Readers Write: Bridging the Outcomes Gap: Transforming Maternal and Fetal Health Outcomes with EHR Technology

Bridging the Outcomes Gap: Transforming Maternal and Fetal Health Outcomes with EHR Technology
By Janet Desroche

Janet Desroche is associate vice president at Meditech.

Despite spending more on healthcare than any other high-income country, the United States continues to struggle with a maternal health crisis, yielding outcomes that are significantly worse than those of peer nations. 

The US reported 22 maternal deaths for every 100,000 live births in 2022. That rate was double or triple that of most other high-income countries, many of which report fewer than five deaths per 100,000 live births. Over 80% of these deaths are considered preventable, which underscores the urgent need for systemic improvements in care delivery.

These outcomes are characterized by severe disparities. Black women are at a disproportionately higher risk, with a pregnancy-related mortality ratio more than double that of white women. Furthermore, fetal and neonatal outcomes remain a concern. Infants born small for gestational age (SGA), with neonatal abstinence syndrome (NAS), or with intrauterine growth restriction (IUGR) face increased risks of adverse neurodevelopmental outcomes, including cognitive delays and neuromotor disabilities.

Programs That Measure and Recognize Care Quality

Several initiatives have been established to identify and recognize organizations that are delivering optimal care. Globally, the World Health Organization’s “baby-friendly” hospital designation recognizes facilities that adhere to the highest standards of care for breastfeeding and mother-baby bonding.

Nationally, the Centers for Medicare & Medicaid Services (CMS) established the “birthing-friendly” designation, a public-facing quality status that helps families choose hospitals that have demonstrated a commitment to maternal health. This designation identifies facilities that participate in Perinatal Quality Collaboratives and implement evidence-based safety bundles to improve outcomes.

Additionally, the American College of Obstetricians and Gynecologists (ACOG) and the Society for Maternal-Fetal Medicine (SMFM) have standardized “levels of maternal care.” This framework promotes regionalized care, which ensures that high-risk pregnancies are matched with facilities that are equipped with the appropriate subspecialists and critical care resources, ranging from Level I (basic care) to Level IV (regional perinatal health care centers).

Leveraging Technology and the Electronic Health Record for Positive Impact

Healthcare organizations are using their EHRs to incorporate evidence-based guidance. By embedding best practices and clinical decision support directly into the workflow, they are driving early detection and timely intervention for the leading causes of maternal morbidity. These interventions are associated with improved outcomes and tangible lives saved. 

• Obstetric hemorrhage. Obstetric hemorrhage is a leading preventable cause of maternal death. To address this, EHR toolkits now align with the Association of Women’s Health, Obstetric and Neonatal Nurses (AWHONN) guidelines, replacing visual estimation of blood loss with quantitative measurement. The system automatically calculates quantitative blood loss (QBL), determines the hemorrhage stage (Stage 1–3), and prompts the care team with stage-specific interventions and order sets, ensuring that life-saving protocols are initiated immediately.
• Preeclampsia and hypertension. Timely recognition of hypertensive crisis is critical to preventing stroke and seizure. Advanced surveillance tools can monitor vital signs in real time, flagging patients who meet specific criteria, such as systolic blood pressure greater than 160 or diastolic pressure greater than 110, that persist for 15 minutes.
• Maternal sepsis. Early recognition reduces sepsis mortality. EHR surveillance systems continuously analyze patient vitals and lab results to identify those meeting sepsis criteria. Once identified, automated screening tools and order sets guide clinicians to immediately initiate evidence-based care bundles.
• Maternal addiction and opioids. Technology also plays a vital role in combating the opioid epidemic’s impact on maternal and fetal health. ACOG and SMFM recommend a non-punitive approach to improve outcomes for pregnant women with opioid use disorder that includes universal screening, early intervention and referral, medication for opioid use disorder (MOUD), naloxone access, and postpartum support. These interventions have been incorporated into many EHRs and can be effective in improving outcomes and reducing harm.
• Infection control. Beyond sepsis, surveillance dashboards help differentiate between active infections (like C. difficile) and colonization. This automation reduces unnecessary testing and isolation while ensuring compliance with stewardship protocols.

Organizations have used their EHR to achieve measurable improvements in maternal care and safety. EHR surveillance supports Joint Commission measures by identifying hemorrhage and hypertension risks early and prompting treatment protocols early to reduce maternal complications. Decision support tools within an EHR can help ensure SEP-1 compliance and reduce sepsis mortality rates. These features show how EHRs embed best practices into workflows and support earlier intervention, enabling healthcare systems to move beyond reactive care to proactive, lifesaving management of maternal and fetal health.

Readers Write: Chatbots Are Repeating a Familiar Healthcare Mistake

Chatbots Are Repeating a Familiar Healthcare Mistake
By Robin Monks

Robin Monks is chief technology officer of Praia Health.

I have five healthcare applications on my phone: multiple patient portals, a lab app, a health tracker, and a record repository from a prior provider. None of them communicate with each other. I work in healthcare technology and build patient experiences for a living, and this is still the reality. That should be a red flag.

Health systems have spent years layering digital tools onto already complex environments. The latest additions are chatbots and AI assistants, each of which are designed to solve a narrow problem. Individually, they make sense. Collectively, they recreate the same fragmentation that digital transformation was supposed to fix.

This pattern has played out before. Mobile strategies in the 2010s produced a flood of standalone apps that patients downloaded once and then abandoned. The industry burned time, money, and credibility getting them into market.

A similar cycle is now underway with AI. Tools are being deployed quickly, often without a coherent strategy. The World Economic Forum has already warned that most health systems lack a defined generative AI strategy despite rapid adoption. That gap between enthusiasm and discipline rarely ends well.

Multiple chatbots now exist within the same health system, each tied to a specific function. From a patient perspective, distinctions between vendors or capabilities are meaningless. Every interaction reflects on the organization. When one tool fails, trust erodes across all of them.

Many of these tools cannot complete the jobs they start. A scheduling request ends with instructions to call an office, and a refill request redirects patients to another app. Each handoff introduces friction, and each point of friction increases abandonment. The industry has built an impressive number of ways to begin a task and very few reliable ways to finish one.

Identity is another weak point. Many tools operate within a single channel, often SMS or a standalone app, without a consistent identity layer. Context is lost as soon as a patient moves between touchpoints. The system does not recognize the same individual across channels, so the experience resets.

The result is confusion about how to complete even basic care tasks. The ONC reports that most individuals now juggle multiple patient portals, yet almost none use tools to manage them. When the path forward is unclear, engagement drops, preventive care is delayed, and follow-ups are missed. The gap between intent and action widens.

Clinical systems learned this lesson years ago. Fragmentation in clinician workflows was treated as a patient safety issue, driving consolidation into unified records. The same logic applies to the patient experience. Fragmentation on the front end produces the same outcome: missed steps, incomplete information, and avoidable risk.

Adding more tools will not fix this. The problem isn’t a lack of functionality, it’s a lack of orchestration.

A workable approach starts with data that spans the full care journey rather than being trapped in individual systems. National efforts such as TEFCA and standards like FHIR are making this increasingly feasible. The infrastructure is emerging, but the industry’s track record suggests it may still find a way to misuse it.

Identity must also be treated as foundational rather than optional. A consistent, portable identity allows continuity across channels and services. Without it, every integration is shallow, and every experience is brittle.

Most importantly, digital experiences must enable action. Many current solutions are little more than read-only interfaces. They show information, but cannot do much with it. A useful system allows scheduling, making payments, obtaining referrals, and performing follow-through without forcing patients to navigate a maze of disconnected tools.

None of this is conceptually difficult. The challenge is discipline. Health systems continue to approve new tools faster than maximizing existing ones. Vendors continue to sell point solutions that solve isolated problems while ignoring the broader experience. The result is more complexity, more fragmentation, and diminishing returns.

Healthcare is approaching another familiar fork in the road. One path continues the current trajectory: deploy more AI tools, watch adoption plateau, and quietly move on to the next trend. The other path requires doing the harder work of integration and orchestration, using emerging interoperability infrastructure to build experiences that actually hold together.

The technology is no longer the limiting factor. The limiting factor is the willingness to stop adding digital point solutions and start designing systems that function as a whole. Without that shift, the industry will repeat a cycle it should have already outgrown, replacing one generation of digital clutter with another.

Readers Write: AI in Revenue Cycle Demands More Than Innovation

AI in Revenue Cycle Demands More Than Innovation
By Patrice Wolfe

Patrice Wolfe, MBA is CEO of AGS Health.

​It’s hard not to conclude that the US healthcare system is at an inflection point. After more than 40 years in this industry, I feel that few other moments (perhaps COVID?) have carried the same weight of urgency, disruption, and potential.

