Sherpas Healthcare Solutions is a medical record retrieval company that takes on the burden of fulfilling these requests. In a recent video interview, we had a chance to chat with Kaylan Blice-Mullins, Senior Director of Provider Relations at Sherpas Healthcare Solutions, and Brian Sitongia, Medical Records Manager at one of their clients, Wills Eye Hospital, to learn more about how Sherpas Healthcare Solutions is helping relieve the burden of all these requests.

Sitongia describes Wills as the oldest and most highly rated eye care facility in the United States, with seven or eight specialty clinics. Before hiring Sherpas, each record request would either require personal attention from Sitongia or a visit to the facility from a requester, who in turn would require a tutorial on how to get into their medical records.

Now, a single technician from Sherpas does the work, using his own login account. Blice-Mullins says any provider can benefit from this service. Plus, she hinted at a new tool they are rolling out which will speed up request handling tremendously, reducing response times from hours to minutes.

When it comes to how working with Sherpas has changed Sitongia’s workflow, he said that at most he has to upload the PDF to the Sherpas portal and he’s done. The requester is billed, and the provider pays nothing. That’s right, Sherpas offers this service at no cost to the provider organization.

Currently, Sherpas doesn’t handle individual patient requests from doctors at Wills Eye Hospital, but Sitongia hopes to transfer that job to Sherpas as well.

Check out our interview with Sherpas Healthcare Solutions and Wills Eye Hospital to learn more about how they’re improving the process of ROI requests.

Learn more about Sherpas Healthcare Solutions: https://scanningsherpas.com/

Learn more about Wills Eye Hospital: https://www.willseye.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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Sherpas Healthcare Solutions is a proud sponsor of Healthcare Scene.

The following is a guest article by Christopher Rogowski, Partner of Strategic Advisory at Pivot Point Consulting, a Best in KLAS Healthcare IT Consulting Leader

It’s no secret that rural hospitals are grappling with a multitude of existential challenges. 

Rural healthcare issues differ sharply from those in urban areas, with economic, social, educational, and policy barriers, along with geographic isolation, driving disparities and limiting access to care.

Facts and statistics collected by the National Rural Health Association reveal the breadth and depth of rural healthcare challenges:

• Rural residents often lack sufficient access to care, with the patient-to-primary care physician ratio in rural areas standing at 39.8 physicians per 100,000 people, compared to 53.3 physicians per 100,000 in urban areas
• Rural residents tend to be poorer than their urban counterparts, with more than $9,000 less in annual per-capita income
• People in rural areas often face transportation barriers, requiring them to travel long distances to access doctors or hospitals
• Chronic conditions like diabetes and coronary heart disease occur more often in rural communities than in urban ones

As a result, rural healthcare providers are feeling the pinch. Today, more than 40% of rural hospitals operate in the red, according to some studies, and more than 200 rural hospitals have closed or converted to models that exclude inpatient care since 2010. More than 400 rural hospitals remain vulnerable to closure.  

Yet for every challenge, there is a corresponding opportunity for technology to lighten the load. A new generation of AI tools is reshaping how care is delivered, and few hold more promise for rural providers than ambient clinical intelligence. Microsoft Dragon Copilot is one of the most compelling examples.

Supporting Clinicians at the Point of Care

Dragon Copilot is an AI-powered tool that combines ambient listening with front-end speech recognition to reduce administrative burden, enhance workflows and save providers time. It’s offered at a discount to eligible rural hospitals through Microsoft’s Rural Health Resiliency Program. (Disclaimer: The firm for which I work partners with this program.) Rural hospitals may register for the Program here.

In practice, the workflow is refreshingly simple. When a patient comes in, the provider activates a listening device or microphone and begins the visit as they normally would. They make eye contact, ask how the patient is feeling, and let the conversation unfold naturally. In the background, Dragon Copilot composes a complete clinical note. When the visit ends, the provider taps to pause and pushes the note into the EHR, either as a whole document or section-by-section. Templates and prompts can be customized to match how each clinician practices. Feedback has been positive, with many providers able to leave the office at the end of the day rather than catching up on charts late into the night.

