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Healthleap Raises $38M to Expand AI Screening for Overlooked Conditions in Hospitalized Patients

Health systems are under pressure to improve patient outcomes while managing staffing shortages and rising costs. Healthleap, a clinical AI platform which works within the EHR to screen hospitalized patients for conditions that might otherwise go undiagnosed and untreated – such as malnutrition – is supporting hospitals in finding at-risk patients sooner.

The San Francisco-based company announced last week that it has secured $38 million in combined seed and Series A funding from Sequoia Capital, First Round Capital, and Hummingbird Ventures. The company plans to use the funds to expand its screening technology to more conditions, including congestive heart failure, aspiration pneumonia, delirium, pressure ulcers, and certain hospital-acquired infections.

“Every week, millions of patients leave hospitals with unresolved health conditions that will erode their health and cost up to 10x more to treat late,” said Healthleap CEO Josiah Meyer in a news release. “Finding them now is prevention, and it’s a win for everyone. Patients get treated before they get sicker.”

Healthleap’s platform is currently used by more than 50 hospitals, including Penn Medicine, Houston Methodist, Cedars-Sinai, Emory and UMass Memorial. Its current focus is malnutrition, which can often go unnoticed and worsen patient outcomes.

“The model uses weight, body mass index, lab results, diagnoses, medications, intake and output, nutrition orders, and vitals,” said co-founder and clinician Jemima Meyer in an email. “The AI system combines all of these signals into one priority score from 0 to 10 for each patient. A second column shows whether the patient’s risk changed since the last assessment. Dietitians see both columns in their existing EHR worklist every morning. The dietitians sort the list and start with the highest-risk patients.”

Healthleap’s technology screens every adult inpatient daily. Typical malnutrition screening relies on a manual process conducted by the admitting nurse or an in-hospital dietitian. Patients who are confused, intubated, or critically ill are often unable to answer, putting them at particular risk for being missed. The company’s website claims that its model showed 88% higher sensitivity than the nursing screening, linking to a peer-reviewed study which backs that number. Each severe malnutrition diagnosis and treatment supports improved patient outcomes and, according to Healthleap, allows hospitals to claim around $11,000 in reimbursement. Each patient flagged had a 1.6-day shorter average hospital stay, according to Jemima Meyer.

As Healthleap expands into screening for additional conditions, each must first undergo further clinical validation, with a focus on smoothly integrating into provider workflows.

“Every condition runs on the same nightly chart review and the same EHR connection”, she said. “Healthleap does not send interruptive alerts. Physicians and nurses see no pop-ups. The score appears as a column in a worklist that the care team already uses.”

Healthleap wants to turn earlier risk detection into better patient care – their next challenge is expanding the results achieved with their malnutrition screening platform across a broader range of conditions.

Photo: John Lund, Getty Images

October 14, 2026
Why Agentic AI Works in Hospitals When You Strip It Down
Imagine an agentic AI solution that plans and executes multi-step tasks like triaging referrals, drafting notes, flagging deteriorating patients, and surfacing care recommendations. After the flawless demo, the committee approves what is supposed to be the answer to operational inefficiencies in healthcare. Fast forward six months: the system formally operates, but it no longer influences any real clinical decisions.

Many hospitals are now trying to use AI to automate processes and free up staff time, running simultaneous pilots to perform as many tasks as possible. This quickly leads to pilot sprawl — a state where organizations can no longer even say which agents are still in active use.

As one widely cited MIT analysis has shown, 95% of enterprise AI pilots fail to deliver measurable ROI, and I would add that they fail predictably. From my experience, I would distinguish the four most common scenarios:

The Big Bang: an attempt at a long, all-department transformation, so by the time anything goes live, the requirements have already changed, and the budget was spent before a single user has seen value.

Pilot Purgatory: a project that looks impressive in a controlled environment (demo) but cannot pass the reality check, working with human users, actual workflows, and unexpected interruptions.

Technology-First: the platform gets built before the problem it should solve is clearly defined, so the team ends up searching for at least some use case after the investment is already made.

Governance Paralysis: long months spent designing the perfect oversight model while the initiative quietly loses momentum, ownership, and executive support.

In fact, all four scenarios share the same fundamental mistake: the system is designed to look intelligent, convincing, even flashy, but not as one you can trust in a real healthcare workflow.

What especially shines in a demo often arrives as opaque in practice. Each new agent and each additional step creates another space where something can go wrong.

An annoying glitch in a consumer app translates to delayed critical care or missed clinical alerts in a hospital. The human is never genuinely in the loop, so staff make the safest and most obvious decision and stop using the system they cannot fully rely on.

Even if the model is robust, an agent operating outside the chart creates too much friction for daily use. Recently, Jonathan Bush argued in MedCity News that orchestration layers must be built above the EHR. While his diagnosis of EHR limitations is spot on, the proposed solution misses the reality of clinical workflows. An agent must live directly inside the patient chart via standard integrations. If it exists as a separate overlay, it will simply be abandoned.

After a decade of working on EHR and HL7 FHIR integration projects, I see this exact friction more often than almost any model problem. For example, the clinician has to leave the record, open a second tool, and then come back just to act on the recommendation. A strong triage engine can be abandoned for exactly that reason.

How do we handle all these issues? The fix is not a better model, but the right sequence. First comes assessment, then the pilot, then change management, and only then scaling. Between these phases there must be a real decision gate, and you should be ready to stop if there is no good reason to go further.

Assessment: The assessment should be brief — about four weeks. We need it to reduce risks before building anything. The potential use cases must be evaluated based on such criteria as value, feasibility, and personal relevance. Value shows the return you get. Feasibility shows whether it is achievable with the available data and infrastructure. And personal relevance is about whether the solution is useful to a particular person in their daily work. If the assessment is honest, the result is a readiness scorecard and a go/no-go call before the money is spent.

