Thursday, April 30, 2026

< + > Leading Hospital at Home Programs

In February, Congress extended the Acute Hospital Care at Home program through the end of 2030. The program provides waivers to hospitals to provide inpatient-level care at home to qualified Medicare beneficiaries. The five-year extension will help participating hospitals – in place at more than 400 organizations – demonstrate the value of hospital at home and, according to the American Hospital Association (AHA), provide evidence to other payers that the program can work.

Many hospital at home programs predate the program approved by Congress, which the Centers for Medicare & Medicaid Services (CMS) first launched during the COVID-19 pandemic. Here’s a look at some of these key programs and what makes them successful.

Johns Hopkins Medicine has been operating its hospital at home program since 1994. Positive outcomes were first reported in 2005, including a 32% drop in care costs and a 35% drop in length of stay. Along with clinical care services, Baltimore-based Johns Hopkins now offers social services, home health aides, and help with day-to-day household management. Not surprisingly, the health system’s success has served as a model for Hospital at Home programs around the country.

Advocate Health launched its North Carolina-based program in just 10 days during the pandemic, AHA said, and has achieved a 15% reduction in readmission rate coupled with higher patient satisfaction scores. Patients can transition from a hospital or skilled nursing facility (SNF) to home-based care, and the program covers short- and long-term care.

Atrium Health serves about 90 patients per day in its North Carolina-based hospital at home program, which was also the model for what the health system said is the first-of-its kind pediatric hospital at home. The program also offers transitions to advanced care or palliative care at home.

The Cleveland Clinic Florida program, launched in 2023, has seen some of the highest patient satisfaction scores across all inpatient wards at the health system. It helps that the health system contracts with community-based providers. The health system’s Clinically Integrated Virtual Care (CIViC) Center covers remote monitoring and virtual care.

Kaiser Permanente has reported smoother care transitions and better patient experiences for its program based in Northern California, which provides care for more than 1,000 patients annually. It’s part of a larger care at home strategy for the health system, which in 2021 partnered with Mayo Clinic and Medically Home to found the Advanced Care at Home Coalition.

Los Angeles General Medical Center emphasizes virtual, concierge-level care, though rideshares can be dispatched if patients need to be evaluated at the hospital. The public safety net hospital’s program has reduced inpatient stays by 4 days and saves the system about $5.6 million annually, and leaders say it’s a model for providing “financially responsible” care.

Wisconsin’sMarshfield Clinic Health System is another early adopter (2016), according to the American Medical Association. The Home Recovery Care program covers 30-day medical care or 60-day rehabilitation care. The health system has reported a 44% reduction in readmission rate, a 35% decrease in average length of stay, patient satisfaction of more than 90%, and increased physician satisfaction.

At Mass General Brigham, the readmission rate for the hospital at home program is less than one-third the rate for inpatient care, AHA said. Aling with typical clinical and ancillary services, the hospital offers medically tailored meals and supports in-hone X-rays. The program has also been adapted to provide hospital-level care for veterans experiencing homelessness.

Mayo Clinic Arizona has reported a 35% decrease in readmission rate for hospital at home patients. The program also demonstrates equivalent rates of patient safety and higher rates of patient comfort. Patients receive a technology kit that, in addition to medical devices, can include a direct-dial phone, Wi-Fi extender, and backup power supply.

Mount Sinai Health System launched its program in 2014 thanks to a CMS grant, according to AHA; it built on an existing program providing home-based primary care to homebound New Yorkers. Only 7% of participating patients need to return to the hospital. The program also includes at-home palliative care, dialysis, and infusion.

The Ohio State University Wexner Medical Center has focused on disadvantaged neighborhoods. AHA reported readmission rates are roughly half as high as inpatient care, and 95% of patients rate the experience as 9 or 10 out of 10. Available services include an in-home safety assessment to help reduce the risk of falls.

Oschner Health prevented hospitalization and readmission for 92% of eligible emergency department patients in its initial pilot program, which it subsequently expanded in 2024. Along with covering chronic conditions, the program is available for Louisiana-based patients recovering from a transplant or those with a cancer diagnosis.

Presbyterian Healthcare Services launched its hospital at home program in 2008 in partnership with Johns Hopkins Medicine. Most patients receive two care visits per day for several days before discharge. The cost of care is 42% lower than inpatient hospitalization, AHA reported. The New Mexico-based health system also tripled at-home admissions capacity during the pandemic.