For rural hospitals, this kind of efficiency is more than a convenience. It frees clinicians to spend more time with patients, reduces burnout, and supports retention of the providers these communities can least afford to lose.

Six Fundamental Dimensions of Readiness

The technology is only part of the story. The difference between organizations that realize the full value of Dragon Copilot and those that quietly shelve it almost never comes down to the product. It comes down to readiness. The following six fundamental dimensions consistently determine whether a deployment will succeed.

1. Governance

Leaders often assume their boards are aligned on AI, but engagement is not the same as alignment. Strong governance brings clinical, operational, IT, security, and community voices into the same room before a single license is deployed.

2. Technology and Digital Capabilities

Broadband, cloud readiness, and EHR integration all matter, particularly in rural areas where connectivity remains uneven. These are also the dimensions leaders tend to be most prepared to discuss.

3. Security and Incident Response

AI introduces a different threat level, and rural hospitals need a plan for responding to a breach involving an AI tool and explaining that response to their community.

4. Measurement

Without clear metrics and a regular cadence for monitoring them, leaders lose the ability to course correct. Return on investment here is broader than the bottom line. It includes provider satisfaction, patient retention, note turnaround time, and billing velocity. Decide what you are optimizing for, then build the measurement plan before go-live.

5. Training

Whether the model is train-the-trainer, certified external trainers, or help desk support, training must be scoped early and matched to how providers actually learn. Putting clinicians in a trainer role rarely works.

6. Change Management and Community Trust

In small, often county-owned hospitals, board members are community members, and leaders need a clear, plain-language answer to how they will explain ambient AI to the people they serve. That is a governance and communications exercise, not an IT exercise.

Looking Ahead

Rural healthcare faces real and persistent headwinds, but the arrival of mature ambient AI tools like Dragon Copilot represents a genuine inflection point. With thoughtful governance, a sound technical foundation, disciplined measurement, well-scoped training, and proactive community engagement, rural hospitals can shift from a workforce-strained, documentation-heavy environment to one where clinicians spend more time with patients and less time at a keyboard. 

The technology is finally catching up to the mission, and for the providers and communities who have carried rural healthcare for decades, that is a reason for real optimism about what comes next.

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

• IT Applications Engineer V, Medical Imaging – Kaiser Permanente
• Chief Information Officer (CIO) – MarinHealth
• Patient Service Representative – Abrazo Health
• Healthcare Value Analyst (Remote) – Sentara Health
• Regulatory Coordinator II (Remote) – Cedars-Sinai
• Epic Certified EHR Systems Analyst III (EpicCare Ambulatory) – Adventist Health
• ROI Medical Records Specialist – MRO
• Clinical Engineering/BiomedicalEngineering Cyber Specialist – TechnoSphere, Inc.
• Provider Relations Analyst – Texas Children’s Hospital
• Epic Applications Analyst Professional – Children’s Hospital Colorado
• Grievance and Appeals Analyst I – Elevance Health
• Virtual Chief Information Officer (vCIO) – Healthcare – Meriplex
• Lead IT Security Analyst – HIPAA, HITRUST, FISMA – NYU Langone Health
• Community Health Worker – RWJBarnabas Health
• Analyst, Pharmacy Data & Analytics – Molina Healthcare
• Health Information Management Analyst – Boston Medical Center (BMC)
• Chief Information Officer (CIO) – Commence
• Information Systems Manager – Mount Sinai Medical Center
• Associate Director, Finance Technology, AI Data & Innovation – Amgen
• Director – Information Systems Analytics & Reporting – Woman’s 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.

Healthcare IT Today sat down with Justin Campbell from RLDatix to discuss legacy data management. The conversation explored how modernizing data archives is tightly tethered to reducing clinician burden and improving patient safety outcomes.

Key Takeaways from the Legacy Data Management and Patient Safety Conversation with Justin Campbell

• Legacy systems contribute to burnout. Clinicians wasting time fishing through multiple decommissioned databases is a cause of frustration and fatigue.
• Incident reporting needs historical context. Without immediate access to legacy records, providers cannot truly understand or prevent patient safety issues.
• AI uncovers the missing reason. Mining incident reports and legacy data with artificial intelligence helps pinpoint systemic issues that may have been missed due to the low frequency of incidents.

