< + > Weekly Roundup – June 20, 2026

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…

How Payers Can Modernize Operations Without Ripping Everything Out. John Lynn chatted with Brian Yavorsky at Imagenet about taking a step-by-step approach to implementing AI and automation – starting with the “high frustration areas” that require a lot of human labor. 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…

Healthcare Facilities Can’t Tell if They’ve Been Hacked Until It’s Too Late. Once a breach is contained, organizations still struggle to determine how attackers got in and whether that door is actually closed. Audit trails are everything, provided they’re reviewed regularly, said Chris Skipworth at Passpack. 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…

AI-Driven Quality: The New Standard for Healthcare IT Service Desks. Dan O’Connor at HCTec said organizations gain visibility into how users engage with support by analyzing IT service desk interactions to improve efficiency and ultimately enable proactive support. Read more…

Healthcare’s Agentic AI Future Will Be Decided by Infrastructure, Not Models. The limiting factor in AI adoption will be whether healthcare systems can support action from accessing data to triggering real-time workflows, according to Sagnik Bhattacharya at Rhapsody. 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…

Funding and M&A Activity:

• Network performance management vendor Clarify Health acquired Loyal Health Holdings, a patient activation platform.
• ealth Catalyst divested Vitalware and the Vitalware business unit, its mid-revenue cycle business, to Med-Metrix.
• Elsevier acquired Wellsheet, which aggregates patient data from EHRs to surface clinical intelligence at the point of care.
• Surgical video analysis startup Uncovr announced $7 million in seed funding.

Thanks for reading and be sure to check out our latest Healthcare IT Today Weekly Roundups.

< + > Military Intelligence Meets Pharma Strategy – Life Sciences Today Podcast Episode 66

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:

• Apple Podcasts
• Spotify
• iHeartRadio
• Amazon Music
• Pandora
• YouTube

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.

Thanks so much for listening!

< + > Approved but Underpaid: How Payers are Quietly Redrawing the Rules of Hospital Reimbursement

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.

A Recurring Pattern with Rotating Targets

This pressure has been building for years. Medicare Advantage claim denials rose 55.7% between 2022 and 2023, according to the AHA. A peer-reviewed study in Health Affairs found initial denial rates of 17% across MA-submitted claims; that figure likely understates the real impact, because partial adjustments, including DRG downgrades, were not captured in the analysis.

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.

Health Systems are Voting with Their Contracts

Across the country, health systems have been severing ties with major MA plans, citing the LLS policy and persistent reimbursement shortfalls that have made certain contracts financially untenable. In South Carolina, one health system formally requested Aetna remove the policy, and when Aetna declined, allowed its contracts to expire rather than accept the revised terms — taking both its Medicare Advantage and commercial plans out of network simultaneously. The opposition has also reached federal court: at least one health system has filed a federal lawsuit arguing the policy violates CMS’s two-midnight rule and breaches its Aetna contract, seeking an injunction to block implementation. The court has not yet ruled. In public statements, Aetna has maintained that the policy complies with all applicable federal law and the terms of its provider contracts, and that its stated intent is to speed up inpatient approvals by removing the upfront medical necessity review.

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.

< + > Clarify Health Acquires Loyal Health to Create Healthcare’s First Closed-Loop Network Intelligence and Patient Activation Platform

Combination Unifies Referral Intelligence and Quality Analytics with Purpose-Built Care Activation, Closing the Gap Between Knowing Where Patients Need Care and Moving them Into It

Clarify Health, a tech-enabled outcomes company delivering referral intelligence, quality measurement, and network performance solutions for health systems, today announced it has completed the acquisition of Loyal Health Holdings, Inc., a healthcare-specific patient activation platform. The combination creates the industry’s first closed-loop network intelligence engine, a single platform that identifies where patients need to go for care, activates them into the right provider, and measures whether it worked.

Loyal’s Care Activation Platform manages more than 80,000 provider and location profiles. It powers millions of patient searches and real-time scheduling transactions annually across a nationwide base of health system customers. Paired with Clarify’s Meridian intelligence platform, the combined company provides health systems with end-to-end visibility and control over patient access, referral performance, and network growth. Existing customers of both companies will continue to receive the same products, services, and support they rely on today.

“Health systems know that better referral management drives better outcomes,” said Todd Gottula, Co-Founder and CEO at Clarify. “What they haven’t had is a single platform that connects the intelligence, where should this patient go, with the activation that gets them there. Clarify provides the intelligence. Loyal provides the activation. Together, we’re giving health systems the ability to run the full loop from referral to measured outcome.”

