There was a time when legacy applications in healthcare were viewed as an inconvenience. They were something to manage, but not something to worry about.

That time has passed.

Today, the accumulation of outdated systems and inaccessible data has become a systemic risk, one that extends far beyond IT departments. It is showing up in delayed care, incomplete clinical decisions, rising cybersecurity exposure, and increasing regulatory scrutiny.

What many organizations still treat as a technical challenge is, in reality, a clinical and operational one.

When Data Exists but Can’t Be Used

From a clinician’s perspective, access to complete and accurate patient information is not optional. The reality for many clinicians and the patients they serve is that it is foundational to safe care.

Yet in many health systems, critical pieces of a patient’s history remain locked in legacy applications. Retrieving that information often requires navigating multiple systems, each with its own interface, structure, and limitations. In time-sensitive situations, that friction matters.

When clinicians cannot easily access a complete medical history, the consequences are real: missed context, duplicated tests, delayed treatment, and increased risk of error. 

This is not a hypothetical scenario. It is happening every day in organizations where data technically exists, but is not practically accessible.

The industry has spent years focused on data capture and retention. The next challenge is ensuring that data can actually be used when it matters most: At the point of care.

The Growing Complexity Beneath the Surface

Over the course of my career, I’ve watched healthcare IT environments grow exponentially more complex.

It is no longer unusual for organizations to manage dozens, or even hundreds, of applications across clinical, financial, and operational domains. Many are remnants of past EHR transitions, departmental systems, or mergers and acquisitions.

Each system represents not only a repository of data, but also a point of dependency and potential failure. In fact, the average organization may be running a multitude of systems simultaneously, with data footprints expanding rapidly over time. 

This complexity creates fragmentation. Fragmentation creates blind spots. And blind spots, in healthcare, create risk.

Cybersecurity Is Amplifying the Stakes

If fragmented data environments create clinical risk, they also create an equally serious security risk.

Healthcare has become one of the most targeted industries for cyberattacks. Outdated applications, which often lack modern security protocols, are particularly vulnerable. These systems frequently house sensitive patient information, yet may not support current standards for authentication, monitoring, or threat detection.

The scale of the issue is difficult to ignore. In 2024 alone, hundreds of millions of patient records were compromised across the industry, with legacy systems playing a significant role in many breaches. 

For healthcare leaders, the question is no longer whether legacy environments pose a risk. The question is how much risk they are willing to accept.

The Financial Argument Is Only Part of the Story

Much of the conversation around legacy systems focuses on cost and for good reason.

Maintaining outdated applications requires ongoing investment in infrastructure, licensing, and support. These costs compound over time, often consuming a significant portion of IT budgets.

But focusing only on financial impact understates the broader issue.

The true cost includes:

• Time clinicians spend searching for information instead of delivering care 
• Delays in responding to audits, subpoenas, or compliance requests 
• Increased exposure to breaches, fines, and reputational damage 
• Lost opportunities to invest in innovation and patient experience 

In many cases, organizations are spending heavily to maintain systems that no longer deliver meaningful value—while simultaneously absorbing the risks those systems introduce.

M&A Is Turning a Challenge into a Cycle

Mergers and acquisitions add another layer of urgency.

Each transaction brings together multiple application environments, often with overlapping systems and inconsistent data structures. Without a clear strategy, organizations inherit not only new capabilities, but also new complexity. Legacy data management becomes a recurring challenge rather than a one-time event.

As highlighted in recent industry analysis, healthcare M&A activity continues to drive the expansion of application portfolios, with each merger introducing additional systems, silos, and integration challenges. Without a sustainable approach, organizations risk carrying forward the same issues, only on a larger scale.

The Most Common Misstep: Treating It as a One-Time Project

One of the most consistent patterns I see is organizations approaching legacy data management as a project rather than a program. A new EHR is implemented. Systems are replaced. And only then does the question arise: what happens to everything left behind? By that point, the organization is already managing the consequences.

Legacy data management needs to be part of the strategy from the beginning – whether that is an EHR transition, a merger, or any major transformation initiative. Planning for how data will be retained, accessed, and governed over time is just as important as selecting the system that will replace it.

A Shift in Mindset: From Storage to Stewardship

At its core, this is a mindset shift.

For years, success was defined by how well organizations could store and retain data. Today, success must be defined by how effectively that data can be accessed, understood, and used. This requires moving beyond the idea that legacy data can simply sit in read-only systems indefinitely. It requires treating data as a living asset, one that must remain available, usable, and secure, regardless of the applications that created it.

A Path Forward

Addressing this challenge does not happen overnight. But it begins with a few fundamental questions:

• Do we have a complete understanding of where our legacy data resides? 
• Have we rationalized why we have it and what we need to do with it?
• Can we access and produce that data quickly when needed? 
• Are we confident in the security of the systems that house it? 
• Are we managing this as an ongoing strategy, or reacting to it as issues arise? 

These are not just IT questions. They are questions of patient safety, organizational resilience, and trust. Ultimately, how healthcare organizations manage their data reflects how they deliver care.

And in an environment where every decision matters, incomplete or inaccessible information is a risk no organization can afford to carry indefinitely.

About Kel Pults

Kel Pults is Chief Clinical Officer & VP, Government Strategy at MediQuant. With 26 years of nursing and patient care experience, Pults applies her deep healthcare and health informatics training and expertise to build effective health information solutions that support hospitals’ and health systems’ clinical data management needs.

What You Should Know

• Diagnostic medical imaging network Scan.com has closed $220M in combined equity and debt financing, comprising a $90 million Series C equity round and $130M in debt facilities.
• The equity financing was led by Noteus Partners, with participation from Aviva, Concord Health Partners, YZR Capital, and Oxford Capital; debt facilities to support M&A and working capital were provided by VerisFi Capital and Atempo Growth.
• Follows a period of rapid growth where Scan.com doubled revenue over the past year, surpassing a $165M annualized run rate and serving more than 900,000 patients globally across the U.S. and UK.
• Scan.com embeds AI across the scheduling and routing workflow: matches referrals based on live scanner availability, transparent pricing, and clinical subspecialty, while routing diagnostic reports to subspecialized radiologists to deliver results within 48 hours.

A Unified API for Diagnostic Imaging

While national commercial laboratory networks like Quest Diagnostics and Labcorp consolidated clinical blood testing decades ago, diagnostic imaging has historically lacked a comparable digital backbone.

