This blog provides a preview of what you’ll find in that deeper dive.

​What’s Being Promised:

• Automated prior authorizations and appeals in payer operations
• Clinical documentation and decision support
• Ambient listening for providers
• AI agents in trial site selection and adverse event detection for pharma

What’s Working Now:

• Clinical ambient tools (Nuance DAX, Abridge, Suki) are helpful but still human-in-the-loop
• Automation of narrow workflows like intake triage or formulary lookups
• Some trial modeling, where data quality is strong and regulatory paths are clear

The Gap:

• APIs struggle with fragmented or unclean data
• Regulatory bodies still require auditability and explainability
• Firms overpromising agentic autonomy rarely disclose how brittle their models are

Consulting Firms: The Connective Tissue
It’s not just product companies shaping this space. Many consulting firms—Cognizant, Deloitte, EPAM, Accenture, Slalom, and others—play a unique hybrid role. They may:

• Build their own agentic tools (sometimes as ISVs)
• Partner with platform vendors like Salesforce or AWS
• Drive real-world implementation and integration of AI in healthcare settings

Far from adding confusion, these firms often bring much-needed structure, compliance rigor, and domain context. They’re helping AI move from lab demo to daily workflow.

Case in Point:

British “AI” firm Repliq was exposed by the Financial Times for passing off manual processes as generative AI, with junior developers writing responses behind the scenes. It was a textbook case of vaporware wrapped in buzzwords.

Read the White Paper:
The companion white paper explores:

• Why Informatica fills a critical data hygiene gap in Salesforce’s Data Cloud
• How to spot poser AI companies (regardless of whether they’re vendors or services firms)
• The regulatory drag on agentic deployment in clinical vs. administrative workflows
• Who’s actually building credible healthcare AI—and how to tell

Conclusion:
AI won’t save healthcare overnight. But real, responsible agentic AI—built on clean data, governed properly, and validated openly—can still move the needle. We just have to know where to look.

Read more: 
Get the Full White Paper - Agentic AI in Healthcare: Sorting Real Innovation from Vaporware

I work in the digital healthcare business — helping healthcare organizations build systems that talk to each other, share data, and ideally reduce friction for both patients and care teams. I’ve also spent time working on digital front doors—the slick, app-like experiences many hospitals and other providers now use to engage patients. These solutions are effective, but they’re not too cheap, both in licensing costs and the services required to put them together.

Recently, I’ve been thinking about what happens to all of this infrastructure—the APIs, middleware, and patient portals—when the funding starts to disappear.

The signals are there: pressure on Medicaid, Medicare, VA services, and public health agencies is rising. In previous posts, I’ve explored the downstream risks (The Big Beautiful Bill, Can We Automate Our Way Out, The Cost of Early Death). But it’s clear that interoperability itself may also be in the crosshairs.

What Interoperability Meant—And Why It Might Be Changing
Interoperability has been around for a while, but was supercharged about 15 years ago with the HITECH Act (Health Information Technology for Economic and Clinical Health). The Office of the National Coordinator for Health Information Technology (ONC) define interoperability across four levels:

• Foundational: Data exchange between IT systems
• Structural: Standard formats and syntax (e.g., HL7, FHIR, CCD)
• Semantic: Shared meaning of data between systems
• Organizational: Consent, governance, legal agreements, workflows

(HealthIT.gov, 2023)

These definitions assume continued growth and investment—backed by Meaningful Use, Cures Act mandates, and adoption of EHRs. However, if federal reimbursement begin to shrink, this framework may no longer hold up.

Digital Front Doors—What Happens When the Budget Gets Tight?
Digital front doors, including mobile apps, chatbots, appointment engines, and patient access APIs, are not free. In fact, a 2023 Chilmark Research report noted that digital front door initiatives often exceed $500K in upfront investment for midsize systems—not including maintenance and integration costs (Chilmark Research, 2023).

If funding goes away, some possible outcomes may be:

• Front-end features dropped in favor of bare-bones portals
• Shifts toward payer-controlled ecosystems
• ​Use of white-labeled third-party tools that sacrifice customization for cost

This is not theoretical--state-level Medicaid agencies have already pulled back on HIE access in some cases (KFF, 2024).

Have We Engineered Ourselves Into a Privacy Trap?
Modern interoperability assumes real-time, cross-entity data sharing. The Trusted Exchange Framework and Common Agreement (TEFCA) is supposed to enable this while protecting consent and governance (ONC TEFCA Overview, 2024). But things have gotten messier.

• Weak HIPAA enforcement at the federal level has left state-level agencies scrambling with inconsistent protections
• Growing use of third-party apps under the 21st Century Cures Act creates new privacy risks—especially when developers fall outside HIPAA’s scope (GAO, 2023)
• Centralized architectures, like some cloud data lakes, create single points of failure—convenient but exposed

Interoperability doesn’t inherently weaken privacy, poor implementation and deregulation can.

What Happens When the Money Dries Up?
If proposed federal cuts materialize, the interoperability ecosystem will feel it in three key ways:

• Stalled Modernization: Small and rural providers may halt FHIR upgrades or delay EHR replacement cycles (CMS, 2024)
• Shrinking HIE Participation: Public health departments and Medicaid MCOs may exit expensive exchange networks
• Vendor Consolidation: Fewer dollars may push smaller integration vendors out, consolidating the market around major players

We should expect increased demand for cloud-native integration platforms, Pay-as-you-go API solutions, and simplified FHIR middleware that minimizes custom development.

How We As Consultants, Product Teams, and Strategists Can Respond

For Consultants & Integrators:

• Push modular architectures—ditch "all-in-one".
• Help clients develop interoperability tiers based on urgency and budget.
• Prioritize data governance and privacy early in the design phase.
• Create exit strategies for fragile HIEs and legacy networks.

For Vendors & Product Developers:

• Build tools that can scale down gracefully—not just up
• Make FHIR-native functionality the default, not a premium
• Explore federated data models and zero-trust architectures
• Data isn't always going to be great. Deliver value using imperfect, incomplete, or disconnected data—perfection will be rare

Where the Market Is Shifting
This took some research on my part, but it looks like a number companies are well-positioned for what’s next:

• Redox, Health Gorilla, Particle Health: - API-based  vendors with national reach (Redox, Health Gorilla, Particle Health).
• Skyflow, TripleBlind: Startups focused on privacy-preserving data exchange and federated AI (Skyflow, TripleBlind).
• AWS HealthLake, Google Cloud Healthcare API, and Microsoft Cloud for Healthcare offer elastic infrastructure for payers and providers trying to do more with less.

Final Thought: Strategy Over Nostalgia
Interoperability isn’t collapsing—but it looks like it is evolving. Consultants, technologists, and product leaders will need to adjust expectations, revise architectures, and help clients prioritize privacy and value over perfection.

This new era we are in is marked by constrained budgets, decentralization, and (not always strategic) tradeoffs. We are going to have to build things differently.

Sources & Citations

• ONC – What is Interoperability?
• ONC TEFCA Overview (2024)
• Chilmark Research – Digital Front Door Trends (2023)
• CMS – Strategic Vision 2024 (Not sure how much this vision will evolve)
• KFF – Medicaid & HIE Participation Trends
• GAO – Privacy Gaps in App Ecosystems (2023)
• Redox
• Health Gorilla
• Particle Health
• Skyflow
• TripleBlind