The Referral Gap

Health systems manage thousands of referral decisions daily. Industry research estimates that 30 to 50 percent of referrals never result in a completed visit, costing health systems 10 to 30 percent of potential revenue. With median hospital operating margins hovering near 1%, even modest referral leakage can be the difference between a sustainable operation and one that cannot invest in its mission.

But the referral gap is not only a financial problem. It is also a quality problem, an access problem, and a strategic problem, and many health systems are solving each in isolation. The root cause is structural: the intelligence about where patients need to go for care lives in one system, while the tools to move patients into that care live in another.

What the Combination Delivers

Referral intelligence to patient activation. Meridian identifies high-value referral corridors, quantifies leakage, and surfaces growth opportunities. Loyal activates patients into those pathways through targeted outreach, digital engagement, and real-time scheduling.

Closed-loop outcomes measurement. The combined platform measures the full chain from referral identification through patient conversion and downstream utilization in a single view.

Healthcare-Native, AI at every layer. Loyal’s platform was built from the ground up for health systems, with clinically validated taxonomies and native EHR integrations. Clarify’s machine learning models for quality, cost, and network performance combine with Loyal’s AI-powered scheduling, chat, and predictive propensity engines to deliver decision-grade intelligence and automated activation across the full referral lifecycle.

“Loyal has built a platform that close to 500 hospitals rely on to connect patients with the care they need,” said Nanette Oddo, former CEO at Loyal Health. “This combination ensures that work continues with the resources, data, and scale to make it even more impactful for our customers and the communities they serve.”

“Clarify is becoming the system in which health systems manage referral performance and network integrity,” said Will Reed, General Partner at Spark Capital and Clarify Board Member. “The industry is shifting toward AI-enabled outcomes delivery, and Clarify, especially now with Loyal’s activation capabilities, will be the platform to power it.”

Leadership and Organization

Todd Gottula, Co-Founder and CEO at Clarify, leads the combined company. Loyal operates as a wholly owned subsidiary of Clarify Health.

Terms of the transaction were not disclosed.

Orrick, Herrington & Sutcliffe LLP served as legal counsel to Clarify. Goodwin Procter LLP served as legal counsel to Loyal. OM Partners served as financial advisor to Loyal.

About Clarify Health

Clarify Health is a tech-enabled outcomes company that helps health systems see and act on what matters most: where patients need care, how networks are performing, and where value is being created or lost. With the acquisition of Loyal Health, Clarify now combines the Meridian intelligence platform with Loyal’s Care Activation Platform to deliver the industry’s first closed-loop system from insight to activation to outcomes. Headquartered in San Francisco. For more information, visit clarifyhealth.com.

About Loyal Health

Loyal Health is a healthcare-specific patient activation platform that empowers health systems to acquire, engage, and retain patients through provider data management, intelligent care search, real-time scheduling, reputation management, AI-powered chat, and CRM. Founded in 2015, Loyal was among the first companies to bring AI to digital patient engagement. Loyal is now a wholly owned subsidiary of Clarify Health. For more information, visit loyalhealth.com.

To learn more about the combined platform, visit clarifyhealth.com or contact your account team directly.

Originally announced June 4th, 2026

< + > The AI That Watches You Operate: Inside Stanford’s Breakthrough for Surgical Residents

I am always looking for unique, high-impact uses of AI in healthcare. While attending the annual Society of American Gastrointestinal and Endoscopic Surgeons conference (SAGES), I sat through a presentation that completely delivered by modernizing the surprisingly manual world of surgical training.

Dr. Chloe Nobuhara, a Stanford general surgery resident, showcased a tool originally conceived by her Principal Investigators, Dr. Yeung-Levy and Dr. Jeff K. Jopling. What they built is an AI application that watches laparoscopic surgical video, analyzes the surgeon’s performance, and gives them the feedback they need to improve.

Here is the inside scoop on how this innovation works.

Key Takeaways from the Discussion with Dr. Nobuhara at SAGES 2026

• The Time Trap. Teaching hospitals are drowning in unwatched surgical video sitting on thumb drives. By running this footage through a specialized AI (dubbed Surgical Learning Model or SLM) to automatically segment the operation and analyze technique, residents receive instant, targeted feedback without draining an attending physician’s schedule.
• Generic AI Fails in the OR. Off-the-shelf LLMs choke on the length and context of multi-hour surgical footage. Training a dedicated model on specific anatomical and directional language creates a highly accurate tool that understands the nuances of the operating room.
• The Limits of Manual Assessment. Relying solely on humans to assess surgical skill is inherently subjective and difficult to scale. Establishing an AI-driven, objective baseline for surgical performance today paves the way for fairer board certifications and prepares the industry for the impending era of autonomous robotics.

