Saturday, March 28, 2026

< + > Weekly Roundup – March 28, 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.

What GLP-1 Drug Coverage Reveals About Our Healthcare System. Insurers are increasingly limiting GLP-1s to their original purpose of treating diabetes and no longer covering the drugs for weight loss. According to Andy Oram, this reversal represents healthcare in the United States in a nutshell: Thinking the problem is solely a matter of high costs and not the result of several complex and intersecting issues. Read more…

The Right Information at the Right Time for Post-Acute Care. John Lynn sat down with PointClickCare CMO Dr. Hamad Husainy to talk about why discharge summaries need to be summarized for post-acute care – and why actionable information benefits patients and providers. Read more…

How AI Can Shift Healthcare to an Abundance Mentality. Heidi CMO Simon Kos joined John to explain how automating clinical workflows can double healthcare’s capacity and provide more equity to vulnerable populations whose access is limited by cost, distance, or language barriers. Read more…

Maintaining Data Across Mergers and Acquisitions. John caught up with Sharon Cook at Harmony Healthcare IT to learn why archiving and other data management needs must be considered before a merger if organizations want savings to extend beyond reduced spending on system maintenance. Read more…

Acute Care and Post-Acute Care Collaboration Benefits Value-Based Care. Our first of several dispatches from key HIMSS 26 sessions explored how University of Maryland Medical System and PointClickCare partnered to better coordinate care for patients discharged to a SNF by focusing on high-risk care pathways. Read more…

Stop Wishing Fax Away; Embed Agentic AI and Fix the Outcome. A staggering 70% of healthcare communication still relies on fax. Documo CEO Denis Whelan explained how automated document processing reads text, classifies documents, extracts data, and matches it to patient records. Read more…

Keys to Success With Virtual Nursing. Baptist Health and Caregility offered strategies for scaling virtual nursing, including building trust and consistency, ditching the computer on wheels, and finding opportunities to expand to virtual care. Read more…

A Future Ready Platform for Innovation. Finally, representatives from MEDITECH and HCA Healthcare UK discussed how to create a sustainable path for innovation using systems that are future-proof, intelligent, and extensible. Read more…

Life Sciences Today Podcast: The Benefits of the Virtual Lab. Josh Haimson at Inductive Bio talked to Danny Lieberman about running in silico experiments in a virtual lab to surface the strongest hypotheses to test in the wet lab. Read more…

CIO Podcast: Navigating the Changing World of Healthcare. John sat down with Rusty Yeager at Encompass Health to talk about what it takes to lead a healthcare IT team today. Read more…

From Programmer to Director: My 25-Year Journey Into the Heart of Data. The recently retired Doug Buell recapped his career at the Dana-Farber Cancer Institute, where he learned lessons about data stewardship, metadata, redundancy, trust, and the lasting power of tape. Read more…

The Costs of Missing What We Could Never See. An EHR shows the “whole picture” of a patient, but AI can surface the “right picture,” said Regard CMO Dr. David Kirk. That eliminates the blind spots that go unnoticed amid a flood of documents and data points. Read more…

Scalable IT Security Solutions Should Be Healthcare’s Top Priority. Nearly 82% of nurses have experienced workplace violence, noted 911Cellular President & CEO Chad Salahshour. Low-lift, high-impact technology such as mobile apps and “panic buttons” let staff send alerts quickly and discreetly. Read more…

It’s Time for “Actioning Information” to Move Beyond Endless Data Streams. Simply having data isn’t the same as acting on what the data tells you, according to Watershed Health CEO Effie Carlson. The answer is a mix of incentives tied to actions that show improvement, support for post-acute care, and workflow integration. Read more…

Discipline Over Speed: Personal Reflections After HIMSS26. Consultant Adam Cherrington noticed a shift in the conversations in Las Vegas this year – from chasing technology to asking better questions about how AI impacts clinicians, staff, and patients. Read more…

This Week’s Health IT Jobs for March 25, 2026: The Bay Area’s Alameda Health System is looking for a CMIO. Read more…

Bonus Features for March 22, 2026: 54% of providers have used non-clinical resources to learn about preventive care; 47% of orgs report low perceptions of safety culture. Read more…

Funding and M&A Activity:

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



Friday, March 27, 2026

< + > What GLP-1 Drug Coverage Reveals About Our Health Care System

Like the recent Alzheimer drugs that threaten to bust the Medicare budget, GLP-1 medications are a ray of hope for millions of people but a nightmare for payers. More and more insurers are limiting the use of GLP-1 drugs to their original purpose: diabetes treatment. This article explores the GLP-1 dilemma to ask what the U.S. health care industry can do to control costs.