Our complex healthcare ecosystem has always operated under pressure. Financial constraints, reimbursement changes, and a shifting regulatory environment are constants in the revenue cycle and across the broader system. What is different now is the pace and scale of technological change, particularly with artificial intelligence (AI).​

Healthcare has never been known for leading in technological innovation. Our industry is deeply tied to regulatory requirements and complex data structures and infrastructures that tend to slow adoption. Even so, we are seeing rapid movement in several pockets of our industry. AI is no longer a future consideration. It is becoming central to how revenue cycle operations and care delivery evolve.​

At the same time, the conversation has shifted from possibility to practicality. The question is no longer what AI can do in theory, but what works in real-world environments that are constrained by margin pressure, operational complexity, limited data liquidity, and uncertainty.​

The One Big Beautiful Bill Act (OBBBA) and other recent legislative and policy changes are beginning to translate into real financial impact. Analysis from Premier Inc. suggests that as much as $68 billion in hospital revenue could be at risk, with some provider organizations facing net patient revenue declines of up to 10%. For many health systems, revenue cycle optimization has already been a key strategic priority. It is increasingly becoming a necessity across the board.​

At the same time, insurance coverage continues to shift. Federal Marketplace enrollment declined 5% in 2026. That is better than expected. But signup numbers are a poor proxy for coverage. Enrollees have until March 31 to pay their premium bill, and after that, coverage will be retroactively terminated, driving higher uninsurance rates. We won’t have a clear picture until July 2026 of the impact that this will have on the insurance mix.

Pressure is also coming from the payer side, where AI adoption has progressed more quickly. Roughly 20% of claims are now being denied, and more than 60% of those denials are never appealed. That represents both a growing challenge and an opportunity for providers to recover revenue more effectively.​

Against this backdrop, health systems are taking a more disciplined approach to AI investment.

Interest in denials management, prior authorization, automation, and clinical documentation integrity remains high. The use cases are compelling. However, the standard for adoption has changed. Organizations are demanding clear, measurable return on investment before committing to solutions that often require high upfront cost and operational change.​

This shift is reflected in conversations across the industry. One health system CIO recently described being approached by a steady stream of AI vendors, each pledging transformation. His response was direct. Show proven results in comparable environments or the conversation does not move forward.

That perspective is increasingly common. Emphasis is shifting to pragmatism over experimentation. Even with that focus, implementation is not simple.​

AI adoption requires more than selecting the right use case. It depends on underlying capabilities that many organizations are still developing. Cybersecurity architecture and governance must be strong enough to support more advanced technologies. Oversight, both operational and regulatory, remains in flux. Federal-level AI regulation has shown some movement, but clarity is limited on what that framework will ultimately look like. In the meantime, organizations are moving forward in an environment that is defined by uncertainty.​

Given these conditions, the way forward is not about broad, rapid adoption. It is about targeted, disciplined execution. There is real opportunity. Modeling from McKinsey & Company suggests that AI could reduce provider collection costs by 30% to 60% over time. Realizing that potential will require a measured approach that balances automation with skilled human expertise.​

Innovation on its own is not enough. Solutions must function within existing workflows, not outside of them. Healthcare revenue cycle workflows are complex, and successful transformation depends on adopting technology that reduces friction rather than adds to it. When done effectively, this can streamline manual work, boost financial performance, and improve both patient and provider experience. The common thread is execution. ​

Healthcare does not lack ideas or innovation. What it requires now is the ability to apply both in ways that are practical, scalable, and measurable. AI will play a central role in that transformation, but only if it is deployed with discipline and a clear understanding of what success looks like in actual conditions.

Readers Write: Revealing Hidden Rural Health Funding Opportunities

Revealing Hidden Rural Health Funding Opportunities
By Phil Sobol

Phil Sobol is chief commercial officer at CereCore.

Rural healthcare leaders are some of the most resourceful people in the industry. But even the most seasoned administrators are often surprised to learn how many funding opportunities exist beyond the federal bills that dominate the news cycle, including state-specific grants, national resource hubs, and coalition programs. The money is more accessible than you think. Here is where to start.

It’s Not Just About Federal Funding

Sweeping federal legislation like the Rural Health Transformation Program creates meaningful opportunities for rural communities that are working to reimagine care delivery and outcomes. That program alone supports systemic transformation at scale. But for many rural hospitals and health systems, waiting for large legislative vehicles to materialize and then competing for a slice of a heavily subscribed pool is not a funding strategy.

Another path is to look at the full ecosystem of available funding, much of which carries fewer restrictions and less competition than headline-grabbing programs.

State-Level Funding Is Underused

One of the most overlooked categories of rural health funding is state-specific grants and programs. States vary enormously in what they offer, but patterns emerge when they are studied closely. Several states have developed dedicated funding streams specifically for coalition formation. Rural healthcare delivery increasingly depends on networks of providers coordinating care rather than isolated facilities doing it alone.

Funding themes that recur across states include clinical integration, access and infrastructure investment, and health information exchange. The specific states prioritizing each theme differ, which means that a funding opportunity that is perfectly suited to one organization might not exist for a neighbor two states over.

Geography matters. Knowing your state’s funding priorities and how those align with your organization’s strategic goals is not optional background knowledge. It is the foundation of a viable grant strategy.

National Resources That Deserve More Attention

Beyond state-level programs, several national resources provide structured pathways to funding that rural health leaders should bookmark and revisit regularly.

The Rural Health Information Hub is one of the most useful and underused resources. It is available to organizations in all states. It functions as a centralized library, aggregating funding opportunities, implementation tools, evidence-based models, and best practices from across the country. For organizations without a dedicated grants team, it’s an accessible entry point into what is available and what has worked elsewhere.

The Health Resources & Services Administration (HRSA) offers multiple grant programs that are specifically relevant to rural and underserved communities. Among these are programs that support coalition development and cross-provider partnerships, funding categories that are often better fits for rural organizations than infrastructure-heavy grants that assume resources and capacity those organizations simply don’t have.

Technology Is Often an Eligible Use of Funds

This is where the conversation gets particularly interesting for health system leaders who are thinking about long-term sustainability. Many of these funding programs explicitly support technology acquisition and modernization. That means that eligible organizations can use grant funding to purchase or upgrade core components of their technology stack, EHR systems, care coordination platforms, telehealth infrastructure, cybersecurity tools, and broadband connectivity.

For rural hospitals operating on decades-old systems, this changes the math significantly. Technology upgrades that once felt financially out of reach become viable when grant funding offsets or covers the cost entirely.

Telecommunications infrastructure is a particularly underused category. Rural facilities may qualify for programs that reduce or eliminate the cost of voice, data, and broadband services, which directly enables telemedicine, improves EHR performance, and strengthens care coordination across dispersed networks.

The key is to understand which programs allow technology as an eligible expense and structuring your application to demonstrate how that investment serves the broader clinical or community health outcome that the grant is designed to support.

Where to Start

If rural health funding feels overwhelming, the practical first step is not to research every available program simultaneously. It’s to get clarity on your organization’s most pressing strategic needs, whether that’s clinical integration, cybersecurity, telehealth capability, or a long-overdue technology upgrade, and then systematically identify which funding streams align with those priorities.

Start with the Rural Health Information Hub to understand the national landscape. Check HRSA’s current grant offerings for programs relevant to your community type and focus areas. Investigate what your state specifically offers, including any coalition-focused programs that may have fewer applicants and less competition than federal grants.

Funding will never solve every challenge that rural healthcare faces. But the right resources, pursued consistently and strategically, can meaningfully change what’s possible for your patients, your staff, and your community. That is worth the effort of knowing what’s available, and this is a good place to start.

Readers Write: RHTP is Money for Rural Hospitals, But States Say Maybe Not

RHTP is Money for Rural Hospitals, But States Say Maybe Not
By Mike Lucey

Mike Lucey, MBA is president of Community Hospital Advisors.

What happened to “hospital?”

“Protecting Rural Hospitals and Providers” is the title of Chapter 4 (section 71401) of the Big Beautiful Bill, which defines the Rural Health Transformation Program (RHTP). It is six pages of clear instruction on how $50 billion will be funneled into rural healthcare over five years. It outlines who gets it and how they can spend it.

This is an impressive economy of words for a federal document with such a bold objective. The word “hospital” appears 15 times in those six pages. The longer federal Notice of Funding Opportunity (NOFO), which calls on states to submit applications for this funding, includes about 80 instances of “hospital,” which reveals an understanding that rural hospitals are the main access points for a wide variety of care, services, and resources for these communities.

But in many state applications, the word “hospital” fades or completely disappears. How is it possible that a law that was written to protect rural hospitals can morph into a series of state programs and agencies with no explicit mention of hospitals in their objectives?

Delivering rural healthcare faces two key challenges: too much space and too little money. When it comes to money, things are clear. The AMA reports that half of rural hospitals are operating at a deficit. The reasons are complex and varied, but location is intrinsic to all of them.

Too much space makes it less profitable to provide healthcare in a rural setting. Rural patients are, on average, two to three times farther from care than urban or suburban patients. The farther people are from anything, the less they do it. It doesn’t matter whether it is a gym, a bar, a parent, or a doctor. Distance becomes a reason or an excuse for why we can’t get there.