Pilot: It is not just a slideshow, but a real demonstrator that works with real users and real data in production, typically over twelve weeks. To pass the reality check and avoid getting lost in pilot purgatory, it must do one clearly defined and practical thing. If you cannot explain to a floor nurse in one sentence what exactly this AI agent does, you do not have one project but several. And this agent must surface in the chart clinicians already work in, not in a separate tool they have to open and use on its own. The system’s rationale must be clear — the specialist using it must understand why a particular case or patient surfaced. And of course, the human must be in the loop, not for show. The system can work and make sense only when the agent surfaces recommendations while a specialist makes the final clinical decision. On one documentation project built this way, follow-up documentation time fell by about a third and compliance with scheduled check-ins doubled. This aligns with broader industry validation; for instance, the AHA Market Scan noted a comparable 27% reduction in documentation time at Intermountain Health.

Change Management : Change management is probably the most underrated step that many teams skip. If no one wants to use the agent, it doesn’t matter how technically excellent it is. So, before you scale, you need not only to train the team how to use the solution, but also build it into daily processes and see whether it has actually caught on. You can see it in very simple signals — how often a nurse accepts or declines a suggestion and what share of recommendations she opens versus ignores. Those metrics are both proof of adoption and a map of what still needs improvement.

Scale: Finally, it makes sense to talk about scaling. And not because “everyone liked the pilot,” but because it met specific predefined criteria and showed real results, such as time saved per shift, error rate, and task compliance. If something is still missing in the pilot, you iterate or stop. If the demonstrator clears the bar, you earn a slightly broader next scope, one use case at a time. That’s how you can see value by week twelve, rather than postponing it for a year and a half. Evidence first, hype never. Federal transparency rules, such as the HTI-1 final rule, are already moving this way by demanding strict algorithm transparency in certified health IT.

In healthcare, the systems that scale are not the most ambitious ones, but the ones that are easiest to trust, govern, and use in real workflows.

The agents that succeed tend to do one narrow task clearly, visibly, and with a human still making the decision.

And the hospitals that will pull ahead over the next couple of years are not the ones running the most impressive pilots. They are the ones running the fewest — and finishing them.

Photo: akinbostanci, Getty Images

Mykhailo Hentosh

Mykhailo Hentosh is Head of Engineering and Solutions at Binariks, where he sets the technical direction for complex software programs in healthcare and AI. A software architect and solution designer with nearly two decades in the field, he is a core member of the company’s AI Center of Excellence. Much of his career has been spent on enterprise healthcare platforms, EHR and HL7 FHIR interoperability, and AI deployments that have to hold up in real clinical workflows. He is known for cutting through hype toward systems that make a measurable difference, and for challenging conventional thinking when the evidence does not support it.

This post appears through the MedCity Influencers program. Anyone can publish their perspective on business and innovation in healthcare on MedCity News through MedCity Influencers. Click here to find out how.
October 14, 2026
Happy Health’s Cuffless BP Device Wins FDA Clearance

Blood pressure is typically taken with a cuff wrapped around the arm, providing a reading in one moment in time. These fleeting snapshots may not always reflect a person’s average blood pressure, and even simple factors like the stress of being at a doctor’s appointment can drive up the numbers. Continuous monitoring offers a broader picture, but many wearable devices still require calibration with a standard blood pressure cuff reading – until now.

Happy Health’s new smart ring – the Happy Ring – is designed to measure blood pressure continuously, without a cuff or invasive device, and without the requirement for external calibration that many other wearable blood pressure devices require. The Austin, Texas-based digital health company announced last week that Happy Ring’s blood pressure measurement capabilities have received FDA clearance and is available for consumers to buy directly or access via referral from their own doctor or a physician from Happy Health’s network. The company claims it is the first BP-measurement device requiring no external calibration. The blood pressure monitoring capability is an add-on feature to the Happy Ring, which is primarily used to track sleep.

The company raised $75 million in August 2026 from ARCH Venture Partners and OpenLoop to build out its platform for home-based care. Their devices focus on assessing sleep quality and other health measurements, providing both patients and physicians data that can help support medical care.

“This is blood pressure’s CGM moment,” said Dustin Freckleton, MD, founder and CEO of Happy Health in a news release last week, referring to continuous glucose monitoring devices for patients with diabetes. “High blood pressure is the most prevalent and modifiable risk factor for heart disease, stroke, dementia and all-cause mortality, which makes seeing it in real life incredibly powerful. Just as CGMs freed glucose monitoring from the finger stick, we’ve freed blood pressure from the cuff, turning an occasional measurement into a passive signal that can reveal risk throughout everyday life, including overnight.”

The ring combines two technologies to provide its cuffless, continuous blood pressure measurements: photoplethysmography (PPG) and bioimpedance. PPG is an optical sensing method in which various wavelengths of light measure how blood volume changes with each heartbeat. Bioimpedance uses an electrical signal to measure the properties of the surrounding tissue, which helps to determine how much blood is present.

Freckleton explained that, “We then use physics-based models of how blood vessels expand and contract to translate those measurements into an absolute blood pressure in mmHg. Because factors like hydration, temperature, medication, vascular tone, etc. can influence these signals, the system also evaluates the physiological state in which each measurement is taken. If those conditions fall outside the ranges we’ve validated, the ring can withhold the measurement rather than report a blood pressure that may not be reliable. This is what allows the Happy Ring to measure blood pressure without an inflatable cuff and, importantly, without requiring a cuff measurement for calibration.”

Happy Health is not the only company with a cuffless BP device. Biobeat, which makes the Biobeat chest patch, offers 24-hour, cuffless, ambulatory blood pressure monitoring combined with a mobile app and automated physician report. However, unlike Happy Health’s device which does not require calibration against a traditional cuff measurement, Biobeat’s does.

Happy Health validated its technology against invasive arterial line measurements, which is the clinical standard for continuous blood pressure measurements. They also tested the device in people with varying BMIs, skin tones, and health conditions, as these variables can also impact blood pressure readings. The company claims that the mean difference was less than 1 mmHg for both systolic and diastolic blood pressure.