Resisting the future

Let’s look at the amazing recent claims made for GLP-1 drugs. Their treatment for diabetes, which all insurers cover, is well established by studies. The medications are also used for weight loss, with FDA approval or off-label, and many people have lost far more weight on the drugs than they could through other means. But in addition, there is strong evidence that these drugs “improve outcomes in people with cardiovascular, kidney, liver, arthritis, and sleep apnea disorders.” A recent survey further suggest that people taking the GLP-1 drugs are less likely to become addicted, or if already addicted, less likely to be hospitalized or suffer overdoses. This makes intuitive sense: if a drug can diminish cravings for food, why wouldn’t it diminish cravings for other ingested substances?

Being under patents, GLP-drugs can easily cost more than $1,000 per month. They are so successful that a significant number of patients pay this out of pocket. The cost of the medications drove a recent deal between pharma companies and the U.S. federal government. In many countries, including China and India, some GLP-1 medications are going generic—but in the U.S., patents will be extended for an unknown length of time.

There are other downsides to GLP-1 drugs too. They have side effects that can get painful enough to cause some people to quit the drugs. People who cease using them tend to gain back all the weight (which is why many doctors try to pair the drugs with behavioral therapy).

Therefore, a lot of insurers, some of whom tentatively funded the use of GLP-1 drugs for weight loss, have decided to restrict their coverage to the original diabetes treatment. The Centers for Medicare & Medicaid Services (CMS), which historically has not covered weight loss drugs, has re-affirmed that it won’t pay for GLP-1 drugs for anything except diabetes. Kaiser Permanente says that it doesn’t cover the GLP-1 drugs for weight loss, although there are ways to challenge the policy.

In my own state of Massachusetts, Blue Cross Blue Shield declined to cover the drugs for weight loss, and the organization providing health insurance for state workers, the Group Insurance Commission, followed suit. (Federal cuts to Medicaid certainly contributed to the funding crunch, but the drugs would have been a challenging budget item in any case.)

A Moral and Financial Dilemma

The health care system eats up more of the economy in the U.S. than any other country. As a percentage of GDP, health care was 18% in 2024, and has come close to 20%. It’s easy to say that payers should cover anything that can be potentially helpful, but that just isn’t feasible. The U.S. economy is like a 100-pound runner trying to wend her way through a race carrying a 25-pound kettlebell.

A lot of the problems in health care are high costs concealed as other phenomena: for instance, the frequent closures of hospitals and long-term care facilities. A common sequence of an events is for a private equity firm to buy the facility and then make cuts and degrade care until the facility is forced to close. Although it’s convenient to blame the private equity firms (who have never done much to win public sympathy), the underlying problem is that the health care facilities were underfunded to start with, a problem particularly endemic to organizations that depend on Medicaid and Medicare. In other words, we’d be paying even more for health insurance if facilities were funded to the level that would cover their costs.

Many authors have projected the results of this crisis on the economy and the public, although different commenters assign the blame to different familiar actors. The multiplicity of suspected culprits indicates that high costs are a systemic problem in which all actors carry some blame, but no single actor can fix it.

Although most economically advanced countries organize their health care better, aging and shortages of staff have created problems for many.

Once the need for triage is posed, sacrificing coverage of GLP-1 medications for weight loss make sense because:

  • Alternative treatments for obesity are available, notably behavioral therapy. Long-term successes are disappointingly rare, but it can be done.
  • Obesity has many known impacts detrimental to health, but in itself it isn’t life-threatening. To make the situation more complicated, there’s a sociopolitical movement to accept high body weight without stigmatization or “medicalization.”
  • Outside of diabetes and weight loss, the benefits of GLP-1 drugs for other conditions will depend on further study.

We must match empathy, which causes us to urge coverage for treatments that can help people, with a quest to reduce costs. The goal is to enable the health care people need by delivering it more efficiently. Doing so is by no means simple, though—if it had been, the U.S. would have done it long ago. Fixing some of the root causes of illness (such as pollution, the economics of food production, and stressful working conditions) would be even harder. Let’s look at some common recommendations for improving health care.

Streamline billing and insurance

The irrationalities and inefficiencies of the U.S. insurance system are familiar to doctors and patients alike. The impact has been quantified, too; one set of researchers said, “A simplified financing system in the U.S. could result in cost savings exceeding $350 billion annually, nearly 15% of health care spending.”