Legacy Data Contributes to Burnout

Keeping a dozen decommissioned systems alive is a massive technical debt. More importantly, it is a daily friction point for clinicians. When doctors have to hunt for old records, care slows down.

“It’s contributed to clinician burnout, switching between multiple systems,” noted Campbell.

Providers need that history at their fingertips. If they are forced to fish around for records in different silos, it’s a barrier to providing safe patient care. Consolidating that legacy portfolio into a single accessible archive removes the friction.

Finding the Root Cause with AI

Storing incident reports is only the first step. The real value lies in connecting those reports to the broader historical patient record. This is where practical artificial intelligence can do the heavy lifting.

By bridging the gap between incident reporting and archived EHR data, organizations can let the data do the detective work.

“It’s about figuring out the why,” Campbell explained. “That’s the missing part, and that’s what AI is particularly adept at”.

Analyzing these combined datasets allows leaders to benchmark safety profiles before and after digital transformations. It turns static archives into active risk mitigation tools.

The Bottom Line

The connection between legacy data management and incident reporting is clear. Leaving historical data scattered across forgotten systems creates blind spots in patient care. Bringing that data together creates a foundation for actionable insights. Health IT leaders who treat their legacy estate as a safety asset will be better positioned to protect both their patients and their staff.

What Healthcare IT Leaders Are Asking

How does legacy data archiving and management impact patient safety?
When legacy data is scattered across multiple decommissioned systems, clinicians lack a complete historical view of the patient. This missing context can lead to clinical blind spots. Consolidating records into a single archive ensures providers have the information they need to make safe clinical decisions quickly.

Why apply AI to incident reporting and legacy data?
Traditional incident reporting captures that an event occurred, but it often misses the underlying cause. Applying AI to a combined dataset of incident reports and legacy records helps uncover hidden patterns. This allows organizations to identify specific workflow failures and address them proactively.

What is the hidden cost of maintaining multiple legacy EHRs?
Beyond the obvious licensing and server maintenance expenses, keeping multiple legacy systems running contributes significantly to clinician fatigue. Forcing providers to log into different interfaces to piece together patient histories drains their time and energy. Modern archiving solutions reduce this cognitive load while keeping the organization legally compliant.

Learn more about RLDatix at https://www.rldatix.com/

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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RLDatix is a proud sponsor of Healthcare Scene.

When all calls were handled manually, results were so bad that they replaced their phone system several times, with no improvements. Many patients weren’t answering their phones, calls were missed or dropped, others would crave contact and keep staff on the phone for a long time. Average wait times were 30-40 minutes, and one long wait led to a negative online review which was problematic for the organization.

As an eClinicalWorks site, the health system decided to try healow Genie to handle calls. It was a simple integration, and worked “out of the box.” The approximate 400 calls they get each day are now spread out, as patients take advantage of 24/7 availability. And when a call does require staff, healow Genie gets a live agent person on the phone within a minute and 20 seconds.

More importantly healow Genie handles about 80% of all calls with no staff interaction, answering the phone within 14 seconds versus previous wait times which regularly reached 30-45 minutes.

Although some staff were worried that the AI system would replace them, they found instead that staff had more time for important tasks outside of answering the phones. When healow Genie does connect a patient to a live agent, the agent can handle a call faster because healow Genie shares basic information with the live agent about the issue before connecting the patient.

As for the patients, they took a little while to accept the system. Causey says it was actually less popular among young patients as well as the elderly. But he explained to patients that all institutions are moving in the direction of AI-handled calls.

healow Genie helps Capital Area Health Network with appointment scheduling, medication refills, and simply providing information about what services the system provides or what a patient’s own treatment plan is. Causey advises managers who install such systems that the staff need education in order to accept the system.

Check out our interview with Capital Area Health Network to learn more about their implementation of the healow Genie call center agent.

Learn more about Capital Area Health Network: https://cahealthnet.org/

Learn more about eCW: https://www.eclinicalworks.com/

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

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

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

eClinicalWorks is a proud sponsor of Healthcare Scene.

