For the 195th episode of the Healthcare IT Today Podcast, we are taking a look at healthcare IT failures! We kick this episode off by debating what we think has been the biggest health IT policy failure. Next, we share our thoughts on the biggest healthcare technology failure. Then, we take a look at the healthcare system to see what its biggest structural failure is. We then end this episode by sharing a personal failure we experienced or were a part of in health IT.
Here’s a preview of the topics and questions we discuss in this episode:
What’s been the biggest health IT policy failure?
What’s been the biggest healthcare technology failure?
What is the biggest failure, structurally speaking, of the healthcare system?
What’s a personal failure you have experienced or been part of in health IT?
Now, without further ado, we’re excited to share with you the next episode of the Healthcare IT Today podcast.
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If you work in Healthcare IT, we’d love to hear where you agree and/or disagree with the perspectives we shared. Feel free to share your thoughts and perspectives in the comments of this post, in the YouTube comments, with @Colin_Hung or @techguy on Twitter, or privately on our Contact Us page. Let us know what you think of the podcast and if you have any ideas for future episodes.
The following is a guest article by Pooja Walia and Rajat Rawal
Most health systems we work with have passed the pilot stage for AI. Ambient documentation tools are in exam rooms. Revenue cycle models are running against live claims. Clinical decision support is nudging diagnoses across specialties. The tools are in the building.
What we keep seeing is that the governance that looked solid before go-live starts to slip a few months after. The policies are still on the shelf. The risk assessment was signed. The oversight committee met. But the AI that is running today is not quite the AI that was reviewed, and the people meant to govern it have no easy way to tell.
This matters more than it did a year ago, because the regulatory picture has changed. The proposed HIPAA Security Rule overhaul, expected to be finalized in 2026, removes the “addressable” safeguard loophole and brings AI systems that handle electronic protected health information explicitly into scope. The FDA’s revised Clinical Decision Support Software guidance, published in January 2026, narrows the exemptions many AI tools have been operating under. Texas, California, Colorado, and a growing list of states are adding disclosure and governance requirements for AI in clinical decisions. The NIST AI Risk Management Framework, which the federal government increasingly treats as the reference standard, expects ongoing oversight, not a single point-in-time review.
The common thread is that these rules assume health systems know what AI is running, who is accountable for each tool, and whether the governance is still accurate months after go-live. For many organizations, those assumptions do not yet match reality.
Here is where governance tends to break down, and what tends to help.
The Review was a Snapshot, The Tool is a Movie
Most AI governance work is front-loaded. Before go-live, the model gets tested. Bias is checked. Data flows are mapped. A risk assessment is written. On day one, the tool is genuinely well-governed.
Then it runs. Patient populations shift. Vendors push model updates on their own schedule. Clinicians use the tool in ways that were not part of the original design. Edge cases show up that never appeared in the test set.
The risk assessment still describes the system as it existed at launch. The system no longer exists that way. The gap widens quietly, and usually no one notices until something visible goes wrong.
The proposed HIPAA rule expects annual risk assessments that reflect the current state of the system, not the state at deployment. The NIST AI RMF’s MEASURE function expects continuous monitoring for the life of the deployment. Both point to the same practical need.
What helps: set a monitoring baseline at go-live and review it on a schedule. Performance. Drift. Override rates. Vendor update logs. Monthly for routine tools, more often for anything touching clinical decisions. This is not a new committee. It is a standing 30-minute review.
The Workflow on Paper is Not the Workflow in Practice
Governance documents describe how the AI is supposed to be used. A clinician is supposed to review each output. The AI is supposed to be one input among several. The recommendation is supposed to be advisory.
The real workflow often looks different. Busy clinicians rely on the tool more than the design assumed. A suggestion meant to be one data point becomes the anchor. Advisory outputs become the default because the reviewer does not have time to second-guess them.
None of this is negligence. It is what happens when thoughtful design meets an overloaded schedule. But if governance is only looking at the intended workflow, it misses what is actually happening.