Imagine that you went to the gym every day and were faced with a totally different set of equipment with a bewildering set of controls. That’s what it’s like for the coders and billers in U.S. health care. Introducing another middleman (pharmacy benefit managers) must inevitably increase costs, as much as they swear that they’re saving payers money.

By the way, we really shouldn’t call the payers “insurance” anymore. Insurance is for events like fires that one hopes will never happen, and that don’t actually happen often. In contrast, we know we all need health care, and we’re just spreading the costs over many years. The problem is we can’t afford it, even distributed over a large population and a long time.

CMS has tried to simplify prior authorization and make it more like a vending machine instead of a roulette wheel. Other aspects of billing could also be rationalized. The logical extreme of this movement is universal, single-payer coverage. However, by ending competition, monopolies tend to increase bloat and ultimately costs.

Still, there is no revenue cycle utopia. Somebody, whether in a private company or the government, will have to review claims to make sure the doctor is requesting appropriate tests and treatments. There’s always a good reason for an MRI; you might turn up something!

Standards of care are wonderful, but patients are unique and there must be channels for requesting special care. So billing is going to require doctors’ time and attention, plus that of other experts throughout the supply chain. Standards can also provide the wrong incentives: when diagnostic manuals list specific symptoms that must appear in order to assign a diagnosis, scads of patients miraculously turn up matching precisely those symptoms.

I think that universal coverage is a fine goal, but if we just add patients to the system without actually making them healthier, we’ll exacerbate the coverage problems we’re now seeing. Lower-income people deserve coverage, but poverty (as everyone who has been poor knows) leads to a higher risk of poor health. Bringing people out of poverty is a prerequisite to curing their conditions, and that’s a daunting task.

Reduce chronic conditions

The main causes of death have shifted historically from acute conditions (tuberculosis, postpartum sepsis) to chronic conditions (diabetes, congestive heart failure). By now, “Ninety percent of the nation’s $4.9 trillion in annual health care expenditures are for people with chronic and mental health conditions.”

So let’s eliminate Type II diabetes, CHF, and all the rest! All we need is to change our diets, overcome addiction to smoking and other drugs, get a personal trainer, put away our screens, and sleep through the night!

You go first.

It’s clear why this is difficult; probably more difficult than taking on the forces that prevent reform of the insurance system. Health care reformers have put forward bold structural changes to improve our behavior. Let’s see where each is headed and the barriers it faces.

Preventative care

Early detection can head off many serious diseases by advising patients to alter their behavior, or provide medications for symptoms such as high blood pressure. For this reason, insurers offer many common generic medications free of charge, and the Affordable Care Act funds regular visits to a PCP without a copay. The challenges that remain include:

  • Getting busy people to the doctor. Telemedicine often fills this gap. But lots of people still miss appointments because they have work or children to care for, feel too bad to come or too good to think they need to come, get distracted for other reasons, or fear the costs of treatment. (Haven’t you ever been charged for a visit that was supposed to be “preventative”?)
  • Getting patients to follow through with treatments. It’s commonly found that only “50% of patients prescribed chronic medications stick to their treatment plans.” The same site says, “medication nonadherence is linked to up to 25% of all hospitalizations.”
  • Allowing time to adequately explore patient needs. PCPs and pediatricians are historically underpaid and underappreciated. Because not enough doctors choose those disciplines (or all disciplines, for that matter), the ones who soldier on are increasingly burdened with too many patients and too much bureaucracy. We’ve seen a proliferation of new clinical disciplines that support the physician while requiring less training, but we haven’t kept up with demand. In short, the strategy of preventative care is thrusting the responsibility for chronic conditions and their costs onto one of the most vulnerable and overstressed parts of the health care industry.

Digital monitoring and behavioral support

Engineering minds look for structural solutions to complex problems, and technology usually plays a role. To humanists who believe that the personal connection between clinician and patient is the cornerstone of medical care, the technology advocates promise that they’re just automating the routine and burdensome parts of the day to free up the clinician for what they want to do. The advocates also recognize patient fears that they’ll be pigeonholed into digital slots, and try to allay those fears by promising more patient access and control. One promising area for digitization and AI is detecting and deterring fraud.

The digital intervention into health care, sometimes called connected health, is a complicated machine with many mong parts. Through devices attached to their bodies or scattered through their homes, patients report data that is fed into doctor’s electronic health records, then run through analytics to recommended behaviors or treatment changes that are then routed back to the patient.

