< + > Blue Matter— AI Transformation in Drug Discovery – Life Sciences Today Podcast Episode 75

We’re excited to be back for another episode of the Life Sciences Today Podcast by Healthcare IT Today. My guest today is Tara Austraat-Churik, Partner at Blue Matter Consulting. Her background spans IBM Watson Health, where she led $50M+ enterprise deals during the first AI wave in pharma, EY’s Health Science and Wellness practice, an MSc in Translational Medicine from Edinburgh, and time as an FBI intelligence analyst. She calls it “Compound Expertise” — depth earned across domains that produces a kind of judgment no single career path can replicate.

In this episode, I sit down with Austraat-Churik and dig into how traditional life science companies compete when AI-native tech companies start discovering drugs without being pharma companies at all.

Check out the main topics of discussion for this episode of the Life Sciences Today podcast:

• How did you end up doing consulting like this?
• Tell me about value creation in the work you do. 
• Are you more of a horizontal AI person, or is there a particular therapeutic area that’s a sweet spot for you?
• What is your moat?
• Trust or scale – what do you think is more important and keeps people coming back to you?
• How many people are on your team?
• Is this more of a corporate initiative or more of a local initiative?
• Techbio companies are now moving up the value chain to develop their own molecules and pipelines. What is your take on this? Is it a trend? What are companies doing? How are pharma/biotech companies dealing with this when computational/AI companies start stealing their turf? How does that work?
• What is the biggest anti-pattern in your industry?
• Will we see a vertically integrated techbio company that does everything?

Subscribe to Danny’s newsletter to get strategic patterns for life science leaders building a defensible business.

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Along with the popular podcasting platforms above, you can Subscribe to Healthcare IT Today on YouTube.  Plus, all of the audio and video versions will be made available to stream on Healthcare IT Today. As a former pharma-tech founder who bootstrapped to exit, I now help TechBio and digital health CEOs grow revenue—by solving the tech, team, and go-to-market problems that stall your progress. If you want a warrior by your side, connect with me on LinkedIn.

If you work in Life Sciences IT, we’d love to hear where you agree and/or disagree with our takes on health IT innovation in life sciences. Feel free to share your thoughts and perspectives in the comments of this post, in the YouTube comments, or privately on our Contact Us page. Let us know what you think of the podcast and if you have any ideas for future episodes.

Thanks so much for listening!

< + > Clinical Revenue Cycle vs. Middle Revenue Cycle

The following is a guest article by Kevin Coloton, CEO at HURC

If you’ve spent any time in hospital finance or operations, you’ve heard two terms used, sometimes interchangeably, sometimes like they’re rivals: clinical revenue cycle and middle revenue cycle. Entire org charts, budgets, and vendor categories have been built around the distinction between the two.

The truth is they are defined differently, simply for organizational convenience— not because they represent different work. In practice, they represent the same billion-dollar battleground, the area where revenue is either protected or lost.

Why These Terms Exist at All

Traditionally, revenue cycle has been divided into three parts:

• Front-End: registration, eligibility, authorizations
• Middle (or Clinical): documentation, utilization review, coding, charge capture, payer communication
• Back-End: billing, collections, denial follow-up, cash posting

The term clinical revenue cycle emerged to emphasize that much of the middle-cycle work is rooted in clinical decision-making—medical necessity, documentation quality, length of stay, and treatment pathways. The term middle revenue cycle came from finance and operations, meant to define the phase between intake and billing. Both terms describe the point where clinical reality must be translated into something payers will actually reimburse. In practice, this is also where organizations either protect margin or lose it, depending on how effectively clinical, operational, and financial teams coordinate their workflows.

Where Revenue is Truly Won or Lost

Hospitals can have flawless registration and aggressive collections, but if the middle cycle breaks down, the full value is not achieved. This is the point where documentation gaps turn into denials, where utilization decisions extend length of stay, and where unclear payer communication creates delays, write-offs, and appeals that never should have existed.