When I became a cancer patient, I had already spent decades building healthcare technology. I helped pioneer remote patient monitoring. I understood data systems, interoperability, and the promise of digital health. None of that prepared me for what it feels like to be inside the system as a patient.

At 60, I knew something was wrong. I had fatigue, unexplained weight loss, and a persistent sense that something in my body was off. I pushed for testing. I had scans, labs, and specialist visits. The data existed. It was extensive. The conclusion was that nothing serious was happening. Weeks later, after a separate emergency room visit with a new care team, I was diagnosed with an aggressive blood cancer.

What stayed with me was not just the diagnosis. It was the realization that nothing new had been discovered. The signals were already present in my medical record. They simply had not been connected. That experience changed how I think about healthcare technology.

The Problem isn’t a Lack of Data

Healthcare does not suffer from a lack of information. It suffers from fragmentation. Every system in healthcare does its job reasonably well in isolation. Labs generate results. Imaging produces reports. EHRs store documentation. Wearables now add continuous streams of patient-generated data. However, none of these systems are designed to synthesize across domains.

Clinicians are left to do that work manually, under severe time constraints, across dozens or hundreds of patients. Even the best physicians are operating within limits that make comprehensive synthesis difficult to sustain. The result is not a lack of knowledge. It is a lack of connected understanding.

AI as a Synthesis Layer, Not a Replacement

In my case, AI did not uncover something hidden or mysterious. It reviewed the same data my doctors had already seen and connected patterns across it. It identified a constellation of signals that pointed toward a deeper issue and highlighted follow-up steps that, in hindsight, should have been considered earlier. This distinction matters.

The future of AI in healthcare is not about replacing clinicians or making autonomous decisions. It is about synthesizing across fragmented data and helping surface what is already knowable but not easily visible. If we frame AI as a discovery engine, we miss the immediate opportunity. Its near-term value is as a synthesis layer that improves how existing knowledge is applied.

The Execution Gap in Precision Medicine

Once you move beyond diagnosis, a second issue becomes clear. Modern oncology has already entered the era of precision medicine. We understand that every cancer is biologically unique. We have genomic testing, targeted therapies, and rapidly expanding treatment options. But care delivery has not kept pace.

Treatment decisions are still largely guided by standardized protocols designed for populations. These protocols are essential, but they are inherently generalized. They cannot fully reflect the molecular uniqueness of each patient. This creates a widening gap between what is scientifically possible and what most patients actually receive.

In my own case, analyzing my data through AI helped me understand that my cancer had specific characteristics that made alternative treatment approaches worth exploring. That insight did not replace my care team. It allowed me to engage with them more effectively and pursue a path better aligned with my biology. The issue is not a lack of innovation. It is the system’s ability to carry that innovation into real-world care.

Why Health IT Infrastructure is the Bottleneck

For health IT leaders, this is where the challenge becomes concrete. If medicine is becoming more personalized, our infrastructure must support longitudinal, integrated understanding of each patient. Today’s systems are optimized for documentation, billing, and episodic encounters. They are not optimized for continuously integrating data across sources and over time.

Even basic interoperability remains inconsistent. Patient data often exists across systems but is not normalized, structured, or accessible in a way that supports meaningful analysis. In some cases, it technically moves between systems but loses fidelity or usability along the way.

This is not just a technical inconvenience. It directly impacts clinical insight. AI cannot function effectively on fragmented or incomplete inputs. If we want better outputs, we need to rethink how data is aggregated, structured, and maintained as a longitudinal record.

The Emergence of the Patient-Side Intelligence Layer

One of the more interesting shifts is where intelligence begins to sit in the system. Traditionally, complex cancer cases rely on tumor boards, where multiple specialists review a case together. That model works, but it is episodic and resource-constrained. What I built for myself was a continuous version of that concept. I created multiple AI agents representing different clinical perspectives and synthesized their outputs into a unified view. It functioned as an always-on, patient-side layer of analysis.

This is not about bypassing clinicians. It is about augmenting the system so that both patients and providers have access to a more complete, synthesized understanding of the case. Over time, we will likely see these intelligence layers emerge both within provider systems and alongside them.

