We reached out to our wonderful Healthcare IT Today Community to ask — how are health IT platforms enabling direct-to-consumer care models such as virtual visits, digital front doors, and on-demand services? The following are their answers.

Jason Griffin, Managing Director of Digital Health Strategy and Cyber Security Practice at Nordic
Health IT platforms are making direct-to-consumer care possible by connecting what patients want—convenient, digital-first access—with the clinical and operational systems that healthcare organizations already rely on. Consumers expect healthcare interactions to feel as seamless as the digital experiences they encounter in banking, retail, and travel. Platforms today support virtual visits, self-scheduling, online registration, digital intake, mobile communications, and personalized outreach through a unified digital front door. The goal is to reduce friction and give patients more control over how and when they access care.

These are no longer standalone tools. The most successful organizations are integrating consumer-facing applications with EHRs, CRM platforms, analytics solutions, and operational systems so patients can move smoothly between digital and in-person care. When that foundation is in place, organizations can deliver more convenient experiences while maintaining continuity across the care journey.

Michael Dalton, Founder and CEO at Ovatient
Health IT platforms, broadly speaking, are enabling direct-to-consumer care by making it easier for patients to find, schedule, access, and engage with care outside of the traditional clinic-based channels. The engagement and insights that these platforms are utilizing are what I find most fascinating.

I think what is going unappreciated from traditional models is that patients, because of LLMs and wearables, have never had more insight into their health. Patients are empowered with data and the questions to pose either to their favorite AI tool or their provider. We’re seeing a broader health ecosystem built principally around medication access, behavioral support, wearable data, and ongoing engagement.

I think this has also been one of the reasons for the exponential growth in MyChart messages between patients and providers, as recently shared in JAMA. We’re not going back, and I don’t think this will slow down.

Ben Maisono, SVP Strategy at Tendo
Health technology solutions that are being adopted have a consumer-grade experience that masks the complexities of access in healthcare and makes it easier. Convenience is the ultimate attribute for consumers with on-demand mindsets. AI triage is helping patients get recommended high-quality providers in a step-by-step evaluation process. This is introducing more shopping behavior than in the past because information is curated and organized in digestible steps for consumers now. We are seeing these engagement experiences adopted heavily in the commercial plan space for members as well to access Centers of Excellence care models.

Kamya Elawadhi, Co-Founder and President at Doceree
The shift to direct-to-consumer care is not a distribution change — it is a reimagining of where healthcare begins. The platforms enabling this most effectively are not simply digitizing the waiting room. They are embedding clinical intelligence, affordability tools, and care navigation directly into the first moment a patient engages. The most effective digital front doors anticipate need, surface relevant information, and reduce friction before care is even initiated. When consumers can understand options, verify coverage, and access support in a single, seamless interaction, care initiation and continuation improve significantly. What makes these platforms different is building around the clinical journey, not bolting onto it.

Dr. Scott Schell, Chief Medical Officer at Cognizant
Health IT platforms make direct-to-consumer care possible by moving more of the front end of healthcare outside the four walls of the clinic. Scheduling, intake, eligibility checks, virtual visits, remote monitoring, payment, and follow-up can now occur through consumer-facing digital channels. The larger shift is that these platforms increasingly act as orchestration layers rather than point solutions.

Consumers expect healthcare to behave more like every other service they use: accessible, responsive, and available on demand. Delivering that experience requires connecting access, clinical workflows, revenue cycle, and payer interactions into a single operating model. The organizations succeeding in direct-to-consumer care are not necessarily the ones with the best app. They are the ones that can translate consumer demand into coordinated clinical and administrative action at scale.

Kei Ishii, MD, Medical Strategy & Clinical Insights at Ubie
In digital healthcare, much of the discussion focuses on convenience, accessibility, and efficiency. However, one of the most overlooked aspects is trust. Patients gain confidence not simply because they receive an answer, but because they feel understood. In clinical practice, trust is built when a physician explores the patient’s symptoms thoughtfully, considers multiple possibilities, and demonstrates a genuine effort to understand what the patient is experiencing. As AI becomes a patient’s first point of contact, the challenge is not only providing the right recommendation, but also creating that same sense of understanding. Preserving trust and reassurance will be just as important as improving access and efficiency.

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

How do you do think health IT platforms are enabling direct-to-consumer care models such as virtual visits, digital front doors, and on-demand services? Let us know over on social media, we’d love to hear from all of you!

The following is a guest article by Casey Williams, SVP of Patient Engagement at RevSpring

Healthcare affordability debates tend to focus on the price of care: what procedures cost, what insurers cover, and what patients owe. That conversation matters, but it skips an issue providers can address right now, which is that the financial experience matters just as much as the total on the bill. 

A patient who receives a clear, timely, personalized bill with real payment options is more likely to pay, return, and trust their provider with future care. That means meeting the patient where they are financially: reaching them through the channel they already use, whether text, portal, or mail, and offering a payment plan fit to what they can actually afford. When patients don’t have those options, confusion breeds inaction. Someone who receives an unclear statement three months post-discharge faces a different set of pressures entirely. The cost feels like a surprise, the process can feel adversarial, and the relationship with the provider pays the price.