As noted above, the middle—or clinical—revenue cycle spans:

• Utilization review and denials management
• Clinical documentation improvement (CDI)
• Medical coding
• Ongoing payer communication during care

Failures here don’t always show up immediately, but surface weeks later as denials, underpayments, or unexplained revenue leakage. By then, the clinical moment has passed, and the leverage is gone.

According to recent data from the American Hospital Association Cost of Caring Report, in 2025, hospitals spent nearly $18 billion on overturning claims denials alone. The AHA also estimates that hospitals spent a staggering $43 billion in 2025, trying to collect payments insurers owe for care already delivered. In addition, it found that the average hospital employed about 64 administrative and billing staff dedicated to these functions — roughly 6.5% of total hospital employment.

That’s why CFOs feel the pain here so acutely, and why clinicians often feel caught in the middle, asked to fix revenue problems after the fact.

The False Divide Between Clinical and Financial

Calling it clinical revenue cycle was meant to elevate the role of clinicians, and calling it middle revenue cycle was meant to structure operations. But separating the two conceptually has created silos that no longer work in today’s payer environment.

Payers don’t care how hospitals label the function. They only care whether medical necessity is clearly documented, whether utilization aligns with policy, and whether claims are defensible the first time. That’s why the most effective models don’t treat this as a handoff between departments, but as a single, continuous workflow—one that operates in real time during the course of care.

Tech-Enabled Services are Changing the Equation

Hospitals have tried partial fixes: more software, more staff, or full outsourcing. Each helps, but none fully solves the problem alone. What’s changing now is the rise of integrated, tech-enabled service models that combine technology with experienced operators and embed directly into existing hospital workflows.

Instead of forcing hospitals to choose between tools or talent, these models facilitate the entire utilization review and payer communication function. They adapt to how hospitals already work, reduce onboarding time, and relieve internal teams from constant policy translation and appeal churn.

This approach can help bring about dramatic reductions in clinical denials, shorter lengths of stay, faster post-acute placement, and meaningful net revenue gains, all without reducing staff. In fact, many hospitals are reallocating internal teams back to patient-facing roles where they add the most value.

Whether you call it clinical revenue cycle or middle revenue cycle, the goal is the same: Make sure the care delivered is accurately documented, appropriately coded, medically necessary, and defensible to payers before the claim is submitted.

< + > Kyndryl Announces Agreement to Purchase Healthcare IT Leaders, LLC | EnableComp Acquires Helix Advisory

Check out today’s featured companies who have recently completed an M&A deal, and be sure to check out the full list of past healthcare IT M&A.

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Kyndryl Announces Agreement to Purchase Healthcare IT Leaders, LLC, to Accelerate AI-Led Modernization

Planned Acquisition to Strengthen Kyndryl’s Ability to Serve U.S. Healthcare Organizations Across Applications, Infrastructure, and AI

Kyndryl, a leading provider of mission‑critical enterprise technology services, today announced its intent to acquire Healthcare IT Leaders, LLC, an enterprise IT services provider for hospitals and health systems. The acquisition will enable Kyndryl to more effectively address growing customer demand for its AI‑led business modernization across healthcare providers and payors by leveraging Healthcare IT Leaders’ healthcare consulting and application managed services expertise.

“Healthcare organizations are under increasing pressure to advance complex clinical, operational and workforce systems while maintaining resiliency, security and compliance,” said Jamie Rutledge, President of Kyndryl U.S. “By combining Healthcare IT Leaders’ healthcare consulting expertise with Kyndryl’s AI-led modernization capabilities, we will be better positioned to support providers and payors.”

Following the close of the acquisition, Kyndryl will combine Healthcare IT Leaders’ consulting expertise in applications across clinical, operational, and workforce platforms with Kyndryl’s infrastructure leadership and AI capabilities to enable healthcare organizations to work with a single provider across applications, platforms, and underlying IT environments.

