What You Should Know

• Healthcare capacity management and predictive operational software market leader LeanTaaS has acquired Aidin, a specialized care transition platform.
• Integrates Aidin’s network—which serves 200+ hospitals across ~20 health systems and coordinates more than 2 million referrals annually across 21,000+ post-acute providers—directly into LeanTaaS’ iQueue for Inpatient Flow.
• The acquisition expands LeanTaaS’ market footprint, currently deployed across 200 health systems in 1,200+ hospitals, centers, and clinics, where iQueue for Inpatient Flow generates an average of $10,000 in annual savings per inpatient bed.

LeanTaaS Acquires Aidin to Unify Capacity Intelligence with Post-Acute Care Transitions

For inpatient hospitals, a patient being clinically ready for discharge rarely means they are out the door. Approximately 40% of discharged Medicare patients require post-acute care—such as skilled nursing, inpatient rehabilitation, or home health—yet these cases account for more than 55% of all avoidable hospital days.

When discharge-ready patients remain stuck in acute beds waiting on insurance pre-authorizations, family selections, or post-acute facility placement, the bottlenecks ripple upstream: emergency department boarding times surge, surgical admissions are delayed, and hospitals absorb non-reimbursable bed days.

To link predictive bed capacity with post-acute placement execution, healthcare operations platform LeanTaaS has acquired Aidin a care transition platform that coordinates referrals across acute and post-acute providers. Financial terms of the transaction were not disclosed.

Connecting Inpatient Flow Intelligence to Post-Acute Execution

LeanTaaS’s core platform, iQueue for Inpatient Flow, uses predictive analytics and machine learning to optimize hospital bed utilization, track discharge barriers, and level-load clinical staffing across more than 1,200 hospitals and clinics nationwide.

The acquisition bridges LeanTaaS’s operational capacity engine with Aidin’s external referral and placement network, which spans 200-plus hospitals across roughly 20 health systems, coordinating more than two million referrals annually across 21,000 post-acute providers.

The integration targets critical failure points along the discharge pathway:

• Reducing Avoidable Length of Stay: Aidin’s standardized workflows have demonstrated an average reduction in inpatient length of stay (LOS) of 0.86 days, cutting placement latency by 50% and generating an average of $1.7 million in annual savings per hospital.
• Closing the Placement Loop: Combines internal discharge barrier tracking with external post-acute workflows—including electronic referral broadcasting, payer prior authorizations, patient choice compliance, and direct post-acute facility communication.
• Upstream Discharge Orchestration: Initiates post-acute placement workflows earlier in the patient’s hospital stay rather than waiting until the day of discharge, freeing acute bed capacity for incoming emergency and surgical admissions.

“Identifying where a patient needs to go next is only the beginning,” said Russell Graney, founder and CEO of Aidin. “Completing a complex care transition requires care teams to coordinate across patients, families, payers, and providers, often under enormous time pressure. We built Aidin to give the people involved in those transitions a shared workflow, greater visibility, and more accountability to move patients forward. By joining LeanTaaS, we can connect that work earlier in the patient journey and create a more seamless approach to patient flow.” 

AgeTech is reaching an inflection point—but not for the reasons expected.  

Investment in technologies designed to support aging adults has surged, fueled by a rapidly expanding industry projected to be a $120 billion market by 2030, drawing in Big Tech, startups, and legacy healthcare companies alike — all racing to enable seniors to live safely, independently, and with dignity at home. 

Yet despite this momentum, more technology is not consistently translating into better care outcomes.

That influx of capital and innovation has accelerated progress, but it has also created a familiar challenge: a fragmented ecosystem of point solution (e.g. standalone PERS pendants, smart scales, and other single-purpose devices) that rarely work together. Compounding the problem, many of these tools operate in closed data environments, built to support only their prescribed devices and shut out others, like an external blood pressure cuff. The result is more devices, more alerts, and more data without the interoperability or coherence necessary to meaningfully improve outcomes. 

In practice, this often shifts complexity onto caregivers and clinicians rather than reducing it.

This fragmentation is not surprising. With more than 10,000 technology-enabled care organizations now operating across digital health and therapeutics — and over $10 billion invested in the sector in 2024 — innovation has outpaced integration. Advances in artificial intelligence continue to accelerate development, but not necessarily coordination.