Freckleton discussed the clinical implications of being able to monitor blood pressure continuously, particularly overnight.

“Sleep apnea is a good example of what that additional context can reveal. It can repeatedly stress the cardiovascular system overnight and contribute to hypertension,” he said. “When a physician can see blood pressure alongside what is happening with a patient’s sleep and breathing, they can begin to understand those relationships rather than looking at each measurement in isolation.”

For Happy Health, the longer-term challenge is establishing how continuous blood pressure measurements should inform diagnosis and treatment. Translating those patterns into clinical decisions will require further evidence, but for now, Freckleton is hopeful that the device will help patients to smoothly integrate at-home blood pressure monitoring into their routine.

“Our vision is much simpler: put on a ring when you go to bed, take it off when you wake up, and automatically get the health measurements that matter to you and your doctor without having to remember to measure anything,” he said. Instead of asking patients to become better at monitoring their blood pressure, we can make the monitoring fit naturally into their lives.”

Photo: Waldemarus, Getty Images

October 13, 2026
What Corporate Venture Funds Get Wrong about Strategic Value

A venture fund’s objective is to invest capital into early-stage companies to fuel growth, increasing the probability that they are acquired or reach public markets. That’s how an “exit” is achieved, with the invested capital returned at a risk-adjusted profit. Corporate venture capital (CVC) differs from orthodox venture capital because, alongside financial returns, a CVC’s goal is often to create strategic value for a core business.

Delivering this strategic value is much easier said than done, and therefore not well addressed by most CVC funds.

Outlined below are four distinct practices that optimize strategic and financial value.

Define strategic value alongside financial return

Strategic value comes in many forms. Pharma corporate venture should develop investments that might generate a valuable new target, drug, or vaccine candidate, improve the probability of pipeline success, reduce the cost of R&D, accelerate clinical trials — and more through the entire value chain. It is not necessary to acquire such businesses outright, but it is necessary to cultivate them into standardized, scaled solutions for scientists, regulators, clinicians, patients, and payors.

Assessing the value of an asset — financial and/or strategic — is as much art as science. Financial value can be estimated using discounted cash flow analysis, which relies on pro forma projections and assumptions about future market pricing and multiples, or through comparable-company analysis. Quantifying strategic value is much more difficult. One derivative indicator of strategic value is whether the core CVC business engages with the portfolio company, through piloting, partnering, licensing, and/or purchasing products or services. A CVC that can’t generate this type of engagement is just delivering capital without a strategic return.

Lead internal collaborations that power sourcing and diligence

Placing relevant strategic investments requires deep familiarity with the parent company’s objectives and priorities. This means dedicated internal workstreams with business leaders and subject matter experts who help refine and prioritize use cases and understand unmet needs. In-house CVC resources also help with pipeline building and technical, scientific, and clinical due diligence. Proper engagement is time-consuming and requires operational, process, and communication excellence — skills anathema to the normal concerns of most venture investors.

Many benefits accrue from generating that upfront buy-in. Deep collaboration raises the CVC’s probability of succeeding in highly scientific and technical markets. It also instills early business awareness and engagement, so portfolio companies develop faster pilots and larger partnerships, executable in a shorter timeframe.

Drive leverage through syndication

Most CVC investors are equipped to lead negotiations and price rounds, but there’s a strong case for prioritizing syndicate creation.

Syndication provides leverage through agile, coordinated activity on negotiating terms, dividing or delegating board activities, and considering future mergers and acquisitions. It also extends financial leverage, allowing a fund to cover more of the market and back a broader range of strategic interest areas. Joining syndicated deals priced by expert financial investors tends to produce a fair market price. A CVC can always walk away when pricing runs ahead of what its internal modeling supports.

Even with reduced board involvement, a fund can stay engaged through shareholder and information rights — quarterly financials, board materials, regular check-ins — whether it holds a board or observer seat or is simply an shareholder.

Occasionally, a CVC will need to play a more significant company-building role, In such cases it can place a convertible note or lead a round outright — but always with an eye toward delegating responsibility over time. The correct level of involvement is a case-by-case decision, considering the strategic relevance of the platform, the stage of the company, and the experience of its board and management team.

Deliver the payoff

None of this works as one single move, but as a pattern: sourcing shaped by internal collaboration, pricing and development sharpened by syndication, and continued engagement all the way to exit. Funds that operate this way tend to see their strategic and financial returns reinforce each other over time rather than trade off against one another.

The key lies in recruiting and training leaders who roll up their sleeves and work seamlessly with internal partners. When portfolio investments help a business develop better therapies or reach patients faster, they deliver strategic value — and ultimately value to patients and providers.

Photo: Hong Li, Getty Images

David M. Rubin

David M. Rubin, Ph.D., is a Managing Director in Merck GHIF, where he leads the drug discovery and precision medicine practice. Dr. Rubin ran and helped build life sciences companies in the USA, UK, Denmark, Hungary, and India. He was awarded National Institutes of Health and American Cancer Society Fellowships to support his research at Harvard Medical School on ubiquitin-dependent protein degradation. He received a B.A. in biology from SUNY Binghamton and a Ph.D. from Temple University. Junk Birds, his collection of previously published short stories, was published in 2025.

This post appears through the MedCity Influencers program. Anyone can publish their perspective on business and innovation in healthcare on MedCity News through MedCity Influencers. Click here to find out how.

October 13, 2026
Pediatric Innovation Isn’t a Small-Market Problem, It’s an Incentive Problem

The case for investing in children’s health should be self-evident. As a society we have a responsibility to invest in the next generation, and few investments carry greater long-term benefit than helping children reach their healthiest possible futures.

When we diagnose and treat conditions early, we do more than improve a child’s health in the moment. We are altering the trajectory of their entire life: development of their personal confidence, resilience, mental wellbeing, learning capacity, and in many cases, also reducing decades of downstream public and private healthcare costs.