Scan.com provides a two-way application programming interface (API) that integrates directly into independent imaging centers’ electronic medical records (EMR) and practice management systems. This enables digital health platforms, employer health plans, third-party administrators (TPAs), and workers’ compensation networks to search live availability, view upfront transparent pricing, and book outpatient scans with a single integration.

Key operational features include:

• Intelligent Routing: AI models match referrals against real-time machine availability, out-of-pocket costs, and clinical subspecialties.
• Automated Triage & Subspecialty Reading: Imaging reports and DICOM files are routed directly to subspecialized radiologists, with results delivered back to referring clinicians and patients typically within 48 hours.
• Human-in-the-Loop Navigation: Dedicated care coordinators manage intake paperwork, pre-authorizations, and patient questions to ensure automation does not replace clinical touchpoints.

To date, more than 900,000 patients have accessed diagnostic imaging through the platform globally.

“The US runs around 600 million medical imaging scans a year, and there is still no national infrastructure behind them,” said Charlie Bullock, co-founder and CEO of Scan.com. “Labs got that decades ago with Quest Diagnostics and Labcorp. Imaging never did, and that is what we have built. An employer, a health plan or a digital health app can now connect to imaging capacity nationwide through a single API, and their patients get scanned at a quality-checked center in days rather than weeks, at a price they can see before they schedule. This investment is about making that the standard in US healthcare, rather than the exception.”

Scan.com, which already operates the largest private medical imaging network in the United Kingdom, entered the U.S. in 2023 and now generates the majority of its business domestically. The company plans to use the capital to scale its nationwide footprint of imaging centers and expand its agentic AI scheduling infrastructure.

What You Should Know

• The Peterson Health Technology Institute (PHTI) will conduct a comprehensive new independent evaluation of digital health tools for type 2 diabetes, slated for publication in 2027.
• The evaluation re-evaluates the market three years after its landmark March 2024 assessment, which determined that widely used digital diabetes tools relying on noncontinuous glucometers failed to deliver meaningful clinical improvements and drove up net healthcare spending.
• Focuses on the expanded therapeutic landscape since 2024—specifically evaluating how modern digital platforms integrate data from Continuous Glucose Monitors (CGMs) and support GLP-1 receptor agonist therapies alongside traditional blood glucose tracking.

How PHTI Re-Evaluates $1B in Digital Diabetes Tools Across Clinical and Economic ROI

The digital chronic care management, self-insured employer benefits, and health plan procurement sectors face a defining validation moment: billions of dollars are flowing into digital diabetes platforms, but healthcare purchasers remain cautious after initial generations of point solutions failed to demonstrate durable clinical or financial return on investment.

According to national health data, more than 40 million Americans—approximately one in eight adults—are living with diabetes, with type 2 diabetes accounting for 90% to 95% of cases. The disease represents the seventh leading cause of death in the United States and generated $413B in direct and indirect healthcare expenditures in 2022, consuming one out of every four healthcare dollars spent nationwide. Consequently, diabetes management continues to rank as the highest-priority chronic disease category for commercial payers and self-funded employers.

When the Peterson Health Technology Institute (PHTI) released its initial assessment in March 2024, the findings disrupted the digital health industry. That baseline evaluation examined eight prominent digital tools that relied on traditional, noncontinuous glucometers paired with remote coaching, concluding that they failed to achieve clinically meaningful glycemic reductions (HbA1c) while increasing total cost of care. Since that report, the clinical standard of care has undergone a massive structural shift.

Digital diabetes companies have raised over $1B in new capital, rapidly pivoting their product roadmaps to ingest continuous glucose telemetry and manage the complex adherence, side-effect profiles, and dosing protocols of GLP-1 receptor agonists.

To determine whether these expanded technological and pharmaceutical integrations have translated into verified clinical efficacy and economic savings, the Peterson Health Technology Institute has announced a new evaluation of digital diabetes management solutions, scheduled for release in 2027.

Evaluating a New Era of GLP-1s and Continuous Biosensing

PHTI’s upcoming 2027 assessment establishes an updated methodological framework to evaluate how digital health platforms manage modern chronic metabolic care:

• Expanding Beyond Spot-Check Glucometers: Incorporates the widespread adoption of Continuous Glucose Monitors (CGMs), assessing how platforms ingest, harmonize, and contextualize high-frequency interstitial glucose telemetry into actionable clinical interventions.
• Evaluating Digital GLP-1 Management: Assesses how digital platforms support initiation, titration, dietary behavioral modification, side-effect management, and long-term medication adherence or off-ramping protocols for GLP-1 receptor agonists.
• Rigorous Clinical and Economic Assessment: Evaluates whether contemporary multi-modal solutions produce statistically significant, durable HbA1c reductions and whether they successfully lower downstream medical expenses to justify their subscription and licensing fees.
• Informing Enterprise Healthcare Purchasers: Provides health plan actuaries, employer benefit leaders, and health system CMOs with objective, third-party evidence to guide procurement decisions and formulary benefit design.
• Expanding Independent Digital Health Surveillance: Adds to PHTI’s established body of independent evaluations across gastrointestinal conditions, opioid use disorder, depression and anxiety, hypertension management, and musculoskeletal care.

“When we released our 2024 evaluation, people were disappointed to learn that widely-used digital diabetes solutions were not meaningfully improving clinical outcomes for patients,” said Caroline Pearson, Executive Director of PHTI. “That evaluation remains one of our most widely read, and we continue to receive more questions about it than any other. PHTI assessments are meant to drive better innovation, and it is encouraging that the landscape today appears to have improved. We are eager to see if these digital solutions have evolved to better serve patients.”

What You Should Know

• Labcorp launched major digital enhancements to Labcorp Global Trial Connect, redesigning both the Labcorp Sponsor and Labcorp Investigator portal experiences.
• Co-developed and guided by direct product testing and feedback from more than 80 biopharma sponsors and 100 clinical investigator sites worldwide.
• Features embedded in-portal kit ordering that cuts manual data entry by 75% while standardizing inventory tracking and supply replenishment.
• Introduces a unified Study Oversight Dashboard that aggregates real-time study progress, site metrics, sample statuses, and supply logistics into a single operational interface.
• Incorporates a guided Report Wizard to simplify custom reporting workflows, alongside end-to-end specimen tracking across the entire sample lifecycle.

Designed with Sponsor and Site Input

The platform updates were built directly on user feedback, product testing, and operational insights gathered from more than 80 biopharma sponsors and 100 clinical investigator sites worldwide.