Therefore, one of the biggest challenges of the technological solution is the technology itself. First, clinicians must get patients to accept all those devices, and payers to cover their use. In 2015 it was reported that, “About 42 percent of people quit using their fitness trackers within six months.” Compliance might have improved since then with the introduction of Apple Watch.

After that, it’s difficult to get data into and out of legacy EHRs, and institutions often refuse to share data that can contribute to analytics. (The institutions usually cite privacy concerns for withholding data, but more often it’s due to proprietary hoarding.) Finally, it takes enormous expertise to calculate the analytics that turn that data into actionable recommendations, especially taking into account the patient’s life and the institution’s social setting. AI has the potential to make all this work, but meanwhile it introduces more complexity, risk, and uncertainty—and after all that, the doctor has to accept liability for the outcomes.

The goal of the digital interventions is the “hospital without walls,” where a patient gets support and recommendations every few minutes or hours instead of once a year. Psychological research has long shown that small, fast feedback improves a person’s performance more than general, long-term recommendations. That insight feeds the popularity of video games—and many say, social media—and indeed many technologies love the idea of “gamifying” health care. I attended a conference called Games for Health for several years.

One can find studies for digital monitoring medicine that reliably demonstrate their effectiveness, but the studies usually cover only a few months of the patient’s life. We’ll need more time to find out what works long-term.

Ultimately, improving chronic health means wrenching people away from the pleasures or comforts that led them to eating or smoking too much or hanging around on the couch in the first place.I’m reminded of Bertolt Brecht’s famous satirical line, “Wouldn’t it then be easier for the government to dissolve the people and elect another?”

Furthermore, not everybody has a choice. A lot of illness is exacerbated by how people are treated by their employers, their landlords, and the institutions in their area. In recent years the health care field has started talking about social determinants of health, but doctors have little say over whether local markets offer fresh foods (a few experiements are underway), much less over whether a power plant opposed by the community gets built.

Cap treatment costs

It certainly seems that hospital prices are too high. The news is full of stories of people who found themselves using an out-of-network ambulance or specialist and going bankrupt over it. But flagging hospital costs obscures a tremendous income gap: hundreds of hospitals in the country (particularly in rural areas where health care coverage is already thin) are at risk of closing, while a few famous hospitals rake in money and expand (although even these are often in the red).

Pharmaceutical companies are another favorite target, particularly since they manipulate the patent system to preserve high prices.

An industry as big and important as health care needs regulation, but it’s highly regulated already, and regulations rarely take into account the costs of compliance. Additionally, drug companies and biotech firms warn that intervening in a complicated and uncertain market introduces the risk of stifling innovation, and their warning should be taken seriously. Uncertainties about major upheavals in regulation can dissuade investments in drug development, whose average time has been estimated at seven or even as high as fifteen years.

Price negotiation, where two parties are engaged in a business push-and-pull and are considering the pros and cons of a deal, is different from capping prices by fiat. Every health care institution negotiates prices except, it seems, the federal government, and it’s a positive development for the federal government to start doing the same.

Reduce end-of-life expenditures

A common claim in health care is that costs are high because we waste money on futile treatments for people on their way out. One article cuts this expectation down to size, saying, “approximately 13% of the $1.6 trillion spent on personal health care costs in the United States was devoted to care of individuals in their last year of life.” That percentage isn’t negligible, but the cited article also goes on to analyze possible remedies and concludes that we couldn’t save much money. The article recommends focusing on chronic conditions, not end-of-life care in particular.

Promote consumer control over health care dollars

Many proposals for health insurance reform pair cutbacks in government subsidies with a policy of directing more money directly to the recipients of healthcare. There might be some logic to this plan if one postulates that prices are high because neither insurers nor health care providers have an incentive to rein them in. But you and I, even when spending our own money, can’t force providers to lower prices or even find out what providers are charging. (There is practically no transparency or choice for patients in health care pricing.) Many parts of the country are lucky to have access to even one health care provider for a given procedure.

Buy out the pharmaceutical manufacturers

Sometimes a health crisis affects society so much that governments drive innovation; Operation Warp Speed during the COVID-19 vaccine is the classic example. (It has nearly been forgotten in the wake of more recent efforts by its very proponents to weaken epidemic response and vaccine development.) Compulsory licensing, usually employed for HIV and AIDS medication, has produced a lot of successes.