Kyndryl already supports a broad set of healthcare organizations by running large-scale, highly regulated IT environments across the U.S. The addition of Healthcare IT Leaders’ business will deepen Kyndryl’s relationships with leading national healthcare systems and expand Kyndryl’s access to the application and consulting layer of those environments. Together, the two companies will…

Full release here, originally announced August 10th, 2026.

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EnableComp Acquires Helix Advisory, Advancing Zero Balance Review Technology in Its Complex Revenue Recovery Platform

Hospitals No Longer Leave Money on the Table Because a Claim is Too Small, Too Complex

EnableComp, the leading provider of technology-driven complex revenue cycle management (RCM) solutions, today announced its acquisition of Helix Advisory, a Cincinnati-based revenue recovery firm whose technology identifies underpayments that traditional rules-based audit systems are structurally unable to catch.

The investment expands EnableComp’s Zero Balance Review capabilities as part of its broader platform roadmap. The acquisition strengthens EnableComp’s platform with three specific capabilities:

• the ability to identify underpayments that standard audit logic cannot see
• clinical signal detection that flags discrepancies between how a case was coded and what a payer actually reimbursed
• root-cause analytics that trace why an underpayment occurred, so clients can prevent future losses, not just recover past ones

For EnableComp’s hospital clients, the integration means underpayment recovery that previously required a standalone audit vendor — or that didn’t happen at all because the claim fell below a manual review threshold — now runs natively inside the same platform handling their complex claims and denials.

“Most recovery programs are built to find what they’re told to look for. Helix’s technology finds what nobody told it to look for — that’s the difference between a rules engine and real intelligence,” said Frank Forte, CEO at EnableComp…

Full release here, originally announced August 12th, 2026.

< + > Great Stats from Epic UGM

In case you missed it, we shared our roundup of announcements from Epic UGM.  One of the other parts I love at Epic UGM is all of the stats they share about the impact that Epic and their users are having on healthcare.  Below you’ll find our compilation of the various stats that were shared at the event:

• Epic customers train over 90% of medical students
• 17 countries in the Epic community; Germany is the newest as Charité joins
• 1.4 million clinicians assisted by AI in Epic each month
• Monthly gen AI activity in July 2025 was 50 million and is now 2 billion as of July 2026
• Art clinician and nurse summaries are in use at more than 300 health systems
• Chart with Art is live in more than 70 unique specialties and multiple clinical roles
• Chart with Art for nursing is live at 11 health systems
• More than 1/3 of Emmie conversations happen after-hours
• Professional Billing Coding Assistant reduced coding-related denials by 33% across 160 organizations
• In Basket Art reduced time spent reviewing patient messages by 50% (Texas Children’s)
• Ask Emmie – 1,000+ hours saved for call center staff over 10 months (Ochsner Health) and 73% fewer billing-related In Basket messages (Community Health Network)
• Dynamic Scheduling Templates saw a 16% increase in online availability (OhioHealth)
• Patient Flow saw a 15% increase in bed days using AI Transfer Assistant (Loma Linda University Health)
• MyChart Central has 346 organizations live in 50 states and available to 115 million patients
• SlicerDicer SideKick reduced time spent creating queries by up to 85% (RWJBarnabas Health)
• Epic Staff did 31,126 R&D Immersion Days in 19 countries since last UGM
• Epic used to release new versions every 18 months, now they do so every 3 months
• A single quarterly Epic release now carries roughly the enhancement volume of an old 18-month
  release. The latest release contained 27% more code than the same version a year ago

In the last year…

• When ordering or administering a medication, clinicians changed course 224 million times based on an advisory from Epic
• 127,000 gallons of blood were saved in the last year by using automatic order adjustments in Epic to combine draws and avoid unnecessary sticks for patients
• 413,000 lung nodules were identified in radiology reports using AI for discrete follow-up tracking helping 2,050 patient start treatment for lung cancer earlier
• Dentists changed medications 278,000 times based on warnings shown in Epic
• Patients were seen an average of 30 days sooner with Fast Pass in MyChart
• Patients saved $300 million on medications when providers switched to lower-cost alternatives thanks to alerts in Epic
• 9.3 billion patient records exchanged across 50 states and 10 countries – 50% between Epic & non-Epic – 90% of Epic organizations are live or installing TEFCA
• 21,000 duplicate imaging tests were prevented thanks to images exchanges across health systems