Yet, pediatric innovation remains chronically and systemically undervalued in healthcare.

That is not because the market for pediatric healthcare is small, it is because the incentives are misaligned. The cost of treating a child’s condition is incurred immediately. However, when early pediatric interventions are effective, their benefits often compound over decades.

No single stakeholder is incentivized to properly and accurately consider the full return on early pediatric interventions and support their adoption. The average tenure of a person staying on the same health plan is two to three years. As a result, private insurers are focused on cost savings that will be realized within that time frame, which for pediatric treatments especially, may only represent a small proportion of the overall benefit.

On the public insurance side, children are largely covered by Medicaid, and while pediatric health is a central tenet of Medicaid’s mission, the program is too often hamstrung by its structure. Unlike Medicare, which is entirely financed at the federal level, Medicaid is jointly financed between the federal government and each individual state. The program is often left underfunded, as its budget is subject to annual appropriations decisions by state legislators who have limited fiscal flexibility. Priorities are constantly changing, making long-term planning next to impossible for Medicaid decision makers.

That misalignment has consequences well before a new technology ever reaches a payer’s desk. When long-term pediatric outcomes are undervalued, the ripple effect moves backward through the entire innovation pipeline, shaping what gets funded, what gets studied, and what ultimately reaches patients.

Investors, wary of uncertain reimbursement and lower near-term returns, often direct capital toward adult indications with clearer paths to market. Drug and device developers may follow the same logic, deprioritize pediatric programs or skip pediatric studies. The result is a self-reinforcing cycle: limited investment leads to fewer pediatric success stories, which then reinforces the false perception that pediatric markets are too small or too risky to justify meaningful investment.

This is why pediatric innovation in healthcare must be evaluated differently.

Payers should require robust clinical evidence to make responsible coverage decisions. But the decision making framework for pediatric products should reflect the unique realities of childhood conditions: the value of early intervention, the long-term costs and consequences of undertreatment, and the potential for a therapy to alter a child’s life path before preventable challenges become permanent ones.

Policy leaders should also examine whether existing pediatric coverage pathways are actually keeping pace with modern innovation. In theory, we already have a powerful mechanism for this: the Early and Periodic Screening, Diagnostic, and Treatment (EPSDT) benefit. Under federal law, EPSDT mandates that Medicaid-enrolled children receive all medically necessary services to correct or ameliorate important pediatric conditions, regardless of whether those services are explicitly covered in a state’s standard plan. It is a visionary policy designed to ensure that no child is left behind by the structural limitations of our healthcare system. In practice, awareness of the EPSDT benefit among Medicaid beneficiaries is low, and Medicaid plans need to do more to streamline the approval process for EPSDT-covered technologies.

Investing in pediatric care is not simply a moral imperative. It is one of the most practical long-term investments the healthcare system can make.

The question is not whether children’s health is worth the investment, that answer is self-evident. The real question is whether our healthcare ecosystem has the foresight to correct these incentive structures before another generation of children bear the cost of our delay.

Photo: Suriyapong Thongsawang, Getty Images

Scott Xiao

Scott Xiao is the co-founder and CEO of Luminopia, a therapeutics company developing software-based interventions at the intersection of ophthalmology and neuroscience. Luminopia’s lead product is a novel FDA-cleared prescription digital therapeutic for amblyopia, the leading cause of vision loss in children, affecting approximately 1 in every 30 children. Scott founded Luminopia after leaving Harvard University at age 17, inspired by a classmate’s experience with amblyopia and the limitations of traditional treatment. He has led the company from early development through clinical validation, regulatory clearance and national commercialization, with the therapy now prescribed to more than 12,000 patients by 1,300+ providers at 600+ top eye institutes, children’s hospitals and private practices. Under his leadership, Luminopia became the first FDA-cleared virtual reality–based treatment for any medical condition. Scott has also spearheaded Luminopia’s real-world evidence strategy, including the development of the PUPiL Registry, which has been instrumental in securing both FDA label expansion and major payer coverage decisions. Scott has been recognized on Forbes 30 Under 30 list in Healthcare, MIT Technology Review Innovators Under 35, and Modern Healthcare’s 40 Under 40. Luminopia was also named one of TIME’s Best Inventions of 2023. Scott is a strong advocate for the role of technology in improving patient engagement and outcomes and is focused on expanding Luminopia’s pipeline to address additional neuro-visual conditions, including strabismus and vision loss following stroke or traumatic brain injury.

This post appears through the MedCity Influencers program. Anyone can publish their perspective on business and innovation in healthcare on MedCity News through MedCity Influencers. Click here to find out how.

October 12, 2026
Loneliness And Diabetes: Harnessing an Opportunity for Better Outcomes

In recent years, health plans have invested heavily in improving diabetes care, with expanded access to medications, continuous glucose monitoring (CGM), and digitally enabled care management programs. Yet despite these advances, only around 40% of adults with diabetes achieve recommended glycemic targets, highlighting that important gaps remain despite substantial advances in diabetes care.

If the clinical infrastructure for diabetes care is so much better, but real-world experiences and outcomes are declining, it’s time to consider that there are other powerful factors influencing the ability of people with diabetes to effectively manage their care.

One important factor may be human connection — or the lack thereof.

There’s no question that modern society is struggling with increasing isolation. Nearly one in three adults reports experiencing loneliness, and one in four say they don’t have social or emotional support in their lives.

This includes over 3 in 10 adults with diabetes, who often find that loneliness increases the challenges of completing daily self-care activities, including monitoring glucose levels, taking medications, and making positive choices around diet and exercise.

With emerging research suggesting that loneliness may influence how successfully individuals can manage their chronic conditions, health plans need to consider more comprehensive strategies for identifying and addressing members who could benefit most from additional education and engagement.