Can RHTP Help? Yes!

RHTP provides an opportunity to address these challenges by providing hospitals with the resources to hit new standards for how they provide care, especially in how they use technology. Nothing is going to change the length of a mile. But technology can close the access and contact gaps that distance creates.

Telehealth and home medical devices are great care tools that continue to get better over time. Full-access portals allow scheduling and reminders, and make messaging clinicians as easy as texting. Transportation can be scheduled and managed for patients with mobility issues through a fully functioning patient portal.

Once technology is in place to increase frequency and consistency of patient contact, technology can enhance these interactions and the quality of care with AI-augmented applications for notes, orders, and coding. These improve provider workflow, decrease burnout, allow better physician-patient interaction, and set the stage for AI clinical guidance. Finally, robust analytics and data management systems will improve the exchange of clinical data between facilities and providers, allowing high quality care regardless of location or specialty.

This vision for better rural care through technology is at the heart of RHTP, and these objectives are stated plainly in those six pages of section 71401. They are worthy and important goals. Improving just these three areas: patient contact, care delivery, and data exchange, improves care for every patient accessing every service the hospital provides. This care foundation can then expand to improve chronic care, nutrition, behavioral health and substance use disorder services, all of which are stated goals of RHTP.

RHTP exhibits a good understanding of the rural “too much space, too little money” challenge. It identifies the problems that space causes and then offers solutions and the money that is needed to deliver those solutions.

But somewhere between the authoring of the original bill and the allocation of funds from the states, many programs veer off course. Money wakes the bureaucratic beast, and the word “hospital” begins to fade.

But that doesn’t mean that the Rural, Critical Access, and Community Hospitals that serve one in five Americans, should accept defeat.

How Do Hospitals Stay at the Table?

My first encouragement to hospitals: don’t be complacent. Don’t take it as inevitable that this money is going to get siphoned off by large and connected entities. States vary widely in how friendly or not they are toward hospitals, but all will make some funds available directly. The difference in how much may well depend on how many hospitals are presenting well-constructed, justifiable projects.

Second: don’t be patient. States are just now assembling staff and drafting processes that will eventually become a method to distribute funds. Now is the time to get to work.

• Create your project list. Not the list every rural hospital has, which includes things you will get to when you have the money. It is that list plus all the things that you have not even let yourself think about because the budget was so restricted.
• Tie each project to your state initiatives and to the federal Use of Funds. Include estimated cost, timeline, and metrics.
• When your state publishes its protocols, format your request to be compliant.
• Whenever possible, team up with other sites. A collective of voices is harder to ignore.

Finally, don’t get discouraged. These processes are intentionally painful. OK, that is my opinion, but I find the process painful and have come to believe it is meant to cull the number of applicants and leave just the group that makes the process a profession. Stick with it.

Patients in rural communities are being left behind. RHTP is an opportunity to change that. There will always be too much space in rural healthcare, but with the right investment and execution, hospitals can close the gap and make a meaningful difference.

Readers Write: Patient Access Has Evolved. The Operating Model Hasn’t.

Patient Access Has Evolved. The Operating Model Hasn’t.
By Steve Nilson

Steve Nilson is acting director of access and experience with Tegria.

Nearly every health system calls access a strategic priority. Once considered an operational outcome, patient access is now discussed in board meetings, embedded in growth strategies, and linked to financial sustainability and digital transformation. That’s real progress.

But in many organizations, the operating model still reflects an older reality. It’s an all-too-familiar model, one where scheduling, digital tools, workforce planning, and financial accountability are governed separately. We have elevated access to the boardroom. We just haven’t rebuilt the system around it. The result is misaligned leadership and a persistent gap between ambition and execution.

Recognition Isn’t the Problem

In conversations across health systems, leaders describe access as foundational to growth, patient experience, and margin performance. Executive teams review access metrics regularly. Investments have flowed into centralized scheduling, digital front doors, automation tools, and AI-enabled communications.

Yet appointment availability remains constrained. System transparency is lacking. Wait times persist. Workforce shortages strain capacity. Digital tools are layered onto workflows that were never redesigned.

The issue isn’t awareness, it’s integration. Access touches operations, clinical leadership, IT, strategy, and finance. In most organizations, responsibility is shared across these groups. Shared ownership can be healthy, but without clearly defined decision rights and coordinated governance, it often diffuses accountability. If everyone influences access, who owns the outcome?

The Structural Gaps

Three structural gaps appear repeatedly.

• Governance without coordination. Access strategy may be discussed at the executive level, but operational decisions still sit within departmental silos. Template design lives in ambulatory operations. Digital configuration sits with IT. Workforce planning sits elsewhere. Financial oversight operates on its own cadence. When these domains are not aligned around common priorities and shared metrics, execution slows. Decisions are made locally that affect enterprise performance globally.
• Technology before workflow redesign. Many systems have invested heavily in digital tools to enable access, from online scheduling to automated outreach, to AI-driven communications. These capabilities matter. But technology does not correct poorly designed templates, unclear referral pathways, or misaligned incentives. Without disciplined workflow redesign and provider alignment, digital optimization becomes surface-level improvement. The underlying constraints remain.
• Workforce treated as a supply problem. Workforce shortages are real and significant. But many organizations frame the issue solely as a recruitment and retention challenge. Less attention is given to productivity design, top-of-license utilization, and care team restructuring. When capacity constraints are treated only as a hiring issue, operational redesign opportunities are missed. Access transformation requires rethinking how care teams are structured, not just how many FTEs are available.

Finance Must Be in the Room

Another pattern is limited structural involvement of finance in access governance. Access is expected to drive growth and protect margin, yet ROI attribution and capital discipline are not always tightly integrated into strategy development.

That disconnect creates tension. Operational leaders pursue experience and throughput improvements. Finance leaders require near-term, measurable return. Without shared governance and aligned performance metrics, access initiatives can stall in prioritization cycles.

Access cannot be an operational initiative with financial consequences reviewed later. It must be governed as a financial strategy from the start.

What Actually Changes the Trajectory

Organizations that close the execution gap do a few things differently:

• They clearly define what success looks like.
• They establish enterprise-level governance with defined decision rights for access.
• They align operational, clinical, digital, and financial leaders around a shared scorecard.
• They challenge internal policies and requirements that add complexity to processes.
• They redesign workflows before optimizing technology.
• They treat workforce design as a strategic lever, not just a staffing problem.
• They narrow priorities rather than spreading resources across fragmented pilots.
• Most importantly, they recognize that access is not a project, but an enterprise priority.

From Initiative to Operating Model

The next phase of access transformation will not be defined by how many tools are deployed. It will be defined by whether organizations align governance, workforce, finance, and digital infrastructure around a cohesive operating model.

Access has been elevated appropriately. Boards are paying attention. Executives are engaged. Investment continues. But elevation alone doesn’t produce integration. Until access is governed with the same structural rigor as finance, quality, and growth, health systems will continue optimizing components rather than transforming performance.

The opportunity isn’t to declare access strategic. It’s to build the system that makes it executable.

Readers Write: A Global Perspective on Advancing Precision Medicine with Genomic EHR Integration

Readers Write: A Global Perspective on Advancing Precision Medicine with Genomic EHR Integration
By Jennifer Ford

Jennifer Ford, MBA is manager of clinical product management and genomics at Meditech.

The promise of precision medicine is simple, using genetic data to identify the best treatment for each patient as quickly as possible.

During my travels to South Africa and Namibia, healthcare leaders in both urban and remote areas shared enthusiasm for the role of EHRs in incorporating genomic data to guide treatment decisions. However, it also made wonder that if the passion for advanced technologies like genomics is so universally embraced, then what barriers are holding us back from widespread adoption?

The Challenges of Adopting a Precision Medicine Program

Despite its promise, adoption of genomics and precision medicine has been slow. Several challenges, both real and perceived, are hindering its adoption:

• Costly testing. While the costs of personal genetic testing have declined significantly in recent years, many patients still face difficulties accessing genetic testing due to high out-of-pocket costs and limited or no coverage.
• Limited availability of testing. Not every health system offers this type of testing, either due to a lack of local testing facilities, insufficient funding, or the absence of a service line.
• Lack of understanding testing value. Many healthcare providers are unfamiliar with the use of genomics in diagnosis and treatment, particularly those working in environments where genomic data is not a prevalent part of the EHR.
• Lack of EHR Integration. Providers often don’t have access to this data within their EHR workflows, and if they do, it is as a static document that is attached to the patient’s record and is too cumbersome to sift through.
• Result data is not actionable. The lack of standardized clinical alerts or decision-support systems that incorporate genomic data means providers may lack the tools or training to make genomically informed decisions.
• Testing is reserved for academia. Precision medicine remains more prevalent in academic and research centers than in community-based health systems, where most care occurs.

These challenges and misconceptions often stem from experiences that predate the integration of genetic data into the EHR, but the paradigm can change.