Are there other home health programs that you know about?  Let us know on social media.

#hospital



< + > Koda Health and UPMC Enterprises Collaborate | Click Therapeutics and Boehringer Ingelheim Announce Series D

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


Koda Health and UPMC Enterprises Collaborate to Prove Out the Value of Advance Care Planning (ACP) at Scale

Backed by Strategic Investment from UPMC Enterprises, Koda Health Scales AI-Enhanced Advance Care Planning Across Complex Populations

Koda Health, an AI-enhanced Advance Care Planning (ACP) platform, today announced a strategic investment from UPMC Enterprises as part of the company’s Series A raise.

The investment reflects UPMC Enterprises’ conviction that digitally guided, values-based advance care planning represents a critical and underbuilt layer of serious illness infrastructure.

Koda’s platform guides patients through condition-specific care planning conversations via video and guided education, helping them document their values, care wishes, medical decision-makers, and treatment preferences. High acuity patients are paired with a dedicated Koda Member Advocate — a clinician with a background in palliative nursing or social work — who provides longitudinal support throughout the care planning process. These advocates ensure advance care plans are complete, surrogates are aligned, and that members receive the care that matters most to them during serious illness. Patient preferences flow directly into clinical workflows, ensuring care teams have access to patient goals at the moments that matter most.

An estimated $200 billion is spent each year on care that patients would not have wanted had they been engaged in their care planning earlier. ACP is proven to close that gap, but has historically been difficult to deliver at scale. Koda Health has demonstrated a 79% reduction in terminal hospitalizations, a 38% reduction in ICU utilization, and a 19% reduction in total cost of care for patients in the last year of life in a third-party validated study.

“UPMC Enterprises’ investment is a meaningful signal, not just to Koda, but to the broader market. It validates that health systems are ready to invest in infrastructure that makes advance care planning work the way it should: proactively, at scale, and with the human support that these conversations require. Having UPMC Enterprises as a strategic investor puts us in a unique position to prove what’s possible,” said Dr. Desh Mohan, Co-Founder and Chief Medical Officer at Koda Health.

“UPMC Enterprises invests in companies building infrastructure that improves how care is delivered for patients who need it most,” said Kathryn Heffernan, Senior Director at UPMC Enterprises…

Full release here, originally announced April 28th, 2026.


Click Therapeutics and Boehringer Ingelheim Announce Series D Investment and Funding to Advance Commercialization of CT-155

Boehringer Ingelheim and Click Therapeutics today announced a strategic agreement to support the commercialization of CT-155, an investigational prescription digital therapeutic that is being studied for the treatment of the experiential negative symptoms of schizophrenia in adults aged 18 years and older. Under the agreement, Boehringer will transfer full product responsibility, including all commercial and marketing authorization rights, to Click Therapeutics. To support this transition, Boehringer has made a $50M Series D strategic investment and provided dedicated commercial funding to help bring CT-155 to patients, if cleared by the FDA. CT-155 was co-developed by Boehringer and Click.

“Boehringer Ingelheim’s selection of Click to deliver CT-155 to patients is powerful validation of our vision and the capabilities we have spent over a decade building,” said David Benshoof Klein, CEO and founder of Click Therapeutics. “We are eager to take the lead with CT-155 and are focused on getting this FDA-designated Breakthrough Device to patients after clearance by the FDA.”

At the core of Click’s commercialization strategy will be the clinical data from the Phase III CONVOKE study (CONVOKE; NCT05838625). The randomized, double-blind, controlled study investigated the effectiveness and safety of CT-155 versus a digital control app as an adjunct to standard of care antipsychotic therapy in people diagnosed and living with schizophrenia experiencing negative symptoms.

The study met its primary endpoint, as presented at the 38th Annual European College of Neuropsychopharmacology (ECNP) Congress, which was change in experiential negative symptoms from baseline to 16 weeks as measured by the Clinical Assessment Interview for Negative Symptoms, Motivation and Pleasure Scale (CAINS-MAP). Treatment with CT-155 demonstrated a Cohen’s D effect size of -0.36 (p value= 0.0003) reflective of a 6.8-point improvement of negative symptoms severity as measured by CAINS-MAP at 16 weeks (vs. 4.2-point in digital control arm), representing a 62% relative improvement.