For rural and critical access hospitals, the stakes are higher. These organizations operate where the margin between operating and closing can be razor-thin, leaving little room for uncollected revenue. When patients can’t navigate their bills, they don’t pay. The Consumer Financial Protection Bureau reports that 68% of adults with unpaid medical bills didn’t pay because they thought their insurance was covering it, while 44% reported that they didn’t pay because they weren’t sure the bill was accurate. In communities where that’s the primary patient base, the shortfall can accelerate a facility toward closure. Removing the friction that causes payment to stall in the first place can help by presenting costs clearly, offering payment plans matched to what patients can actually afford, and communicating through the channels patients will actually use. For rural systems, improving the financial experience is just as much a revenue priority as it is a patient satisfaction initiative. 

Affordability as a Behavioral and Financial Function

CMS reports that patient out-of-pocket spending in the U.S. reached $556.6 billion in 2024. When costs feel unmanageable or unpredictable, patients struggle to pay their bills, but financial pressure also changes their behavior, resulting in delayed or avoided care.

Recent data shows that 79% of consumers have experienced “bill shock,” and 50% have cut back on care due to cost. Those patients who disengage due to cost concerns are less likely to return and more likely to present with more acute needs later on. The core barrier isn’t always the total on the bill, but that patients don’t understand what they owe or why. Confusion drives inaction, which costs providers real dollars and compounds clinical risk for patients. 

Closing these gaps means connecting the financial experience across the full journey, from pre-service estimates to post-visit payment. 

The good news is that providers can help patients understand and manage what they owe by reaching patients before a balance is due to set expectations, delivering statements that are clear and consistent, and offering payment pathways that reflect each patient’s actual financial situation. Analytics that draw on behavioral and financial data across the patient relationship can surface prior payment behavior, preferred contact channels, and one’s propensity to pay. Providers can use this data to personalize communications for individual patients and inform the most impactful outreach. For example, a patient who consistently pays online shouldn’t receive a mailed statement, and a patient likely to need a payment plan should be offered one proactively, before the balance ages. 

Rural Healthcare Carries a Heavier Load

More than 400 rural hospitals are at risk for closure this year, with 41% operating in the red. The Medicaid population in rural communities tends to be larger, lower-income, and more reliant on coverage that is increasingly unstable. Pending federal budget provisions could reduce Medicaid spending in rural areas by as much as $137 billion over the next decade, with uncompensated care costs rising by an estimated 35% as a result. Rural revenue cycle management teams are lean by necessity. In addition, workforce shortages make it hard to retain experienced billing staff, while patient protection and compliance rules add increased complexity that small teams are stretched to manage. That combination of thin margins, vulnerable patients, and limited administrative capacity makes reducing payment friction a top priority for rural systems, and one that alleviates multiple priorities at once. 

Health systems that consolidate patient data across touchpoints can help reduce the fragmentation that drives billing errors and missed collections. When a patient’s insurance status, prior payment history, and channel preference live in separate systems, the billing experience and ultimately collections break down. Removing friction cuts what it quietly costs providers: delayed collections, staff time chasing balances, and preventable write-offs.

Consolidated platforms also reduce the manual labor required to manage outreach. Automated, analytics-driven communication workflows let smaller teams do more with the resources they have. The systems seeing yield improvements treat financial engagement as a discipline with its own data and feedback loops, rather than a back-office function. When outreach is timely, personalized, and matched to what a patient can realistically afford, traditionally underserved populations get the guidance they need to navigate their bills and stay connected to care, rather than falling out of the system because the financial experience was too hard to manage.

The Billing Strategies Providers Can Act on Today

Healthcare affordability won’t be solved with a single billing statement. Policy, coverage, and reimbursement reform are all necessary, but there are steps providers can take today, especially for the rural healthcare leaders keeping the lights on today. Starting with clearer bills, more personalized communication strategies, and proactive financial conversations about what patients can afford to pay can help alleviate payment friction before balances age. This ensures that patients get the care they need, and providers can continue to serve their local communities. By taking a patient-first approach to billing and communications, providers are better positioned to weather the continued financial disruption in healthcare.

Savista Acquires ABW Medical, Expanding Ambulatory and Non-Acute Revenue Cycle Management Capabilities

Savista, a healthcare operations company with more than 35 years of revenue cycle management (RCM) experience, today announced the acquisition of ABW Medical, a trusted ambulatory RCM company serving the non-acute healthcare market. The acquisition expands Savista’s footprint into ambulatory and non-acute care segments while giving ABW Medical clients access to Savista’s broader capabilities, infrastructure, and operational scale.

ABW Medical serves an established base of clients, including federally qualified health centers (FQHCs), medical groups, managed service organizations and virtual care providers. ABW also holds preferred RCM partner status within the athenahealth ecosystem, a relationship that strengthens Savista’s position across the ambulatory market and adds further diversity to its EMR expertise.

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

This acquisition marks a pivotal moment for Savista, strengthening its ability to deliver comprehensive, tailored revenue cycle management solutions to a broader spectrum of healthcare organizations. By addressing the unique needs of community and rural health providers, this expansion aligns seamlessly with Savista’s mission to enhance financial performance and operational efficiency across diverse healthcare settings. The timing is critical as regulatory demands and financial challenges are intensifying, making scalable solutions essential to driving sustainability and supporting long-term growth for clients.