Overcoming the Challenges of Adopting a Precision Medicine Program

I’ve worked with healthcare leaders who are integrating genomics into the EHR. The result has been that when genetic data is ingested discretely into the EHR, clinical alerts become available for each patient based on their genetic information, enabling personalized patient care.

Genetics is not just for academic centers. I’ve seen the value that community hospitals gain when patients receive genetically-led services locally rather than traveling to larger academic medical centers. By equipping community clinics with a user-friendly, plug-and-play solution, they can focus on translational research that will lower costs, improve accessibility, and achieve better patient outcomes.

The Benefits of Adopting a Precision Medicine Program

The benefits of genomics in healthcare are becoming increasingly clear. The use of genomic data extends beyond cancer treatment, as health systems are using it to improve behavioral health treatment, newborn and pediatric care, and health and weight management. Having effective technology that can analyze genomic data to provide clinical support empowers clinicians to deliver more targeted patient treatment and support population health objectives. Adopting a genomics program can also support service line growth.

Global Precision Medicine Initiatives

Various initiatives worldwide are bringing genetic testing to the forefront of healthcare. Each area of the world faces distinct challenges related to geography, patient demographics, and scaling testing opportunities.

In South Africa and Namibia, healthcare leaders shared their desire to improve access to genetic testing in African nations. To reduce costs and maximize the benefits of genomic data, they are experimenting with leveraging social determinants of health to identify and prioritize patient cohorts to whom they will deploy testing. Where technological infrastructure may be limited, national labs are looking for ways to more equitably transport and perform testing from remote villages using drones, satellite internet services, and other technologies.

In England, the National Health Service (NHS) announced a £650m investment to provide every baby in England with DNA screening to identify potentially fatal diseases and to offer personalized healthcare as part of the government’s 10-year plan. The NHS recognizes that when patients receive personalized healthcare to prevent ill health before symptoms begin, it will reduce the pressure on NHS services and help people live longer, healthier lives. In the US, a similar approach has been announced in Florida’s Sunshine Genetics Act, which funds newborn genome sequencing pilots. These efforts are helping shift the paradigm toward proactive, personalized healthcare.

In Maryland, Frederick Health operates a dedicated precision medicine and genetics clinic that uses genomic data for precision medicine in behavioral health and beyond. In a Scottsdale Institute presentation, they shared how they addressed cost concerns by negotiating testing costs with laboratories and started a rapidly growing clinical trials program. They use genomic data to identify patients for clinical trials, increasing enrollment and improving care. They have found that moving clinical trials into the community hospital space increased revenue.

Ontario Shores Center for Mental Health Services in Canada announced that it would offer free pharmacogenetic testing of eligible patients to improve outcomes. The testing is initially focused on improving the treatment of patients who are admitted with schizophrenia or schizoaffective disorder, with plans for future expansion to use pharmacogenomics in behavioral health management.

Final Thoughts: Adopting Precision Medicine in Clinical Care is Essential

The more that genetic data is integrated into the EHR, the faster widespread deployment will occur. As clinicians find meaningful utility in genetic data, the importance of a strong precision medicine program shifts from a nice-to-have to a must- have. The key factor is how the EHR can leverage genetic data to improve patient outcomes.

As applications for genetic data evolve, an established genetic program becomes essential to improving physician satisfaction by empowering them with the advanced tools that they need to provide the best possible patient care.

Readers Write: When the Cloud Becomes the Attack Surface

When the Cloud Becomes the Attack Surface
By Brian McManamon

Brian McManamon, MBA is general manager of managed security and managed cloud services at Clearwater.

Healthcare organizations often talk about cloud as though it is a destination. In reality, for most hospitals, it has become an operating layer that keeps expanding.

That expansion did not usually happen through one formal strategy. It happened incrementally through SaaS adoption, remote access, vendor integrations, analytics tools, backup environments, and acquisitions. What many organizations now manage is not a clean cloud migration, but a hybrid environment made up of on-premises systems, cloud platforms, and third-party services that are tied together through identity and connectivity.

That matters because the cloud is no longer just part of the technology stack. In many environments, it has become part of the attack surface.

For many hospitals, “moving to the cloud” does not mean shutting down the data center and rebuilding everything as cloud-native. It usually means adding cloud services around existing operations. Clinical and business systems may still sit on-premises while identity, disaster recovery, remote access, analytics, and collaboration tools increasingly depend on cloud services. SaaS expands the footprint even further, often without being treated internally as part of the organization’s cloud environment.

That is where risk begins to grow quietly.

One of the most common misconceptions is that cloud is secure by default because the provider is secure. Major providers such as AWS, Azure, and Google Cloud invest heavily in securing their platforms. What they do not secure is each customer’s implementation.

Hospitals still own the responsibility for identity, configuration, access controls, logging, monitoring, and governance. If those areas are weak, cloud adoption can expand exposure faster than teams realize.

The opposite misconception is also common. Some organizations assume that keeping critical systems on-premises limits cloud risk. In practice, many of those same organizations have already adopted cloud identity, SaaS, remote vendor access, and external integrations. They have become hybrid whether they planned to or not. The difference is that they may not be managing that reality with a clear operating model.

Hybrid itself is not the failure. It is normal. In many cases, it is the natural result of smart teams making practical decisions over time.

A department adopts a new SaaS platform. IT centralizes identity. A cloud backup initiative begins. A new analytics platform is introduced. An acquisition brings another tenant, another domain, or another set of inherited tools. None of those decisions is inherently problematic. The problem is that governance and visibility often do not scale at the same pace.

That is when the cloud starts to become the attack surface.

The risk shows up first in identity. In hybrid healthcare environments, identities increasingly function as the control plane. Privileged roles accumulate. Service accounts remain active without clear ownership. Exceptions to MFA or conditional access persist longer than intended. Shared administrative access and standing privileges expand the potential blast radius of a single compromise.

An attacker no longer needs to move through the environment in the old ways if they can come through a valid account, exploit a policy exception, or take advantage of weakly governed permissions in a cloud-connected system.

The problem is compounded by visibility gaps. Many healthcare organizations do a strong job monitoring endpoints and network activity, yet cloud signals often remain fragmented. Logs may live across multiple consoles, subscriptions, tenants, and SaaS environments. Security teams may be watching the perimeter closely while missing critical changes in role assignments, application permissions, data shares, or service account behavior.

When those signals are not centralized and correlated, detection slows down. In some cases, it never happens at all.

Data sprawl adds another layer of risk. Healthcare environments generate copies of sensitive data for backups, archives, exports, analytics, and testing. Over time, protected health information can end up in more places than intended, sometimes with broader access and weaker protections than production systems. The issue is not only where the data started, but where it moved, who can reach it, and whether that movement is being governed consistently.

This is why cloud security in healthcare cannot be treated as a narrow infrastructure question. It is a governance question, an identity question, and ultimately a resilience question.

Cloud can improve resilience, but only when it is designed deliberately. Redundancy, scale, and operational flexibility can be real advantages. But those advantages weaken quickly if identity becomes a single point of failure, if disaster recovery exists only on paper, or if dependencies across cloud, SaaS, and legacy systems are not fully understood. In a hospital, resilience is not just uptime. It is the ability to support patient care when systems are under stress.

Good governance in that environment does not mean a large policy binder sitting on a shelf. It means a small number of clear, enforceable standards.

Hospitals need defined ownership for subscriptions, accounts, and services. They need baseline guardrails that prevent unsafe defaults. They need identity governance that prioritizes least privilege, manages non-human identities, and reviews exceptions regularly. They need enough centralized logging and alerting to see meaningful changes in the environment and act on them.

Most importantly, governance has to work in a 24/7 clinical setting. That means building models that support urgent care delivery without abandoning accountability. Exceptions may be necessary, but they should be time-bound, documented, owned, and reviewed.

The cloud is not the problem by itself. Unmanaged cloud is.

For healthcare leaders, one of the most useful next steps is a practical reality check. Inventory the tenants, subscriptions, service accounts, and privileged identities that are already in use. Confirm ownership. Review standing administrative access. Identify where visibility into cloud activity is missing. In most organizations, the attack surface has expanded gradually enough that no single decision created the problem. That is exactly why it deserves attention now.

In healthcare, the fundamentals still apply. Know your environment. Govern identity and access. Maintain visibility into critical systems and data flows.

The cloud becomes dangerous when organizations stop treating it as infrastructure and start assuming it will govern itself.

Readers Write: Healthcare’s Quietest Financial Problem

Healthcare’s Quietest Financial Problem
By Reed Liggin

Reed Liggin, RPh, MBA is co-founder and CEO of SlicedHealth.

Payer contracts are negotiated with extraordinary care. They often involve detailed financial modeling, language review, and extended debate among finance and managed care leaders who understand that the margin implications extend beyond the current fiscal year.

By the time an agreement is signed, the organization usually understands clearly what it expects to be paid and how those numbers fit into broader margin targets.