How loneliness provides the missing context behind diabetes outcomes

Healthcare organizations have become increasingly sophisticated in identifying clinical risk. Claims data, utilization patterns, medication adherence metrics, and biometric indicators all provide valuable insights into which members may be struggling.

However, those data points do not always explain why two individuals with similar diagnoses, treatment plans, clinical profiles, and access to clinical resources can experience dramatically different outcomes.

Loneliness may help explain part of that difference.

Unlike social isolation, which refers to limited social contact, loneliness reflects a person’s perception of connection. An individual can be surrounded by family members, coworkers, and community networks while still feeling alone in managing a chronic condition.

Loneliness can influence motivation, confidence, resilience, and engagement. Together, these factors shape diabetes self-management. Because the vast majority of diabetes care occurs outside the healthcare setting, people must make dozens of health-related decisions every day, often without immediate clinical support.

For example, a recent poster presented at the American Diabetes Association Scientific Sessions examined adults with diabetes who initiated CGM as part of a structured education and coaching program. The findings provided important insights into the relationship between loneliness and diabetes outcomes.

At baseline, individuals reporting loneliness demonstrated significantly poorer glycemic profiles than those who did not.

Average blood glucose levels were nearly 185 mg/dL among lonely participants compared with approximately 162 mg/dL among non-lonely participants. Glucose management indicator (GMI), an estimate of A1C derived from CGM data, was also significantly higher, while time in range was substantially lower.

In short, participants experiencing loneliness entered the program at a clear disadvantage. What happened next suggests that individuals experiencing loneliness may have the greatest opportunity to benefit from structured education, coaching, and ongoing human support.

When participants received structured diabetes education and ongoing coaching from certified diabetes care and education specialists, those experiencing loneliness demonstrated some of the most substantial improvements observed in the study.

Within three months, average glucose levels declined by more than 34 mg/dL, GMI improved by nearly one percentage point, and time in range increased by more than 17 percentage points.

While some variation occurred over the remainder of the nine-month study period, outcomes remained improved relative to baseline and narrowed many of the initial differences between lonely and non-lonely participants.

These findings suggest that loneliness is not simply a marker of poor outcomes, but also a valuable data point that can pinpoint individuals who have the most to gain from meaningful support and engagement.

The research shows a promising opportunity for health plans to connect with members who may benefit the most from more personalized engagement with experienced diabetes coaches, educators, and clinicians and therefore reduce the delta in outcomes for people with loneliness in their lives.

Recalibrating how health plans investigate poorly controlled diabetes

The study highlights the need for a more comprehensive understanding of the underlying reasons a person may be experiencing poor control of their chronic disease.

When looking at a member who is having trouble maintaining their health, the first questions usually revolve around how to connect a member with medications, CGM devices, and clinical care management resources.

But if all those are in place and the member isn’t experiencing improvement, it should trigger a deeper dive into the member’s nonclinical circumstances.

Who helps this person manage their diabetes? Do they have a caregiver, or are they trying to juggle being a caregiver for someone else? Are they socially connected outside of the home? Are they struggling with depression, anxiety, or other mental health conditions? Do they have the health literacy and confidence to participate in decision-making around their own health needs?

Asking the right questions about community support, caregiving responsibilities, emotional well-being, and the availability of trusted social networks can establish important context for a person’s outcomes — and spark conversations about social needs that might never otherwise take place.

As healthcare continues moving toward more personalized and holistic models of care, it is important to consider that “success” with diabetes care isn’t just about achieving glycemic control, but also about improving the quality of life for the whole person in multiple dimensions.

Photo: FrankyDeMeyer, Getty Images

Coni Dennis

Coni Dennis DNP, RN, NE-BC, is SVP and Chief Clinical Officer at CCS, where she leads evidence-based chronic care strategies focused on improving outcomes for health plans, providers, and individuals living with chronic conditions. Prior to CCS, she spent 16 years at Humana leading clinical strategy and population health initiatives at scale, providing deep experience across care delivery, quality improvement, and value-based healthcare operations.

This post appears through the MedCity Influencers program. Anyone can publish their perspective on business and innovation in healthcare on MedCity News through MedCity Influencers. Click here to find out how.

October 12, 2026
Join Us at INVEST Digital Health for a Conversation on Consumer-Centered Care across the Healthcare Ecosystem

The annual INVEST Digital Health conference is coming up! Scheduled for October 29 in Dallas at Pegasus Park, in collaboration with Health Wildcatters, consumer influence in healthcare is the theme this year. Consumers are ordering their own diagnostics, tracking their health with wearables, accessing care directly and expecting healthcare to work more like the other services they use every day. The conference will explore how the consumer experience is impacting visits to clinicians, self-insured employers, GLP-1 drug developers and more.

In a preview of the conference, we’ve included an updated list of the speakers and the agenda. In addition to the panel discussions, attendees will also be offered a tour of BioLabs, a state-of-the-art incubator lab and office space for emerging life sciences startups. They will also get the chance to view Bridge Labs, a purpose-built, institutional-quality life sciences building, providing flexible lab and office spaces. An innovation showcase will spotlight health tech companies The conference will conclude in a networking reception in Health Wildcatters offices.

To register for INVEST Digital Health, click here.

How Convenience Is Reshaping Clinical Strategy

Patients’ evolving preferences and expectations have forced health systems and digital health companies to rethink how care is delivered. This panel will dive into how providers are offering patients the level of consumer-centric convenience they’ve become accustomed to in other areas of their life — and how this shift can lead to improved loyalty, adherence and outcomes.

Hubert Zajicek, CEO Health Wildcatters (moderator)

Angeline Chen, Founding Director of Product, Novellia

Arpan Parikh, CEO, Kin Health

Travis Rush, CEO, Reperio Health

Adam Weinstein, Chief Product Officer, Teladoc

The Rise of Consumer Diagnostics

From gut health analysis to STI check-ups to whole-body biomarker testing, patients are increasingly turning to direct-to-consumer diagnostics platforms for answers about their health. In this session, healthcare experts in this space will talk about the level of personalization, clinical rigor and consumer engagement needed in order for people to navigate this type of personal health data.