Key enhancements to the Global Trial Connect ecosystem include:

• 75% Reduction in Kit-Ordering Data Entry: Introduces embedded in-portal collection kit ordering that automates inventory oversight, auto-populates site details, and cuts manual data entry by 75%.
• Unified Study Oversight Dashboard: Consolidates trial metrics, investigator site performance, sample statuses, and logistics supplies into a single operational view.
• End-to-End Sample Tracking: Provides real-time visibility across the entire biospecimen lifecycle—from on-site patient collection through transit, central lab intake, testing, and biobanking.
• Guided Report Wizard: Simplifies data extraction and operational reporting with an intuitive builder designed to generate customized study reports without custom database querying.
• Role-Based Navigation: Delivers a streamlined user interface with role-tailored dashboards and workflows to reduce software onboarding friction for site staff.

“As clinical trials become increasingly complex, sponsors and investigator sites need technology that helps them work more effectively,” said Dr. Brian Caveney, Executive Vice President, President of Biopharma Laboratory Services, and Chief Medical and Scientific Officer at Labcorp. “The latest enhancements to Global Trial Connect make it easier to access insights, manage study activities and navigate trial complexity with confidence, allowing customers to focus less on operational tasks and more on advancing research and supporting patients.”

Labcorp’s central laboratory and trial support footprint spans operations across roughly 100 countries, supporting more than 85% of the new therapeutic drugs approved by the FDA in 2025. The company plans to continue rolling out automated data and logistics features across its Biopharma Laboratory Services portfolio.

What You Should Know

• Healthcare technology firm WellBeam formed a strategic partnership with Kettering Health (a regional health system in western Ohio) and Alternate Solutions Health Network (ASHN) (a national provider of home health joint ventures).
• Embeds home health clinical workflows—including order management, electronic plan-of-care signing, and clinical communication—directly into existing physician electronic medical record (EMR) environments.
• Eliminates the traditional post-acute coordination bottleneck of manual faxes, unlogged phone calls, and physical paper chasing between health systems and home health agencies.
• Automatically captures billable physician oversight charges for Kettering Health clinicians who are already performing home care management, unlocking compliant fee-for-service and value-based reimbursement.

Eliminating Faxes to Automate Post-Acute Management

Under the partnership, Kettering Health’s network of physicians and ASHN’s field clinicians will connect through WellBeam’s platform to manage home health workflows inside the daily charting environment.

Instead of juggling paper forms and external web portals, doctors can electronically review and sign home health care plans and orders directly within their EMR in-basket. Once completed, documentation is automatically routed back into the patient’s longitudinal medical record.

The integration targets three core operational priorities:

• Workflow Automation: Replaces manual document management with standardized digital order routing, speeding up home health start-of-care turnaround times.
• Automated Charge Capture: Automatically logs billable physician time and oversight activities associated with home health care management that often go unbilled due to fragmented documentation.
• Cross-Setting Communication: Creates a direct digital communication channel between hospital-affiliated physicians and home visiting nurses to coordinate real-time medication adjustments and care escalations.

There is an interesting shift happening in healthcare’s digital transformation efforts. We are no longer simply using software to support clinicians; we are operating within a software-defined clinical environment. Today, clinical workflows, revenue cycles, patient engagement platforms, analytics pipelines, and AI models run on shared digital infrastructure affecting key care outcomes. A radiology model, for example, is not just producing an image; it is feeding clinical decision engines, influencing billing documentation, etc. 

As these tools become more intelligent and autonomous, a new problem has surfaced. Healthcare organizations are struggling to keep track of how they actually behave in real time. Interactions between data pipelines, user decisions, and infrastructure conditions have created dependencies that are often invisible in aggregate. As this black-box logic – where the internal workings of the system remain hidden – finds its way into high-stakes clinical and operational workflows, it creates new, invisible risks. 

Why Point-in-Time Governance Is Already Obsolete 

Healthcare governance mechanisms are still structured around periodic review: audit cycles, committee oversight, pre-deployment assessments, retrospective safety analysis, quarterly financial reviews, etc. This approach made sense when software behaved predictably and defined inputs produced consistent outputs.

AI changes this dynamic. Risk no longer concentrates in single deployment decisions; rather, it accumulates in system behavior over time and does not trigger the obvious alarms. A triage algorithm or predictive sepsis model, for example aren’t static asset that can be certified once and left alone. They behave more like an ongoing participant in care delivery and experience performance drift as patient populations change, workflows evolve, or upstream systems are modified. 

When governance is periodic, but systems are continuous, risk slips in through the gaps. Let’s say a routine EHR update introduced a drift in a model. The precision drop will not be immediate. There might be a small percentage dip over a few months. By the time a quarterly review catches it, the accumulated exposure may already be affecting clinical decisions and patient safety. These issues also lead to a loss of trust in the system and affect clinic adoption. 

This gap needs a control pane and that’s where observability comes in. It addresses this structural mismatch by making system behavior visible in real time across infrastructure through continuous monitoring of data, models, and user interactions.

From Monitoring to Observability

In IT environments, observability emerged when distributed systems became too complex for surface monitoring. Logs, metrics, and traces used to answer a simple question – what is happening inside the system right now, and why? However, with AI in the picture, and in a regulated industry like healthcare, the stakes are higher than just system uptime. We don’t just need to know if the AI is running; we need to know if the AI is “reasoning” correctly -by leveraging the context of the clinical workflow. 

Which is why observability must extend beyond logs to answer questions like: 

• Why was a specific treatment recommended?
• Why did care quality metrics degrade after a workflow change?
• Why did cloud costs spike?
• Why did sensitive data flow into an unauthorized system have?

Basically, if traditional monitoring tells you the car stopped, observability explains what led up to it – a drifting sensor, an imbalanced fuel mix, or something else.

The Four Pillars of the AI Control Infrastructure

To matter at the executive decision level, observability cannot live in the IT domain alone. It must be integrated into the strategic reporting of the organization across four distinct layers:

A. The Data Provenance Layer

Regulatory frameworks like the NIST AI Risk Management Framework (RMF) and emerging HHS mandates are moving toward a requirement for traceability.

If a patient suffers an adverse event where an AI was involved, the organization must be able to produce an immutable audit trail:

1. What version of the model was running?
2. What specific data inputs did it receive?
3. What exact outputs did it deliver and in what form?
4. What was the “confidence score” of its output?
5. Which clinician saw the output, and how did they react? Is there a record of the clinician using/accepting the output in their clinical decision-making?