What is less clear, in many cases, is whether those expectations hold once the contract is operational. Negotiation is a focused event. Execution is an ongoing process that depends on claims configuration, payer adjudication logic, and a long list of small decisions that rarely receive executive attention. The contract may be sound, yet its performance in practice can drift in ways that are difficult to detect without deliberate review.

That is where healthcare’s quietest financial problem tends to live.

Most reimbursement misalignment is not dramatic. It does not look like a denial spike or a system outage. It looks like an almost-right payment, then another that is almost right, and then a few thousand more that are almost right. Those are the hardest errors to spot because they do not feel like errors.

The reason is simple: the contract is rarely a single rate. The contract is a set of conditions.

A surgical case might be paid under a base methodology, while the implant follows a different rule. A drug might be carved out if the NDC is present and paid under a different schedule if it is not. An outlier threshold might apply only after a cost calculation that depends on how charges were mapped and which revenue codes were recognized. A quality adjustment might be effective on paper in January, effective in the payer’s configuration in March, and not visible on the remittance in a way that makes it easy to reconcile.

Because the discrepancies are usually small, they tend to be absorbed into ordinary variance explanations. Margins fluctuate for many reasons. When reimbursement is directionally correct, it is easy to conclude that the contract is performing as intended. The temptation is to move on, because there is always something louder competing for attention. Over time, however, small differences across high-volume services can accumulate in ways that are more consequential than any single remittance would suggest.

The structure of most organizations reinforces this pattern. Contract negotiation is concentrated within managed care and finance leadership. Payment posting and denial management sit within revenue cycle. Financial performance is reviewed at an aggregate level.

Each function operates responsibly within its own scope. The precise alignment between negotiated language and adjudicated payment exists somewhere between those scopes, which makes it harder to see and harder to measure consistently.

Sampling can confirm that the world is not on fire, but does not reliably detect systematic drift across a high-volume population, especially when the drift is small and distributed across service lines, modifiers, and carve-outs. Those issues do not typically surface through a single appeal or an isolated audit finding. They reveal themselves gradually, if at all.

A practical constraint is that appeal timelines move quickly. Reconstructing the intent of a negotiated provision after months of operational activity is not simple. By the time a pattern becomes visible, the administrative effort required to pursue it may outweigh the perceived benefit, especially when the variance per claim appears modest. The economics of attention often favor larger, louder problems.

Healthcare finance is disciplined in many ways. Budgets are scrutinized. Forecasts are refined. Variance explanations are debated. But reimbursement accuracy, when it is mostly right, rarely commands the same intensity.

The difficulty is that reimbursement is foundational. When performance is directionally aligned but not exact, the difference can remain invisible inside aggregate results.

None of this implies widespread failure. It reflects the increasing complexity of reimbursement and the reality that operational systems interpret legal language through their own logic. Small gaps are easier to tolerate than large ones, and quiet gaps are easier to overlook than noisy ones.

In an environment where margins are narrow and expectations are high, quiet misalignment has a way of persisting longer than anyone would prefer. It does not demand attention. It rarely introduces itself. It simply continues in the background, one remittance at a time.

The contract ends with signatures. Its performance unfolds slowly, in details that are easy to assume and harder to verify. That space between intention and execution is where healthcare’s quietest financial problem tends to reside.

Readers Write: How AI is Helping Providers Navigate Regulatory Uncertainty

How AI is Helping Providers Navigate Regulatory Uncertainty
By Mindy Fortson

Mindy Fortson is COO of Experian Health.

Healthcare organizations have always had to navigate change. But lately, it may feel like the ground is constantly shifting, with The One Big Beautiful Bill Act (OBBBA) being the latest example.

While its full impact won’t be felt until next year, many providers who were surveyed say that they are not prepared and expect major challenges with eligibility and billing.

For many revenue cycle teams, this uncertainty may be creating real anxiety in day-to-day operations for staff who are already overextended. They must prepare for stricter eligibility checks, increased reporting mandates, and the likelihood of more patients cycling in and out of coverage. Providers are turning to AI not as a futuristic concept, but as a stabilizing force that brings consistency, clarity, and efficiency to increasingly complex operational demands.

Providers are asking these questions about OBBBA, and responsible AI-driven support may help answer them.

How Will Providers Know Which Patients Are About to Lose Coverage?

One of the most immediate concerns surrounding OBBBA is coverage volatility. The Congressional Budget Office anticipates that 11.8 million individuals could lose health insurance over the next decade due to policy changes, including new community engagement requirements and more frequent eligibility reviews. For providers, the challenge is not only that coverage may change, but that it may change quickly and unpredictably, creating instability at the front end of the revenue cycle.

That raises an urgent operational question: How do we identify coverage risk early, before it turns into denied claims or uncompensated care?

Many eligibility workflows are manual and fragmented and were not built for this level of change. Revenue cycle teams are already balancing staffing shortages, rising claim denial rates, and growing payer complexity.

AI solutions can help providers build more consistent operational foundations. They can identify accurate patient information, automate eligibility and insurance discovery checks, flag incomplete documentation, and lessen the burden of manual tasks.

Denials are likely to become a bigger pressure point under OBBBA, especially when coverage status changes between scheduling, registration, and billing. When teams are already stretched thin, even small documentation gaps can quickly turn into delayed reimbursement and rework. Operational consistency will be one of the most important safeguards providers can build in the years ahead.

Are Providers Ready for a Surge in Self-Pay and Uncompensated Care?

OBBBA is expected to increase the number of patients moving into self-pay categories, which is a group that already represents the highest share of bad debt write-offs. Loss of coverage doesn’t mean that patients stop needing care. But it does mean that providers face greater financial unpredictability.

Providers are asking: How do we maintain financial stability while patient responsibility grows?

Many providers need more reliable ways to understand patient populations, anticipate payment challenges, and engage patients with clearer payment options earlier in the process. AI-driven solutions can bring structure to this complexity by analyzing large amounts of patient data, demographic indicators, and billing patterns to support segmentation and reduce guesswork in collection strategies.

These tools can also identify potential charity eligibility and help providers better anticipate which patients may struggle to pay. Many providers need more predictable workflows for both staff and patients in an increasingly uncertain coverage environment.

How Will Providers Navigate Additional Operational Complexities?

OBBBA introduces a new layer of operational complexity. Each state will implement the provisions of this law differently. Providers will need to understand both state and federal rules to ensure compliance. Eligibility may hinge on employment hours, participation in training programs, or exemption status that can change month to month. Documentation may be incomplete, delayed, or interpreted differently across states.

For providers, the question becomes: How do we confirm coverage status with confidence when eligibility itself is more dynamic?

The risk isn’t only that patients lose coverage. It’s that coverage appears active at one point in the process and changes before a claim is adjudicated. That creates exposure to retroactive terminations, denials, and rework that strain staff.

Managing this kind of volatility requires more than manual verification. AI can help monitor eligibility timelines, flag missing or inconsistent documentation, and prompt earlier intervention when redetermination windows approach.

In addition, providers need access to broader, more complete data than a single insurance record. They will need to know the correct order of benefits if a patient has more than one insurance and whether they are likely to qualify for Medicaid if they appear uninsured. Eligibility may increasingly depend on data elements that providers have not traditionally needed to consider, like employment status or volunteer activities and income verification.

AI can help pull together these disparate data points and support more consistent front-end decision-making, especially when eligibility is dynamic and documentation requirements are evolving.

As implementation unfolds, operational consistency will depend on building workflows that can adapt to these requirements without adding unnecessary friction for staff or patients.

Preparing Now Means Building Stability into Core Workflows

Providers don’t need every answer today, but they do need to be asking the right questions:

• Which patients may fall through coverage gaps?
• How will self-pay growth change financial exposure?
• Where are administrative processes most vulnerable?

In a time of constant change, providers are searching for stability and workflows that are clearer, more consistent, and less reactive. AI, applied thoughtfully and responsibly, can help bring that stability into the revenue cycle. This technology is one of the best ways to ease administrative strain and help staff focus on what matters most.

Readers Write: All I Needed to Know to Disrupt Healthcare I Learned from “Seinfeld,” the Epilogue: The Summer of George

All I Needed to Know to Disrupt Healthcare I Learned from “Seinfeld,” the Epilogue: The Summer of George
By Bruce Brandes

Bruce Brandes, MBA is co-founder and board chair of WhaleHawk and CEO of Mindyra Health.

In 2014-15, I authored a seven-part blog series at the encouragement of Mr. HIStalk to reflect on my years of lessons learned in this industry through the satirical but surprisingly parallel lens of the greatest sitcom of all time.

Posts like Do The Opposite, And You Want to Be My Latex Salesman, and Yada Yada Yada were intended to reorient the mindset of how healthcare solution companies approach their go-to-market activities.

Similar to my TV friend Larry David as he wrapped “Curb Your Enthusiasm,” over a decade later, I felt compelled to pen this as a bit of an epilogue to my old HIStalk series, while also illuminating a next-generation path forward as we rethink commercial relationships in healthcare.