This gap has compliance consequences now. The FDA’s updated CDS guidance looks at how the tool is used in practice, not just how it was designed. State laws like California’s AB 3030 require disclosure when AI meaningfully contributes to a clinical decision, which means the organization has to know when that threshold is crossed in the live workflow.
What helps: look at usage data. Which outputs are clinicians accepting without edits? Which are they overriding? Which are they clicking past? The answers tell you how the tool is really being used and where governance assumptions no longer match reality.
Escalation Paths Exist on Paper, Clinicians Cannot Find Them
A pattern we see often: a clinician notices something off about an AI tool. An output looks different. Confidence scores shifted. Results feel inconsistent with what the tool was producing last month. The clinician has a gut sense that something is wrong, and no idea who to tell.
Compare this to how other clinical technologies work. If a medication is wrong, there is a reporting process everyone knows. If an imaging machine misbehaves, biomedical engineering is a phone call away. When an AI tool drifts, the path is usually unclear. Is this an IT ticket? A vendor issue? A safety event? Who owns this?
Under the proposed HIPAA rule, incident response is a formal requirement, and it has to be operational, not just documented. ONC’s algorithm transparency rules expect certified health IT to support similar accountability.
What helps: every AI tool gets a named owner. Clinicians know who to contact. The process does not have to be elaborate. It has to be clear, and people have to know it exists. Treat AI systems the way you already treat other clinical technologies, and most of this gap closes.
Human-in-the-Loop Only Counts if the Human can Actually Review
This is the phrase we hear most often in healthcare AI, and it is also the one most likely to be a formality. Having a clinician click “approve” is not the same as having the clinician meaningfully review the output. If the workflow pushes them to approve twenty outputs in a minute, they are not reviewing. They are rubber-stamping.
Governance that assumes careful review, when the workflow makes careful review impossible, creates a gap between documentation and reality. The record will say a human reviewed each case. The reality will be different. Several state AI laws now explicitly require meaningful human oversight, not just nominal review, which means this gap is increasingly a legal exposure, not just a clinical one.
What helps: design the workflow so that real review is possible in the time clinicians actually have. If a real review is not possible, face that directly. Either invest in the time and structure to do it properly, or pull back on how much the AI is trusted to do on its own. Do not let the phrase carry weight it does not earn.
The Feedback Loop is Usually Missing
The better AI programs we have seen treat what happens after deployment as part of the system, not an afterthought. Clinicians can flag outputs. Overrides are logged. Patterns get aggregated and fed back to vendors or internal teams. Changes to the model or the workflow trigger a review instead of just happening quietly.
Most programs do not have this yet. It is the piece that turns governance from a document into a practice, and it is where the NIST AI RMF’s MANAGE function expects organizations to operate. Without it, the organization is flying on instruments that were calibrated at takeoff and never checked again.
Where this Leaves Us
AI in healthcare is past the stage where governance can stop at the point of deployment. The regulations are catching up fast, and they are catching up in the same direction: continuous oversight, named accountability, meaningful human review, and a feedback loop that captures what the system is actually doing in production.
The organizations that will hold up are the ones that treat post-deployment governance as part of the job. A monitoring baseline. A named owner for every tool. An escalation path that works in practice. A workflow that supports real clinician review. A feedback loop that learns from the live system.
None of this requires a new framework. It requires treating AI the way healthcare already treats everything else that affects patient care, which is as something that needs ongoing attention, not a one-time sign-off.
That is the work now.
About the Authors
Pooja Walia
Pooja Walia is a seasoned IT professional who works with healthcare organizations to design and operationalize secure, scalable, and compliant AI systems in regulated environments. Her work focuses on translating AI innovation into reliable, real-world systems.
Rajat Rawal
Rajat Rawal is a technology leader who supports healthcare organizations with implementing cloud and AI solutions, with a focus on operational scalability, system reliability, and navigating critical deployment challenges.
The views expressed in this article are the authors’ own and do not reflect the views of their employer.
Check out today’s featured companies who have recently completed an M&A deal, and be sure to check out the full list of past healthcare IT M&A.