Solving the “Thumb Drive” Dilemma

What I didn’t realize and what Dr. Nobuhara highlighted, was how manual and time-consuming surgical training really is. Most laparoscopic surgeries by surgeons in training are recorded, but there is not enough the time to review them.

“We as residents and attendings have hundreds of hours of video that we’ve collected,” explained Dr. Nobuhara. “We don’t go through it nearly as often as we should because it’s a time-consuming process. In the ideal world, it’s an attending and a resident sitting down reviewing the case that they just did together.”

To fix this, Dr. Yeung-Levy, Dr Jopling, Dr. Nobuhara and their team built computer vision models into a web app. Residents upload their videos, and the AI goes to work detecting errors and assessing the skill of the surgeon.

“The idea here is that residents can self-study on their own,” continued Dr. Nobuhara. “They can watch the videos, get an AI-generated summary, and then come to the attending the next day or when they have time to review together.

Even better, the AI automatically “chunks” the operation into distinct segments. For example, with a typical laparoscopic cholecystectomy operation, their app divides it into five steps. This allows educators to instantly pull specific clips of basic “clipping and cutting” for interns, or complex dissections for senior residents. It transforms a lengthy three-hour video file into a precise moments that can help viewers focus on specific points in time – a fantastic teaching tool.

Building a Surgical Learning Model (SLM)

So, why not just upload these videos to Gemini or Claude? Because off-the-shelf LLMs today choke on the length and context of a multi-hour operation.

“We built out a specialized surgical video language model that can use more specific surgeon language, including anatomy,” explained Dr. Nobuhara.

I called this a “Surgical Learning Model” (SLM) and it is already being piloted by over 60 users across four hospitals. The feedback has been overwhelmingly positive – users trust the AI’s segmentation and love the efficiency it offers.

The Stanford team is already looking beyond the resident’s video review. They are laying the groundwork for broader applications, like objective board certifications and the coming age of autonomous robotics.

“How do we grade the robots that are doing surgeries on their own?” is a question that Dr. Nobuhara and the team at Stanford are now asking.

The Bottom Line

AI might not be replacing surgeons anytime soon, but tools like this can help them improve their skills. In the end, better-trained surgeons mean safer patients which is a future worth getting behind.

Learn more about Standford at https://med.stanford.edu/

Listen and subscribe to the Healthcare IT Today Interviews Podcast to hear all the latest insights from experts in healthcare IT.

And for an exclusive look at our top stories, subscribe to our newsletter and YouTube.

Tell us what you think. Contact us here or on Twitter at @hcitoday. And if you’re interested in advertising with us, check out our various advertising packages and request our Media Kit.

< + > Why Healthcare Facilities Can’t Tell if They’ve Been Hacked Until It’s Too Late

The following is a guest article by Chris Skipworth, CEO at Passpack

When a hospital gets locked out of its own systems mid-shift, staff reach for paper. Charts get hand-written. Medication orders get verified in hallways. Procedures get delayed. The public got a vivid look at this reality through HBO’s The Pitt, which dramatized the chaos of a cyberattack on a busy emergency department. Anyone working in healthcare would have recognized it immediately. The attack sequence, the scramble, the recovery.

What’s harder to recognize, even for those closest to it, is how much of the exposure starts not in the network but at a login screen, and how little visibility most facilities have into what happened there once the incident is over. The breach gets contained. Systems come back online. But the question that determines whether it happens again often goes unanswered: how did they get in, and is that door actually closed? 

How Exposure Accumulates

Compromised credentials account for roughly a third of all cyberattacks on healthcare organizations. That figure isn’t surprising to anyone who understands how access actually works inside a hospital.

Consider a travel nurse who joins a cardiology ward on a 13-week contract and gets provisioned with a login on day one. When the contract ends, that account may sit open for weeks while IT works through a backlog. Multiply that across the volume of contract and temporary staff moving through a mid-size hospital in a single year, and the number of dormant but active credentials in the directory becomes effectively untrackable.

Shared logins at ward terminals make it worse. Despite HIPAA requiring unique credentials for every staff member, 73% of healthcare professionals in one study reported using a colleague’s login to access medical data. Speed is the justification, and in a clinical environment, it’s hard to argue with. The workaround becomes the norm, and the audit trail disappears with it.

Third-party vendors extend the exposure further. Biomedical technicians, EHR contractors, and billing service providers often hold standing credentials with no expiration and minimal oversight, moving between client environments carrying access that was rarely designed to follow them there. The AHA has noted that the majority of patient records stolen in recent years came from third parties rather than hospitals directly, a predictable outcome of how vendor access typically gets managed.