From Population Medicine to N-of-1 Care

The broader shift underway is from population-based medicine to truly individualized care. Every patient represents a unique case. Every disease evolves.

Static guidelines and episodic decision-making are increasingly insufficient. AI creates the possibility of continuously integrating new data, adapting to changes, and aligning treatment decisions more closely with the individual. But this only works if the underlying systems support it.

What Comes Next

My experience did not convince me that healthcare needs more data. It convinced me that healthcare needs better ways to connect and apply the data it already has.

There are likely many patients today whose optimal treatment paths already exist somewhere in clinical literature, trial data, or another physician’s experience. The problem is that no one has the time or tools to connect those dots consistently.

That is the opportunity. AI will not replace clinicians, and it will not eliminate uncertainty. But it can help bridge the gap between what we know and what we deliver.

For a system under increasing pressure from complexity, that may be the most important innovation ahead.

About Steve Brown

Steve Brown is a technologist, filmmaker, and AI practitioner whose work explores how emerging technologies reshape human decision-making and complex systems. He is the Founder and CEO at CureWise and has previously built and led multiple companies in digital health and software, including ventures acquired by Apple and Bosch.

Sagility Acquires CareSeed to Accelerate AI-Led Quality Operations and Medicare Advantage Performance Transformation

Acquisition Expands Sagility’s Healthcare Quality, HEDIS, and Care Gap Orchestration Capabilities for Health Plans

Sagility, a leading tech-enabled healthcare operations and transformation company, today announced its acquisition of CareSeed, a U.S.-based healthcare analytics company specializing in NCQA-certified HEDIS quality reporting, medical record review, chart abstraction, and regulatory analytics for health plans.

Founded in 2012 and headquartered in Kansas City, Missouri, CareSeed serves 30 small and mid-sized U.S. payers, with a strong footprint in Medicare Advantage. The company’s cloud-native platforms, Forecast and Harvest, help health plans improve HEDIS performance, streamline chart abstraction and medical record review workflows, strengthen audit readiness, and manage increasingly complex regulatory requirements.

The acquisition represents a strategic expansion of Sagility’s healthcare quality and Stars capabilities and advances the company’s broader vision of moving health plans beyond retrospective HEDIS reporting toward integrated, member-level quality orchestration.

By combining CareSeed’s technology with Sagility’s healthcare operations, clinical services, and AI-led transformation capabilities, Sagility will deliver an end-to-end quality operations continuum.

The combined offering will support health plans across the full quality lifecycle — from HEDIS abstraction and reporting to prospective gap closure, provider engagement, care coordination, and continuous performance monitoring.

“CareSeed has built strong capabilities in quality measurement, HEDIS reporting, and healthcare analytics that have helped health plans navigate an increasingly complex regulatory environment,” said Ramesh Gopalan, Managing Director and Group Chief Executive Officer at Sagility…

Full release here, originally announced June 11th, 2026.

Model N Acquires Kalderos to Expand its 340B Capabilities and Gross-to-Net Revenue Management

Deal Adds Claims-Level Visibility to Help Manufacturers Identify Duplicate Discounts Earlier and Reduce Revenue Leakage

Model N, a leading end-to-end commercialization, revenue optimization, and compliance platform for life sciences companies, announced it has acquired Kalderos, a technology company that provides a gross-to-net (GTN) intelligence platform with rich capabilities for driving visibility into 340B drug discount programs.

The acquisition will expand Model N’s capabilities as a leading revenue management platform for life sciences manufacturers and extend its 340B offerings to better serve customers. 340B, a federal program that requires drug manufacturers to sell outpatient drugs at discounted prices to eligible safety-net hospitals and clinics, is a core and growing component of the overall GTN value chain.

Manufacturers are managing 340B amid Medicare drug price negotiations, potential future pricing models such as most-favored-nation (MFN), and growing scrutiny of 340B claims-level data requirements. These pressures are increasing the need to identify discount discrepancies and optimize revenue. Model N’s 2026 State of Revenue Report found that only 1% of life sciences revenue leaders have real-time visibility into 340B discounts, Medicaid and Medicare rebates, and utilization rebates.