Without an observability infrastructure, reconstructing this timeline is nearly impossible. As data moves across platforms, vendors, Shadow AI, and internal teams, observability can provide its lineage.  Where did this PHI (Protected Health Information) originate? Which model processed it? Did it cross a regulatory boundary it shouldn’t have? Where was the data privacy policy not applied or overlooked? This traceability helps demonstrate how compliance is maintained continuously.

B. The Model Integrity Layer

 Pre-trained machine learning models are optimized on their training datasets. Drift occurs when live data diverges from training data. A new clinic opening in a different zip code can shift population characteristics enough to reduce the accuracy of a readmission predictor for that location.

Observability surfaces these localized performance changes early. It allows teams to detect subgroup degradation, flag anomalies, and escalate to human review before small deviations turn into systemic issues. When it comes to foundation models such as the SOTA large language models (LLMs), training or retraining them again is not possible or practical. In that case, grounding LLMs with proper context and applying guardrails for it to stay within the contextual boundaries is of utmost importance. Performance evaluation and continuous monitoring of such models require different evaluation frameworks and metrics than traditional ML models. 

C. The Workflow & Trust Layer

Trust in AI is fragile. When an AI tool provides an incorrect output, clinicians start to disengage and begin to override the system. On the other hand, too much trust might lead to “cognitive surrender” and have grave consequences of acting on incorrect recommendations. Both situations are undesirable in the healthcare context. With observability mechanisms in place, organizations can track these behavioral signals in real time and take corrective action. 

D. The Economic & Compute Layer

AI is expensive and the inference cost, particularly with large models running across multiple departments, can spiral if not monitored. For the CFO, observability connects the dots between compute usage and clinical value. It helps the organization identify redundant models, vendor sprawl, and Ghost AI tools that are consuming resources without contributing to the margin. This visibility enables rationalization decisions grounded in both performance and economics.

An Executive Framework for Healthcare Observability

For boards and CIOs, observability belongs alongside cybersecurity and enterprise data governance. It shapes how AI risk is identified, measured, and managed.

Practical implementation requires:

1. Treating observability as non-negotiable core infrastructure, not just tooling.
2. Ensuring visibility from data ingestion through model behavior to clinician interaction and final outcomes
3. Defining thresholds aligned to patient safety, regulatory exposure, compliance, privacy obligations, and financial sustainability.
4. Connecting those thresholds to automated response paths — review, pause, or escalate.
5. Designing for scale early to avoid fragmented controls later.

This creates disciplined AI growth, sustained leadership visibility, and manageable financial exposure as deployments expand.

Control as a Catalyst for Speed

The common misconception is that governance slows things down. In reality, the opposite is true. While it takes upfront investment and commitments to design and implement governance controls, it pays dividends in the long run. 

In healthcare, the organizations that will win the AI Race are not those who deploy the most models, but those who can manage the most models at scale without losing institutional control. When you have a control pane for trust, you can move faster, innovate more aggressively, de-risk, and scale with the confidence that the system is behaving exactly as intended.

The next phase of healthcare is not just AI-led; it is observability-governed. For the modern CXO, this isn’t just a technical requirement; it is the foundation of the architecture of accountability.

About Kaushik Raha

Kaushik Raha, PhD, Vice President, CitiusTech leads the development and deployment of AI-driven innovations for healthcare and life sciences. With more than two decades of experience spanning healthcare, pharmaceuticals, clinical research, and health information services, he has held leadership roles at CitiusTech, Elsevier, Johnson & Johnson, and GSK. Trained as a scientist with advanced degrees from AIIMS, Penn State University, and UCSF, Kaushik brings a rare blend of deep scientific expertise and practical AI leadership, helping organizations translate emerging technologies into measurable healthcare outcomes. 

What You Should Know

• Musculoskeletal technology leader Globus Medical, Inc. announced the acquisition of Higgs Boson Health, a digital healthcare experience company based in Durham, North Carolina, and incubated out of Duke University.
• Integrates Higgs Boson’s software engineering and artificial intelligence research teams into Globus Medical to develop a seamless, connected digital care environment spanning the entire musculoskeletal patient journey.
• The acquisition positions Higgs Boson’s technology within Globus Medical’s “surgical intelligence pillar,” establishing a closed-loop analytics and continuous-learning framework that links pre-operative planning, intraoperative execution, and longitudinal post-operative recovery. 

Power AI-Driven Musculoskeletal Surgical Intelligence

In orthopedic and spine surgery, the success of a complex spinal fusion or joint reconstruction depends heavily on what happens before and after the operation—from pre-operative conditioning to years of post-surgical rehabilitation. Yet medical device companies have historically focused almost entirely on the hardware placed inside the operating room, leaving long-term recovery and patient compliance tracked through disjointed apps or lost entirely once a patient is discharged.

To bridge that gap and link operating room robotics with longitudinal patient outcomes, musculoskeletal technology manufacturer Globus Medical has acquired Higgs Boson Health, a digital health and patient experience company incubated out of Duke University.

Financial terms of the transaction were not disclosed.

Building a Closed-Loop “Surgical Intelligence” Platform

The acquisition brings Higgs Boson’s software engineers and artificial intelligence researchers directly into Globus Medical’s enabling technologies division. The software will serve as the core asset within Globus Medical’s surgical intelligence pillar, creating a closed-loop system that connects pre-operative planning, robotic surgical telemetry, and multi-year patient recovery data.

The digital care platform aims to address key operational hurdles across the surgical continuum:

• Long-Term Outcome Tracking: Directly supports Globus Medical’s overarching clinical objective to achieve and verify 95% positive clinical outcomes at 10 years across its orthopedic and spine procedures.
• Pre- and Post-Operative Navigation: Deploys interactive mobile tools to guide patients through pre-habilitation checklists, post-discharge physical therapy regimens, and remote symptom tracking.
• Algorithm-Driven Feedback: Feeds real-world functional recovery metrics back to surgical care teams, allowing surgeons to refine patient selection criteria and procedural techniques over time.
• Portfolio Integration: Complements Globus Medical’s hardware and robotic lines, including its ExcelsiusGPS robotic navigation platform, joint reconstruction implants, trauma systems, and biomaterials.

What You Should Know

• Gartner advises healthcare Chief Supply Chain Officers (CSCOs) to replace manual inventory counting and legacy scanning tools with autonomous supply rooms driven by computer vision and artificial intelligence.
• Garner reveals advances in computer vision, spatial sensing, and workflow automation enable Periodic Automatic Replenishment (PAR) rooms to autonomously monitor inventory levels, predict demand, and trigger reorders with minimal human involvement.
• Autonomous supply rooms integrate overhead camera tracking, predictive replenishment algorithms, voice-directed guidance, illuminated pick-to-light bins, and electronic shelf labels (ESLs) to streamline clinician item retrieval.