Implementation and Technical Service Stats

• 300k new users live
• 35 full go-lives
• 460 Add-ons
• 35k Gold star featured turned on resulting in 2000 more Gold Stars
• 1,500 upgrade complete

Cosmos and Cosmos-enabled insights:

• 320 million unique patients, 23 billion encounters, 99 billion lab results, 186 million surgeries, 170 million home care visits, 25 million cancer cases, 18 million rare disease patients, 15 million mom-baby links in Cosmos.
• More than 220 papers published (10 journals) based on research from Cosmos
• Look-Alikes used more than 25,000 times last year.
• Best Care Choices for My Patient live at more than 60 organizations.
• 20 customers are early adopters of Cosmos Curiosity.
• 56 organizations live with Best Care Choices for My Patient
• 1 million monthly admissions use Cosmos Median Length of Stay

Those were most of the stats shared at Epic’s 2026 UGM.  Which stats stood out to you?

< + > The Role of Data Governance and Master Data Management in Ensuring Accuracy and Consistency Across Back Office Systems

Data is a major part of everything we do in healthcare; back-office health IT systems are no exception to this. There is a tremendous amount of data that is needed to successfully run all claims processing, medical billing, patient scheduling, etc. Gathering data isn’t usually the issue; however, governing and managing it is. For example, it’s not very hard to create a bill for rendered services, but what is hard is tracking what was paid in full, what is on payment plans, what charges are in dispute, and making sure that information is only accessible to the relevant parties.

To get a better picture of the importance of data management in back office health IT systems, we reached out to our wonderful Healthcare IT Today Community to ask — what role do data governance and master data management play in ensuring accuracy and consistency across back office health IT systems? Below are their responses.

Ashley Murgatroyd, Director of Healthcare Strategy at LexisNexis Risk Solutions
Data governance and master data management are critical to back-office health IT systems because they create a single, consistent foundation for patient data across fragmented administrative workflows. By resolving identities and eliminating duplicate records, organizations can improve data accuracy, streamline claims processing, and reduce costly errors tied to misidentification. These capabilities help maintain a cohesive, longitudinal view of patient records as they move through billing, eligibility, and other back-office functions, which can improve operational efficiency and support more reliable outcomes.

Ultimately, strong governance and master data management practices enable health systems to reduce financial leakage, protect sensitive data, and ensure consistency across the systems that power day-to-day administrative operations.

Denis Whelan, CEO at Documo
They’re critical. Automation is only as good as the data governance frameworks backing it up. Data governance and master data management are foundational business drivers. When you invest in clean, governed master data, you create the trust required to fully embrace automation—allowing you to scale your back office, wipe out administrative burnout, and protect the financial and operational health of your organization.

Even the most advanced AI and Machine Learning models encounter ambiguity—like a smudged, handwritten fax or a poorly scanned invoice. Data Governance dictates the exact protocol for what happens when data drops below a specific confidence threshold.

Instead of letting a system guess and corrupt the database, a strong governance framework should route that specific file to a human-in-the-loop workflow. A team member verifies or corrects the record, and that human intervention is used to train the model to be more accurate next time. This ensures that the master database remains untainted while maintaining high operational velocity.

John Squeo, Senior Vice President at CitiusTech
Without a trusted, enterprise-wide master record for entities like patients, providers, vendors, and cost centers, back-office analytics produce conflicting outputs that erode executive confidence and slow decision-making. Data governance and MDM platforms, including Microsoft Purview, Databricks Unity Catalog, Snowflake Horizon, Informatica, Reltio, and Profisee, are increasingly essential for maintaining a single source of governance across fragmented Health IT landscapes. Strong data governance frameworks establish accountability structures, including data stewards, ownership policies, and quality SLAs that sustain accuracy beyond the initial implementation.