“Joining Savista is an exciting next step for our team and our clients,” said John Bozin, Chief Executive Officer at ABW Medical…

Full release here, originally announced August 27th, 2026.

Cureety Acquires Reimagine Care to Build the Future of Precision Oncology Care

Combination Brings Together Therapy-Specific Precision Monitoring and Clinical Intelligence, AI-Supported Patient Engagement, and Oncology-Specialized Care Teams to Extend More Personalized, Continuous, and Sustainable Cancer Care

Reimagine Care, an AI-powered oncology care organization, today announced that it has been acquired by Cureety, a precision oncology care company. The acquisition brings together Cureety’s therapy-specific monitoring and clinical intelligence with Reimagine Care’s AI-supported patient engagement and oncology-specialized care teams to support more personalized, continuous care beyond the clinic.

Together, Cureety and Reimagine Care form a profitable organization that supports more than 250 cancer centers and 100,000 patients across five countries, with a combined team of approximately 150 people.

The acquisition comes as cancer treatments are becoming increasingly personalized and complex, more care is moving outside the hospital, and oncology teams are supporting growing patient populations with limited clinical resources. Yet care delivery has not evolved at the same pace. The next generation of cancer therapies requires the next generation of cancer care. The combined organization aims to extend the reach of oncology teams beyond scheduled visits, helping them maintain greater visibility between encounters, prioritize patients who need attention, and deliver more personalized, continuous care.

“Cancer is a global challenge, and advances in treatment are creating many of the same opportunities and pressures for oncology teams regardless of where they practice,” said François-Guirec Champoiseau, Co-Founder and CEO at Cureety…

Full release here, originally announced September 2nd, 2026.

An initiative in Australia is doing just that. They’re using free and seamless data exchange to reduce screen time so that clinicians can focus more on conversations with patients.

Healthcare IT Today sat down with Kirsty Maunder, Senior Business Analyst at CSIRO’s Australian e-Health Research Centre. We discussed their Smart Health Checks initiative, which uses Smart on FHIR technology to reduce data entry and foster human connection at the heart of care.

Key Takeaways

• Make interoperability a front-of-house benefit. Pre-populating health checks eliminates screen time and allows a clinicians to focus on having a natural conversation with their patient to gather important information.
• Smart forms prevent manual transcription errors. By writing data directly back to the clinical information system, it pushes updates directly into downstream pathways for better chronic disease management and national reporting.
• Address daily frustrations of your clinical staff. The push for a smart form did not start as a technology mandate, but evolved naturally from workshops where community groups expressed frustration over repeated data collection.

Making Interoperability a Front-of-House Benefit

Clinicians spend too much time staring at screens instead of speaking with patients. The Smart Health Checks project by the Australian e-Health Research Centre addresses this by auto-filling digital templates with existing patient data before the visit even begins. This frees the provider to engage in what Aboriginal and Torres Strait Islander communities call “yarning”, which is a natural conversational flow.

Maunder explained the goal clearly: “By pre-populating as much data as possible and then [allowing the clinician to] really focusing on listening, it expediates the process for the clinician and offers a better experience for the patient”.

Eliminating Transcription Errors with Write-Back Capabilities

Data capture only matters if the information flows easily to the next step of care. Forcing a clinician to re-key data guarantees transcription errors and wastes valuable time. The CSIRO team made sure to eliminate this by ensuring their smart forms communicate in both directions.

“The other thing that a SMART Form can do is it can write back to the clinical information system,” Maunder noted. “All of that information that pre-populated, you can update or add new information, and it’ll write back to the clinical information system so you are able to use it for downstream pathways such as e-referrals, chronic condition management plan, national key performance indicator reporting.”

Building Solutions from Clinical Frustrations

Most successful IT projects start with listening to the people doing the work. CSIRO did not launch their Smart Health Checks initiative because of a government mandate. It was in response to real pain points raised by Australian health organizations. Maunder pointed out that the idea grew from community workshops.

The Bottom Line

Data standards and interoperability are often dismissed as back-office IT concerns. For decades, true data liquidity has remained an elusive goal. It is time to tie data exchange directly to clinical and administrative relief in order to accelerate adoption. The Smart Health Checks initiative in Australia shows what happens when interoperability is baked into tools doctors actually want to use.

Questions Healthcare IT Leaders Are Asking

How does SMART on FHIR improve the patient encounter?
SMART on FHIR applications can extract existing data from the electronic health record to pre-populate clinical forms. This automation prevents clinicians from re-typing known information. As a result, the provider spends less time looking at a monitor and more time engaging directly with the patient.

Why is data write-back critical for clinical workflows?
Write-back functionality allows a third-party application or smart form to push new data directly into the core clinical information system. This eliminates manual transcription errors and ensures the patient record updates immediately. It also accelerates downstream workflows like chronic disease management plans and national quality reporting.

How should IT leaders identify which clinical workflows to digitize?
The best digital tools originate from the daily frustrations of front-line staff. IT teams must conduct collaborative workshops to identify operational pain points, such as redundant data entry and fragmented templates. Addressing those specific barriers directly leads to faster user buy-in and higher adoption rates.