The Audit Trail is Everything

The most damaging consequence of poor credential hygiene tends to show up after the breach, when the investigation begins, and the data needed to answer basic questions simply isn’t there.

When three clinicians share a ward login, and that login appears in an access log at 2 a.m., no one can determine with certainty who was behind the keyboard, or whether any of them were. The log exists, but it doesn’t tell you anything useful. HIPAA’s Security Rule is explicit on this point: shared credentials make it impossible to determine when specific individuals accessed protected health information. The audit infrastructure the organization has been maintaining becomes worthless at exactly the moment it’s needed.

This is why incident response often stalls out. The forensic trail doesn’t exist. The facility is left unable to confirm the breach came through a particular account, and equally unable to rule it out. Containment becomes guesswork, and the exposure that created the breach remains unaddressed because no one can point to it with confidence.

The Change Healthcare attack, widely described as the most significant cyberattack in U.S. healthcare history, demonstrated what that looks like at scale. One compromised third-party clearinghouse disrupted operations at hospitals across the country. The infrastructure connecting them was built for care coordination. It had no mechanism for containing what traveled across it once a credential was compromised. 

Building the Audit Trail

Organizations that can move quickly after a breach tend to build accountability into access management from the get-go. They’ve seen what happens when a response is delayed, and they’ve made a deliberate choice not to be in that position again.

Strict credential management is the starting point. Every staff member needs an individual credential, including agency nurses, rotating residents, and short-term contractors. Shared logins are treated as a compliance violation, and staff are regularly reminded why this is necessary.

Offboarding is automated and tied to contract end dates, not left to an IT queue that clears on its own schedule. When a travel nurse’s 13-week contract ends, her access ends the same day.

Vendor access is managed the same way. Every third-party credential carries a defined expiration date and is renewed only through explicit review. Quarterly access audits, assigned to a specific owner, ensure that standing permissions don’t persist simply because no one flagged them for removal. A centralized credential vault keeps the full access history on every vendor account, queryable in minutes.

Access logs are reviewed on a regular schedule, not pulled reactively after something has gone wrong. A monthly review cadence surfaces anomalies while they can still be investigated. When an incident does occur, the organization can pull a clean timeline, identify which accounts were active, confirm who held them, and demonstrate that access was revoked when it should have been. 

Everyone Holds a Key

The scene in The Pitt captured the chaos a breach produces at the point of care. What it didn’t show is what happens in the hours and days after, when investigators try to trace the incident back to its source. That trail, when it can be followed at all, often leads back to a mismanaged credential. An account that was left open. A login shared for convenience. A vendor with access that no one reviewed.

Cybersecurity in healthcare is not solely an IT problem. Every staff member who logs into a clinical system is part of that trail. And each has a responsibility to keep their piece of it clean.

About Chris Skipworth

Chris Skipworth is the CEO at Passpack, a password management platform that helps healthcare organizations manage and protect access credentials across distributed teams, contractors, and third-party vendors.

< + > Uncovr Raises $7 Million in Seed Funding from Index Ventures to Build the System of Intelligence for Surgery

Early Analysis of Surgical Robotic Procedures Found Missed Billable Steps in 16% of Cases and a ~10% Reimbursement Gap, Highlighting Broader Breakdowns in How Surgery Is Understood and Documented

Uncovr, a surgical AI company transforming how surgery is analyzed, documented, coded, and learned, has today announced $7 million in seed funding led by Index Ventures, with participation from Seedcamp, Frst, No Label Ventures, and Entrepreneurs First. The round also includes Jean Nehme (Founder of Digital Surgery, acquired by Medtronic), Othman Laraki (CEO at Color Health), and Charlie Songhurst (Meta Board Member) alongside a group of exceptional surgeons and operators. Uncovr is already working with leading hospitals in the US and Europe and has built a pipeline representing more than 400 operating rooms. In a few months, their system has analyzed thousands of hours of surgical video, building the first AI systems capable of turning complex surgical procedures into structured clinical and operational data at scale.

Uncovr automatically generates procedural coding and operative reports directly from surgical video and intraoperative workflow data, enabling hospitals to automate coding from the ground truth of a procedure rather than from documentation written after the fact. The platform improves reimbursement accuracy, strengthens clinical records, and increases visibility into surgical workflows across the operating room. The platform is being deployed across operating rooms in the U.S. and Europe.

