Tahasin Alam

Unclear policy rules create friction across the healthcare ecosystem — patients are unsure, providers over-document “just in case,” and insurers face growing FWA risks. Aconiq tackles this root cause by providing real-time clarity on exclusions, eligibility, and pre-existing conditions. When everyone knows what’s covered, FWA naturally decreases — and trust increases. 🪩 Fast ○ Fair ○Transparent 🪩 Aconiq is redefining how insurers and providers manage claims. #insurtech #healthinsurance

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Health Data Interoperability: What Sanity Could Look Like (see prior post) If insanity is doing the same thing repeatedly while expecting different results, healthcare can choose sanity today. We've spent decades solving the wrong problems or implementing only partial solutions—building standards, funding EHRs, aggregating data, debating ontologies, fielding HIEs—all delivering unusable documents and fragmented data. Outcomes haven't changed, burnout has increased, costs have skyrocketed. Sanity isn't about perfection. It's about RESULTS that matter, achievable through pragmatic innovation. It requires treating data as the core asset—usable and reusable when trusted, contextual, available, and fit for purpose across your ecosystem. Interoperable Data is a Choice Sanity starts with accepting reality: You cannot control how all data is created or received. Wearables, external EHRs, patient-generated data, social determinants—data creation is distributed and expanding. That's innovation, not the problem. The problem is lacking an approach or hoping someone else fixes it. The fix: control what you can and establish processes for how data becomes trusted and reusable at your organization's boundary. Principles for Interoperable Data: 1.     Establish Trust at the Boundary – Validate, enrich, extract, and flag issues when data enters 2.     Categorize Data Strategically – Different data types require different management 3.     Process Once, Use Everywhere – Handle once, maintain processing history; make available based on user needs 4.     Extract and Ascertain Data Systematically – Make unstructured data computable with full context 5.     Reconcile Continuously – Create single current truth as new data arrives 6.     Make Context a First-Class Asset – Preserve meaning, timing, and intent with data 7.     Support Multiple Temporalities – Match processing speed and storage to actual needs The Framework in Practice Data arrives → Trust established → Categorized by value → Processed once to extract meaning and preserve context → Reconciled continuously → Available immediately → Usable and reusable across all systems. Technology exists to create longitudinal records of health for individuals and use data fabric for other needs. What's missing is focus on outcomes and commitment to treating data as the core asset. Healthcare is investing billions in AI, precision medicine, value-based care, and digital health. All require the same: trustworthy, contextual, interoperable data. You can't AI your way out of bad data. You can't achieve population health with fragmented facts. You can't deliver precision medicine without reconciling data. But you can make data a core asset with processes that create trust, preserve context, and efficient use. You can stop using failed approaches. Sanity is pragmatic, achievable, and measurable. To experience different outcomes, you need to REIMAGINE delivery of interoperable data.

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Today’s Situation Report offers a roundup of the latest news in federal health policy like payers aligning with CMMI’s ACCESS model as questions emerge about financial viability, CMS ending its signature primary care model early, new FDA modernization proposals, the agency’s reversal to review Moderna’s flu vaccine, and insurers accelerating AI adoption for oversight.  We’re also sharing a new action opportunity: the EMERGE Initiative is gathering input on how changes to federal statistical data have affected researchers, practitioners, and policy work. If federal data disruptions have impacted your work, you can share your experience through their anonymous survey: https://lnkd.in/e5ppE3y5  Read more here: https://lnkd.in/e-fuihzN 

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We're eliminating the guesswork on how to curtail health spend. Our actuaries are helping health plans identify the costs that actually move trend. Great insight Stuart Sutley. https://lnkd.in/ennsPScY

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Warning: Overgeneralization Ahead The range of AI expertise in healthcare is, in my view, broader than in any other industry. We’ve long relied on rigorous studies—FDA approvals, actuarial tables, sophisticated financial analyses for insurers—but AI adds layers of complexity that raw data alone can’t capture and relying on that expertise is sometimes misleading. Across sports data provides different levels of insight. In baseball, detailed stats offer deep insights into performance, but in football or basketball, what’s missing from the numbers can matter just as much as what’s recorded. The same is true in care delivery. Think for a second of two hardworking doctors. One that is average and the other that is very good. The very good doctor can look at the chart of the patient but never gets to see them; the average doctor has full access to the information and the patient. It’s not clear which doctor you should prefer without understanding your situation. It depends on the situation. Is it a fast-moving ICU crisis or a months-long mystery illness? How reliable and comprehensive is the data in the record—beyond column headers, what’s the quality of the observations? In many critical contexts—like the ICU—“stats” can lag behind reality. Seasoned doctors often say, “I just knew something was off.” Are they simply better clinicians, or are they picking up on patterns not encoded in the EHR? If we build AI without grappling with what data we’re missing—and what we’ll never capture in legacy systems—we’ll leave its true potential untapped. AI must learn to “see” and “hear” the patient: facial expression, breathing patterns, speech nuances, even subtle movements. Relying solely on today’s EHR data will certainly create value—but it will also miss the signals that matter most for patient care. PS - the below are the stats from two basketball players for the playoffs over a decade. Who are you taking?

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Pre-AI SaaS EHRs will lose marketshare for the following reasons: 1. Legacy infrastructure is not built to support agentic applications: EHRs can't just add a plug-in and claim agentic automation. Every 3 months the technology advances and this means the infrastructure needs to be AI-native in order to keep up with these product shifts. 2. Shift in user behavior: doctors and other frontline healthcare workers are no longer willing to wait for anyone to give them permission to adopt AI technology. The old moat was on stickiness and EHRs maintained it with health system consolidation. This shift in AI product adoption challenges EVERYTHING we've known about pre-AI EHR SaaS distribution. 3. Custom frontend applications can now be vibe-coded: IDEs are for engineers but ADEs are being built for professionals. This means that vibe-coding development is no longer just creating prototypes with AI slop. These are real production-ready agentic applications that are secure and built using EHR backend data. #LetDoctorsVibe #VibeMedicine *ADE = Agent Development Environment

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Insurers, Hospitals Push Back Against CMS Proposal To Bring Non-Network Plans To ACA Exchanges Fierce Healthcare reports, “Insurers and hospitals have come together to rebuke” an Administration “proposal to roll back limits of plan designs that may be listed on the Affordable Care Act’s (ACA’s) exchanges.” The CMS last month “included in its Notice of Benefit and Payment Parameters for 2027 Proposed Rule a plan to allow some non-network plans to obtain qualified health plan (QHP) status.” In response, “groups representing health plans (AHIP, Association for Community Affiliated Plans and Alliance of Community Health Plans) as well as hospitals (Federation of American Hospitals and America’s Essential Hospitals) crossed the aisle to submit a joint comment letter to CMS calling on the administration to rethink its approach.” In the letter, they explained their top concern is that people shopping for coverage on the exchanges “won’t be able to grasp the differences between network and non-network plans, potentially exposing them to higher-than-expected out-of-pocket costs.” #cms #non-networkplans #ahip

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"ID.me looks forward to supporting CMS's modernization efforts. Secure, interoperable digital identity is foundational to building a more patient-centered healthcare system."

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