Why is interoperability especially critical in markets like Australia?
Unlike environments dominated by one or two giant enterprise electronic health records, Australia’s healthcare landscape features a wide variety of independent primary care systems, allied health applications, and regional point solutions. Because providers do not share a single monolithic software platform, open standards like HL7 FHIR and SMART forms are essential to bridge these separate systems, prevent repeated data collection, and ensure continuity of care across disparate providers.

Learn more about CSIRO’s Australian e-Health Research Centre at https://aehrc.csiro.au/

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Healthcare organizations have gotten better at asking the first AI compliance question: “Will the vendor sign a BAA?”

If an AI vendor will create, receive, maintain, or transmit PHI on your behalf, a BAA is foundational. But it’s not a HIPAA certification, a security assessment, or a blanket approval for every feature and workflow you build around that vendor.

A BAA governs a defined relationship and a defined scope. Your team still owns the hard part: controlling how PHI moves through the application, who can see it, and where it goes next.

That distinction is becoming more important as healthcare AI moves beyond a single model API call. The riskiest gaps I see are rarely in the signature process; they emerge after the agreement is in place.

1. Treating AI Logs Like Ordinary Application Telemetry

AI deployments generate unusually sensitive logs: prompts, model responses, user IDs, timestamps, tool calls, and sometimes entire clinical narratives. Teams know they need visibility to investigate errors and improve performance. The problem is that observability systems often become a parallel repository of PHI with far broader access than the clinical system that generated it.

HIPAA’s Security Rule requires organizations to implement access controls and audit controls appropriate to their environment, while the Privacy Rule’s minimum-necessary standard requires role-based limits on PHI use and access. That means “engineers need it for debugging” should not translate into standing, organization-wide access to raw prompts. HHS’s Privacy Rule guidance and Security Rule guidance are clear on the underlying principle: access should be tied to a legitimate role and purpose.

In practice, this means you must inventory every place AI interaction data lands, minimize what you log, redact or de-identify where feasible, and make access to unredacted records time-bound and auditable.

2. Missing the Restrictions Behind the BAA

A vendor may offer a HIPAA-ready API or enterprise product, but that does not mean every use case (or every product feature) is in scope. The BAA is only one part of the operating terms. The more consequential restrictions often sit in implementation guidance, product documentation, and feature-specific coverage tables.

Anthropic provides a useful example. Its BAA requires customers to follow an Implementation Guide for HIPAA Entities, and that guide restricts certain clinical uses, including direct patient-provider interactions, diagnosis, and treatment purposes. Those constraints are not a minor footnote: they shape what teams can safely build, even after a BAA is signed. (For example, Anthropic states that data sent to third parties through MCPs or connectors is not covered by its BAA).

That is why product names are not compliance boundaries; data flows and approved use cases are. Before enabling a new capability, ask four questions:

• Is this exact feature covered, not just the vendor or account?
• Is the intended workflow permitted under the vendor’s implementation guidance?
• What data leaves the product boundary?
• Has the feature changed since the last vendor review?

A BAA that covered last quarter’s chat workflow may say nothing about today’s autonomous agent.

3. Treating Behavioral Health Data as Ordinary PHI

Behavioral-health workflows require additional care, especially when substance use disorder (SUD) treatment records are involved. Those records may be subject to 42 CFR Part 2, which imposes confidentiality requirements beyond a standard HIPAA analysis.

The mistake is not simply failing to add “Part 2” to a contract. It’s assuming that a HIPAA BAA, by itself, resolves every permission, notice, redisclosure, and data-segmentation question that may apply to SUD records.

Before an AI tool enters a behavioral-health workflow, the security, privacy, legal, and clinical teams need to trace the data journey together, and the trace should end in a design decision, not a meeting. Which records originate from a Part 2 program, and can the retrieval layer label them at ingest? The Part 2 amendments that took effect in February 2026 simplified consent; they did not simplify segregation. Where a system cannot reliably separate Part 2 records, the conservative posture is to apply Part 2 handling to the whole set. That matters most when an agent pulls from several sources into one response, because the answer inherits the most restrictive rule among its inputs. If you cannot say which passage came from where, you cannot show which rule applied.

That’s the design work that keeps a useful AI workflow from creating a sensitive disclosure problem.

4. Forgetting the Vendors Around the Model

Modern AI systems are rarely one vendor, one API, and one BAA. They include logging platforms, vector databases, cloud storage, workflow engines, retrieval systems, web-search tools, connectors, and MCP servers. Every additional component creates another potential PHI recipient and another question about safeguards, contractual coverage, and access. For healthcare organizations working across several AI products, managing BAAs across multiple AI vendors is ultimately an operational discipline, not a one-time contracting exercise.

HIPAA’s Security Rule also requires business associates to obtain appropriate assurances from subcontractors that handle ePHI on their behalf. HHS’s Security Rule summary explains that downstream obligation.

The right review artifact is not a folder of executed BAAs; it’s a living data-flow map with user input, retrieval source, model, tool call, log destination, human reviewer, and retention path. If your organization cannot draw that chain, it cannot credibly claim to control it.