Unlike George Costanza, I was not special enough to get hired at PlayNow, nor was I Penske material. Instead, over the past 10 years, I’ve been fortunate to have had firsthand experience in growing transformational healthcare companies, including Livongo, Teladoc, Care.ai, and Stryker.  

Operating a healthcare organization has never been more difficult. Financial pressures, dizzying technological advances, workforce challenges, and daily policy uncertainty are among a litany of existential issues. Consequently, every solution company needs to completely reimagine how it discovers, approaches, engages, and closes new business. More importantly, the focus on value, outcomes, and building enduring relationships is paramount. Who knows more about enduring relationships than Jerry, Elaine, George, and Kramer?

Through “Seinfeld” wisdom, combined with my career journey, I’ve developed an understanding of how healthcare executives prioritize investments, navigate buying decisions, and set partnership expectations. Moreover, I’ve discovered the secrets, strategies, and tactics of successful solution companies and their most effective sales leaders and account reps.  

Go-to-market in healthcare takes too long and costs too much. Reps commonly prioritize the wrong accounts, engage at the wrong time, and make a pitch that sounds like everyone else’s and is more focused on what they want to sell than the problems their prospect seeks to solve.

Like George Costanza’s invention of the IToilet (only “Curb” loyalists may get that reference), the power of agentic AI and a treasure trove of digitized industry data are creating a better way to make your life easier.

I see many healthcare sales reps using ChatGPT, Claude, Gemini, etc. to help them conduct market and account research. My question is, does this actually help or hinder a solution company’s go-to-market success?

Are we simply accelerating unwanted outreach? Does rogue, individualized use of generic LLMs exacerbate the inconsistency of a company’s approach and messaging? Is decision-making in healthcare different enough to warrant a more customized approach?

I contend that using generic LLMs for some research is OK, but the findings are superficial and insufficient if you aspire to improve the overall ROI you are getting on your sales and marketing investment.

We must train LLMs to more deeply understand how selling and decision-making in healthcare is different from any other industry. Sales cycles are long because, more often than not, the optimism of a sales rep does not reflect the realism facing buyers. LLMs must be customized to create sales acceleration agents that are deeply trained in our industry dynamics, on each specific account, and on each individual decision maker, contextualized to the unique solution and best practices.

Three key agentic deliverables will ensure the focused, efficient path to growth every company seeks while enabling a more collaborative relationship with clients.

Know WHERE to Go

Is your go-to-market plan rooted in legacy marketing investments, dated market data subscriptions, and antiquated sales enablement tools? Smarter market segmentation must refine your ideal customer profile at a much deeper level than “academic medical centers” or “community hospitals with 250+ beds.” Real data intelligence is informed by patterns across an array of less obvious variables, such as operating metrics, financial trends, workforce dynamics, governance, leadership histories, community influences, etc. so you don’t waste time chasing accounts that will likely never make a buying decision.

Know WHEN to Engage

How well do you understand the priorities of your prospects and honestly assess your solution’s relevancy, respectfully not persisting when your offering is not a fit or the timing isn’t right? A custom healthcare LLM can continuously monitor tens of thousands of digital healthcare-specific data sources — across government reporting, podcasts, industry news, policy trends, videos, clinical journals, financial filings, and social media — and correlate those insights with the context of your value proposition. That allows you to be the first to make timely connections when a potential buyer would be most receptive to your outreach.

Know HOW to Win

Are all of your reps consistently engaging in a way that is hyper-personalized, but rooted in your proven best practices? Too often, companies lead with spam emails, unwanted LinkedIn messages, trade show chocolates, texts, and unsolicited calls that waste time and money while detrimentally littering our industry and damaging your brand. Proper use of modern agents will create customized playbooks that guide informed, personalized conversations and organizational insights that demonstrate your diligence and expertise that will save time for your best reps to manage more accounts and ensure that every rep performs more like your best reps.

George Costanza once warned that “A George divided against itself cannot stand.” Take heed, and rethink how engaging healthcare-specific, custom LLM-trained agents can reduce ineffective sales and marketing efforts to catalyze a new approach for growth, leading to a less-cluttered industry and better outcomes for all.

Readers Write: AI Can’t Feel Emotions, But It Can Be Designed to Care

AI Can’t Feel Emotions, But It Can Be Designed to Care
By Richard Mackey

Richard Mackey, MBA is CTO at CCS.

AI-assisted chronic disease management is becoming a reality. Some of the biggest AI companies have set their sights on healthcare with the launch of solutions like ChatGPT for Health and new personal health data management tools like those offered by Claude from Anthropic.

Chronic diseases like diabetes, heart disease, and depression require not just medical oversight, but consistent engagement, trust, and behavioral support. AI tools are starting to offer just that, both inside and outside of the traditional care environment.

Still, if those AI interactions feel cold, impersonal, or judgmental, they can drive disengagement, the opposite of what’s needed to improve long-term patient outcomes.

Done poorly, AI can amplify the very problems that it is supposed to solve. Done well, empathetic AI becomes a force multiplier, extending the reach of human care, building trust at scale, and helping people feel supported, even when interacting with a “machine.”

When Empathy Is a Design Challenge

Empathy in AI isn’t about simulating emotions or pretending to be human when it’s not. AI shouldn’t try to be human, but it does need a native understanding of the emotional context of the interaction and an ability to respond in a way that feels respectful, supportive, and authentic. In other words, empathy in AI is a design problem, one that spans data, UX, language, and intended purpose.

Consider the example of a patient managing type 2 diabetes. If a patient stops using their continuous glucose monitor, a typical automated system might flag it as noncompliance. But an empathetic AI agent that is trained not just to process the data but also to understand human behavior might recognize subtle signals in the data that indicate emotional burnout or socioeconomic barriers, and adjust the tone of outreach accordingly. That could mean offering reassurance instead of reminders, or escalating the case to a human clinician or social worker for follow-up.

Striking the right tone and balance in the design of communication with the agent, seeking to understand or offer encouragement, for example, will make a meaningful difference in whether a patient reengages or shuts down.

The ROI of Empathy

In value-based healthcare, where providers and health plans are financially accountable for outcomes, empathetic AI that is embedded in chronic disease management workflows can have measurable impact. AI can use sentiment analysis or behavioral cues to help identify patients who are at risk of disengagement or decline, triggering proactive interventions from human outreach staff.

AI can also handle routine administrative tasks with appropriate tone and timing and without clocking out at the end of an eight-hour shift, freeing up human clinicians to focus on complex, relationship-based care that fosters engagement and sustains motivation.

The result is fewer hospitalizations, higher therapy adherence, improved satisfaction scores, and ultimately, better chronic experiences and better health outcomes at lower cost.

Designing for Trust in the Age of Automation

As AI becomes more embedded in the healthcare ecosystem, its ability to convey empathy in a transparent way must be a priority. Research has already shown that it’s possible, with human respondents identifying AI responses as more empathetic and engaging across scenarios ranging from crisis situationsand cancer care to everyday communications from healthcare providers.

The consumer world is quickly operationalizing this approach, with companies like beauty brand Sephora and airline Qatar Airways scoring accolades for their AI assistants’ optimal blend of digital efficiency, personalization tools, and engagingly empathetic personality. As companies like OpenAI and Anthropic turn their attention to healthcare, they are likely to lean into a similar empathy-first approach to assist individuals with healthcare-specific tasks.

The key to success will be maintaining transparency and trust in the AI-powered healthcare ecosystem as we leverage the technology’s seemingly near-limitless potential. The bottom line is that we don’t need AI to have feelings, but we do need it to understand ours, especially when and where support and care is needed most as a patient.

Readers Write: Healthcare’s Seasonal Surge is Upon Us. Is Your Health System Ready?

Healthcare’s Seasonal Surge is Upon Us. Is Your Health System Ready?
By Dusti Browning, RN

Dusti Browning, RN, MSN is VP of growth and client solutions for Conduit Health Partners.

Seasonal surges happen every year, and 2026 is particularly brutal. The flu was already associated with 120,000 hospitalizations and 5,000 deaths by the end of 2025.

The winter months often bring with them a tidal wave of respiratory viruses, influenza, RSV, and COVID. Clinicians expect them. But while these spikes in patient volume are predictable, too many health systems find themselves in a challenging supply-and-demand environment that can negatively impact patient care and the bottom line.

A recent report found that 60% of nurses are experiencing a significant uptick in patient volume and case complexity amid the current flu season. As seasonal surges collide with ongoing emergency department (ED) overcrowding and staffing shortages, health systems face mounting pressure to find scalable, practical solutions.

The national report surveyed 64 nurses, half working in triage and half in transfer centers, and found that 70% of nurses believe that offering 24/7 virtual nurse triage prevents unnecessary ED visits. In fact, additional industry data points to an ED avoidance rate of 72 to 76% over the past two years, meaning nearly three out of four triage encounters are resolved without an ED visit.

While hospitals and health systems can’t eliminate seasonal surges, they can anticipate them and implement systems that reduce strain.