Gartner Advises Healthcare CSCOs to Deploy Computer Vision for Autonomous Supply Rooms

Hospitals have spent decades equipping clinical staff with handheld barcode scanners, RFID wands, and smart inventory cabinets. Yet despite those digital tools, the underlying operational model has remained unchanged: human workers still spend countless hours manually walking stockrooms, counting boxes, and scanning shelves.

When materials technicians get bogged down in manual audits, visibility lapses, medical supplies expire quietly on back shelves, and bedside nurses lose valuable clinical time hunting through chaotic supply closets.

According to new research from Gartner, Inc., healthcare Chief Supply Chain Officers (CSCOs) should prepare to phase out manual stock counting entirely in favor of autonomous supply rooms driven by ceiling-mounted computer vision and predictive AI.

From Task Automation to True Operational Autonomy

Gartner draws a sharp operational distinction between technologies that merely assist manual tasks and systems that deliver true inventory autonomy. While barcodes, RFID tags, weighted bins, and badge-access smart cabinets improve individual data capture steps, they still force hospital staff to scan or count items.

Autonomous Periodic Automatic Replenishment (PAR) rooms eliminate human auditing from the equation:

• Continuous Optical Sensing: Ceiling-mounted cameras monitor stock levels, bin fullness, and shelf positions in real time, removing the need for manual physical counts.
• Predictive AI Replenishment: Machine learning models analyze historical usage patterns and upcoming clinical procedure schedules to automatically trigger purchase orders or warehouse picks before items hit critical shortages.
• Point-of-Care Clinician Guidance: Integrated pick-to-light bins, electronic shelf labels (ESLs), and voice prompts guide nurses directly to needed supplies in seconds.

Measuring Investments by Work Removed

Gartner advises supply chain executives not to let legacy hardware investments dictate future strategy, urging leadership to evaluate emerging technologies based on the physical labor they eliminate.

“Hospitals have spent years buying technology that helps employees count supplies, but employees are still doing the counting,” said Bruce Gilmore, VP Analyst in Gartner’s Supply Chain practice. “AI and computer vision create an opportunity to remove that work, rather than make it incrementally faster. This can enable nurses to spend more time with patients, rather than searching for missing items in stockrooms.”

Gartner clients can read more in: Getting to Inventory Autonomy: Build the Self-Managing Supply Room. Nonclients can learn more in: CSCO Roadmap for Building a Supply Chain AI Foundation.

What You Should Know

• Philips secured an award of up to $33.7M from the Advanced Research Projects Agency for Health (ARPA-H) under its Autonomous Interventions and Robotics (AIR) program, led by Dr. Ileana Hancu.
• Develops AI-enabled robotic procedure automation and remote-assisted capabilities for endovascular stroke treatment (mechanical thrombectomy) built on the Philips Azurion image-guided therapy platform.
• The program integrates AI navigation algorithms, robotic catheter control, and real-time imaging guidance into the Philips Azurion image-guided therapy platform to enable remote-assisted and supervised autonomous endovascular interventions.

The Geography Problem in Acute Stroke Intervention

In acute ischemic stroke, “time is brain.” When a large-vessel occlusion cuts off cerebral blood flow, approximately 1.9 million neurons die every minute the vessel remains blocked. Mechanical thrombectomy—a catheter-based procedure that physically navigates the neurovasculature to retrieve the clot—is among the most effective interventions in modern medicine, frequently reversing severe neurological deficits.

However, the procedure suffers from a severe geographic bottleneck. In the United States, approximately 335,000 patients qualify for thrombectomy each year, yet only 12% ever receive it. More than half of the U.S. population lives over an hour from an advanced comprehensive stroke center capable of performing neurointerventional procedures. On a global scale, the disparity is even starker: fewer than 3% of eligible patients with large-vessel occlusions receive mechanical thrombectomy. Because transferring an acute patient to a distant tertiary facility often consumes the critical therapeutic window, thousands of patients are left with permanent disability or die simply due to proximity constraints.

To dismantle these physical barriers, ARPA-H selected Philips to lead an initiative under its Autonomous Interventions and Robotics (AIR) program to engineer remote-assisted, supervised autonomous endovascular surgery.

Azurion Integration, Autonomous Navigation, and Steerable Catheter Engineering

The ARPA-H project unites medical device manufacturing, academic engineering, and clinical neurosurgery to establish supervised robotic autonomy in endovascular therapy:

• Azurion Image-Guided Platform Integration: Embeds endovascular robotics, smart micro-interventional devices, real-time fluoroscopic imaging, and AI workflow orchestration directly into the Philips Azurion architecture, supporting progressive levels of supervised procedural autonomy under expert physician control.
• Autonomous Endovascular Navigation (Johns Hopkins): Led by Axel Krieger, PhD, Associate Professor in Mechanical Engineering at Johns Hopkins University, this development stream focuses on algorithmic path-planning and automated sensorimotor navigation to guide catheters safely through complex arterial vascular trees.
• Advanced Steerable Catheter Development (Boston University): Co-led by Tommaso Ranzani, PhD, and Sheila Russo, PhD, Associate Professors in Mechanical Engineering and Materials Science & Engineering at Boston University, this team is engineering miniaturized, flexible, steerable catheter hardware designed for precise robotic actuation in delicate cerebral vasculature.
• Neurosurgical Translation & Remote Triage (Weill Cornell Medicine): Guided by Dr. J Mocco, Chair of Neurological Surgery at Weill Cornell Medicine, this initiative establishes clinical validation protocols, safety envelopes, and remote-intervention pathways to enable specialists to oversee or perform procedures from distant hubs.
• Global Scale and Public-Private Track Record: Capitalizes on Philips’ installed footprint of over 20,000 image-guided therapy systems operating across more than 80 countries, where a patient is treated every second.

“This ARPA-H program accelerates our long-term vision for image-guided intervention,” said Bert van Meurs, Chief Business Leader Image-Guided Therapy at Philips. “We’re helping shape a future where expert care is no longer limited by geography or workforce constraints. The goal is not to replace expertise, but to make expertise scalable and accessible.”

Every industry reaches a point where incremental improvements are no longer enough. I believe healthcare has reached that point. 

In recent decades, we’ve invested heavily in digital transformation. EHRs, patient portals, cloud platforms, and now AI have fundamentally changed what’s possible. 