The Post-Signature Checklist

A BAA should be the beginning of operational oversight, not the end of procurement. After signing, ask:

• Who can access AI prompts, outputs, and logs containing PHI?
• Is every enabled product feature covered under the agreement and configured correctly?
• Does the workflow involve SUD records or other specially protected data?
• Which third parties (including connectors and tool servers) can receive PHI?
• Has the organization updated its risk analysis as the workflow changed?

Healthcare organizations don’t need to choose between moving quickly with AI and protecting patient data. They just need to stop treating the contract as the control.

The teams that build durable AI programs will be the ones that operationalize governance at the data-flow level where the risk actually lives.

About Mat Steinlin

Mat Steinlin is Head of Information Security at Aptible, a compliance-first infrastructure platform for regulated healthcare and AI companies. He has led security assessments for digital health companies through HIPAA, HITRUST, SOC 2, and PCI compliance for over a decade.

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

• Director of Home Health – South County Health
• ROI Medical Records Specialist – MRO
• Director Information Services – IM Regional Ops – CHRISTUS Health
• VP, Technology Operations – Expion Health
• Behavioral Health Scheduler & Survey Liaison – Nemours Children’s Health
• EMR/EHR Implementation Specialist – Alignerr
• Clinical Application Specialist – Arcare
• Computational Health Informatics Data Manager I – Boston Children’s Hospital
• Deputy CIO, Care Technologies & Informatics – Information Solutions – MUSC Health
• Associate Consultant – Behavioral Health – Oracle
• Health Partner Engagement Specialist I – CareSource
• Manager, External & Scientific Communications – Flatiron Health
• Clinical Transformation Specialist – Revenue Cycle – Ascension
• Analyst-Health Information Management – Baptist Memorial Health Care
• Chief Technology Officer – HCLTech
• Community Health Worker (Childhood Onset Chronic Healthcare Needs) – Duke University
• VP, Practice Technology – OneOncology
• Biomedical Cybersecurity Specialist – Connected Medical Devices – MaxIT Consulting
• Health Information Analyst/HIM Operations – Hartford HealthCare
• Compliance & Privacy Manager – Corporate Compliance – NewYork-Presbyterian Hospital

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

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

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

I recently sat down with Jolie Ritzo, CEO at Civitas Networks for Health, Anne Santifer, Executive Director at Arkansas State Health Alliance for Records Exchange, and Dan Cranshaw, Executive Director at KC Health Collaborative, to learn more about what’s happening in the Civitas community and get a preview of their annual conference.  One thing they shared was how often healthcare infrastructure serves an important purpose but goes unrecognized. This is by design.  For example, an HIE (Health Information Exchange) may make a patient’s information from multiple organizations available to a clinician at the point of care.  The clinician and patient often have no idea how the information from external organizations was made available to them, but having that data often improves care. Good infrastructure like an HIE quietly does this work in the background, often with little recognition.

Along with Civitas members’ role as critical infrastructure for healthcare, we talked with our panel of HIE and health collaborative experts about some of the hot topics in the community as a preview of what will be discussed at the upcoming Civitas Annual Conference.  One of the most important topics in healthcare in general is the cuts and changes to Medicaid.  These cuts are going to have a significant impact on most healthcare organizations, but that also presents an opportunity for HIEs and health collaboratives to assist provider organizations whose patients may need extra assistance and care.

While rural health issues are not a new focus area for the Civitas community, Ritzo, Santifer, and Cranshaw all mentioned that the RHTP (Rural Health Transformation Program) presents a really great opportunity for partners in the Civitas community to support states in improving rural health.  They are hopeful that the funds back rural providers and communities with innovative approaches to solving some of the big challenges they have faced for too long – access to quality care and services, legacy data systems, workforce shortages, the digital divide, to name a few.  Santifer mentioned that one of the things she’s most excited about at the Civitas Annual conference is hearing all of the different ways that states are approaching the rural health funds.  We’ve definitely seen that no two states are the same, and so a lot of great innovation funded by RHTP will be shared.This is a tremendous opportunity to hear what is working and, in some cases, what is not.

With all of the various healthcare interoperability regulations out there, Kill the Clipboard, CMS Tech Ecosystem, TEFCA/QHINs, etc., I also asked the panel about the positive impact these regulations are having and some of the ways they could be improved.  The panel suggested that there’s a lot of nuance to these regulations.  For example, it may seem like a great idea to Kill the Clipboard, but many patients in some areas still really like the clipboard.  We have to take these local nuances into account.  That said, it is an exciting time for innovation and moving policy into practice. Examples of this will be discussed throughout the annual conference.

Finally, we asked the panel to share what they think will be some of the top conversations at the Civitas Annual Conference.  Check out our interview with these experts to learn more about the latest developments in health data, health information exchange (HIE), and cross-sector approaches to improve community health.

Learn more about Civitas: https://civitasforhealth.org/

Learn more about Arkansas State Health Alliance for Records Exchange: https://sharearkansas.com/

Learn more about KC Health Collaborative: https://www.kchealthcollaborative.org/

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

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Civitas is a proud sponsor of Healthcare Scene.

Data is a multi-dimensional representation of reality. A simulacrum. Its function is to aggregate events and human behavior to account for the past and inform decisions for the future. Digital data takes the form to another level of abstraction. Electricity is sent through logic gates that together form the basis of modern computing.