Governments are unlikely to take responsibility for drug development in general. We already have a drug development system that depends on independent public research, which in turn depends on government funding. That’s the economic structure that needs to be defended and expanded.

Develop new cures

The rosiest hope for health care is to find miracle cures that eliminate disease. Ironically, GLP-1 medications represent just such a miracle cure, albeit with side effects and other problems listed at the beginning of this article. Other candidates for miracle cures include mRNA research, which of course has been seriously reduced, and genetic testing. But breakthrough treatments tend to cost a lot (even millions of dollars per patient).

We can’t reasonably hope for a new kind of health care; we have to figure out how to fund the one we’ve got. GLP-1 drugs were not the first advance to place burdens on the system, and they are not likely to be the last. But payers cannot ensure the continuation of the system by resisting progress either.



< + > Virtual Labs with Josh Haimson – Life Sciences Today Podcast Episode 54

We’re excited to be back for another episode of the Life Sciences Today Podcast by Healthcare IT Today. My guest today is Josh Haimson, Co-Founder and CEO at Inductive Bio! Josh Haimson joins me to talk about how their virtual lab can run millions of in silico experiments to predict how molecules will behave in the body and surface the strongest hypotheses to test in the wet lab.

Check out the main topics of discussion for this episode of the Life Sciences Today podcast:

  • Tell me about your journey.
  • How do you create value for your customers?
  • How do you measure the value?
  • What are three things you’d like to achieve in the next 12-18 months?

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.

Thanks so much for listening!



< + > From Programmer to Director: My 25-Year Journey into the Heart of Data at Dana-Farber

The following is a guest article by Doug Buell, retired Technical Director, Research Computing Services at Dana-Farber Cancer Institute

When I walked into Dana-Farber, nonprofit cancer treatment and research center based in Boston, 25 years ago, I didn’t think of myself as a “data person.” I was hired as a computer programmer – someone who made systems work, fixed what broke, and built tools when nothing existed.

Over time, the work revealed something deeper: data isn’t the byproduct of science. It is the science.

Back then, data lived in scattered documents across shared drives, desktops, and even floppy disks. Clinical research teams were using Adobe to link protocol files together. The master file was constantly overwritten, connections were lost, and no one trusted what they were looking at.

My first major assignment was to bring order to that chaos. We built a web-based system to organize hundreds of protocol documents, forms, and amendments, making them accessible and consistent. That project taught me an early lesson in stewardship: if you don’t control your information, it controls you.

Evolving from Developer to Data Leader

For years, I stayed close to the code – building applications, managing clinical trial systems, supporting administrative data needs. The real shift came when I stepped into leadership and was asked to guide the institute’s research data ecosystem.

That’s when the full scope of the problem became clear. Data wasn’t centralized, tagged, cataloged, or consistently backed up. Dozens of aging servers held billions of files across multiple petabytes, with little redundancy beyond RAID. A single catastrophic event could have erased years of irreplaceable research.

When a power outage exposed that vulnerability, we faced a hard truth: storage isn’t strategy. We needed a comprehensive data management approach.

Building a Modern Data Infrastructure

My goal became straightforward: make research data discoverable, resilient, secure, and trustworthy.

We rebuilt from the hardware up, adopting a three-tier model:

  • Hot copy for day-to-day research access
  • Warm copy for operational recovery
  • Cold copy for long-term preservation

We explored AWS Deep Glacier early on, but hidden retrieval costs and multi-day delays made it impractical for research environments that require responsiveness. Tape – surprisingly – emerged as our most reliable and cost-effective long-term solution. Multiple petabytes on-premises, fast retrieval, predictable costs, and no tolls to access our own data.

Sometimes the future looks like a smarter version of the past.

Mediaflux and the Power of Metadata

We had been using Mediaflux to manage our initial tape system, and it became the backbone of our file management strategy. It allowed us to track data across environments and harvest rich metadata.

But we made an early mistake: we treated Mediaflux like a newer version of our old system instead of leveraging its full tagging and cataloging capabilities. That limited our ability to find and interpret data after the fact.

Which brings me to one of my strongest convictions: Metadata must be created at birth.

Trying to tag data retroactively is nearly impossible at scale. We still have hundreds of reel-to-reel tapes from the 1970s stored off-site. We know they contain data – but no one knows what data. Without metadata, they’re essentially useless.

The same problem applies today. New instruments generate multi-terabyte datasets in hours. Without tagging at creation, neither researchers nor AI systems can meaningfully interpret what they’re looking at.