In value-based care environments especially, flawed provider or payer master data directly translates into misdirected payments and compliance risk. Data governance is particularly critical when modernizing with AI to establish data provenance, traceability, and governance of metadata collation that drives user trust in generated insights and reliability in automated processes. Forward-looking organizations are now treating data governance and MDM as foundational infrastructure for AI readiness, recognizing that model quality is only as good as the data underneath it.

Kevin Erdal, President, Advisory Services at Nordic
Healthcare organizations generate massive amounts of operational, financial, workforce, and supply chain data, but without strong governance structures, they often struggle with duplicate records, inconsistent definitions, and fragmented reporting.

Master data management establishes a single source of truth for critical enterprise data, including vendors, suppliers, employees, locations, the chart of accounts, and operational metrics. This improves consistency across systems and enables more accurate reporting, forecasting, and analytics.

Strong data governance also helps organizations build trust in their data. When finance, HR, supply chain, and clinical teams are aligned around standardized definitions and ownership models, leaders can make decisions with greater confidence.

This becomes even more important as organizations invest in AI and advanced analytics. AI tools are only as effective as the quality and consistency of the underlying data. Organizations that prioritize governance and data integrity are better positioned to scale automation, improve interoperability, and generate meaningful operational insights.

Monte Sandler, Chief Operating Officer at WebPT
Data quality is foundational in healthcare. If patient or payer information is inaccurate at the front end, those issues create denials and delays later in the revenue cycle. Strong data governance helps organizations standardize how information is captured and updated, while also making it easier to identify patterns and root causes across workflows. Reliable data is what makes proactive, “shift left” strategies possible.

Rachel Blum, VP, Emerging Markets and Partners at Verato
Highly accurate MDM is essential and absolutely critical to ensure data accuracy and consistency across not just back office systems, but all Health IT systems. The downstream impact of poorly executed MDM is flawed reporting, compliance risk, and operational inefficiencies that can ripple across the organization. At its worst, mismanaged data erodes trust in the system entirely, forcing teams to rely on manual workarounds instead of the technology meant to support them. Trying to scale to support AI? Better get MDM right, or AI will quickly expose these flaws in your data infrastructure.

Sherri Atchley, AVP for Altera Managed Services at Altera Digital Health
Data governance and master data management play critical roles in ensuring accurate patient matching, workflow integrity, financial reconciliation, and operational consistency across integrated health IT systems. Without strong governance practices, organizations face increased risks of duplicate patient records, incorrect patient associations, payment posting discrepancies, delayed processing, and downstream operational errors.

As automation expands into back office workflows, such as inbound fax filing or remittance/payment posting, maintaining strong governance standards and audit controls becomes increasingly important to ensure accurate document routing, indexing, reconciliation, and financial integrity.

While robotic process automation (RPA) can help enforce process consistency and reduce manual errors, organizations still require strong governance frameworks, clearly defined business rules and ongoing monitoring to maintain high-quality operational and financial data across integrated applications.

Dr. Scott Schell, Chief Medical Officer at Cognizant
Data governance is now operationally essential. Organizations cannot scale analytics, automation, or AI with inconsistent enterprise data. Master data management creates consistency around identities, locations, workforce structures, and financial attribution. Governance establishes accountability for maintaining that consistency over time.

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

What role do you think data governance and master data management play in ensuring accuracy and consistency across back office health IT systems? Let us know over on social media, we’d love to hear from all of you!

< + > Agentic AI in Healthcare Operations: A Build-vs-Buy Framework for Health-System CIOs

The following is a guest article by Chandresh Patel from Bacancy Technology

Agentic AI is no longer just a concept for healthcare. Many health systems are already exploring how AI agents in healthcare can automate administrative and clinical workflows. However, the bigger question today is whether to build an agentic AI solution in-house, buy a ready-made one, or combine both approaches.