• AND: The output is 1 (true) only if all inputs are 1; otherwise, it outputs 0 (false)
• OR: The output is 1 if at least one input is 1; it only outputs 0 if all inputs are 0
• NOT (Inverter): Takes a single input and reverses it; a 1 becomes a 0, and a 0 becomes a 1
• NAND: The exact opposite of an AND gate; it outputs 0 only if all inputs are 1, and outputs 1 in all other cases, because any other logic gate can be built from it, the NAND gate is considered a “universal gate”

We see the descendants of these gates in queries and code today:

• Select * from med_data where diagnosis NOT like ‘%chest” OR ‘%heart%’
• Select sandwich from restaurant where array_contains(sandwich,’baloney’)

When a concept is lost in translating reality to its representation, it’s known as lossy. It’s a way of compressing knowledge and experience into smaller, faster, and more accessible formats. Vinyl becomes CDs. Cathode ray tubes become digital LEDs. Specifications for Databricks platform best practices become “Storage is cheap. Compute is expensive. Materialize.” The meaning is preserved while the context and density shrink. The upside of that compression is portability- concepts, data shapes, and logic can be translated into new contexts to compound value instead of requiring new patterns to be created to match each new situation.

So when we look at healthcare data, we have to think about what the data actually means and what it’s supposed to represent. A perfect example is NULL. At the surface level, the meaning is obvious- we don’t have that data. But NULL can mean a lot of things in different contexts, especially in healthcare:

• Patient was asked but refused
• Patient does not have a specific diagnosis
• Patient doesn’t know
• Provider didn’t ask
• The data itself was lost

In healthcare informatics, this can be referred to as NullFlavor. It’s the difference between what the data says, which is that there’s nothing there, and what it means- real-world events like the ones outlined above. That differentiation belongs to a category of meaning called semantics- how we define things. Semantic conformance is when, instead of simply displaying the data that is received, we translate it into a set of known and agreed-upon definitions to make communication easier.

Jack sees a pail, Jill sees a bucket— they can both agree it’s a water container.

Semantic interoperability is the foundation of modern healthcare data exchange. A contract must be created whenever terminology is shared across organizations. Some orgs may say NP or PA, others may call them MLP or Mid-Level Providers. Machines don’t understand the difference, so a contract must be made to have an understanding of the terminology. Both sender and receiver must have a shared translation layer.

Data does not fall from the sky, and computers are not magic. Data is physical- stored on spinning magnetic disks or microscopic transistors. It generates heat. AWS is not actually in the “cloud”. It’s a warehouse full of slow, spinning disk hard drives that are known to fail. The value comes from scale- the drives are cheap, so they can store the same data shards across hundreds or thousands of servers. If one fails, there’s always somewhere else to grab it. People use it because it is cheap and reliable. This is where much of the healthcare data in the US is stored.

What is Healthcare?

Healthcare is the scientific method applied to human maintenance through social care. It allows humans to survive longer and more comfortably through knowledge compounded and preserved in a lossy form. Every band-aid and every MRI machine are the result of generations of humans working together to figure out how to live better, healthier lives through inherited processes.

Humans have been doing it for a while. Healed bones, normally a death sentence in ancient times, have been found as far back as early Homosapiens. Healthcare is a biological, social process rooted in anthropological data.

What is Healthcare Data?

We now have a basic understanding of data and healthcare. Now it’s time to see how they fit together. Enter John Snow (not the grumpy guy in a black coat).

John Snow was a doctor in London during a cholera outbreak in 1854 with an obsession with the purity of water that eventually proved quite useful. As cholera spread through an area in London, the prevailing opinion was that “bad air” was the problem. Snow rejected this explanation and investigated further. Hundreds died and many fled. Snow took note of where deaths occurred and where deaths did not occur. He inquired where each person acquired their water. He put it all on a map, and began to look for patterns— two emerged:

1. Those who primarily acquired their water from the Broad Street well were the ones most likely to contract cholera
2. Those who worked at the brewery on Broad Street were generally fine; they drank beer, not water

Snow did not possess the technology to prove the water in the well was contaminated, but the data was compelling enough to persuade local authorities to remove the pump handle. People stopped dying.

That is the power of health data. Instead of walking around town trying to figure out the problem, he walked around collecting stories. He compressed those stories into a lossy data format- who died, who’s alive, where do they get water, where do they work. Recording physical events and symptoms that appear to have no correlation. The compressed data was then shifted to a new context- a map. In the new context, a pattern emerged that told the story of what was going on. The pattern was clear enough to convince humans to act, saving lives.

That is health data engineering.

That is what we do in clinical software and population health.

• Finance: What can we afford to do?
• Management: What should we do?
• Ops: Humans doing things
• Data: Records of humans doing things
• AI/ML: Predicting what humans will likely do next
• Engagement: How do we get people to do things?
• Reporting: Did they do the things? What was the impact?
• Marketing: Look at all the things we’re doing!

There has to be a shared understanding between those workflows. Marketing has to understand what clinical and technical claims are valid. The sysadmin has to keep a record of who has access to what resources. AI/ML needs to understand how members are being engaged to accurately predict intervention opportunities. Sales needs engineers to negotiate technical requirements, semantic interoperability, and data products.