The next phase of AgeTech won’t be defined by smarter individual tools. It will be defined by whether fragmented data can be aggregated into a coherent picture that supports better decisions — for seniors, caregivers, and care teams alike — within a care model increasingly oriented toward prevention and value-based outcomes in the home. 

Aggregation Becomes the New Battleground 

The first wave of AgeTech focused on visibility: tracking movement, sleep, heart rate, hydration, medication adherence, and more. That visibility is now table stakes. 

What caregivers and care teams increasingly need is coherence. 

Caregivers don’t want ten dashboards. They want a single, integrated view that brings together insights from multiple devices and highlights what actually matters. Without aggregation, even the most sophisticated tools struggle to deliver meaningful preventive value. 

Aggregated data enables: 

• Earlier detection of subtle changes across conditions 
• A more holistic understanding of health trends over time 
• Less caregiver anxiety 
• More informed, timely decision-making 

As the AgeTech market matures, platforms that remain siloed will struggle to scale. Technologies that connect disparate data into meaningful, longitudinal insight will become the backbone of modern aging care. 

Preventive, Multi-Condition Care Moves into the Home 

Aging rarely follows a single condition. 

Most older adults manage multiple, co-morbid conditions — mobility changes, sleep disruption, cardiovascular risk, cognitive decline — that are clinically intertwined and compound one another over time. 

The shift toward aggregated data is what makes preventive, multi-condition care possible in the home. When data from multiple sources is viewed together, small deviations become visible earlier, enabling interventions before issues escalate into acute events. 

Technology alone, however, is not enough. Human guidance remains essential. Not as in-home labor, but as scalable interpretation and support that helps families and care teams prioritize what matters most. 

The future of aging care is hybrid: smart technology paired with human insight, delivered remotely and at scale. 

AgeTech as Infrastructure for Home-Based Value-Based Care 

Policy and payment models are accelerating a shift toward care in the home. Momentum continues to build around remote monitoring and wearable technologies as part of standard care rather than optional add-ons. 

As health systems and payers expand value-based models, AgeTech will play a central role in making those economics work. 

Aggregated, interpretable data enables: 

• Earlier intervention that reduces avoidable utilization 
• Fewer hospitalizations and emergency visits 
• Better outcomes without increasing in-person care 

Value-based care in the home depends on aggregation. Without it, remote monitoring shifts complexity and stress onto caregivers and clinicians instead of reducing it. 

The most effective AgeTech platforms will increasingly function as care orchestration layers — translating home-based data into decisions that support value-based outcomes at scale. 

Wearables Grow Up 

Wearables—ranging from smartwatches and pendants to body-worn patches and other sensors—are entering a new phase as aging Boomers adopt technology with higher expectations and far less tolerance for friction. This shift is reinforced by a broader groundswell of support from policymakers: the Centers for Medicare & Medicaid Services (CMS) has publicly sought input on advancing a seamless, patient-centric digital health ecosystem that empowers Medicare beneficiaries through greater access to innovative technologies and improved data interoperability across systems, signaling a more supportive environment for connected devices and remote monitoring. 

As a result, the next generation of wearables will not win on novelty or raw metrics alone. They will win on how seamlessly they integrate into purpose-built, senior-focused ecosystems that support caregivers and care teams alongside older adults themselves. 

For seniors, the value of a wearable isn’t the data it collects—it’s the reassurance it provides and the timely response it enables when something changes. 
 

Where AgeTech Finally Starts to Matter 

The next phase of AgeTech will not be won by companies that build better devices, but by those that create the connective tissue between them. 

As care continues to move into the home, and as payers and providers increasingly operate under value-based care models that reward outcomes over volume, the ability to see the whole person becomes essential. That shift demands AgeTech function less like a collection of point solutions and more like true infrastructure: integrated, designed specifically for seniors, and supported by scalable human guidance that helps data translate into action. 

Until that happens, the industry risks scaling complexity instead of care.

The opportunity ahead is not simply to monitor aging, but to make home-based care more sustainable — clinically, operationally, and economically. 

About Gary Guiser 

Gary Guiser is the President and COO at ClearWellness,  a proactive wellness and lifestyle management service that pairs seniors with Certified Health and Wellness Coaches.  With 25 years of senior leadership experience, Gary brings extensive expertise in tech-enabled health and social care services to ClearWellness. His work focuses on delivering compassionate care solutions to seniors, caregivers, and disadvantaged populations while guiding companies through growth and transformation. 