CT-155 was well-tolerated and demonstrated an adverse event (AE) profile consistent with past studies. The AE rates with CT-155 and the digital control arm were 8.3% vs 13.4%, respectively. There were no trial discontinuations attributed to CT-155 and two (2) for the digital control arm. There were no serious AEs related to either group…

Full release here, originally announced April 9th, 2026.

#koda

Wednesday, April 29, 2026

< + > Measuring Clinical, Operational, and Financial ROI of AI Initiatives

We’ve broken AI down into many pieces to analyze the many different aspects of AI and how it affects the different areas of healthcare organizations. One area we haven’t talked about enough yet is the return on investment of AI initiatives once they’ve been deployed in the real world. Today, we set out to fix this wrong and learn more not only about the financial ROI of AI initiatives but also the clinical and operational ROI.

We’ve reached out to our brilliant Healthcare IT Today Community to ask — how do you measure the clinical, operational, and financial return on investment of AI initiatives once they are deployed in real-world settings? The following are their answers.

Elevsis Delgadillo, SVP, Customer Success at KeenStack
There’s no need to reinvent reporting to measure AI ROI. Most organizations are already tracking the right metrics. In referral management, that might be leakage or time to schedule, and in clinical use cases, it could be outcomes like hospital-acquired sepsis rates. In the revenue cycle, it’s collections and denials. The key is enabling an AI workflow in a specific area and measuring what changes so the impact can be clearly attributed.

Shay Perera, Co-Founder & CTO at Navina
Meaningful ROI in healthcare AI must be assessed through a combination of clinical, operational, and financial metrics. Clinically, we measure factors like improved risk adjustment accuracy and care gap closure rates; operationally, reductions in chart review, documentation, and coding time, and administrative burden, alongside high clinician engagement with the solution; financially, enhanced documentation quality contributes to audit readiness and more predictable revenue under value-based contracts.

Jared Hamilton, Cyber Managing Director at Crowe LLP
Physician feedback is one of the most important measures of return on investment. In clinical settings, we look closely at whether AI tools are meaningfully reducing time spent on documentation and administrative tasks, such as manual charting, and allowing providers to spend more time interacting directly with patients.

That impact shows up not only in efficiency metrics, but also in the patient experience. Most of us have been patients ourselves, and we understand the difference between a provider who is fully engaged in the conversation versus one focused on a keyboard. When AI helps shift attention back to the patient, it delivers both clinical and experiential value, which ultimately supports provider satisfaction and long-term operational and financial returns.

Denis Whelan, CEO at Documo
ROI for AI initiatives is measured using the operational and financial metrics organizations already track. This includes reduced manual processing time, faster referrals and authorizations, fewer document errors, and lower cost per transaction. On the clinical side, teams focus on metrics like improved turnaround times, fewer delays in patient care, and improvements in population health metrics.

For example, one healthcare organization using AI to process inbound documents was able to cut handling time by 40-50% while redeploying staff to higher-value tasks – achieving measurable efficiency gains without adding headcount.

Joe Russolello PT, DPT, MBA, Senior Vice President, Growth at WebPT
ROI only becomes real when it shows up in the clinician’s experience first. Clinically, that means less burnout and higher satisfaction driven by reduced documentation burden.

Operationally, the most consistent metric is time. Often, one to two hours per clinician per week is reclaimed and significantly less after-hours charting. Financial gains follow when those efficiencies reduce denials, accelerate billing cycles, and shrink AR days, with many organizations reaching sustained ROI within the first year.

If AI doesn’t make clinicians’ lives meaningfully easier, the financial returns rarely last.

Deepak Prakash, Co-Founder & CTO at Sonio
Comparing key benchmarks of time, cost, and performance of initiatives prior to AI integration in a health system can show the stark differences in operational efficiencies due to its use, such as lessening clinician documentation burden, greater reimbursement rates, and faster diagnostic results. Yielding earlier-stage diagnostics from AI-powered software allows patients to make more informed decisions, and can be measured in defining the decreased time spent per appointment, higher patient engagement rates, and more personalized care journeys.

Lisa Israelovitch, Co-Founder & Chief Executive Officer at AssistIQ
Real-world settings such as hospitals and other care facilities often drive clear return on investment from integrating AI platforms in their networks, seen through tangible outcomes in areas such as reduced cost per case, lower inventory waste, and time saved per procedure. Setting recurrent assessments around operational efficiencies in the early stages of new AI initiatives creates a useful lens to compare strategy effectiveness with previous benchmarks.