Without a shared understanding, everything has to be held in human memory and translated at each interaction. We lose the compounded value of shared language. But we can only realize that value if we turn it into infrastructure.

Meeting templates. Standardized specifications. The documentation becomes the physical artifact of every new efficiency, every workaround, every decision made and problem solved. Available to anyone on any team to reuse so we’re not solving the same complicated baseline problems more than once. We only have to keep track of the central document.

Documentation materializes the result of  expensive human processing:

• Investigation becomes a specification
• Agreement becomes a definition
• Experience becomes a template
• A workaround becomes a reusable pattern
• A decision becomes an artifact

The same thing applies to Databricks. Data is stored there in 3 groups:

1. Raw record of things that happened – labs, medical care, contact attempts, all preserved in their original format
2. Like reusable documentation, the silver layer normalizes data into a shared form; buckets and pails become water containers – Female, Fem, f become F; this is the core
3. This is how we combine the data to meet the needs of other teams— custom data sets, data marts, AI modeling data— each requiring a custom level of complexity and scale; the majority of work has already been done, the function here is to turn that into something useful to humans and machines

The Medium is the Message

Data tells stories. When multiple people ask a developer for multiple different things, there’s no way to tell if they’re actually the same request if there’s no translation to standard business concepts.

In order to avoid doing the same work 3 times, the initial work of defining the business meaning and needs of each separate department needs to be solved once. That solution becomes both documentation and infrastructure. Like any other infrastructure, it has to be monitored, tracked over time, and governed by humans who understand how the data fits into different workflows.

Technology is not a solution to problems. It is the means by which humans manage scale and complexity. Documentation and policy bridge the gap between the magic box and what we need to do our jobs.

But Wait, There’s More!

Humans inside an organization have no pre-defined business needs. Business needs, legal constraints, and federal policy shape the data of organizations.

Every new table is the materialization of a customer’s needs. Patient data can be difficult to find by design, to ensure we align with HIPAA’s concept of Minimum Necessary Use- people shouldn’t access private data unless there’s a documented reason why. We are contractually obligated to preserve source data. The outside constraints define the boundaries and shape of the data.

And it doesn’t stop there! Healthcare policy is the social process that defines what healthcare is and what it should do.

For a long time, the socially produced model was Fee-for-Service, incentivizing treatment volume and detailed accounting. Those incentives determined the data requirements for clinical software companies- reporting, receipts, workflow optimization. Platforms like SQL Server and reporting tools like Tableau were the shape these incentives pushed data into.

The shift towards value-based care changed the incentives. The emerging incentives center around improving results, preventing health problems, and reducing unnecessary care.

The details are complicated, but the concept is simple. Healthcare organizations are paid a fixed amount to keep a specified group of patients healthy. This creates new business needs:

• Treat healthcare conditions before they become expensive healthcare crises
• Test according to what a patient’s specific needs are, not what will increase the volume of tests
• Predict and intervene where engagement can lead to improved health outcomes for patients

The concept is called capitation. It’s one of the core reasons why the new incentives require data to take new shapes. New data shapes require new data platforms that can store and receive data efficiently.

In SQL Server, data lives in a flat format. A field for every business need that humans have. Tableau is the industry standard for how to take this raw, flat data and make it usable by humans to do their work. For Fee-for-Service, this was the power couple, and it worked brilliantly.

Under VBC, the shape of data changes at a fundamental level. Data is no longer an accounting of what happened. It is the raw material to create predictions about what will happen in the future. Those predictions drive human behavior in the form of engagement and intervention.

To be able to make those predictions, machines have to compress vast amounts of aggregated data into forms it can use for prediction.

AI and machine learning are not some sci-fi self-aware overlords. They are John Snow’s process of compressing human activity into data, shifting the context, and predicting probable outcomes or causes in digital form. The main difference is that technology is now assisting humans with the scale and complexity of the task.

Machines don’t have to filter context on the fly like humans do. They don’t have to think about dinner, putting the kids to bed, or worry about whether their comments could be misread. They are physical machines that do what humans design them to do. 

Machine learning takes in more data than could be stored in human working memory to do complex math at scale to make the predictions defined by engineers to solve problems defined by humans, shaped by incentives and constrained by regulation.

Theoretically, an LLM could be built with paper, pen, and shelves instead of logic gates. But theory is limited by physical constraints:

• The time it would take could only be measured by using the age of the universe as the unit of measure, and it would still be in the millions
• The physical space required would be over 100000 larger than the square footage of Earth
• It would require immortal humans to keep the manual process running

Even then, the task of defining what the AI should be doing must come from regular humans, because machines have no inherent incentives outside their physical constraints. That warehouse full of spinning disks and logic gates is not defining purpose. It is executing commands to coordinate human activity, as defined by humans.

Humans define the problem from accumulated experience. Technology compresses and coordinates that experience into a form that can be acted upon at scale. The results return to humans, who test them against reality, preserve what works, correct what does not, and begin the process again.