What You Should Know

• Oncology drug development platform N-Power Medicine closed a $32M Series B financing round to scale its community oncology clinical research network and expand adoption of its Prospective External Control Arms (ProECA) technology.
• The financing features Labcorp Venture Fund as a new strategic investor, alongside continued participation from Merck Global Health Innovation Fund (Merck GHI), Innovatus Capital Partners, and a leading U.S.-based biotechnology investor.
• Solves a critical clinical trial bottleneck by generating trial-grade, standalone prospective external control arms directly from routine community cancer care, significantly reducing the need for traditional patient randomization, decreasing trial sample size requirements, and compressing drug development timelines.
• N-Power’s initial ProECA program in non-small cell lung cancer (NSCLC) is active across more than 40 clinical sites spanning multiple large community health systems, currently used by biopharma sponsors to accelerate early-phase and Phase 4 post-market studies.
• Pipeline expansion includes the upcoming rollout of ProECA platforms for colorectal cancer and prostate cancer later this year.

Generating Regulatory-Grade External Controls in Routine Care

N-Power Medicine embeds research operations directly into everyday community oncology workflows, allowing standard-of-care patients to contribute to clinical evidence without disrupting routine practice.

The new capital will accelerate the expansion of its community research footprint and drive broader biopharma adoption of ProECA, its proprietary Prospective External Control Arm platform:

• Trial-Grade Prospective Harmonization: Unlike retrospective chart reviews, ProECA prospectively mirrors active clinical trial protocols—capturing protocol-aligned inclusion/exclusion criteria, next-generation sequencing (NGS), standardized imaging intervals, and bio-sample collection schedules directly from routine patient visits.
• De-Risking Early-Stage Pipelines: Enables biopharma sponsors to run smaller, single-arm or augmented Phase 2 studies, accelerating Phase 2-to-Phase 3 transition decisions and Phase 4 post-marketing commitments while reducing the number of enrolled control subjects needed.
• Active NSCLC Deployment & Multi-Tumor Expansion: N-Power’s initial non-small cell lung cancer (NSCLC) ProECA is active across more than 40 community oncology sites within regional health systems, with planned rollout into colorectal cancer and prostate cancer cohorts later this year.

“We’ve built a model that creates value for drug developers, community oncologists, and cancer patients. By bringing prospective external control arm programs directly into everyday cancer care, we dramatically speed up drug development while giving many more oncologists and patients access to research and innovative therapies,” said Dr. Mark Lee, co-founder and CEO of N-Power Medicine. “With Labcorp Venture Fund joining our existing investors, this financing lets us scale and set a new standard for how oncology drug development gets done.”

What You Should Know

• Flosonics Medical secured a major development contract with the Biomedical Advanced Research and Development Authority (BARDA), part of the Administration for Strategic Preparedness and Response (ASPR) within the U.S. Department of Health and Human Services (HHS).
• Awarded under BARDA’s Burn and Blast Medical Countermeasures program (Contract No. 75A50126C00006), the agreement includes initial funding of approximately $15 million, with a total potential value of up to ~$70 million if all milestones and options are exercised.
• Funds the development of FloPredict, an AI-enabled software algorithm designed to detect internal bleeding and hemorrhage following acute traumatic injury and mass casualty events (including blast scenarios and nuclear detonations).
• Integrates directly with FloPatch, Flosonics’ FDA-cleared, wireless, wearable Doppler ultrasound device worn on the neck to assess carotid blood flow and real-time hemodynamic changes.
• FloPatch has already been deployed across more than 13,000 patients in 30+ U.S. hospitals, primarily supporting bedside fluid resuscitation decisions in sepsis, shock, and hypotension workflows.

Extending Wearable Doppler Ultrasound to Mass Casualty Triage

The federal funding will support the development of FloPredict, an artificial intelligence software enhancement for the company’s FDA-cleared FloPatch device. FloPatch is a wireless, adhesive Doppler ultrasound sensor applied directly over the carotid artery to capture real-time blood flow dynamics. To date, the device has been deployed across more than 30 U.S. hospitals on over 13,000 patients, primarily guiding fluid resuscitation decisions for critical care conditions such as sepsis, septic shock, and severe hypotension.