Choosing the right approach has become one of the biggest challenges for healthcare leaders as they move from AI experimentation to enterprise-wide adoption.

According to research conducted by Microsoft and The Health Management Academy, published in the NEJM AI in January 2026, found that 43% of health system leaders are testing or piloting agentic AI. Yet only 3% have deployed an AI agent into a live clinical or operational workflow.

That gap between the pilot and deployment is where most organizations struggle, and it’s rarely a technology problem. It’s a trust problem.

Giving an AI agent the ability to schedule patients, update an EHR, submit claims, or coordinate care is very different from deploying a chatbot. Every action it takes must be accurate, secure, easy to track, and meet clinical and regulatory requirements.

For CIOs evaluating this shift right now, the real decision is how much operational responsibility they are ready to hand an AI agent, what governance needs to be in place, and which implementation approach best fits the organization’s long-term strategy.

These are the conversations we have most often with healthcare leaders exploring enterprise AI. Thus, before choosing a platform or starting development, let’s understand why agentic AI in healthcare changes the way health systems approach the build-versus-buy decision.

What Makes Agentic AI in Healthcare Different From Traditional Build vs. Buy Decisions?

Healthcare organizations have been making build-versus-buy decisions for years. Agentic AI changes that decision because it does more than provide information. It can take action and complete tasks on its own.

While a chatbot answers a query, an agent understands the purpose, makes a decision on what must be done, reaches into multiple systems, and executes the process without manual monitoring.

Think of a denied insurance claim, for example. Rather than just recognizing the problem, an AI agent could confirm eligibility, retrieve the required documents, fix coding mistakes, ensure the claim follows the right approval process, and refile the claim.

That shifts the evaluation from comparing software features to evaluating operational responsibility.

Before selecting any solution, CIOs should answer three fundamental questions:

• Can the agent securely interact with every system required to complete the workflows, whether that’s Epic, Oracle Health, or a claims platform?
• Can every action it takes be reconstructed later for a compliance review?
• And does the workflow reflect something specific to how your organization delivers care, or is it a problem every health system solves the same way?

Where the Evaluation Usually Breaks Down

The biggest mistake CIOs make isn’t choosing the wrong vendor or investing in the wrong technology. It’s assuming that if an AI agent can complete a workflow, it’s ready for production.

A Black Book Research survey of 182 U.S. hospital leaders, reported by Presidio following HIMSS26, found that only 22% feel confident they could produce a complete, auditable explanation of an AI agent’s decision for a regulator within 30 days.

That’s the number CIOs should be reacting to, not the adoption hype. If a vendor can’t show approval logs, role-based access controls, and a clear record of what an agent did and why, the deployment speed they’re selling doesn’t matter. Ask for the audit trail before you ask for the demo.

Build, Buy, or Hybrid: How Do the Options Actually Compare?

The table below is the framework we walk through before any technical conversation starts.

Decision Factor	Buy	Build	Hybrid
Deployment time	Weeks	Months	Weeks to months
Workflow fit	Standard workflows	Custom workflows	Standard + custom
EHR integration	Vendor-supported	Fully customized	Mix of both
Compliance & audit	Vendor-managed	Fully controlled	Shared responsibility
Maintenance	Vendor handles it	Internal team	Shared
Flexibility	Limited	High	High
Upfront cost	Lower	Higher	Moderate
Vendor lock-in	Higher	None	Lower
Best for	Scheduling, intake, basic RCM	Clinical workflows, payer rules, care pathways	Fast deployment with custom logic

Note: Budget often influences the first decision. Buying is usually the less expensive option to get started, while building costs more initially but gives you greater ownership over time. However, without a skilled AI and integration team, maintaining a custom solution can quickly become a challenge.

Why the Hybrid Model Wins for Most Health Systems

Few health systems need to commit to one approach forever. What tends to work best is using a platform to handle standardized, high-volume workflows like patient intake, eligibility checks, and appointment scheduling, while relying on custom development for the workflows tied to a specific payer contract, clinical protocol, or care pathway that no vendor platform was designed to handle.