Some research teams began experimenting with scripts that write meaningful metadata the moment a file is created. It isn’t perfect, but it’s the right direction. True data visibility requires metadata from day one.

The Human Challenge

The biggest obstacle was never technical. It was human.

Researchers treat their data like gold – and they should. When they didn’t fully trust central systems, they kept private copies. Sometimes dozens of them. After several power outages, some labs even built their own mini data centers rather than rely on shared infrastructure.

Rebuilding trust takes years. Losing it takes minutes.

Fragmented data becomes invisible data. And if we can’t see it, we can’t protect it, catalog it, or prepare it for the future – especially a future driven by AI.

Preparing for an AI-Driven World

My philosophy on AI is simple: AI is only as good as the data it learns from.

If AI is going to accelerate cancer research, then:

  • The data must be accurate and timely
  • The metadata must be meaningful
  • The infrastructure must scale
  • The expertise to curate it must grow

One of the most important emerging roles in research is the data librarian – professionals who understand both science and information architecture.

AI can help predict failures, optimize storage, and streamline operations. But it must be fed truth, not noise.

What I Leave Behind

As I retired, there was a quiet symmetry to my final major decision: purchasing a new tape library from Spectra Logic.

This wasn’t the tape system of decades past – cartridges on the floor and manual handling. It was automated, intelligent, scalable to virtually limitless capacity, dramatically more energy-efficient than disk solutions, and fully air-gapped for privacy.

It delivered an on-premises Glacier-like solution – but on our terms. Predictable costs. Fast access. No penalties to retrieve our own data.

The lesson is familiar: old ideas don’t disappear. They evolve.

The challenges ahead are real. Data growth is exponential. Instruments generate larger files every year. Storage needs double rapidly. But the cultural challenge is just as significant: encouraging researchers to tag, curate, and trust.

My successors must balance both – building resilient infrastructure while helping researchers understand how their data becomes searchable, reusable, and foundational for breakthroughs.

What makes this work exciting is that it never stops changing. New data types, new storage models, new computing platforms, new discovery tools – they arrive constantly. Through it all, one truth remains: Data only becomes powerful when it is managed with intention.

That was my journey. And it’s a journey that will continue long after I’m gone.

About Doug Buell 

Doug Buell is a seasoned technology leader and retired Technical Director for Research Computing Services in the Informatics & Analytics division at Dana-Farber Cancer Institute, where he provided advanced computing solutions and user support to the research community. He previously held senior roles in data integration and application support at the University of Massachusetts Medical School and Partners HealthCare, contributing to large-scale research IT operations. With decades of experience at the intersection of scientific research and computing, Doug combines deep technical expertise with a collaborative approach to problem-solving. He resides in Massachusetts and remains engaged with technology and research communities.



< + > Translucent AI Raises $27M Series A Led by GV | HEARTio Announces Closing of $4.25 Million Financing

Check out today’s featured companies who have recently raised a round of funding, and be sure to check out the full list of past healthcare IT fundings.


Translucent AI Raises $27M Series A Led by GV

Translucent Brings its AI Platform to Health Systems Across the Country, like Duly Health and Care, Wray Hospital and Clinic, and Springfield Clinic, to Give Leaders Early Visibility into Financial Risk and a Clear Path to Respond

Translucent, the AI platform for healthcare finance, today announced a $27 million Series A funding round led by GV (Google Ventures), with continued participation from NEA, Virtue, and FPV Ventures. The preemptive, oversubscribed round comes just months after the company’s $7 million seed round, announced in August 2025, reflecting the urgent need for real-time financial visibility in an industry facing unprecedented fiscal pressure.

“We see Translucent as an important tool that has the potential to help us improve the scale and visibility needed to better understand where revenue and operational opportunities exist,” said John Orsini, CFO at Northwestern Medicine.

Healthcare spending was projected to reach $5.6 trillion by the end of 2025, a 7.1% increase from the year prior, and early signs indicate that costs will only continue to rise in 2026. However, that growth isn’t translating into sustainable margins. Healthcare finance has crossed a threshold where the volume and complexity of data across siloed areas like labor, supplies, claims, contract economics, equipment, and other cost centers make real-time, manual reconciliation impractical, forcing human teams to manage the business in hindsight. Without real-time visibility, organizations can’t identify where revenue is leaking or take action fast enough to make meaningful changes. This lack of financial visibility contributed to 20 hospital bankruptcies and 23 hospital and emergency department closures last year. Currently, more than 700 rural hospitals are at risk of closure, displacing patients and making access to care even more challenging.