Under the BARDA agreement, Flosonics will develop machine learning algorithms within FloPredict to analyze real-time Doppler waveforms and flag early hemodynamic indicators of internal hemorrhage. The initiative aims to strengthen emergency preparedness and triage capabilities for high-consequence mass casualty events, including blast trauma and potential nuclear detonations where medical personnel and diagnostic resources are severely constrained.

Key capabilities targeted by the collaboration include:

• Pre-Decompensation Hemorrhage Alerts: AI models designed to detect subtle changes in arterial flow time and velocity profiles that precede clinical shock.
• Rapid Bedside Triage: Enables non-specialist frontline clinicians and first responders to obtain objective hemodynamic data within seconds without waiting for bulky imaging equipment or invasive arterial lines.
• Optimized Resource Allocation: Helps triage teams identify which trauma victims require immediate surgical intervention or blood transfusions during mass casualty surges.

What You Should Know

• Global connected diabetes care leader Glooko, Inc. announced GestationalCare, a dedicated pregnancy-specific clinical module built on its ambulatory diabetes management platform.
• GestationalCare resolves the acute challenge of compressed clinical timelines in pregnancy, replacing disconnected spreadsheets, patient portal screenshots, and paper logs with automated, device-neutral data aggregation.
• Covers the full spectrum of maternal metabolic care: Gestational Diabetes Mellitus (GDM), Type 2 diabetes in pregnancy, and Type 1 diabetes throughout pregnancy.
• Available immediately to healthcare organizations in the United Kingdom, with general availability in the United States scheduled for October 2026.

How Glooko’s Pregnancy-Specific Module Automates MFM and OB/GYN Glycemic Triage

Managing diabetes during pregnancy operates on a compressed clinical clock: hormonal shifts can alter insulin sensitivity within days, while glycemic volatility poses immediate risks for both maternal and fetal outcomes. Despite the need for rapid intervention, prenatal glycemic tracking often relies on disconnected paper logs, patient portal photos of glucometer screens, manual spreadsheets, and unintegrated continuous glucose monitor (CGM) downloads.

When care teams must spend time manually reconstructing a patient’s glycemic history from fragmented records, clinical adjustments are delayed and rising-risk individuals can slip through the cracks between routine prenatal visits. To standardize glycemic tracking across maternal health teams, global digital health platform Glooko has launched GestationalCare, a specialized pregnancy module within its ambulatory diabetes management platform.

Structuring the Pregnancy-Specific Glycemic Workflow

GestationalCare adapts Glooko’s device-neutral connectivity to create a unified clinical workflow for maternal-fetal medicine specialists, OB/GYNs, endocrinologists, certified diabetes educators, and nursing teams. The module supports glycemic tracking across gestational diabetes mellitus (GDM), Type 2 diabetes in pregnancy, and pre-existing Type 1 diabetes.

Key capabilities embedded in the module include:

• Population-Level Risk Prioritization: Surfaces at-risk individuals across an entire clinic population by flagging persistent fasting hyperglycemia, postprandial spikes, and gaps in patient data submissions.
• Contextual Glycemic Pattern Review: Visualizes blood glucose readings against clinical pregnancy targets across fasting and mealtime intervals, presenting nutritional logs, medication doses, and device telemetry in a single view.
• Between-Visit Engagement: Facilitates continuous touchpoints through the patient-facing Glooko Mobile App—including automated Bluetooth device syncing, mobile meal logging, structured educational modules, and secure clinical communication.
• Postpartum Transition Tracking: Maintains continuity of care through the postpartum period to monitor glycemic normalization and transition patients into ongoing primary or endocrine care.

Rollout and Availability

GestationalCare is available immediately to healthcare providers in the United Kingdom and is scheduled to launch for U.S. health systems and ambulatory practices in October 2026.

What You Should Know

• Healthcare AI and utilization management technology company Xsolis announced the general availability of its GenAI Peer-to-Peer Clinical Synopsis solution.
• Directly addresses the critical 24- to 72-hour regulatory and commercial window for peer-to-peer denial reviews, giving physician advisors an instant, automated synthesis of medical necessity before speaking with health plan medical directors.
• Automatically scans the longitudinal electronic medical record (EMR) for vital signs, lab results, active therapies, and daily clinical progress to construct a day-by-day medical necessity narrative and severity profile.