PartsSource Acquires SkillNet to Add Workforce Intelligence to its Enterprise Clinical Technology Platform
New Combination Connects Service Operations, Workforce Capability, and AI-Driven Readiness Planning to Help Health Systems Improve Clinical Capacity
PartsSource, the leading performance platform for clinical technology, today announced the acquisition of SkillNet, a Workforce Intelligence platform for healthcare technology management (HTM) that gives hospitals and health systems real-time visibility into competency compliance and team capabilities, enabling leaders to close critical technician skill gaps and grow care capacity.
Following PartsSource’s introduction of new service optimization and asset performance capabilities at AAMI eXchange in late May, the acquisition of Skillnet expands the PartsSource Enterprise Clinical Technology platform into workforce intelligence. The combination enables healthcare organizations to better align equipment uptime, service delivery, technician capability, and operational readiness across the enterprise.
In concert with the acquisition, PartsSource announced its intention to offer an expanded portfolio of workforce solutions. The new PartsSource PRO Workforce solution will include a multi-vendor, multi-modality Technical Decision Support System that provides AI-empowered diagnostics and repair procedures to help technicians accelerate maintenance and repair of highly complex clinical assets and On-Demand Training from its former acquisitions of RSTI and NVRT Labs.
“Healthcare organizations are under pressure from every direction – rising demand, aging infrastructure, workforce shortages and growing operational complexity,” said Philip Settimi, MSE, MD, President and CEO at PartsSource. “Ensuring healthcare is always on requires more than maintaining equipment. It requires visibility into the people, skills, workflows, and operational systems behind clinical asset availability. The addition of SkillNet strengthens our ability to help health systems manage clinical asset performance holistically by focusing on their most important asset – their people.”
PartsSource is actively co-developing its workforce solution with alpha partners, five industry-leading healthcare systems operating a combined total of 43 hospitals.
“For enterprise HTM teams, workforce readiness is inseparable from clinical asset performance, regulatory compliance and quality outcomes,” said Keith Whitby, SCM Division Chair, Healthcare Technology Management at Mayo Clinic…
Azara Healthcare Closes the Medicaid Coverage Gap for Safety-Net Providers with Addition of Advocatia
Combined Platform Gives Safety-Net Providers and Health Plans a Single, Easy-to-Use, Data-Driven Pathway to Ensuring At-Risk Individuals Maintain Enrollment During Medicaid Redetermination
Azara Healthcare, the four-time Best in KLAS provider of population health and value-based care solutions for the safety net, today announced that it has acquired Advocatia, a digital-first public benefits enrollment platform. The combination delivers the first end-to-end Medicaid coverage retention solution — pairing the Azara DRVS platform’s ability to identify and engage at-risk individuals with Advocatia’s strengths in guiding them through application, documentation, and initial enrollment or renewal of Medicaid coverage and other public benefits programs.
Together, the combined solution enables clients to:
Identify and prioritize patients at risk of losing coverage using Azara DRVS registries and risk stratification
Engage patients at scale through automated, multi-channel outreach
Guide patients through the enrollment and renewal process in 75 languages via Advocatia’s self-service digital platform
Capture documentation and income verification to support redeterminations and new work requirement reporting
Provide health center staff and navigators with real-time visibility into patient progress and completion rates, alerting and allowing them to intervene when additional help is needed
The move comes as community health centers, hospitals, and other safety-net providers prepare for one of the most significant coverage shifts in a generation. Under the One Big Beautiful Bill Act (HR.1), more than 7.6 million Americans are projected to lose Medicaid coverage by 2034, and 80-hour monthly work requirements take effect in January 2027. The financial stakes for safety-net organizations are immediate. Hospitals are estimated to have delivered over $36 billion in uncompensated care to uninsured patients in each of the past 3 years, underscoring the growing pressure on providers caring for vulnerable populations. Compounding the challenge, the National Association of Community Health Centers (NACHC) projects HR.1 will reduce community health revenue by $7 billion annually due to increased uncompensated care — a level of strain that could force 1,800 site closures and 34,000 job losses nationwide.