Protecting System Capacity Remotely

The report found the most frequent patient concerns during the seasonal surge include minor respiratory symptoms, medication management, chronic disease follow-up, and low-acuity infections. Around 75% of nurses report that remote solutions help manage these issues effectively. This is significant given the challenges facing health systems during seasonal surges. A separate study found that 35% of patients that present to an ED during the winter months wait four or more hours for a bed.

Safeguarding capacity in today’s EDs is an imperative, with stats from the Centers for Disease Control and Prevention (CDC) showing that 42.7 visits per 100 people start in the ED. As those numbers continue to increase, virtual nurse triage provides an alternative access point that is proven to reduce strain on health system EDs during seasonal surges.

Notably, the recent patient access and throughput report found that nearly one in three avoided emergency visits associated with nurse triage after regular clinic hours. This demonstrates that real-time clinical access can help patients reach the right level of care at times when they are more likely to turn to the ED. The end result is improved overall access to care, better outcomes, and lower costs. A measurable decrease in staff burden and burnout further strengthens the impact.

Enhancing Patient Experience

When seasonal outbreaks occur, capacity is at a premium, but so is staffing. Burnout continues to be rampant in healthcare. A recent survey conducted by The Harris Poll of 1,504 frontline health care employees revealed that 55% are looking for job openings, interviewing, or planning to switch to a new role in the next year.

While AI and automation are primed to ease administrative burdens in the coming years, the reality is that patients and families in distress often need to speak with a human being. When staff are lacking and already under immense strain, patient experiences are negatively impacted. Lengthy wait times to get to a professional or a frustrating technology-first approach can cause patients to turn to the ED out of desperation. Virtual nurse triage offers a more accessible, clinically appropriate alternative.

The patient access and throughput report found that roughly one in four nurses witness or suspect worsened outcomes due to delays in access or coordination. The findings reinforce the efficacy of virtual nurse triage to address operational challenges of seasonal surges and improve patient outcomes and experiences.

Readiness When Demand Peaks

The CDC predicts that flu activity could continue to rise in the coming weeks. Seasonal surges don’t have to mean bottlenecks and burnout. The data show what works: nurse-first, telephone triage reduces visits to the ED, eases the operational burden of overcrowded waiting rooms, and reduces the risk of worsened outcomes.

As health systems prepare for the next seasonal wave, integrating nurse triage into access pathways isn’t just operational. It is essential for protecting capacity, easing staff strain, and improving patient care.

Readers Write: Lessons from the ChatGPT Health Debate

Lessons from the ChatGPT Health Debate
By Robert Stewart

By Robert Stewart is CTO of Arbital Health.

A recent column by Geoffrey Fowler in The Washington Post that describes his disappointing experience with ChatGPT Health sparked discussion in the health IT community. While many remain optimistic about the long-term potential of platforms such as ChatGPT Health and Claude for Healthcare, Fowler’s piece highlights issues that healthcare leaders, clinicians, and technologists should examine carefully.

Variability and inaccuracy are not unique to large language model (LLM)-based systems. Many clinical diagnostics have known false-positive rates, and repeat testing is routine when results are unexpected. Clinicians themselves may reach different conclusions when presented with the same clinical information months later. Medicine has always operated within a probabilistic framework.

What is different with LLM-driven systems is their non-deterministic behavior when given the same input repeatedly. Identical prompts can generate materially different responses. Fowler demonstrated this when ChatGPT assigned his cardiac health scores ranging from a B to an F using the same underlying data. That level of variability can cause confusion or anxiety when applied to personal health interpretation.

Many consumer health AI tools are built on retrieval-augmented generation (RAG) architectures, in which the model is grounded using user-specific information such as medical records or wearable device data. Even when anchored to structured inputs, however, the LLM’s narrative interpretation can still vary, reinforcing the need for clinician oversight and appropriate guardrails when deploying these tools in consumer health settings.

It’s also important to recognize the potential psychological impact of these tools. Researchers such as Eric Topol caution against indiscriminate screening of asymptomatic individuals because it often produces “incidentalomas,”(findings that lead to unnecessary follow-up testing or treatment without improving outcomes. Consumer AI health scoring systems risk amplifying this phenomenon by continuously surfacing probabilistic interpretations in the absence of appropriate clinical context.

Wearable Data Challenges

Wearable device data introduces another layer of complexity. Anyone who works with longitudinal wearable datasets understands that the signal-to-noise ratio is inconsistent. Devices are removed for charging, replaced every few years, or switched across vendors that have different calibration baselines. Environmental and behavioral factors such as travel, altitude changes, illness, stress, or sleep disruption can produce statistically significant physiological changes that an AI system may misinterpret without broader context.

Jessilyn Dunn, PhD and her lab at Duke University have conducted extensive research that uses machine learning and statistics to extract valuable insights from consumer wearables, but the work remains challenging. Even highly targeted machine learning applications, such as arrhythmia detection platforms developed by companies like AliveCor, still operate with non-trivial false-positive rates. Wrapping a general-purpose LLM around wearable data without similarly rigorous modeling layers is unlikely to deliver clinically reliable outputs.

Security and Privacy Considerations

As consumer AI health tools evolve, security becomes increasingly important. Anyone who uses ChatGPT, particularly those who are sharing sensitive health information, should enable multi-factor authentication (MFA), which is one of the most effective controls for reducing account compromise risk.

Users should also recognize an important regulatory distinction. Information that is entered into consumer AI services is generally not protected under HIPAA. OpenAI’s enterprise offering, ChatGPT for Healthcare, is designed for HIPAA-covered environments and supports Business Associate Agreements (BAAs), but consumer versions operate under different legal frameworks.

The Takeaway for Health IT Leaders

The lesson from Fowler’s experience is not that consumer health AI lacks value, but that context, governance, and clinical integration matter. Non-deterministic systems that interpret noisy consumer data can easily generate variable outputs that users may misunderstand as clinical conclusions rather than probabilistic insights.

For health systems, payers, and digital health innovators, the near-term opportunity lies in combining LLM interfaces with validated predictive models, strong clinical workflow integration, and transparent communication about uncertainty. Without those guardrails, even well-intentioned consumer health AI tools risk creating confusion rather than clarity.

Readers Write: Doing Everything For the Patient, Not To the Patient

Doing Everything For the Patient, Not To the Patient
By Nassib Chamoun

Nassib Chamoun, MS is founder, president, and CEO of Health Data Analytics Institute.

“Do as much as possible for the patient and as little as possible to the patient.”

That single sentence, written by Bernard Lown, MD in “The Lost Art of Healing,” should serve as a universal guide to thinking about medicine, caregiving, and what it truly means to heal. Dr. Lown was my mentor beginning in my early 20s and remained a close friend until his death in 2021 at age 99, He was decades ahead of his time. He believed that medicine should integrate scientific rigor with moral imagination, and that clinical excellence without compassion is incomplete care.

Today, his words feel less like a reflection and more like a challenge. Our population is aging rapidly. Older adults are the fastest-growing consumers of healthcare services.

As more patients approach the later stages of life, the central question facing clinicians, health systems, and policymakers is not whether we can do more, but rather if doing more truly serves the patient. Increasingly, the evidence suggests that quality of life, not simply quantity of life, must be the defining outcome.

This is not a new conversation. In 1974, Balfour Mount, MD, who is widely regarded as the father of palliative care in North America, established the first hospital-based palliative care unit at Montreal’s Royal Victoria Hospital. Since then, the field has grown steadily. Decades of research demonstrate improvements in symptom control, patient and family satisfaction, alignment of care with patient goals, and, in many cases, lower healthcare utilization and costs.

More recently, the World Health Organization issued a call-to-action urging health systems to expand palliative care access. Not only for humanitarian reasons, but also as a sustainable response to the use of our healthcare resources.

Organizations such as the Center to Advance Palliative Care (CAPC) have worked to standardize best practices and train clinicians to deliver high-quality, interdisciplinary palliative care across settings. Leading physician researchers and ethicists have published extensively in peer-reviewed journals, academic texts, and mainstream media.

Despite this robust evidence base, many patients and families still experience end-of-life care as a stark binary: aggressive inpatient interventions on one side, or hospice and “giving up” on the other. Why does this false choice persist?

For me, this question is no longer theoretical. It is deeply personal. As my parents age, I have watched them navigate serious illness, both at home and in the hospital. Again and again, I have seen a system that is reflexively oriented toward intervention — more procedures, more monitoring, and more escalation.

The intent is usually good. But too often the outcome is suffering, including physical discomfort, emotional distress, and a loss of agency at precisely the moment when patients need it most. Where is palliative care in these situations?

End-of-life care should not be an either-or proposition. It should not require patients to choose between life-prolonging treatment that may diminish quality of life or dying at home without support.

Palliative care belongs alongside disease-directed treatment, especially during hospitalizations, where it can provide expert symptom management, clarify goals of care, support families, and guide thoughtful transitions home when appropriate.