Mohan Giridharadas, CEO at LeanTaaS
In the real world, the ROI of AI centers on whether it changes outcomes in a measurable, sustained way. We measure operational ROI by tracking flow and capacity metrics that reflect day-to-day performance: discharge processing time, ED boarding, transfer declines, length of stay, OR utilization, block utilization, and surgical throughput.

Clinical ROI is often indirect but real: when the system runs with less gridlock, patients get to the right care faster, and clinicians spend less time doing manual workarounds.

Financial ROI comes from unlocked capacity and avoided cost: more admissions and surgeries without building new beds or ORs. Specifically in the perioperative space, Rush University Medical Center increased primetime OR utilization by 4% and improved surgeon block utilization by 12%, enabling 1,705 additional surgeries over three years and delivering a 12x ROI in one recent year.

Patrick Sheehan, Vice President of Value-Based Care at Withings Health Solutions
Real-world ROI from AI in healthcare is realized when it improves both how care is delivered and the outcomes it produces. While operational AI is already delivering measurable efficiency gains, the next frontier of real ROI will come from clinical use cases that directly enable earlier, more confident intervention. Clinical AI is advancing rapidly, enabling earlier identification of patient deterioration and giving care teams the confidence to intervene proactively rather than react to symptoms.

This is especially impactful in heart failure, where disease progression patterns vary widely, and early signs of deterioration are difficult to detect, contributing to avoidable hospitalizations. By improving operational efficiency and enabling earlier intervention, AI helps health systems deliver high-quality, scalable care to complex populations and perform better under value-based care models that reward quality and affordability.

Ben Moore, Chief Innovation Officer at PerfectServe
Now that healthcare is deep into the AI hype cycle, the focus needs to shift from experimentation to solving specific, measurable problems based on the wealth of knowledge we’ve already compiled. When the use case is narrowly tailored, the expected results should be easier to anticipate and track. For example, we’ve done some research that suggests the average clinician may spend 30–40% of their time during a shift just trying to communicate with their colleagues to coordinate patient care. That kind of built-in friction is a perfect target for AI. Train an AI agent with all of the rules from our routing engine and deploy it to stem the number of errant or unnecessary communications that flow throughout a hospital. Deploy another agent to execute emergency shift swaps when a provider has a sudden family emergency and can’t cover a shift.

These applications remove toil and stress from important clinical workflows and give time and peace of mind back to clinicians. And because so many hospital processes are touched by communication and coordination, the opportunities for measurement are extensive. Measurement opportunities span call center efficiency metrics—more efficient patient transfers, higher volumes of urgent calls handled, pre/post analyses of calls misdirected to off-call providers, and engagement surveys tracking provider satisfaction with scheduling flexibility and autonomy. As these AI applications mature, the results will shift from promising to proven.

Greg Farnum, SVP GM, Federal and Strategic Advisory at Audacious Inquiry
Just as early time-and-motion studies made the invisible work of information exchange visible and quantifiable, customer-specific language models have the potential to illuminate ROI in ways generic LLMs cannot. While general AI tools can demonstrate time savings, curated SLMs that truly understand organizational workflows, terminology, and decision frameworks unlock a different level of measurement.

The ROI opportunity with customer-specific models lies in their ability to capture organizational friction that’s currently invisible: the cognitive load of context-switching, the emotional burden of repetitive administrative tasks, and the time lost to information retrieval. Like HIE before it, we need proxy measures first—time saved, burden reduced, experience improved—before we can connect these to hard financial metrics.

Ben Scharfe, EVP for AI at Altera Digital Health
Measuring ROI currently relies heavily on leading indicators that signal long-term financial health. While many measurements are currently soft, we focus on physician and patient satisfaction as primary markers. High satisfaction scores are direct predictors of reduced physician turnover and increased patient retention and referenceability, both of which have material financial impacts. Operationally, we still track chart closure times and clean claim rates. When ambient AI reduces the administrative burden, the return is found in the stability of the workforce and the improved integrity of the patient encounter.

So many great experiences here! Huge thank you to everyone who took the time out of their day to submit a quote to us! And thank you to all of you for taking the time out of your day to read this article! We could not do this without all of your support.

How do you measure the clinical, operational, and financial return on investment of AI initiatives once they are deployed in real-world settings? Let us know over on social media, we’d love to hear from all of you!