“Our clients have been telling us the same thing for months: they need help ensuring that all patients still eligible for Medicaid coverage successfully re-enroll before deadlines and coverage loss,” said Jeff Brandes, President and CEO at Azara Healthcare…
Welcome to the weekly edition of Healthcare IT Today Bonus Features. This article will be a weekly roundup of interesting stories, product announcements, new hires, partnerships, research studies, awards, sales, and more. Because there’s so much happening out there in healthcare IT that we aren’t able to cover in our full articles, we still want to make sure you’re informed of all the latest news, announcements, and stories happening to help you better do your job.
News
ONC published a draft of the USCDI+ Quality V2 use case, which builds on the v1 use case released in January as part of an ongoing effort to advance standardized quality data. Public comments will be accepted through July 17.
In addition, ONC published a notice of funding opportunity for LEAP in Health IT, with Leading Edge Acceleration Projects for agentic AI in clinical care, API monitoring, and lab system interoperability. Applications are due July 16.
Arbital Health announced ATLAS, an actuarial modeling capability designed to help ACOs evaluate participation and financial performance under the CMS LEAD Model.
If you have news that you’d like us to consider for a future edition of Healthcare IT Today Bonus Features, please submit them on this page. Please include any relevant links and let us know if news is under embargo. Note that submissions received after the close of business on Thursday may not be included in Bonus Features until the following week.
Happy Father’s Day, everyone! I’ve been told I’m getting something from Dunkin’ Donuts, and I’m 98% sure what my present is, but I’m going to act completely surprised anyway.
Welcome to our Healthcare IT Today Weekly Roundup. Each week, we’ll be providing a look back at the articles we posted and why they’re important to the healthcare IT community. We hope this gives you a chance to catch up on anything you may have missed during the week.
A Breakthrough for Surgical Residents: AI That Watches You Operate. At the SAGES conference, Colin Hung took in a session by Dr. Chloe Nobuhara at Stanford that highlighted the use of computer vision models that detect errors in uploaded videos and assess the skill of the surgeon, cutting training time significantly. Read more…
Measuring Patient Engagement and Satisfaction Outcomes. How does the Healthcare IT Today community make this happen? Answers included treating clinician experience and operational metrics as leading indicators, as well as monitoring care plan adherence (which often reveals more than satisfaction scores). Read more…
How TPMG Cracked the Value-Based Care Code. Jeff Morrison at Virginia-based Tidewater Physicians Multispecialty Group sat down with Colin to discuss aligning EHR use with CMS incentives (through tracking key metrics and automating outreach) and alleviating the burden of quality reporting. Read more…
The Myth of the Single Healthcare Decision-Maker. At Reuters Digital Health 2026, Colin learned why vendors must secure both executive sponsors and clinical champions in the sales process – and what it takes to displace incumbents. Read more…
Quick Takes From HFMA 2026. At the conference, John Lynn heard from healthcare financial management leaders about leveraging price transparency data and aligning AI initiatives to meaningful outcomes (Part 1) and uncovering revenue leakage and pairing AI with clinical governance (Part 2).
Life Sciences Today Podcast: Military Intelligence Meets Pharma Strategy. Tony Page at Within3 talked to Danny Lieberman about helping life science companies move to proactive, intelligence-driven launch strategy. Read more…
CIO Podcast: Adopting AI and Smart Technology. Jill Evans at MetroHealth joined John to talk about evaluating AI functionality when looking at tools that nurses will use, including smart hospital rooms. Read more…
Payers Are Quietly Redrawing the Rules of Hospital Reimbursement. Severity downgrades, retrospective coding challenges, and clinical validation audits reduce payments without outright denials. Organizations need to focus on documentation and claims monitoring, said Missy Harbert at Revecore. Read more…
Curing Healthcare Revenue’s Complexity Problem. Complexity grows as revenue moves across multiple systems, teams, and time horizons, noted Steve Harding at Clari and Salesloft. Successful organizations are building a more structured approach to revenue orchestration. Read more…
This Week’s Health IT Jobs for June 17, 2026: Long Island’s St. John’s Episcopal Hospital at South Shore is looking for an Associate Chief Digital Information Officer. Read more…
Bonus Features for June 14, 2026: Number of patients using telehealth down 48% since 2020; 71% of patients want phone or in-person assistance when they need help. Read more…
We’re excited to be back for another episode of the Life Sciences Today Podcast by Healthcare IT Today. My guest today is Tony Page, Senior Vice President of Insight Analytics at Within3. Page spent years applying military intelligence doctrine — structured analysis, adversarial thinking, decision superiority — before bringing those frameworks into pharma. As SVP of Insight Analytics at Within3, he’s now helping life science companies move from reactive milestone-chasing to proactive, intelligence-driven launch strategy. In this episode, Page and I unpack why pharma launch teams are flying blind, what “insights management” actually means in practice, and why the companies winning in 2026 are the ones treating competitive intelligence as a strategic pillar — not a reporting function.