More than 400 million surgeries are performed each year globally, and the majority are now captured on video through minimally invasive and robotic techniques. Yet after surgery, the operative report, the official record of a procedure, is still reconstructed manually from memory, by an exhausted surgeon juggling cases – often hours after the event has taken place. That operative report becomes the legal and clinical record of the procedure, the basis for billing, compliance, and the reference for future patient care. As a result, critical details are frequently lost, and much of the data generated in the operating room remains unused.

Alongside the funding, Uncovr is releasing findings from an initial real-world analysis of its deployed cases: missed billable steps in 16% of procedures and a ~10% reimbursement gap – driven entirely by documentation gaps that human review had not caught. The revenue was there. It simply was not written down. The finding echoes a growing body of evidence showing a lack of details in the operative report. A multi-institutional study of more than 1,000 surgical cases across 500 health systems found that most operative reports fail to report at least 70% of recommended clinical information – directly linked to higher rates of infection, readmission, and reoperation. These are not two problems. They are the same problem: the operative report is the single artifact that connects what a surgeon did to what a patient receives next, and to what a hospital gets paid. When it is incomplete, everything downstream suffers.

Uncovr addresses this gap by analyzing the only true source of procedural information: the surgical or endoscopic video captured in real time. Rather than relying solely on an operative report written after the fact, its proprietary models identify the steps performed during a procedure and automatically generate procedural coding and clinical documentation directly from the ground truth.

By grounding coding and documentation in what actually happened during a procedure, Uncovr helps hospitals capture revenue more accurately, improve compliance, strengthen clinical records, and create a structured procedural dataset for future surgical AI systems.

“At Uncovr, we are taking what actually happens in the operating room and turning it into something that can be reliably captured and used,” said Ines Iraki, Co-Founder and CEO. “Surgeons should not have to spend their time reconstructing from memory what a camera has already captured and becoming medical coders. The bigger opportunity is what comes after. Every robotic and minimally invasive procedure already generates a rich record of expert decision-making, technique, and judgment. We believe this will become one of the foundational datasets of modern medicine – the basis for how surgical knowledge gets transmitted and applied at scale. Surgery has always been learned by watching. For the first time, AI makes it possible to capture, structure, and transmit that knowledge at scale.”

Beyond operative reports, Uncovr uses surgical video as the source of truth to automatically generate procedural coding, capture missed reimbursement opportunities, and create structured clinical records that can be used across documentation, compliance, quality, and research.

“When we looked at our own cases, we saw clear gaps between what actually happened in the operating room and what was captured in the record and by the codes,” said Dr. Prakash Gatta, Medical Director of Complex Foregut Surgery at Texas Health Resources and VP of Clinical and Medical Affairs at Uncovr. “That has real implications, not just for reimbursement but also for compliance, coding, clinical safety, and continuity of care. This isn’t a marginal issue; it’s a structural gap in how surgery is documented today.”

“Ines, Eric, and Johann have done something rare: earned adoption inside one of healthcare’s hardest environments and moved incredibly fast once inside. By structuring what happens in the OR, Uncovr is building a highly valuable dataset for surgical AI,” said Martin Mignot, Partner at Index Ventures.

Founded in 2025 by Ines Iraki (CEO), Johann Diep (CTO), and Prof. Eric Vibert (Medical Co-Founder), Uncovr was shaped by firsthand experience across surgery, autonomous systems, and frontier AI. While working on healthcare, Iraki spent time inside operating rooms and became obsessed with the gap between what surgical systems capture and what hospitals are actually able to use. Vibert, Chief of Surgery at AP-HP, spent years confronting the clinical consequences of incomplete operative reporting, while Diep previously developed AI systems for autonomous environments in defense and at the European Space Agency.

The team includes engineers, surgeons, and medical coders from institutions including ETH Zurich, École Polytechnique, AP-HP, Mayo Clinic, HEC Paris, and Texas Health Resources/Texas Christian University. The company has expanded across Paris and New York and is accelerating deployment with leading health systems in the U.S. and Europe. The operative report is only the first application. Every year, hundreds of millions of procedures generate one of healthcare’s richest yet least utilized datasets. By transforming surgical video into structured intelligence, Uncovr is building the infrastructure layer for surgical AI, paving the way for real-time assistance, smarter operating rooms, and safer procedures at scale.

For more information, visit uncovr.ai.

About Uncovr

Uncovr is building the system of record for surgery and endoscopy, automatically generating operative reports and procedural coding from surgical video and intraoperative workflow data. By transforming what happens inside the operating room into structured clinical intelligence, Uncovr is creating the infrastructure layer for computational surgery. The company is headquartered in New York and Paris and backed by Index Ventures, with participation from Seedcamp, Frst, No Label Ventures, Baobab Ventures, and leading angel investors.

Originally announced June 10th, 2026