Marissa Plescia, Reporter, MedCity News (moderator)

Leo Grady, CEO, Jona

Rachel Miller, PA, MBA, Head of Product & Growth, Labcorp

Kelly Lacob, CEO, Xella Health

Manish Arora, CEO, LinusBio

Pita Navarro, Chief Scientific Officer, Evvy

The New Age of Consumer Drug Platforms

Consumers want healthcare to work like retail, with transparent pricing, home delivery, intuitive digital experiences and on-demand access. In response, drugmakers are reshaping how patients access and purchase medications by launching direct-to-consumer platforms, which could bring pharma closer to the patient than ever before. How are consumer-facing models challenging traditional healthcare distribution channels? Can these new platforms improve affordability and adherence?

Shelley Bailey, Strategic Initiatives at the Independent Pharmacy Cooperative (IPC) (moderator)

Bethanie Baynes, Director of Payer Relations & Business Development, Amazon Pharmacy • Michelle Davey, CEO, Wheel

Thorsten Wirkes, Managing Director, Bertelsmann Healthcare Investments

Jessin Joseph, Vice President, PBA Markets, Judi Health

The Payer’s Role in the Consumer Health Era

As healthcare becomes more and more consumer-driven, how are payers fitting into the equation? From covering direct-to-consumer digital health tools to integrating members’ wearable data to deploying voice AI for navigation and member support, payers are being pushed to go beyond traditional benefit design.

Brian Whorley, CEO, Paytient (moderator)

Magda Lenartowicz, Chief Medical & Innovation Officer, Clever Care

Michelle Wang, Investor, Wavemaker 360

Erica Murdock, Managing Partner, Unseen Capital by Seae Ventures

Rosemary Gammon, Principal Consultant, CMR Healthcare Consultants

How Are Wearables Changing Health Behavior?

Listen to industry leaders discuss how they’re working to validate wearable data and integrate it into care delivery, as well as what they see coming down the pipeline in terms of reimbursement and regulatory pathways for wearable-driven care.

Ken Nelson, Partner, Nelson Jennings Ventures (moderator)

Tanvi Jayaraman, Clinical Lead for Health AI, ŌURA

Peter Micca, Managing Partner, Caduceus Capital Partners

Patrick Sheehan, VP of Value-Based Care, Withings

Ben Green, Chief Clinical Officer, AliveCor

When Consumer Brands Enter Employers’ Benefits Stack

The line between healthcare, consumer technology and employee benefits is blurring. More and more employers are partnering with consumer brands to deliver health services that feel more like retail experiences than traditional insurance offerings. Hear benefits leaders discuss how this trend is changing access and engagement.

Jack Stockert, Managing Director, Health2047 (moderator)

Ali Diab, CEO, Collective Health

Dana Lujan, CEO, Wellthlinks

Jason Tibbels, Chief Medical Officer, Lantern

Joanna Balogh-Reynolds, Senior Vice President of Strategic Partnerships, Progyny

Building the DTC Weight Loss Economy, One GLP-1 Script at a Time

GLP-1 drugs are one of the fastest-growing segments in digital health.This panel will explore how weight loss startups are structuring their businesses and engaging their patients to achieve health goals, as well as how they’re managing drug supply
constraints and evolving beyond prescriptions into complete metabolic health offerings.

Arundhati Parmar, Editor-in-Chief, MedCity News (moderator)

Adam Johnson, CSO, Twin Health

Alexander Fabry, SVP of Product, Noom

Laura Walmsley, Chief Commercial Officer, Virta Health

Wei-Li Shao, President & Incoming CEO, Omada Health

To register for INVEST Digital Health, click here.

Photo: kasto80, Getty Images

October 12, 2026
Are Hospital Operating Rooms Ready For FDA’s Expectations for Surgical Robots?

Robotics are increasingly being incorporated into today’s complex surgeries, and their capabilities are quickly expanding as they take on new tasks and higher levels of autonomy. The FDA recently published draft guidance on pre-market testing for robotically-assisted surgical devices, raising timely discussion around whether hospital infrastructures are capable of supporting increasingly complex systems.

It describes how robotic surgical devices should be tested prior to clinical use, covering everything from the robot’s movements to the design of its software, its reliability and accuracy, its cybersecurity measures, safety and sterility, how it visualizes patient images, and how it interfaces with the surgical team. The guidance is aimed at manufacturers and is currently nonbinding; it is open for comment until November 24.

As surgical technology becomes increasingly connected, the FDA’s guidance asks manufacturers to consider how different software functions within a single operating room interact. This raises a critical question for hospitals: as surgical robots become more sophisticated, what must the operating rooms around them have to successfully integrate the new technology?

Dr. Nadine Hachach-Haram, a surgeon and the founder and CEO of Proximie, argues that the answer lies in understanding the operating room as a connected clinical ecosystem.

“What stands out to me in the FDA’s guidance is that it treats the robot as part of a much wider clinical system,” Hachach-Haram said. “It looks beyond the device itself to the software, connectivity, training and the environment in which it will actually be used. That matters because, as a surgeon, you quickly see that even very advanced technology has to work within the reality of the operating room.”

Proximie, headquartered in London, is a health technology company whose platform is designed to connect operating room devices and support intra-operative video and data capture. The company says its software is used by more than 700 hospitals worldwide.

Hachach-Haram asserts that it’s important for manufacturers to assess whether the addition of more complex capabilities may impact the performance of a new robotic device, a consideration which the FDA guidance also underscores.

“A great robot should still be a great robot once the video and data sit alongside it, not a lesser robot with a video system attached,” said Hachach-Haram.

The FDA guidance also addresses cybersecurity, a subject that’s top-of-mind for today’s healthcare leaders.