Kalderos’ Truzo solution directly addresses these challenges through an end-to-end drug discount management platform that provides real-time data visibility across multiple drug discount programs, enabling effective compliance monitoring, claim validation, and dispute resolution between manufacturers and covered healthcare providers. The added 340B data also strengthens Model N’s advanced analytics strategy, giving manufacturers more complete information across the revenue lifecycle.

“Model N delivers the commercial platform life sciences leaders rely on to expand market access, optimize revenue, and maintain compliance,” said Bret Connor, CEO at Model N…

Full release here, originally announced June 17th, 2026.

For the 117th episode of the CIO podcast hosted by Healthcare IT Today, we are joined by Jeff Sturman, Former CIO of Memorial Health System and Managing Partner & Practice Leader, Information Technology & Digital at WittKieffer, to talk about the ins and outs of being a CIO! We kick this episode off by discussing why we think so many CIOs only last 3-5 years and whether we think that is a good or a bad thing. Then we list out the things that health systems are really looking for in CIOs today, that they really weren’t before. Next, Sturman shares the key aspects he wishes CIOs understood more about job searches and their careers.

We know that the role of CIO does a lot, but is it too much? We dig into that by seeing what parts of a CIO’s job can be replaced by AI. Next, we debate whether the IT leadership team has become too crowded with CISO, CMIO, CDAO, CNIO, CDIO, etc. We also discuss the keys to successfully leading and creating proper reporting structures in an organization. Next, we talk about what we think is the most valuable thing a CIO could be working on right now to improve their leadership efforts. Lastly, we conclude this episode with Sturman sharing the best piece of advice he’s received in his career.

We release a new CIO Podcast every ~2 weeks. You can also subscribe to the Healthcare IT Today podcast on any of the following platforms:

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If you run a healthcare practice or health platform, your infrastructure is likely overdue for an honest evaluation. It’s not cracked or broken; it’s just that it kept chugging along while the technology and regulatory environment evolved around it. Ultimately, you will need to migrate. The choice is whether you do it on your own terms or wait until an emergency forces your hand.

Let’s look at what a planned migration actually takes, and how to execute it smoothly.

Establish Your Baseline

Before drawing up a migration plan, you need a clear inventory of your current setup. What software versions are you running? Which ones are still actively supported by vendors? What hasn’t been audited in the last year? While these questions seem basic, most practices can only answer them partially. This isn’t due to negligence; it’s simply because quiet, working systems rarely demand attention.

However, risk quietly accumulates in the gap between a system that is merely running and one that is actively maintained. Knowing exactly what is in your environment, as well as its forward lifecycle, is what transforms a migration from a chaotic emergency into a predictable project.

Planned vs. Forced Migrations

Migrations have a bad reputation, but it’s largely undeserved. While concerns about downtime, cost, and operational continuity are valid, they are entirely manageable if you address them upfront.

Take downtime, for example. A well-executed migration runs the old and new environments concurrently. This means any eventual cutover downtime is minimal, scheduled, and communicated well in advance so there are no surprises for your staff or patients.

Cost is another major factor, but inertia carries its own financial risks. Staying put leaves you exposed to compliance penalties on unsupported systems, broken integrations, and compounding technical debt that makes future upgrades even harder. With a planned migration, expenses are predictable; with a forced migration, they rarely are.

Finally, maintaining operational continuity comes down to clear documentation. When you map out the process before turning anything off, your internal team and your migration partner know exactly who handles what. The anxiety surrounding migrations usually stems from a lack of planning, and clear documentation completely eliminates that uncertainty.

Build a Forward-Looking Roadmap

An effective migration roadmap goes beyond a basic upgrade list. It should be a strategic document that you can confidently present to a CFO or compliance officer. It needs to be broken into distinct phases with realistic timelines, accounting for future growth like new locations, incoming integrations, and shifting compliance standards.

Successful healthcare practices treat infrastructure upgrades like any other major business initiative, ensuring clear milestones, assigned budgets, and a defined parallel-running period to guarantee a seamless handoff.