“Healthcare organizations are in trouble, and the financial infrastructure that should help leaders respond is fundamentally broken. That’s why healthcare needs a true financial operating system built for this moment,” said Jack O’Hara, Founder and CEO at Translucent. “We built Translucent to give hospitals and clinics what they desperately need: real-time clarity and control, so they can catch problems early enough to act and keep their doors open for the patients who need them.”

Translucent is the first agentic AI platform for healthcare finance. It consolidates operational, clinical, and financial data into a unified view, continuously monitoring signals across the organization to surface real-time financial insights and identify root causes the moment they emerge. Translucent replaces the patchwork of disparate tools, spreadsheets, and manual operations that have historically defined healthcare financial operations, giving teams a shared source of up-to-date truth. Rather than waiting for month-end close or pulling static reports…

Full release here, originally announced March 11th, 2026.


HEARTio Announces Closing of $4.25 Million Financing for its AI Algorithm to Detect Warning Signs of Heart Attacks

Heart Input Output Inc. (HEARTio), an artificial intelligence healthcare diagnostics company, today announced the closing of $4.25 Million in new seed financing with participation from Intelligence Ventures, Audacious Capital, VU Venture Partners, LifeX (Pittsburgh), Bessel, and others. These funds will enable the launch of HEARTio’s pivotal study seeking FDA clearance for HEARTio’s flagship product, ECGio.

“This successful round of financing is a testament to the momentum we are building in bringing ECGio to market,” said Utkars Jain, Co-Founder and CEO at HEARTio. “Up and down the cap table, we have so many people that believe in us from all around the world — executives, physicians, venture capitalists, and family offices. With this financing, we expect to complete our pivotal study and submit the technology to the FDA for clearance. Our technology has the potential to democratize cardiovascular care and help create a world where no one experiences a heart attack.”

“In addition to our investors, we have been incredibly lucky to work with so many globally renowned experts in Cardiology, Emergency Medicine, Cardiothoracic Surgery, Medical Devices, Biostats, Life Science Tools, and more,” said Adam Butchy, Co-Founder of HEARTio. “This amazing team has allowed us to stay focused on patient care and technological innovation, making sure that we are creating something that is truly disruptive and impactful.”

HEARTio is a digital diagnostics company using AI to detect coronary artery disease (CAD), more quickly, more accurately, and at a lower cost as compared to the current standard of care…

Full release here, originally announced March 16th, 2026.



Thursday, March 26, 2026

< + > The Right Information at the Right Time for Post-Acute Care

Discharges for acute care facilities involve multiple inputs from multiple departments and can be chaotic. After the patient reaches a rehab setting or other post-acute care facility, key data about medications, procedures performed, and necessary interventions is often missing. Plus, problems go beyond mere availability of health data. In our recent interview with Hamad Husainy, Chief Medical Officer at PointClickCare, he shares that a discharge “summary” can be hundreds of pages long which makes it hard for a post acute care clinician to process.

PointClickCare, the leading EHR for post-acute care in the U.S., now uses an AI tool called Discharge Intel to create a 1- to 2-page synopsis of the discharge information. The key to being useful, of course, is to capture what Husainy calls “the right information at the right time.” Expectations for AI are rising in health care, he says: It has to be 99% accurate, or even more. They work hard to understand what clients need and Discharge Intel is a great example of them listening to customers and providing an AI solution that benefits patients and the post acute care providers.

Being useful also require more than interpreting and analyzing data: the systems must drive quality and be accountable. PointClickCare’s synopses include pointers to sources, so staff can go back to original data if needed.

In our interview with Husainy, we also dive into more of PointClickCare’s efforts to provide “AI-powered intelligence” as opposed to just data aggregation.  Plus, we ask him to share how PointClickCare is approaching governance, clinical validation, and accountability in their AI efforts.

On the larger landscape of health care, Husainy laments the “animosity” that exists among some patients and doctors alike. He thinks that technology, by addressing gaps in health care, can build trust and bring people together in the care process.

Watch our interview with PointClickCare to learn more about how they’re improving care transitions by making sure the right information is available at the right time.

Learn more about PointClickCare: https://pointclickcare.com/

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< + > The Costs of Missing What We Could Never See

The following is a guest article by Dr. David Kirk, Chief Medical Officer at Regard

The great promise of healthcare technology, at least for clinicians, has always been a view of the “whole picture” of a patient’s health. If we could just collect everything in one place, then better diagnosis and treatment would surely follow.