This is exactly the gap we close; we offer custom healthcare software development services to build and extend AI solutions around your existing platforms, giving you the flexibility to support unique workflows without replacing the systems you already use.

Instead of forcing you to choose between an off-the-shelf platform and a fully custom build, we help you combine both approaches to gain speed without giving up control.

How Bacancy Technology Helps Health Systems Build Production-Ready Agentic AI

With more than a decade of expertise in offering Healthcare IT services, we’ve learned that getting an agent to work in a demo is one thing; deploying it inside a health system’s real compliance rules and real EHR systems is another. That’s the biggest difference between a successful pilot and enterprise-wide adoption, and it’s where our expertise delivers the most value.

• Instead of replacing the systems you already use, we integrate with Epic, Oracle Health, MEDITECH, Cerner, FHIR R4 APIs, HL7, and your existing scheduling and revenue cycle platforms. This allows AI to work within your current environment without forcing you to replace the tools your teams already depend on.
• Approvals, logging, role-based access control (RBAC), and security guardrails are included in the initial release. This is how we achieve compliance with zero delays in rollouts or rollbacks.
• Prior authorization, denial management, patient access, scheduling, clinical documentation, and care coordination all have different operational challenges. We build around those workflows rather than trying to fit healthcare into a generic AI platform.
• Whether the right fit is Azure OpenAI, AWS Bedrock, Google Vertex AI, LangGraph, CrewAI, MCP, or a secure RAG architecture, we choose the technology based on your business and clinical requirements, not the other way around.
• Some organizations need help evaluating commercial platforms. Others need custom development tied to payer requirements or clinical protocols. Most projects combine both, using a vendor platform with a custom layer to move faster without giving up control of critical workflows.

When the development and implementation of your healthcare system has advanced beyond the pilot phase, this is the point where our discussions typically start. We help you determine whether a build, buy, or hybrid approach best fits your operational, technical, and compliance goals.

If you’re evaluating agentic AI in healthcare for production, our healthcare AI specialists at Bacancy Technology can help you assess your workflows, existing systems, and compliance requirements to determine whether a build, buy, or hybrid strategy best fits your organization.

Conclusion

When it comes to agentic AI in healthcare now, the issue is not whether to build or buy; the critical question is whether the model you choose will be safe to use, able to interact with your existing systems, and sustainable beyond the pilot stage.

The decision should come from your workflows and your compliance requirements, not from which option looks fastest on paper.

At Bacancy Technology, we help health systems plan and implement healthcare AI solutions that fit their workflows, existing systems, and compliance requirements. Whether you need to evaluate a vendor, build a custom solution, or consider a hybrid approach, our team can help you choose the right path and deliver solutions that work in real-world healthcare environments.

About Chandresh Patel

Chandresh Patel is a seasoned technology professional and passionate writer at Bacancy Technology, with years of experience helping businesses navigate the ever-evolving digital landscape. With a strong background in software development and IT strategy, he specializes in delivering technology solutions for the healthcare and finance sectors, translating complex technical concepts into practical, actionable insights for readers of all backgrounds. Chandresh has contributed to numerous industry publications, sharing his expertise on emerging trends, compliance-driven innovation, and digital transformation strategies that drive growth for healthcare providers and financial institutions alike. When he’s not writing, he enjoys mentoring young professionals and staying up to date with the latest advancements in technology.

Bacancy Technology is a proud sponsor of Healthcare Scene.

< + > Blaze.tech Raises $8.5M Pre-Seed | HealthSnap Secures $25 Million Growth Financing

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

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Blaze.tech Raises $8.5 Million Pre-Seed, Marking First Investment from New Healthcare Venture Firm Friale

Announcing $8.5 Million in Pre-Seed Funding, Blaze Enables Healthcare Teams to Create HIPAA-Compliant Custom Software with AI

Blaze.tech (Blaze), the AI app building platform purpose-built for healthcare, today announced an investment from Friale, a new healthcare-focused venture firm founded by the family behind HCA, the nation’s largest hospital operator. Blaze is Friale’s first investment, and the round adds $5 million additional capital to Blaze’s pre-seed, bringing the total to $8.5 million.