#clinical     #measure

< + > The Expanding AI Ecosystem: How PHI Can Quietly Leave the Healthcare Environment

The following is a guest article by Dennis P. Sweeney, MBA, Co-Founder of Vertebrai Solutions Inc., and Consulting Principal at Tellogic Inc.

Healthcare organizations are rapidly adopting artificial intelligence (AI) solutions to support clinical, administrative, and operational workflows. To manage privacy risk and control Protected Health Information (PHI), most healthcare organization deployments follow a familiar pattern. AI systems are hosted inside private, HIPAA-compliant cloud environments under Business Associate Agreements (BAAs) with the major cloud providers.

Hosting in a private HIPAA-compliant cloud environment provides infrastructure safeguards. These architectures, used by legacy healthcare systems with internal interfaces and custom-developed external APIs, manage PHI data exposure concerns. Platforms such as Microsoft Azure and Amazon Web Services provide strong security controls, encryption, audit logging, and established compliance frameworks. With a BAA in place, healthcare leaders can be reasonably confident that protected health information (PHI) stored and processed within those environments is being handled appropriately.

Many organizations deploying large language models (LLMs) believe they have addressed critical privacy concerns. The AI is operating inside a controlled HIPAA environment. Security controls are in place. Compliance requirements are satisfied.

The information technology architecture hosting the system feels safe.

The Valuable AI Work Inside Controlled Environments

AI systems in these healthcare environments are performing valuable work. They summarize patient charts, generate clinical documentation, assist with prior authorization workflows, triage patient messages, support population health analysis, link to research guidelines, and automate administrative tasks that consume large portions of the clinician’s workday. The realization that every system capable of reading the medical record eventually encounters the same reality, Electronic Health Record (EHR) systems are filled with protected health information.

PHI is more than structured data elements. It is a detailed narrative of an individual’s medical history, including diagnoses, medications, laboratory results, imaging findings, clinical notes, and social or behavioral context. Protecting PHI is not only a regulatory obligation under HIPAA, it is also essential to maintaining patient trust and preventing harms such as stigma, discrimination, identity theft, or financial loss resulting from unauthorized disclosure.

The Shifting Question: What Happens After the AI Accesses PHI?

For many healthcare leaders, the central question has historically been whether AI can safely operate within HIPAA-compliant environments. This can be compared to verifying if barn can safely house the farm animals, where the only exit for the farm animals is through the observed front barn door.

A different question is emerging as agentic AI expands in these LLM systems. What happens to patient data after the AI accesses it?

The Rapid Rise of Agentic AI in Healthcare

At the recent HIMSS 2026 conference, numerous vendors prominently promoted their agentic AI solutions, showcasing autonomous agents capable of handling everything from clinical documentation and revenue cycle tasks to patient communications and multi-step care coordination.

LLMs are increasingly being deployed within agentic architectures, where the LLM not only generates responses but also performs actions across multiple systems. Integration frameworks such as the Model Context Protocol (MCP) demonstrate the ease of connecting systems using this new architecture. MCP standardizes secure, structured communication between AI agents and external tools, resources, and data sources, enabling LLMs to discover capabilities, retrieve context, and execute workflows with greater reliability and control. A single AI assistant can retrieve clinical context from the EHR, assemble documentation, query scheduling systems, submit payer requests, and coordinate actions across multiple applications. 

An LLM might call external systems such as pharmacy benefit manager (PBM) databases for real-time formulary and drug-interaction checks, laboratory information systems (LIS) for results verification, revenue cycle management (RCM) platforms for claims processing, telehealth integration services, wearable data aggregators, or third-party population health analytics tools.

Each integration makes the system more useful. This might be compared to the barn housing farm animals; the building is rapidly being renovated to allow more light with new windows and doors, but at the same time, allowing new exits through which the animals might escape. Each agentic AI integration creates new pathways through which patient data can flow. 

Hidden Privacy Risks in Interconnected Ecosystems

A BAA governs how a cloud provider stores and processes PHI within its services. It does not automatically govern how information flows when an AI system communicates with external APIs, third-party software tools, or other connected platforms.

LLM increasingly functions as a bridge between systems by retrieving information from one environment, processing it, and then transmitting relevant context to another system to complete a workflow.

This LLM behavior is exactly what is intended and provides the expected benefit. 