Yet despite extraordinary investment and innovation, many healthcare organizations still wrestle with familiar challenges. Providers spend more time staring at screens than caring for patients. Staff navigate disconnected systems to complete routine tasks. Patients wait weeks for appointments and face unnecessary friction throughout their care journey.  

Technology has evolved dramatically, but the way work gets done has remained stagnant. 

That raises an uncomfortable but necessary question: If we’ve invested so much in innovation, why do so many healthcare organizations still operate as if constrained by workflows designed decades ago? 

The answer isn’t that healthcare has failed to innovate. It’s that we’ve become remarkably good at patching workflows instead of asking why they broke down in the first place. Each patch makes sense in isolation. Collectively, they create operational, experience, and technical debt; collectively, workflow debt. 

If we’re serious about transforming healthcare, we need to stop asking how technology can make today’s workflows more efficient, start asking whether those workflows should exist at all, and intentionally redesign them to reflect how providers want to practice and patients want to get care. 

That is how healthcare starts paying down this debt. 

Every Broken Workflow Eventually Becomes a Patient Problem 

Operational inefficiency is often seen as an internal challenge. We associate it with administrative burden, staffing shortages, poor revenue cycle performance, or clinician burnout.  

However, these are also patient issues. When clinicians spend evenings completing documentation, patients receive less face-to-face attention during the day. When referrals are delayed because information doesn’t flow efficiently between systems, patients wait longer for diagnoses and treatment. When staff must manually verify information across disconnected applications, patients face unnecessary delays.  

It’s true that patients may never see the workflows operating behind the scenes, but they certainly experience the consequences. 

How Did We Get Here? 

Organizations have historically responded to reimbursement changes, regulatory requirements, staffing shortages, and acquisitions by solving immediate operational problems. Each investment made sense in isolation. One new application solved scheduling. Another improved billing, addressed compliance, and so on. 

While these investments created meaningful progress individually, they collectively introduced layers of complexity that few organizations ever stepped back to rethink or redesign. Administrative activities are estimated to account for roughly one-quarter of total U.S. healthcare spending today. While many activities are essential, too much of that cost is paying interest on workflow debt: duplicated effort, disconnected systems, and operational complexity. 

We need to stop servicing this debt and start paying it down with three fundamental interventions:  

Shift 1: Putting Tech in its Proper Place 

Over time, technology has too often become the center of workflow design instead of the enabler of care.  

That distinction may sound subtle, but its impact has been profound. Providers have increasingly adapted to systems rather than systems adapting to providers. Clinical workflows have been constrained by software requirements rather than shaped by patient needs. 

Technology should enable great care, not define it. 

Shift 2: Reimagining Work Altogether 

Healthcare has spent years modernizing existing processes. We’ve digitized paper, automated manual tasks, implemented patient portals, introduced bots, and are now rapidly deploying AI. 

Those investments have unquestionably delivered value, but modernization is not the same as transformation. Too often, we’ve refreshed yesterday’s operating model instead of asking whether it still deserves to exist. 

Adding artificial intelligence to a broken workflow doesn’t transform the workflow. It simply allows us to execute the same process faster. As I often tell our teams: “Magic dust sprinkled on top of broken is still broken.” 

The greatest opportunity AI presents isn’t automation, it’s reimagination. Instead of asking, “How can AI automate this workflow?” healthcare leaders should begin asking: “If we were designing this experience today, knowing everything that’s now possible, would we build it this way?” 

Shift 3: Democratizing Information  

Healthcare organizations have never had more information available to them, from clinical data to operational metrics, financial performance, and quality measures. 

Unfortunately, critical information often remains fragmented across applications, departments, and vendor ecosystems. Every team owns part of the story, but no one has the complete picture.  When clinicians and staff don’t fully trust the information available to them, they naturally feel the need to recreate it. They verify data that’s already been entered, ask patients repetitive questions, and duplicate work. The future belongs to organizations that democratize trusted information rather than silo it.  

AI Changes What’s Possible 

AI has quickly become the defining conversation in healthcare, and for good reason. But AI is not the transformation. It is the accelerator.  

Organizations that automate fragmented workflows simply automate fragmentation. Organizations that redesign workflows before applying AI unlock something far more valuable: capacity. Capacity for clinicians to spend more time with patients, for staff to prioritize tasks, and for leaders to make decisions with appropriate context. 

Perhaps the most exciting future isn’t one where clinicians become more efficient at navigating software—it’s one where software increasingly disappears into the background. Technology should quietly orchestrate work, surface trusted information when it’s needed, and eliminate unnecessary administrative effort. 

De-Normalizing the Broken  

Healthcare has always been defined by resilience. That resilience has carried our industry through extraordinary challenges and will continue to define the people who dedicate their lives to caring for others. 

But resilience was never meant to become a permanent operating strategy. Resilience is what allows an organization to keep functioning while it carries debt. It isn’t a substitute for paying down that debt. The next chapter of healthcare won’t be defined by how well organizations adapt to complexity. It will be defined by how deliberately they retire it. 

About Gautam “G” Shah, MBA, FACHDM

Gautam “G” Shah, MBA, FACHDM, joined NextGen Healthcare in 2026 as Chief Product and Strategy Officer. His 25 years of global experience span product management, AI, digital transformation, clinical workflow optimization, healthcare finance, and patient experience solutions. 

What You Should Know

• GE HealthCare unveiled three new cardiovascular ultrasound systems—Vivid Explorer, Vivid Advanced, and Vivid Focus—showcased at the European Society of Cardiology (ESC) Congress 2026 in Munich, Germany.
• All three systems leverage the software-defined cSound imaging architecture introduced on the ultra-premium Vivid Pioneer, standardizing image reconstruction, contrast, and spatial resolution across care environments. 
• The systems pair with EchoPAC Software Only v211 for offline quantification and reporting; all three systems and EchoPAC v211 have attained CE Mark status and are currently 510(k) pending with the U.S. FDA.

Extending cSound Architecture Across the Hospital Fleet

All three systems are built on GE HealthCare’s software-based cSound imaging architecture, the same image reconstruction platform used in its ultra-premium Vivid Pioneer console. By sharing a common beamforming and software foundation, the systems deliver consistent contrast and spatial resolution regardless of whether an exam takes place in a catheterization lab or an outpatient exam room.