The guidance warned:

“Failure to maintain cybersecurity can result in risks such as compromised device functionality, loss of device availability, loss of data (medical or personal) availability and/or integrity, or exposure of other connected devices or networks to security threats. This in turn may have the potential to result in patient injury.”

Human factors are another focus, with the FDA emphasizing that testing should be conducted in realistic contexts to accurately assess performance within real-world operating room conditions.

As surgical robots continue to evolve, the guidance asks manufacturers to prove that these new technologies work smoothly as part of today’s complex human-and-machine operating team.

Photo: PhonlamaiPhoto, Getty Images

October 12, 2026
What Life Sciences Commercial Teams Can Learn from Digital Health

A cursory glance across job descriptions at most major pharmaceutical companies will, if one is being unkind, look essentially identical to the 2010s: the same divisional divide of analytics, field, launch, CRM, and content, to name just a few, with essential requirements listing a relevant degree and a set number of years in the subdivision. Meanwhile, AI threatens to upend the commercial ecosystem as we know it, if we let it. There’s no shortage of investment in commercial AI: budgets exist, and pilots launch. What’s missing is a redefinition of the realm of commercial possibility. R&D teams talk about AI in terms of what’s possible, where Commercial teams mostly talk about it in terms of what’s faster: faster call planning, faster reporting, faster content approval. In the pharmaceutical industry, we’re not pondering what the Commercial function could become, only what it could do quicker. I believe that the pharmaceutical industry could learn several vital lessons from the digital health teams they are so frequently adjacent to, or in partnerships with.

Today

As it stands, commercial teams still remain largely siloed, despite a decades-long effort to the contrary: the SWAT teams who swoop in for launch, the marketing teams who focus on a “360-degree view of the customer” and pore over agency data on tactic performance, the sales teams who battle daily for two to five minutes with target HCPs, the digital teams optimizing Veeva or Salesforce and wrangling unwieldy data, and the market access teams negotiating, lobbying, and analyzing. Each function is a separate thread in the complex ball of yarn that is a Commercial team in 2026. Each trying to grapple with the pace of change driven by AI for specific, single threaded needs. For example, a rep getting handed a new “AI-driven next best action” tool but it’s bolted onto the same static quarterly system that existed before, and doesn’t address any fundamental underlying model improvements.

Opportunities for Pharma

The real question: if we designed a pharma commercial function from scratch today, would it look anything like this? My hypothesis is no, and that it would look much closer to the world of digital health.

Digital health and Pharma remain distant cousins in the family tree of life sciences and health. Where digital health can focus on specific interventions and inventions, Pharma, Big Pharma at least, has the unenviable task of spreading its approach across indications, therapeutic areas, and ultimately entire patient and HCP populations. The result is that digital health companies have grown up leaner, with commercial functions built as a specific, consolidated, and usually much smaller function than in pharma.

What pharma commercial could borrow from digital health isn’t complicated or novel, but it isn’t obvious in the day to day of a big pharma juggernaut. I once heard that in the beginning days of the internet, the NY Times typed in the full newspaper – just digitizing the physical copy. This is an exact analogy for how most of big pharma is using agentic AI now: focused on re-creation of existing ways of working and increasing efficiencies.

The less obvious side to the speed and efficiency coin is the idea of changing the actual operating rhythm of the portfolio: shipping concepts to production in days rather than months or years. A digital health team ships a feature in weeks based on a usage pattern; a pharma commercial team can spend a quarter just getting requirements gathered. Or radical focus: a digital health commercial function is built around a handful of clear outcomes, not a dozen sub-functions each optimizing their own area. Pairing radical focus with pharma’s strength in regulatory & compliance creates the opportunity to expedite patient outcomes in a way before unseen. Finally, digital health companies build with partners from day one because they have to, while pharma often plans in-house first and partner only once internal options are exhausted, organizational fatigue has taken grip, or the “build” is ready to get going.

Making It a reality

What would it look like if Pharma embraced a digital health style way of working? Potentially, it would be by replacing the sprawl of sub-divisional dashboards and systems with one shared data layer, a single source of truth on HCP engagement, patient journeys, tactic performance, and pipeline, with access rights layered on top rather than entirely separate systems per function. Instead of a field force, maybe it’s a cognitive command center AI that operates across functions instead of within one, flagging a stalled account to market access, content, and the field team simultaneously instead of routing sequentially through three approval chains. And rethinking the field model itself: rather than dispersing reps to chase five-minute HCP windows on a static cycle, that same investment could fund fewer, richer touchpoints, twice-yearly events, and deeper partnerships with different, AI native platforms with distribution & reach like OpenEvidence that HCPs are already choosing to use on their own terms.

None of this requires more commercial budget than pharma is already spending. It requires spending it on reimagining commercial as a function instead of speeding up an existing function. Digital health and pharma have plenty to learn from, and with, each other, and AI is the first real forcing function either industry has had to do it. The Pharma commercial teams that treat this as a chance to redesign the model, not just streamline it, will be the ones that change what’s possible for HCPs and patients. The remainder will only benefit from greater day to day efficiency.

Source: champpixs, Getty Images

Suzy Jackson

Suzy Jackson is a Digital Health & Commercial Pharma subject matter expert and former Digital Health Chief Commercial Officer at RVO Health, as well as former Head of the North America Life Sciences Commercial Consulting team at Accenture. Suzy’s independent advisory focuses on Commercial technology, strategy, Go-To-Market efforts, market entry, partnership shaping, and competitive analysis across Pharma and Consumer Health. Outside of her advisory work, Suzy is an avid scuba diver, traveler, and sits on the Boards of two non-profits focused on women’s rights, legal advocacy, and survivorship from trauma and abuse.

This post appears through the MedCity Influencers program. Anyone can publish their perspective on business and innovation in healthcare on MedCity News through MedCity Influencers. Click here to find out how.