Consider a use case such as prior authorization. The LLM accesses the patient data, including codes, history, and details that make up the patient’s life. It might pull in a quick formulary check from the Pharmacy Benefits Manager (PBM) or verify a lab result and transmit this data to the payer’s Interface. Overall, saving time and speeding up care, but behind the scenes, suspense builds in the quiet; the request can spill more context than planned. External logs gobble fragments of the record. Data is retained outside the controlled HIPAA environment. No malice. Just the task completed. Yet the patient data crossed the line. Slipped away into the unknown.

Figure 1: The Expanding Agentic AI Ecosystem

Agentic AI systems are particularly effective at multi-step workflows that retrieve information, reason about it, and pass structured data between systems, without the user’s intervention. The LLM/AI engine becomes an intelligent conduit through which patient information flows.

Mitigating Risks: The Technical Savior Using PHI Redaction

Mitigating this risk requires architectural safeguards as well as governance oversight.

The most reliable HIPAA Safe Harbor solution is technical PHI redaction. A de-identification layer prevents the LLM from ever receiving the protected data and transmitting it outside the private environment. It replaces the 18 HIPAA identifiers, including names, addresses, phone numbers, and medical record numbers, with pseudonymous tokens. It does this while preserving the clinical facts the LLM needs, including data on labs, vitals, allergies, encounters, diagnoses, clinical notes, and medications. Dates are shifted to maintain sequences without exposing exact values. A secure mapping in the application layer temporarily holds the link back to the original identifiers.

Clinicians act on the provided information, and tokens resolve back. Session ends, mapping gone. No persistent exposure. These safeguards reduce the risk dramatically. The AI flows data safely now. The expanding AI ecosystem? It is now tamed. Patient trust preserved.

Looking Ahead: Balancing Innovation and Protection

The productivity benefit of these systems is real, and their adoption will accelerate in the coming years, if not months. Healthcare leaders need to recognize that AI systems connected to multiple platforms behave differently than traditional software operating within a single controlled private environment.

Once an AI system learns how to navigate the patient chart, it eventually learns how to navigate everything connected to it.

In modern healthcare IT environments, that network of connections and data flows will end up extending farther than most organizations expect.

About Dennis P. Sweeney

Mr. Sweeney is the Co-Founder of Vertebrai Solutions Inc., which released the Vertebrai AI Clinical Assistant at HIMSS26. He is also a Consulting Principal with Tellogic Inc., as a trusted advisor, supporting healthcare organizations for over 30 years, leading the IT & Data/Information strategies, establishing Clinical Integration & Accountable Care Organization programs, leading cross-functional teams, providing program management, technical assessments, business transformation, organizational redesign, software product development, change management, and system implementations.


#ai



< + > This Week’s Health IT Jobs – April 29, 2026

It can be very overwhelming scrolling through job board after job board in search of a position that fits your wants and needs. Let us take that stress away by finding a mix of great health IT jobs for you! We hope you enjoy this look at some of the health IT jobs we saw healthcare organizations trying to fill this week.

Here’s a quick look at some of the health IT jobs we found:

If none of these jobs fit your needs, be sure to check out our previous health IT job listings.

Do you have an open health IT position that you are looking to fill? Contact us here with a link to the open position and we’ll be happy to feature it in next week’s article at no charge!

*Note: These jobs are listed by Healthcare IT Today as a free service to the community. Healthcare IT Today does not endorse or vouch for the company or the job posting. We encourage anyone applying to these jobs to do their own due diligence.



Tuesday, April 28, 2026

< + > MediQuant Reduces Cyber Exposure, Tech Footprint, and Costs with Application Rationalization

Over the decades, IT systems proliferate at health care systems, particularly when individual departments install solutions optimized for their particular use case. Jim Jacobs, CEO at MediQuant, points out that users tend to rely on existing systems and resist having them taken away. But consolidation can help customers meet their defined priorities: reducing cyber exposure, tech footprint, and costs.  A structured application rationalization approach and tool can provide the data needed to make those decisions.

Serving clients since 1999, MediQuant supports what Jacobs calls “responsible AI.” He worries that many organizations move too fast and risk costly mistakes. MediQuant has adopted AI across four pillars: to provide clinical insights and improvements in revenue cycle, to drive improvements in complex implementations, and in identifying more uses for patient data.

MediQuant is also innovating around DICOM, consolidating imaging data from all modalities into a centralized, DICOM-based archive, enabling seamless access to historical studies while reducing costs and eliminating the complexity of fragmented legacy systems.

Check out our interview with MediQuant to learn more about the benefits of application rationalization and the unique ways they’re helping hospitals and health systems get value from their health data archive.