Each console targets a distinct operational setting:

• Vivid Explorer: Built for high-volume echocardiography laboratories and routine interventional suites, supporting 4D, multi-plane, and 2D imaging across transthoracic (TTE), transesophageal (TEE), and intracardiac echocardiography (ICE).
• Vivid Advanced: Designed for mid-sized community hospitals and multi-specialty practices requiring comprehensive cardiac imaging (2D TTE, multi-plane TEE, and 2D ICE) alongside vascular, abdominal, and soft-tissue exams.
• Vivid Focus: Engineered for routine outpatient cardiology, featuring the new 4Sc-RS transducer probe, AI-assisted workflows, and multi-organ exam capabilities.

Automating Quantification to Ease Sonographer Burden

To cut down on repetitive manual caliper placement and reduce exam table times, the new systems integrate an array of automated AI measurement tools:

• AI Auto Measure 2D & Cardiac Auto Doppler: Automatically calculates routine 2D dimensions and Doppler flow velocities, cutting manual keystrokes.
• Easy AutoEF & Easy AFI LV: Automates ejection fraction and left ventricular global longitudinal strain measurements to minimize inter-observer variation across clinician skill levels.

The systems are accompanied by EchoPAC Software Only v211, an updated post-processing platform that allows cardiologists to conduct identical automated measurements, offline quantification, and structured reporting from remote workstations.

All three consoles and EchoPAC v211 have secured CE Mark status in Europe and are currently 510(k) pending with the U.S. Food and Drug Administration.

In medical applications, performance begins at the material level. Whether used in implantable devices, diagnostic tools, or surgical instruments, materials are required to meet stringent standards for biocompatibility and structural integrity. Consistent material properties enable stable processing and component performance, forming a reliable foundation for downstream device manufacturing and validation.

As healthcare systems become more connected, medical devices are expected to operate consistently over extended lifecycles. Within this context, material consistency supports manufacturing stability and contributes to meeting performance requirements defined by device manufacturers.

As digital technologies, including AI, become more integrated into healthcare manufacturing, they are often associated with gains in efficiency and scalability. However, in medical contexts, these advancements must be carefully balanced against strict safety and validation requirements. While technologies such as AI cannot replace the need for high-quality, precisely engineered materials, they can support improved process consistency and quality assurance when applied within validated manufacturing frameworks.

Why Material Precision is Critical in Healthcare

In medical device manufacturing, even small deviations in materials or geometry can introduce variability in processing and component characteristics, requiring careful control during downstream design and validation.

Material selection is equally critical. Precious metals—including gold, platinum group metals, and silver—are widely used in medical devices due to their stability in biological environments and functional characteristics such as electrical conductivity and visibility under X-ray imaging. These characteristics support component-level functionality in applications such as minimally invasive devices, electrodes, and sensing elements, depending on final device design and validation. Material characteristics support component-level functionality and are evaluated as part of broader device design and validation processes.

Digital technologies have the potential to reduce variability in materials and geometry, but only when paired with domain expertise and rigorous manual validation. In medical manufacturing, precision and reproducibility remain the primary priorities, ensuring alignment with defined quality and regulatory requirements.

Enabling the Next Generation of Miniaturized Medical Devices

Medical technologies are becoming smaller, more complex, and increasingly tailored to individual patient needs, pushing the boundaries of what traditional manufacturing methods can reliably achieve. 

Precious metals are commonly selected for miniaturized applications because their material characteristics can be maintained at small scales under controlled processing conditions. For example, silver is used in applications requiring stable electrical performance in compact configurations, while platinum group metals are selected where durability, radiopacity, and compatibility are required, depending on device design.

At smaller scales, even minor variations in composition or structure can affect material consistency and component characteristics, making process control increasingly important.

Producing such materials with the required level of precision involves highly controlled processes and deep expertise in metallurgy and materials engineering. While automation can help standardize production steps, consistent material properties remain essential for supporting stable downstream manufacturing and validation processes.

Empirical Testing and Validation Remain Essential

Advances in digital modeling and simulation are helping to streamline the development of medical technologies by reducing reliance on extensive physical prototyping. These tools allow engineers to better understand how materials and designs may behave under a range of conditions, identifying potential issues earlier in the development cycle.

However, the accuracy of these models depends heavily on the quality of the underlying material data. Predictive tools are only as reliable as the inputs used to build them.

For this reason, empirical testing and validation remain essential. Physical verification ensures that material characteristics align with specified requirements prior to integration into device manufacturing processes, particularly in applications requiring consistent performance over defined operational lifecycles.

This combination of digital analysis and empirical validation supports a more robust development process while maintaining alignment with the stringent requirements of medical applications.

The Role of Expertise and Quality Systems

As new technologies are introduced into medical manufacturing, the role of human expertise remains central. Materials scientists and engineers are responsible for understanding how materials behave under real-world conditions, interpreting data, and ensuring that performance requirements are met. Established quality systems, such as those aligned with ISO 13485, provide the framework for maintaining consistency across materials and processes.

Organizations with integrated capabilities—from material refining to processing—are positioned to support consistent material quality across the value chain. For example, companies such as TANAKA, with experience in precious metals refining and advanced material processing, support consistent material quality used in medical device manufacturing.

Digital transformation tools can enhance visibility and documentation, but they are most effective when used alongside established engineering practices and validated quality processes.

Looking Ahead: Aligning Materials Innovation with Emerging Technologies

As medical technologies continue to evolve, the relationship between materials science and digital innovation will become increasingly important. Emerging tools, including AI, have the potential to support more efficient development and manufacturing processes, delivering value when built on a foundation of consistent, high-quality materials within medical applications.

Ultimately, the future of medical innovation will be shaped by how effectively advanced materials and simulation-driven approaches are integrated to better understand and optimize material characteristics. While AI can enhance development and manufacturing, material integrity will continue to support performance, safety, and long-term reliability within device development and regulatory frameworks. 

By combining controlled material processes with appropriate use of digital technologies, manufacturers can support innovation while maintaining alignment with regulatory and quality requirements.

About Kunihiro Shima 

Kunihiro Shima joined TANAKA PRECIOUS METAL TECHNOLOGIES Co., Ltd. in 1995 and has built nearly three decades of experience in precious metal technology, materials science, and advanced manufacturing.

His technical expertise spans material science and material processing of precious metals for a wide range of industries, including medical devices, automobiles, semiconductors, consumer electronics, and clean energy technologies. Throughout his career, he has contributed to the development of high-performance precious metal materials and components, combining deep metallurgical knowledge with application-driven innovation.