Technology does not reduce complexity. It compresses complex data to be able to coordinate human activity. It is infrastructure built with human knowledge. That’s why a meeting template and a 30 billion parameter LLM are both infrastructure. Everything we’ve covered here is a different layer of the same process:

• Healthcare is the social process of applying the scientific method to improving human health and care
• Policies and regulations create an agreed-upon framework for how we can do that
• Clinical software companies interpret those incentives and constraints, and allocate resources to purchase the technology required to fit the incentives and constraints
• Humans use the technology to manage the scale and complexity of the new forms data takes
• Human workflows, materialized and governed by humans in policies, templates, and runbooks, create the translation layer between human needs and physical computation

You don’t need to know how a calculator works. You need to know what it does, how to use it, and how it is supposed to make your tasks easier. 

About Nat Osit

Nat Osit is a technology synthesist with over two decades of experience across healthcare operations, clinical software, grassroots media, federal policy, interoperability, data engineering, implementation, and systems architecture. Her work focuses on translating human knowledge, organizational processes, and technical systems into reusable infrastructure. She develops MediLacra, an open-source synthetic healthcare data platform, and contributes to interoperability and data-quality work through the HL7 community.

Memorial Hermann Health System Deepens Partnership with Strategic Investment in PartsSource

Investment Will Help Strengthen Healthcare Technology Infrastructure and Clinical Capacity Across More than 1,600 Leading Hospitals Through the PartsSource Platform

PartsSource, the leading performance platform for clinical technology, today announced a strategic investment from Memorial Hermann Health System, a non-profit, award-winning health system committed to creating healthier communities, now and for generations to come.

PartsSource provides device-owners and operators an enterprise technology platform that unifies parts, service, contracts, asset intelligence, and workforce capacity into a single operating system for healthcare technology management. Powered by 25 years of longitudinal data, millions of service events, billions of data points, and today servicing over 15,000 clinical sites across the U.S., the platform delivers real-time visibility, predictive signals, and prescriptive action, enabling health systems to move from reactive repair to proactive management—unlocking clinical capacity from assets already in place.

“As healthcare organizations look to lower cost of service, unlock clinical capacity, and find new revenue within their existing walls, a unified, evidence-based, and data-driven approach to asset management becomes essential,” says Philip Settimi, MD, MSE, President and CEO at PartsSource. “Memorial Hermann joins 150 other health systems who are advancing a scalable model that transforms how mission-critical assets are managed…

Full release here, originally announced August 25th, 2026.

Arintra Raises $25M to Pioneer Revenue Assurance for America’s Health Systems

As the First and Only Autonomous Coding Platform Across 23+ Specialties and Every Health System Care Setting, Arintra is Setting a New Standard for Revenue Cycle Management and Reinventing how Leading Health Systems like UC Davis Health, Mercyhealth, Meritus Health, Rochester Regional Health, Reid Health, Mercy Medical Center, and More get Paid Accurately, Promptly, and in Full

Arintra, the first and only enterprise AI platform for revenue assurance in healthcare, today announced a $25 million Series B funding round, bringing the company’s total funding to $51 million. The round is led by Define Ventures, with participation from existing investors including Peak XV Partners, Yale New Haven Health (YNHH) Center for Health Care Innovation, Endeavor Health Ventures, Y Combinator, Counterpart Ventures, Ten13, and Spider Capital. Endeavor Health, based in Chicagoland, also was an early adopter of the technology, and participated in both the Series A and B rounds.

U.S. healthcare is a $5 trillion system, yet most health systems and provider groups struggle to get paid by payers for the care they deliver — predominantly due to siloed processes that are riddled with inaccuracy, costing health systems, providers, and patients every day. As a longtime function within health system operations, traditional revenue cycle management has consisted of individual point solutions and a fragmented workflow that leaves revenue on the table. Arintra is pioneering revenue assurance, an agentic AI approach built to close the gap between care delivered and revenue earned. The company’s platform has become a core piece of the enterprise health system’s infrastructure, bringing leading health systems the bottom-line impact they deserve.

Driving $5 Billion in Annual Claim Value for Leading Health Systems, Large Provider Groups, and Academic Medical Centers

Arintra has deployed its platform across health systems and large provider groups, partnering directly with them to increase compliant revenue capture and lower cost to collect, reduce claim denials and processing lags, and improve coding accuracy. One of those partners is Rochester Regional Health. “At Rochester Regional Health, our vision extends beyond automating a single specialty — we’re looking at how AI can fundamentally transform coding across the enterprise,” said Karen Linder, Senior Director of Health Information Management & Coding at Rochester Regional Health…

Full release here, originally announced August 26th, 2026.

For the 121st episode of the CIO podcast hosted by Healthcare IT Today, we are joined by Patrick Williamson, Chief Information Officer at Array Behavioral Care, to talk about AI tools and behavioral health! We kick this episode off by discussing why AI governance and security are so critical and different in healthcare and behavioral health. Then we learn how Williamson has been approaching vetting solutions in his organization and why it’s so hard to say no to a solution. Next, we dig into Williamson’s thoughts on using PHI on ungoverned AI tools.

We also see if there are any that clear HIPAA, as well as what it would take to do so. Then we talk about some of the initial applications in behavioral health where AI tools make sense. Next, we analyze the value of behavioral health having visibility into the patient’s full medical record. We then debate over whether we need more AI or if we just need to leverage the tools we already have. Lastly, we conclude this episode with Williamson giving advice to aspiring CIOs who are considering a behavioral health organization.

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