October 9, 2026
AI Can Flag Cognitive Decline Earlier — Health Systems Still Own What Happens Next

A patient completes a brief cognitive assessment during a routine primary care visit. The result shows a pattern associated with possible impairment. It is not a diagnosis, but it is concerning enough to warrant follow up. The neurologist schedule is full. The primary care team is unsure who should explain the finding. The family wants answers that the result cannot yet provide.

This is the practical test for the next generation of cognitive screening. AI can now detect subtle changes in timing, speech, attention and task performance that may be missed during a conventional visit. Blood based biomarkers are also moving closer to routine clinical workflows. Together, these advances could help identify cognitive decline while people still have more options for treatment, planning and support.

Earlier detection is valuable, but it creates a duty that does not end when an alert appears in the electronic health record. Health systems need to decide, in advance, how a risk signal becomes a clinically accountable pathway. Otherwise, screening can shift uncertainty from the clinician to the patient without moving either of them closer to care.

An early signal creates a new clinical state

A screening result occupies an uncomfortable space. It may be more significant than an ordinary abnormal lab value because it touches a person’s memory, judgment and independence. Yet it remains far less conclusive than a diagnosis.

That distinction must be preserved. Cognitive performance can be affected by depression, poor sleep, medication, pain, hearing or vision problems, language, education and acute illness. A digital assessment may identify a pattern that deserves attention, but it cannot explain every cause behind that pattern. Even Alzheimer’s biomarkers are used within a broader evaluation. Recent MedCity coverage of blood based biomarkers noted that imaging or cerebrospinal fluid testing may still be necessary for some patients.

The words used in the chart, patient portal and clinical conversation therefore matter. “Elevated risk,” “possible impairment” and “consistent with pathology” are not interchangeable. A health system should define what each result means, what it does not establish and which next action it triggers. That language should be consistent across primary care, neurology, radiology, laboratory medicine and patient communications.

Assign responsibility before screening begins

No cognitive screening program is ready for scale until someone owns the result. The accountable person may be the ordering clinician, a trained nurse, a cognitive care coordinator or a memory clinic team. The model can vary. The obligation cannot.

The pathway should answer a few operational questions before the first patient is screened. Who reviews the result, and within what period? Which findings call for a repeat assessment, a clinical workup or specialist referral? Who checks for reversible contributors? Who contacts the patient? What happens when the patient cannot be reached or misses the next appointment?

Primary care teams already face uncertainty when cognitive concerns surface. One MedCity analysis of cognitive screening in Medicare wellness visits described how limited confidence in available data can push clinicians toward referral even when specialist capacity is constrained. Adding a more sensitive tool without clarifying clinical ownership may increase the number of referrals while leaving the underlying uncertainty intact.

A useful system does not merely flag risk. It routes the result to a person with the authority, time and protocol to act on it.

Communicate uncertainty as part of care

Patients should not receive a cognitive risk signal as an unexplained score. At minimum, the conversation should cover three points: what the assessment observed, what the result cannot determine and what will happen next. A concrete date or time range for follow up is more useful than a general instruction to speak with a specialist.

The timing of disclosure also deserves attention. If a result can appear automatically in a patient portal, the health system should consider whether clinical review or an explanatory message needs to occur at the same time. Learning about possible cognitive decline alone, through unfamiliar language on a screen, can turn a screening program into a source of avoidable fear.

Family involvement can help, particularly when a relative has noticed changes or supports appointments. It should still follow the patient’s preferences and consent. Caregivers are often treated as an informal extension of the care team, even though healthcare increasingly relies on them to coordinate complex care. They need clear information and defined support, not the responsibility of interpreting an uncertain result on their own.

Capacity is part of diagnostic quality

Earlier identification will increase demand for confirmation, counseling and longitudinal care. That demand should be estimated before screening expands. A health system needs to know how many additional assessments it can complete, how long patients will wait and which cases can remain in primary care with specialist guidance.

New care models may help. NYU Langone’s virtual dementia care partnership was designed to shorten waits for memory care while keeping patients connected to in person diagnostics and treatment when needed. The important principle is continuity. A virtual visit, a memory clinic, a care coordinator or a primary care protocol can all contribute, but the patient should not have to assemble the pathway alone.

Access must also be tested across language, culture, disability, education and digital familiarity. If a screening tool reaches a broader population but follow up remains available mainly to patients who can navigate complex referrals, the program may identify disparities without reducing them.

Measure whether the pathway closes

Model accuracy and the number of completed screens are necessary measures, but they do not show whether patients received care. Health systems should also track the time from flag to clinician review, the time to confirmatory evaluation, missed follow up, unresolved alerts, referral completion and the proportion of patients who understood their result and next step.

These measures reveal whether early detection is functioning as a clinical service or simply producing more information. They also show where responsibility is being lost between primary care, specialty care and the home.

AI can bring cognitive risk into view earlier than traditional observation alone. The benefit will depend on what the health system has built around that moment. A mature program is not defined by how many alerts it produces. It is defined by whether every clinically significant alert reaches an accountable professional, a timely evaluation and a conversation the patient can understand.

Early detection creates more time. Health systems must make sure that time leads somewhere.

Photo: Andrew Bret Wallis, Getty Images

Nargiz Noimann

Nargiz Noimann is a neuroscientist afounder focused on how immersive technology and AI can support cognitive resilience and emotional recovery in clinical and post treatment settings. She has more than 25 years of international experience across neuroscience, psychotechnology, and healthcare innovation, and has studied at institutions including Stanford University. Nargiz is the founder of X Technology, a UAE based healthtech company developing AI powered VR programs designed to support recovery experiences for patients and caregivers. She also leads the Scientific Research Center for Psychotechnologies in Kazakhstan, where her work explores structured approaches to mental resilience, attention, and recovery in high stress and chronic illness contexts.

This post appears through the MedCity Influencers program. Anyone can publish their perspective on business and innovation in healthcare on MedCity News through MedCity Influencers. Click here to find out how.

October 8, 2026