Automated Synthesis for Time-Sensitive Payer Reviews

The new capability is embedded directly as an add-on feature within Dragonfly Advise®, Xsolis’ dedicated workflow platform for hospital physician advisor teams. When a payer issues a denial and a case is escalated for a peer-to-peer review, the generative AI engine automatically ingests the longitudinal electronic medical record—scanning laboratory values, vital sign trajectories, active medication orders, bedside nursing notes, and multidisciplinary progress records.

From that raw data, the software generates a structured, day-by-day clinical synopsis highlighting:

• Severity Indicators: Flags specific diagnostic markers, hemodynamic instability, and comorbid escalations that warrant inpatient admission over observation status.
• Medical Necessity Rationale: Contextualizes physician treatment plans against objective clinical criteria, articulating why care could not be safely delivered in a lower-acuity setting.
• Point-of-Discussion Prep: Provides a concise case overview so the advisor can enter the payer conversation without spending 15 to 20 minutes manually reconstructing the chart.

Expanding the Dragonfly GenAI Portfolio

The launch represents the third generative AI capability Xsolis has deployed across its Dragonfly® platform, joining existing tools built for automated appeal letter generation and initial medical necessity reviews.

In a previous three-month operational pilot conducted with Indiana-based Beacon Health System, Xsolis’ GenAI medical necessity review tool reduced average chart review times by 68%, cutting review durations from 15 minutes down to 4.7 minutes per case.

Multiple health systems have already signed agreements to integrate the Peer-to-Peer Clinical Synopsis into their utilization management (UM) and physician advisor operations, with enterprise implementations rolling out over the coming months. Upcoming roadmap enhancements include enabling the synopsis to export directly into third-party communication tools used by physician advisors during payer calls.

“Peer-to-peer reviews are among the most time-sensitive points in the denial process, and physicians are often preparing for them while managing a full clinical and administrative workflow,” said Joan Butters, Co-Founder and CEO of Xsolis. “By bringing the most relevant clinical facts forward, we can reduce the work required to reconstruct the case, help physician advisors enter the conversation prepared, and surface details that are easy to miss in a complex chart.”

What You Should Know

• AI-native regulatory software company Weave Bio announced a major enterprise collaboration with global biopharma leader Takeda to deploy its regulatory automation platform across 14 active drug development programs in multiple global markets.
• The collaboration directly addresses the heavy administrative bottleneck of regulatory dossier authoring, which has historically trapped critical scientific data inside static, hard-to-maintain document repositories.
• The system handled more than 100 Health Authority Questions (HAQs) and Responses to Questions (RTQs) without a single data extraction error, recording a 68% average answer quality score.
• The deployment follows FDA and EMA joint guiding principles for AI by keeping internal regulatory teams in full control of verification, edits, and final sign-offs.

Validating 97% First-Draft Timeline Compression

The collaboration, which began with automated IND module drafting, has since scaled into active Health Authority Question (HAQ) intake and Response to Question (RTQ) workflows.

According to a study co-authored by researchers from both companies and published on arXiv, the platform demonstrated measurable operational gains while preserving underlying data accuracy:

• Drafting in Hours, Not Weeks: Reduced the time needed to draft nonclinical written summaries (eCTD Module 2) from roughly 100 hours to three hours—a 97% reduction in initial drafting time that drove a 60% compression in overall preparation timelines.
• 95% Source Fidelity: Delivered high fidelity to source nonclinical study reports (eCTD Module 4), minimizing hallucination risk during cross-document extraction.
• Error-Free Inquiry Processing: Logged zero extraction errors across an evaluation dataset of more than 100 regulatory questions, achieving a 68% average response quality score against an internal target of 50% to 70%.
• Audit-Ready Data Lineage: Maps every generated sentence and numerical table directly to its source paragraph in underlying study reports, enabling internal review teams to verify references instantly.

Meeting FDA and EMA Governance Standards

The partnership expands amid increasing regulatory scrutiny surrounding artificial intelligence in life sciences. Following the FDA’s draft guidance on AI in drug development decision-making and joint FDA/EMA principles emphasizing human accountability, the platform operates strictly as an assistive layer. The software automates information extraction, cross-referencing, and initial drafting, while regulatory professionals retain full responsibility for verifying claims and approving final submissions.

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.