Check out the main topics of discussion for this episode of the Life Sciences Today podcast:
What was the moment you realized that intelligence doctrine could solve something broken in life sciences?
Within3 talks about the “invisible college” — the hidden network of experts that actually shapes clinical and commercial decisions. How do you map that, and what does a pharma team do differently once they can see it?
Insights management is often treated as a cost center — a reporting function that feeds decks nobody reads. How do you make the business case for it as a revenue driver, and who in the org actually has to own it?
In data quality, engagement, and transparency — what are the three non-negotiables?
What’s the anti-pattern you keep seeing, and what does the fix actually look like in practice?
Subscribe to Danny’s newsletter to get strategic patterns for life science leaders building a defensible business.
Be sure to subscribe to the Life Sciences Today Podcast on your favorite podcasting platform:
Along with the popular podcasting platforms above, you can Subscribe to Healthcare IT Today on YouTube. Plus, all of the audio and video versions will be made available to stream on Healthcare IT Today. As a former pharma-tech founder who bootstrapped to exit, I now help TechBio and digital health CEOs grow revenue—by solving the tech, team, and go-to-market problems that stall your progress. If you want a warrior by your side, connect with me on LinkedIn.
If you work in Life Sciences IT, we’d love to hear where you agree and/or disagree with our takes on health IT innovation in life sciences. Feel free to share your thoughts and perspectives in the comments of this post, in the YouTube comments, or privately on our Contact Us page. Let us know what you think of the podcast and if you have any ideas for future episodes.
The following is a guest article by Missy Harbert, Senior Solution Advisor at Revecore
For years, hospitals have built denial management programs around a predictable premise: when a payer disputes a claim, it sends a denial. There is a code, a process, a path to appeal. That premise is no longer reliable.
Payers have been engineering workarounds to formal denial obligations for some time: severity downgrades, retrospective coding challenges, clinical validation audits that sidestep regulatory scrutiny. Aetna’s Level of Severity (LLS) inpatient payment policy, effective January 1, 2026, is the most refined expression of that pattern yet.
What the LLS Policy Does
Aetna approves urgent and emergent inpatient admissions for Medicare Advantage (MA) and Special Needs Plan members as usual. But for stays of one to four midnights, it conducts a severity review using Milliman Care Guidelines, which CMS does not sanction for medical necessity determinations. If the stay doesn’t meet those proprietary thresholds, Aetna pays at a reduced rate comparable to observation, regardless of the contracted inpatient Diagnosis-Related Group (DRG) rate.
The critical design choice is what the policy avoids: a formal denial. A traditional observation downgrade generates a denial code, triggering federal appeal rights and CMS oversight. Here, the admission is approved. The payment is simply repriced unilaterally, outside the contract negotiation process.
As the American Hospital Association noted in its September 2025 letter urging Aetna to rescind the policy, hospitals are being paid at rates determined outside the good-faith contracting process. Disputes route to arbitration rather than standard appeals, a more burdensome and far less transparent process, and the resulting adjustments post as paid rather than denied. Without a denial code to trigger a work queue, most of these underpayments go undetected.
DRG downgrades deserve particular attention because of how they compound. Repeated DRG adjustments suppress a hospital’s case mix index, which informs prospective payment rates, meaning artificially lowered severity designations today reduce reimbursement for years to come. Data from Ballad Health puts the share of inpatient discharges affected by level-of-care changes, including DRG downgrades, at up to 10%. Among millions of accounts audited by insurers, analysis published in The Hospitalist found adjustments run almost exclusively in one direction: downward.