Learn more about MediQuant: https://www.mediquant.com/

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

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

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

MediQuant is a proud sponsor of Healthcare Scene.

#mediquant

< + > The Digital Front Door is Locked for Millions of Patients; Health IT Leaders Hold the Key

The following is a guest article by Mike Barton, VP, Communications at AudioEye

Patient Portal Adoption has Surged, but Accessibility Failures are Locking out the Patients who Need Digital Access the Most

The digital transformation of healthcare has made it easier than ever for patients to manage their own care. For the one in four Americans living with a disability, that promise often falls apart at the first click.

A patient checks her lab results at 10 p.m. She logs into her health system’s patient portal, pulls up the results page, and sees what her doctor ordered. Five minutes, start to finish.

Same portal, different patient. This one is blind. The login form has no labels, so her screen reader can’t tell which field is for her username and which is for her password. She guesses. Gets it wrong. The error message that pops up? Invisible to her assistive technology. She never gets past the front door.

That second scenario is not uncommon. According to the ONC’s 2024 Health Information National Trends Survey, over 75% of individuals now have online access to their medical records. But for the one in four American adults living with a disability, the digital front door to healthcare might as well be bolted shut. These aren’t people opting out of digital health. They’re being shut out by the very tools meant to bring them in.

Where Patient-Facing Digital Tools Break Down

The failures in patient portals aren’t random. AudioEye’s 2025 Digital Accessibility Index analyzed more than 420,000 web pages across 15,000 websites and found that the average healthcare page had 272 accessibility issues. They cluster in the exact workflows patients use most.

  • Keyboard Navigation Failures: Patients with motor disabilities often can’t use a mouse, so they navigate entirely by keyboard; healthcare sites averaged 6.1 keyboard-related violations per page – that’s enough to make logging in or filling out a form a dead end
  • Low Contrast Text: If the color contrast between text and background is too low, people with low vision can’t read it; healthcare pages averaged 69.1 contrast violations per page; lab results, medication instructions, appointment details: all potentially unreadable
  • Broken Links and Forms: Screen readers need properly labeled links and form fields to guide someone through a page; without them, it’s guesswork; healthcare sites averaged 5.4 inaccessible links and 4.0 broken form elements per page

All of this is happening on live patient portals, at organizations that would absolutely describe themselves as patient-centered.

The Regulatory Pressure is Real and Immediate

The updated Section 504 rule from HHS, finalized in May 2024, requires organizations receiving federal financial assistance to meet WCAG 2.1 Level AA across their digital properties (45 CFR Part 84).

59% of business leaders said their organizations would face legal risk if audited today, according to AudioEye’s 2026 Accessibility Advantage Report. Yet only 47% describe their accessibility programs as proactive. The rest are operating reactively or meeting bare minimums. If you’re in health IT, that gap should worry you.

A Practical Fix Roadmap for Stretched Teams

Most patient portal accessibility failures are configuration and content issues. Fixable, if you prioritize the highest-impact areas.

Start with the critical path. Map the five to ten workflows patients use most. Audit those specifically against WCAG 2.1 AA using both automated scanning and manual testing with assistive technology. Automated tools catch roughly two-thirds of issues. The rest — screen reader behavior, keyboard flow logic, cognitive accessibility — requires human evaluation.

Fix login and authentication first. A patient who can’t get past the front door can’t use anything else. Then test and fix form labels, link descriptions, and alt text on high-traffic pages.

Someone has to own it. Nearly half of organizations manage accessibility entirely in-house, but 64% of those teams admit they lack the specialized skills. If nobody owns accessibility as an ongoing practice, it won’t survive the next site update.

Build testing into every release cycle. Every portal update, new feature, and vendor patch can introduce new barriers. The organizations that stay compliant test at every release, not once a year before an audit.

The Real Impact is Patients, Not Compliance

Yes, the Section 504 deadline matters. But fines aren’t the real cost here.

If the disability rate in your community mirrors the national average, approximately 25% of your patients have a disability. Even if only a fraction of those people can’t complete basic portal tasks, you’re talking thousands of patients a year who are functionally locked out. They give up on the portal. Call the front desk instead. Skip the follow-up because the phone line was busy. And slowly drift away from their own care.

#digital

< + > Leading Hospital at Home Programs

In February, Congress extended the Acute Hospital Care at Home program through the end of 2030. The program provides waivers to hospitals t...