But in practice, the idea that a clinician can review a complete longitudinal record for every patient is a fallacy. The “whole picture” exists in the EHR as thousands of pages of notes, labs, imaging reports, and medication histories that no human can realistically absorb and act on in a short window of time. Every day, clinicians are asked to do the impossible: spot life-threatening details buried in thousands of lines of chart data while moving at a pace no human can sustain.

The gap between what exists in the record and what a clinician can actually see is where harm occurs. Every clinician ends each shift knowing there was something in a patient’s chart they didn’t have time to review. That awareness is causing moral injury at an unsustainable rate. It’s a symptom of a healthcare system that is drowning in data and starving for insights.

As we enter 2026, the conversation around what healthcare technology can achieve, and more importantly, what it should achieve, must change. Instead of showing clinicians the “whole picture,” AI should augment clinicians by surfacing the “right picture” – the critical details that can materially impact diagnosis, risk, or treatment at the bedside.

In medical school, we’re trained to gather every piece of information we need to evaluate a single patient. I’d spend hours – even days – putting together every piece of the puzzle, because that was the time afforded to me. In practice, that luxury doesn’t exist. In the hospital, clinicians have to juggle multiple patients, each with a mountain of data. Whether it’s the ICU, ED, or clinic, there’s rarely time to fully review every record. According to the American Medical Association, 22.5% of physicians spent more than eight hours on the electronic health record outside of normal work hours in 2024.

As an ICU physician, I often rush to the bedside to save a crashing patient. I have very little time to understand the patient’s complete medical history, yet the charts are getting bigger every day. Meanwhile, emergency departments face growing patient volumes, increasing boarding times, and mounting wait times, making these impossible decisions even more frequent.

In a practice environment like this, clinicians can only rely on our experience and the limited information we can digest and analyze with the time we have. Nearly all patient data goes unseen. As many as 900,000 patient data points on a single critical care bed go to waste every hour, and only 3% of healthcare data is ever used.

These blindspots are why my colleagues and I go home every night worried about what we missed. Every diagnostic error that harms or risks harm to a patient creates moral injury. It’s the root cause of increasing burnout and depression among physicians.

No doctor enters medicine wanting to spend more time analyzing data than caring for patients. Yet, that’s the reality of modern healthcare; it’s a system that asks humans to process an impossible amount of information in an unreasonable amount of time. A 2024 study, for example, found it would take clinicians more time than is available in a single day – 26.7 hours – to deliver recommended care guidelines for an average number of patients per day.

It doesn’t need to be this way. AI can illuminate those blind spots – what I’ve been calling “augmented intelligence” – enhancing clinicians’ ability to use their training and knowledge. It can synthesize an entire patient record and surface the handful of insights that truly matter: a lab that contradicts the working diagnosis, a medication the patient forgot to disclose, an overlooked note buried deep in the EHR.

For clinicians, changing the way we practice medicine isn’t easy.  We’ve been inundated with new tech over the past few decades – tools we were promised would be transformative, but delivered little more than distractions. As a result, many of us have grown used to practicing within a system riddled with blind spots.

But we’ve reached a breaking point. The cost of inaction – of not altering our workflows, of hanging on to the ways we’ve grown accustomed to working – is being measured in moral injury and patient lives.

If adapting to a new, AI-augmented way of practicing medicine means fewer missed diagnoses, fewer sleepless nights, and more moments where we can actually be present with our patients, that’s a change worth making.

The grand promise of AI in medicine isn’t automation. It’s not “efficiency” or “optimization” – it’s the restoration of the quality of care people deserve and doctors expect of themselves. AI has already proven valuable in reducing administrative burdens – documenting visits, summarizing notes, improving billing – but little of that meaningfully improves patient care. The real transformation will come when AI moves beyond paperwork and into clinical decision-making, helping physicians see what we might otherwise miss.

When AI can analyze every data point in a chart and bring forward the insights that matter most, it strengthens our ability to save lives. It buffs away the callouses of moral injury that have built up over years of practicing under impossible conditions.

If AI can surface key insights from the flood of patient data, care gets safer and the people delivering it can finally breathe again. The goal of healthcare technology should no longer be to provide clinicians more data. It should be to help us see the data that matters most in moments of care.



< + > Weekly Roundup – March 28, 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 impo...