“Our mission is to make building healthcare software radically easier, so anyone with an idea to improve healthcare can have the power to build it,” said Nanxi Liu, Co-Founder and Co-CEO at Blaze.tech. “Every week, we see someone build a prototype with AI, demo it to a customer, and then hit a wall because the app isn’t connected to systems healthcare runs on and doesn’t have the compliance that real patient data demands. We built Blaze for that last mile—production apps that handle real patient data, write back into EHRs, and send prescriptions nationwide.”

“Blaze can generate multi-portal applications, fully structured relational databases, and end-to-end workflows in minutes. But in healthcare, speed only matters if you can trust what you ship,” said Tina Wojcik, Co-Founder and Co-CEO at Blaze.tech. “That’s why we pair AI generation with deterministic, auditable workflows, guardrails on every AI action, and separate development, staging, and production environments to protect live patient data. Healthcare teams get the velocity of AI with the control of enterprise software baked in from day one.”

From individual doctors to Fortune 500s, healthcare organizations use Blaze to build HIPAA-compliant solutions tailored to how they operate. With Blaze, patient portals, scheduling, custom EMRs, billing, and prescribing workflows are optimized for each organization’s unique operations. Kiaora runs its entire online GLP-1 and hormone-therapy prescribing business on Blaze; The Care Connexion runs its therapist-referral platform on it. Larger provider groups use Blaze to automate clinical workflows inside their EHRs…

Full release here, originally announced August 4th, 2026.

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HealthSnap Secures $25 Million Growth Financing to Accelerate AI-Powered Virtual Care Management Leadership

New Senior Secured Facility Validates Company’s Category Leadership and Fuels Next Phase of AI Innovation and Enterprise Growth

HealthSnap, the pioneer and category leader in AI-powered virtual care management, today announced it has secured a $25 million senior secured growth financing facility led by Eastward Capital Partners, LLC. The new facility strengthens the company’s balance sheet, refinances existing debt, and provides significant additional capital to accelerate artificial intelligence innovation, expand commercial operations, and scale deployment of its Advanced Primary Care Management (APCM) platform across the nation’s leading health systems.

The financing follows a period of exceptional growth and market momentum for HealthSnap as healthcare organizations increasingly adopt AI-enabled solutions that improve patient outcomes, reduce costs, strengthen clinical efficiency, and extend care beyond the traditional clinical setting.

HealthSnap has helped define the rapidly emerging virtual care management category by bringing together Remote Patient Monitoring (RPM), Chronic Care Management (CCM), Principal Care Management (PCM), Advanced Primary Care Management (APCM), AI-powered clinical workflows, enterprise analytics, reimbursement optimization, and care coordination into a single, EMR-Integrated, intelligent platform that enables healthcare organizations to proactively manage patients at scale – unlocking capacity without adding headcount.

Today, HealthSnap supports more than 80,000 active patient programs across 200 health systems and physician organizations, with the company projecting more than 100,000 active patient programs by the end of 2026. HealthSnap’s platform currently ingests 2 patient measurements every second, and patients enrolled in HealthSnap’s programs see a 97% reduction in alert frequency by their twelfth month, demonstrating remote patient management at scale.

HealthSnap partners with many of the nation’s leading healthcare organizations, including Prisma Health, AdventHealth, Ascension Health, Sentara Health, Tampa General Hospital, UnityPoint Health, Baptist Health South Florida, Mount Sinai Medical Center, and University Hospitals. Even more importantly, several of these organizations, including Sentara Health, Tampa General Hospital, and UnityPoint Health, have chosen to invest strategically in HealthSnap, reflecting their confidence in the company’s technology, leadership, and long-term vision…

Full release here, originally announced August 6th, 2026.