Where Hospitals are Most Exposed
The LLS policy creates the sharpest exposure in service lines where patients stabilize quickly and where clinical decision-making intensity is hard to quantify in the first days of a stay: congestive heart failure exacerbations, chronic obstructive pulmonary disease admissions, pneumonia, syncope, chest pain, and acute kidney injury without dialysis. These are admissions where initial presentation justifies inpatient care but where subsequent clinical stability can be used retroactively to argue lower severity, even when the escalation risk that was managed, or the failed outpatient treatment that preceded admission, tells a different story.
There is also a regulatory dimension to this. Because LLS adjustments don’t generate formal denials, they don’t generate formal appeals. Aetna’s denial and appeal metrics could appear to improve even as payment reductions continue, potentially creating an artificial boost to the Star Ratings measures CMS uses to evaluate plan quality. That is a structural integrity issue for the Medicare Advantage program with implications well beyond any single hospital’s balance sheet.
What’s driving these decisions across health systems is the widening gap between contracted payment and actual payment after payer review. The AHA has documented that Medicare pays hospitals 82 cents for every dollar spent on care. When MA plans layer post-approval repricing on top of that baseline, the arithmetic becomes untenable for a growing number of organizations.
The Contagion Risk
Whether Aetna’s LLS policy survives legal and regulatory scrutiny remains to be seen. For revenue cycle leaders, though, the litigation outcome may matter less than the signal the policy sends to every other MA plan watching. Aetna has demonstrated that a payer may be able to reduce payments significantly on a large subset of admissions while sidestepping the regulatory, transparency, and appeal obligations that attach to formal denials. If that model holds, others could replicate it — adjusting criteria to fit different clinical scenarios while keeping the same underlying structure intact.
What to Do Now
The operational response requires more than adding LLS to a denial work queue. Because these adjustments post as paid, standard denial-tracking workflows won’t surface them. Revenue cycle teams need a dedicated monitoring layer for short-stay MA claims, specifically watching for payment variances against expected contractual rates on one-to-four midnight admissions. Organizations should also pursue formal written confirmation of LLS rate determinations from Aetna; without knowing the rate that should have been applied, variance analysis is impossible.
On the clinical documentation side, CDI teams need to understand that establishing severity matters more than ever. Capturing organ dysfunction, failed outpatient treatment, escalation risk, and the clinical uncertainty that justifies inpatient-level care creates the evidentiary record that protects reimbursement when Aetna conducts its severity review after discharge. That documentation needs to happen early, if not at admission, not days into the stay when the note has shifted in tone from acute management to monitoring. Vague stability documentation without that severity context is an invitation for a downgrade.
It’s critical not to wait for a new payment model to launch. Hospitals that haven’t locked down contract language prohibiting post-approval repricing will find payers far less willing to negotiate after the fact.
The Bottom Line
With retrospective payer audits, a familiar sequence tends to play out: payment is restructured, hospitals detect it late, and revenue erodes before any systematic response is in place. Revecore’s analysis of the LLS policy and its implications points to one common trait in organizations that respond effectively: monitoring infrastructure that treats payment variances as signals, not noise, and clinical teams that understand the direct line between documentation and reimbursement. The organizations that recognize these signals early and build their response accordingly will be better positioned for whatever iteration of this playbook comes next.
About Missy Harbert
Missy Harbert is an experienced revenue cycle executive with a proven track record of leading end-to-end operations, driving revenue growth, and improving profitability across complex healthcare environments. As Senior Solution Advisor at Revecore, she leads strategic initiatives spanning business development, client management, and operational performance.
Missy brings deep expertise across home infusion, hospital-based infusion, inpatient, and outpatient services, with a strong focus on denial prevention and revenue recovery. She is known for leading performance turnarounds, developing new service lines, and building high-impact client partnerships.
She also has extensive experience in mergers and acquisitions across both public and privately held organizations, consistently aligning operational execution with growth strategy to deliver measurable financial outcomes.
About Missy Harbert