Currently, as Senior General Manager of the GLOBAL MARKETING/R&D SUPERVISORY DEPARTMENT, Mr. Shima leverages his extensive technical background and broad industry experience to drive new business creation and support the development of next-generation solutions based on TANAKA’s core precious metal technologies.

What You Should Know

• Open-source healthcare data infrastructure company Metriport secured $26M in new funding led by TJ Parker, General Partner at Matrix, with participation from ARTIS Ventures and Y Combinator, bringing its total capital raised to $28.4M.
• The platform eliminates point-of-care blind spots and weeks-long manual chart chasing across hospitals, pharmacies, and outpatient clinics by unifying and deduplicating medical records into a standardized, searchable data model. Replaces legacy “black box” interoperability tools with an open-source data infrastructure accessible via a single API, cloud data warehouse integration, or native EHR application.
• Metriport provides real-time clinical alerts as patients navigate the care continuum, alongside built-in AI medical record summarization to surface critical history without requiring in-house data science teams.

Turning Raw Data into Point-of-Care Intelligence

Metriport connects directly to major healthcare data exchanges and applies an open-source data engine to match, extract, deduplicate, and standardize disparate clinical records into a single unified schema. Rather than requiring hospital IT teams to build custom data science pipelines, the platform exposes clean patient records through a single API, a data warehouse, or directly inside native electronic health record (EHR) apps.

The platform has already gained traction across prominent care delivery organizations, including Amazon One Medical, Sollis Health, and Color Health.

At concierge medical provider Sollis Health, physicians use Metriport to pull outside operative reports and imaging histories in real time. In one clinical example shared by the company, an emergency physician treating an acute abdominal pain case used the platform to instantly surface a patient’s previous endoscopy records—confirming an early ulcer and initiating targeted treatment immediately without ordering an unnecessary repeat CT scan.

Compliance, Security, and Scaled AI Capabilities

The company recently earned HITRUST r2 Certification and plans to use the Series A capital to expand its artificial intelligence capabilities, specifically focusing on automated clinical chart summarization and agentic workflows for care teams.

Metriport has also placed heavy emphasis on data governance, with an in-house legal team vetting every organization that applies to query health information networks through its API to prevent data misuse.

What You Should Know

• The Trump Administration announced a $93.3M federal investment under the Rural Health Transformation Program (RHTP) to expand telehealth, surgical robotics, and modern care models across rural Georgia.
• Completes Georgia’s $218+ million Year-1 funding commitment under the broader $50 billion federal RHTP framework established by the Working Families Tax Cuts Act.
• Prepares 87 rural Georgia hospitals to transition toward value-based and total cost of care payment models by supporting pre-implementation readiness for CMS’s AHEAD Model (States Advancing All-Payer Health Equity Approaches and Development).
• Deploys next-generation clinical hardware and software to community facilities, specifically surgical robotics, telehealth access points, specialized dementia care capacity, and hospital cybersecurity infrastructure.

Upgrading 87 Rural Facilities for Value-Based Care and High-Tech Delivery

Administered through the Centers for Medicare & Medicaid Services (CMS) and the Georgia Department of Community Health’s GREAT Health initiative, the funding will flow directly to 87 rural hospitals to bridge staffing shortages, address specialized care gaps, and prepare providers for alternative payment models.

Key areas targeted by the investment include:

• Value-Based Care Preparation: Provides pre-implementation capital to help 87 rural hospitals build the data and operational capacity required to participate in CMS’s States Advancing All-Payer Health Equity Approaches and Development (AHEAD) model.
• Workforce Recruitment and Training: Funds clinical education, paramedic and EMT training programs, and retention initiatives to stabilize staffing across rural clinical facilities.
• Surgical Robotics and Specialized Tech: Introduces next-generation clinical hardware—including surgical robotics, expanded dementia care capabilities, and hospital cybersecurity safeguards—to community facilities that historically lacked capital for advanced equipment.
• Expanding Telehealth and Maternal Care: Builds out statewide telehealth access points, transportation networks, newborn screening pipelines, and integrated behavioral and maternal health services.

What Leadership Is Saying

“President Trump understands that every American deserves high-quality healthcare, no matter where they live,” said Health and Human Services Secretary Robert F. Kennedy, Jr. “We are putting resources directly into rural communities to strengthen the health workforce, modernize infrastructure, expand access, and bring care closer to home. We are giving states the flexibility to build solutions around the people they serve, while demanding accountability for results.”

What You Should Know

• Healthcare operations and revenue cycle management (RCM) company Savista has acquired ABW Medical, an ambulatory RCM provider specializing in the non-acute healthcare sector.
• The transaction expands Savista’s operational footprint into community health centers, federally qualified health centers (FQHCs), physician medical groups, management services organizations (MSOs), rural health providers, and virtual care platforms.
• Integrates ABW Medical’s preferred partner status within the athenahealth ecosystem, diversifying Savista’s electronic medical record (EMR) delivery capabilities alongside its established acute-care Epic-certified training workforce.
• Provides ABW Medical’s non-acute client base with direct access to Savista’s broader enterprise infrastructure, global delivery scale, specialized cancer registry services, and advanced eligibility, coding, and accounts receivable (A/R) optimization technologies.

Bridging Inpatient Scale with Outpatient EHR Workflows

The acquisition broadens Savista’s 35-year operations footprint—which spans more than 800 clients across 49 states—into Federally Qualified Health Centers (FQHCs), rural health providers, management services organizations (MSOs), and virtual care platforms.

The tie-up centers on integrating specialized outpatient workflow capabilities with enterprise back-office muscle:

• Athenahealth Preferred Integration: Integrates ABW Medical’s preferred partner status within the athenahealth ecosystem, adding dedicated workflow support for athenaCollector, athenaClinicals, and athenaCommunicator alongside Savista’s established Epic-certified operations workforce.
• Safety-Net Billing Expertise: Directly addresses the complex reimbursement mechanics of community clinics, including Medicaid wraparound payments, multi-payer credentialing, and grant reporting compliance.
• Enterprise Infrastructure Access: Gives ABW Medical’s non-acute client base direct access to Savista’s global delivery network, complex denial mitigation teams, specialized medical coding, and certified cancer registry management services.

“ABW Medical brings deep revenue cycle management expertise, a strong client base, and a delivery model that aligns well with how we operate,” said Jan Grimm, CEO of Savista. “This acquisition expands our capabilities and opens new opportunities to serve community and rural health organizations that often face distinct financial pressures. We look forward to deepening that work and helping these organizations achieve the financial sustainability they need to continue serving their communities.”