I have seen the power of this model first hand. Palliative-focused hospitalizations can be transformative, not only for patients who experience relief from pain and fear, but also for caregivers who gain reassurance, guidance, and partnership. This approach preserves dignity, respects patient values, expands hospital capacity and access, and makes more responsible use of limited healthcare resources. Most importantly, it restores humanity to care.

For me, the conclusion is clear. When possible, our loved ones should not die in hospitals. They also should not have to forgo care, comfort, or hope.

To palliative care clinicians, healthcare leaders, policymakers, advocates, and anyone who has walked this path with someone they love, let us build a healthcare system that truly does everything for the patient, not to the patient. Compassion and evidence are not competing priorities. Together, they form the highest standard of care.

Readers Write: What a Modern Application Managed Services Model Should Deliver

What a Modern Application Managed Services Model Should Deliver
By Scott Gildea

Scott Gildea, MBA is EVP of client delivery for Optimum Healthcare IT.

For years, application managed services in healthcare has been treated as a singular staffing solution. When teams were short-handed or roles went unfilled, organizations added overseas resources to keep systems running. That approach worked until the environment changed.

Today’s healthcare landscape is more complex than ever. EHRs, ERPs, and enterprise platforms are deeply connected to patient care, revenue, and operations. Downtime is no longer just an inconvenience, it is a risk. At the same time, IT teams are burned out and being asked to support transformation while maintaining stability.

In this environment, application managed services cannot be about coverage alone. They must deliver accountability, consistency, and operational confidence.

This is the Moment for Application Managed Services

As a whole, healthcare organizations are at a dramatic inflection point in healthcare IT. Some of the biggest reasons for this include:

• Mounting pressure surrounding increasing costs, stagnant budgets. and fluctuating reimbursement rates.
• Socioeconomic pressures, such as increasing prices.
• Downward pressure from health system executives to be more efficient and forward-thinking.

Application managed services must keep pace with the expedited evolution of technology in healthcare. Change is here for most organizations, whether it takes the shape of AI, the mergers and acquisitions, or the increasing socioeconomic pressures. 

Health systems are no longer asking whether they need managed services. They are asking which models will actually support their organizations over the long term. The answer lies in delivery models that are built specifically for healthcare, designed for accountability, and focused on the people who keep these systems running every day.

What a Modern Application Managed Services Model Should Deliver

Health systems are not looking for another vendor. They are looking for a delivery model that they can rely on every day, not just during go-lives or major initiatives. Traditional approaches often fall short.

What organizations need now is a managed services model that is explicitly built for healthcare enterprise applications, operates as a valid extension of the internal team. and has clear ownership and shared accountability.

A modern application managed services solution should answer a few basic questions:

• Who owns the day-to-day operations?
• How are issues identified before they become incidents?
• How is performance measured and improved over time?
• How does the model scale without disrupting internal teams?
• Will this allow us to keep up with the ever-changing landscape of health IT, including EHR updates, AI advancements, and more?

When managed services are designed well, they reduce operational noise. Leaders spend less time reacting and more time planning. Internal teams stay focused on strategy and improvement instead of constant firefighting. That does not happen by accident. It requires healthcare-specific experience, disciplined delivery, and a model that is built for complex enterprise environments.

Readers Write: Medicare Goes All In on Value-Based Care

Medicare Goes All In on Value-Based Care
By Eugene Gonsiorek, PhD

Eugene Gonsiorek, PhD is VP of clinical regulatory standards for PointClickCare.

If there were any doubts about Medicare’s commitment to value-based care, there shouldn’t be any longer.

Abandoning its former model of rolling out value-based care (VBC) programs one at a time, the Centers for Medicare and Medicaid Services (CMS) between March and December 2025 announced nine new or proposed programs and modifications to five existing programs – an unprecedented pace.

The rush of new programs and the concentrated timing is CMS announcing it is aligning Medicare around VBC to a greater degree than ever before. This is good news for organizations that have been working toward this end and a prompt for those who haven’t made as much progress.

The New Medicare Programs

Let’s take a closer look at the new and proposed programs. 

• ACCESS (Advancing Chronic Care with Effective, Scalable Solutions). A voluntary, 10-year model testing outcome-aligned payments for measurable clinical improvements using technology-supported care for chronic conditions such as hypertension, diabetes, musculoskeletal pain, and behavioral health.
• WISeR (Wasteful and Inappropriate Service Reduction). Launched in mid-2025, this model tests ways to reduce unnecessary services and accelerate prior authorization while safeguarding patients and taxpayers against low-value care.
• GUARD (Global/Universal Accountability in Drug Pricing) and GLOBE (Global Outcomes in Benchmarking and Equity). Proposed mandatory models that aim to test international benchmark-based adjustments to Medicare Part D and Part B drug rebate and pricing systems to help address high drug costs.
• Ambulatory Specialty Model (ASM). Finalized as a mandatory model beginning in 2027 that holds certain specialists accountable for quality, cost, and care coordination outcomes.
• LEAD (Long-term Enhanced ACO Design). Announced as the next generation of accountable care organization models, a 10-year design intended to better support small, independent, and rural providers following ACO REACH (Accountable Care Organization Realizing Equity, Access, and Community Health).
• BALANCE (Better Approaches to Lifestyle & Nutrition). Announced alongside GUARD and GLOBE, this voluntary model is intended to align manufacturers, state Medicaid agencies, and Part D plans to improve metabolic health through GLP-1 access plus lifestyle support, with testing concluded by 2031.

Across these models, several common design features stand out. Time horizons are longer, often extending eight to 10 years. Payment is increasingly tied to measurable outcomes rather than process compliance. Accountability extends beyond primary care into specialty care and pharmaceuticals. In select areas, CMS is requiring mandatory participation to achieve broad system impact.

The ACCESS model illustrates how CMS expectations are evolving. A voluntary 10-year initiative, ACCESS ties payment to demonstrable improvement in chronic conditions such as hypertension, diabetes, musculoskeletal pain, and behavioral health. The focus is no longer service volume or short-term utilization metrics, but sustained clinical outcomes.

Similarly, the WISeR model reframes inappropriate utilization as both a quality failure and a fiscal risk. By targeting low-value services and streamlining prior authorization, WISeR signals CMS’s growing willingness to intervene earlier in care decisions. The goal is not simply to manage spending after it occurs, but to prevent waste before it happens.

Together, these models reflect a clear shift from utilization-based proxies toward explicit accountability for results.

Specialty Care and Pharmaceuticals Move to the Center

Perhaps the clearest departure from earlier value-based care efforts is CMS’s expansion of accountability into specialty care and drug pricing, areas historically insulated from performance-based payment.

The finalized ASM, set to begin in 2027, makes participation mandatory for selected specialists and holds them accountable for quality, total cost of care, and care coordination. This challenges the long-held assumption that VBC is fundamentally a primary care endeavor. It also elevates downstream utilization, including post-acute care, from a secondary concern to a central performance variable.

At the same time, the proposed GUARD and GLOBE models are CMS’s most direct effort to apply value-based principles to pharmaceutical spending. By testing international benchmarking approaches in Medicare Parts B and D, CMS is extending accountability into pricing structures that have traditionally been governed by statute rather than performance expectations.

Long-Term Accountable Care and Prevention as Structural Bets

The LEAD model underscores CMS’s recognition that accountable care requires stability, not churn. By extending participation horizons to 10 years and focusing on small, independent, and rural providers, LEAD acknowledges that organizational transformation and sustained downside risk cannot be achieved on short timelines.

In parallel, the BALANCE model reflects CMS’s growing emphasis on prevention and upstream investment. By aligning manufacturers, state Medicaid agencies, and Part D plans around GLP-1 access combined with lifestyle and nutrition support, BALANCE tests whether earlier intervention in metabolic disease can produce durable improvements in outcomes and spending. By pairing pharmaceutical access with behavioral support, CMS is testing integrated solutions rather than isolated interventions.

The Effects on Patients and Providers

These models collectively raise the bar for providers. Financial accountability is more robust. Timelines are longer. Expectations for care coordination and performance improvement are higher. Independent practices, rural providers, and specialists, groups historically less exposed to mandatory value-based arrangements, are now central to CMS’s policy design.

For patients, CMS’s stated objectives are clear: earlier intervention, fewer unnecessary services, better chronic disease control, and lower drug costs. Whether these outcomes are realized will depend less on policy intent than on execution, particularly provider engagement and the ability to manage care across settings.

From Experimentation to System Design

Taken together, the new model announcements signal that CMS is moving beyond experimentation toward system design. The concentration of releases, the expanded mandatory participation, and the consistent emphasis on outcomes and cost containment all point in the same direction.

CMS is no longer asking whether VBC works. It is redesigning Medicare on the assumption that it must.

As these models move from proposal to implementation, they will shape payment policy, care delivery structures, and provider participation in Medicare well into the next decade. Organizations should prepare themselves for a system in which value-based accountability is no longer optional, but the norm.