Before signing with a technology partner, ask them to walk you through their methodology in detail. Find out exactly how they handle concurrent environments, how they communicate downtime, and what their escalation path looks like when unexpected issues arise. Vague answers at this stage are a major red flag.

Maintaining Momentum Post-Migration

Reaching a modern, supported infrastructure is a great milestone, but the work doesn’t stop there.

In healthcare, patch management must be a continuous process, not a reactive fix. Your BAAs, patch histories, and access logs should always be organized and accessible before an auditor requests them. That way, when the next major security vulnerability is announced, the systems powering your patient portals, revenue cycle management (RCM) platforms, and scheduling tools are already protected.

Ultimately, infrastructure management isn’t about setting it and forgetting it; it’s about eliminating the element of surprise.

Kelly Goolsby is with Nexcess, a managed hosting platform that provides HIPAA-compliant infrastructure and migration support for specialty practices, regional health centers, and healthcare SaaS platforms.

Ready to build your migration roadmap? Let’s talk.

H1 Receives $40M Investment in Round Led by CVS Health Ventures

H1 Helps Healthcare Players Transform Operations with AI

H1 recently received a $40M investment in a round led by CVS Health Ventures, the venture capital investment platform of CVS Health dedicated to driving health care innovation and digital disruption. The investment follows several successful projects that CVS Health and H1 have collaborated on, including a new AI model leading to a substantial improvement in health care provider directory accuracy, helping people connect with health care providers more quickly.

“CVS Health has a long-standing commitment to improving access to care, and this collaboration represents another step in our mission to develop industry-leading solutions that simplify the healthcare experience,” said Justin Brock, Partner at CVS Health Ventures. “We are excited to see how H1’s data and AI capabilities can further drive efficiency and improve the consumer experience across the healthcare ecosystem.”

H1’s mission is to connect the world to the right doctor. The company is known for its AI-powered platform that helps identify and engage the right doctors for critical workflows across pharmaceutical, health plan, health system, and technology companies. 85% of the top 20 pharma companies and 9 out of 10 of the top health plans are H1 customers. The fast-growing company is profitable and well on its way to operating as an above Rule of 40 company in 2026.

“This collaboration with CVS Health Ventures further enables H1 to achieve its mission of connecting the world to the right doctor,” said Ariel Katz, Co-Founder and CEO at H1…

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

Lassie Raises $35M Led by Andreessen Horowitz to Build AI for Small Businesses to Run Themselves

Founded by Early Robinhood and Superhuman Product Managers, Lassie is Working on Autonomous Systems that Handle the Busywork

Currently Operating in 700+ Small Businesses Across 49 States, Providing Business Owners with Over 250,000 Hours of Labor Each Year

Lassie, the company building autonomous systems to run small businesses, announced it has raised $35 million in Series A financing, bringing its total capital raised to $47 million. The round was led by Andreessen Horowitz (a16z) with support from Night Capital, Rahul Vohra, Founder and former CEO at Superhuman, Zach Perret, Co-Founder and CEO at Plaid, Taavet Hinrikus, Co-Founder and former CEO at Wise, Gokul Rajaram, and Brian Balfour, Co-Founder and CEO at Reforge.

AI can do a lot today. It writes software, passes bar exams, and generates realistic videos from simple prompts. But it can do so much more. It can help millions of small business owners by handling their busywork. Up until now, software has not removed this painstaking administrative work and has merely rearranged it. For the first time, software can interpret messy context, move across the endless systems that small businesses have, and do that work for an owner.

Nowhere is this more apparent than in doctors’ offices, the largest type of small businesses behind retail and food and beverage. A typical practice loses 100+ hours a month to administrative work and spends roughly $200,000 a year on staff that owners can barely find, let alone keep.

Today, Lassie’s agent goes into a practice’s insurance portals, pulls reimbursements, reconciles them against records, updates the system-of-record, and verifies the funds in the bank. Operating in more than 700 practices across 49 states, Lassie currently provides businesses owners with over 250,000 hours of labor each year.

“Small business owners should be freed up from doing busywork, so they can focus on what they are passionate about,” said Steijn Pelle, Co-Founder and CEO at Lassie…

Full release here, originally announced June 3rd, 2026.