< + > A Look at Workday’s ERP in Healthcare

Healthcare technology conversations often focus on the EHR and clinical systems, but the technology supporting a health system’s HR, finance, and supply chain operations is just as critical to keeping things running. Workday is increasingly playing a strategic role in healthcare ERP, helping some of the nation’s largest health systems manage the business operations that ultimately support patient care.

At Workday Rising, I sat down with Keith Lohkamp, VP of Supply Chain Management for Healthcare at Workday, to talk about the company’s growing presence in healthcare and where it’s investing next.

AI is becoming a bigger part of that story. Workday’s announcements include Sana as a new front door for the platform, helping bring together the agents and workflows being developed across the system. Lohkamp also highlighted candidate profiles designed to reduce friction when healthcare workers apply for jobs, along with decision intelligence and Data Cloud capabilities that can help health systems connect operational and clinical data to support better planning and decision-making.

Those capabilities have practical applications across the organization. In recruiting, Workday’s talent acquisition agent can help identify candidates and schedule interviews more quickly. Lohkamp pointed to AdventHealth, which has reduced agency spending by more than $60 million using Workday tools. Finance teams can benefit from more automated close and audit processes, while supply chain teams can use AI to make it easier to find products, create contracts, work with suppliers, and capture savings.

Supply chain resilience is another major focus. Lohkamp explained that healthcare organizations have to be ready for shortages, back orders, recalls, natural disasters, and other disruptions that can quickly affect the availability of critical supplies. Workday now works with more than half of the organizations on Gartner’s healthcare supply chain Top 25, and he shared an example of West Coast health systems that had to quickly adjust their purchasing after a major distributor experienced a fire at its distribution center. Workday helped those organizations pivot their ordering and track supplies so patient care could continue without disruption.

Making all of this work also requires connecting Workday to the rest of a health system’s technology environment. Lohkamp discussed the company’s integration approach with EHRs and other systems, including connections for item master, product, pricing, and financial data. He also highlighted Accounting Center, which can bring detailed revenue cycle transactions into Workday to support deeper financial analysis, along with Data Cloud’s ability to connect data across different sources.

Watch the full interview to hear Lohkamp’s perspective on Workday’s growing role in healthcare and how AI, connected data, and more resilient supply chain operations are shaping the next generation of health system ERP.

Learn more about Workday: https://www.workday.com/

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< + > Willis Knighton Health Uses Ambient AI from Commure to Reduce Physician Burden

Before standardizing on MEDITECH for their EHR, the Louisiana provider Willis Knighton Health had 140 different instances of EHRs. Senior VP and CIO Mark Galea said it was critical to get everyone on a single EHR. But he also knew the transition would be challenging for staff and cause a lot of disruption. He felt that the institution owed it to their doctors to see how technology could make their work easier after such a significant change. After a pretty extensive search and evaluation, Willis Knighton brought in Commure to provide an ambient AI scribe to alleviate some of the burden on their clinical staff.

In a recent interview, Galea along with Ian Shakil, Chief Strategy Officer at Commure sat down with us to share the detailed, carefully planned strategy used by Willis Knighton to choose their ambient AI vendors and roll it out efficiently. Shakil and the team at Commure were one of the original creators of ambient AI, and the company has particular expertise in difficult settings such as emergency departments.

One of the key needs of Willis Knighton was that their AI scribe vendor was able to integrate well with MEDITECH. Plus, Galea said he didn’t want Willis Knighton to be the first customer—or even the tenth—because he didn’t want a vendor to “experiment” on them. He talked to and ruled out many ambient AI vendors that at first looked promising, and settled on Commure and one other vendor for pilots.

Some intensive analytics also went into Willis Knighton’s decisions. They looked at what the doctors were actually doing: when doctors are using the EHR, which parts, and how often have to go back to the same place. They tried the two pilot solutions on 28 specialties, one doctor for each. Shakil praised Willis Knighton, saying they were unusual among providers because “they knew what they wanted” when talking to vendors and did the work to measure those outcomes.

One of the advantages of Commure is that it partners closely with clients. Shakil says they emphasize not just technology, but how to measure and report returns so Willis Knighton was a good match.

Deployment actually went faster than expected, with less time spent on support and 14 months to deploy the solution. Galea did not want a lot of customization or optimization up front. Other factors in choosing both the EHR and ambient AI included data security and cost. Like most providers, Willis Knighton has very limited capital.

Galea is a strong believer in using technology strategically to gain a competitive edge. For instance, they use robots in 60% of the operating rooms. He thinks that only in the past four to five years, the investment the health care industry made in digital technologies is starting to deliver on its promise.

Both Galea and Shakil offered thoughts for the future. Shakil said that, proud as he was to invent the ambient AI scribe, he looks forward to helping it disappear into a broader suite of AI-assisted tools for summaries, search, and other use cases embed in workflows. He also wants Commure to move into more niche settings and RCM.

Galea and Shakil agree that providing more data to consumers “will shift the playing field.”

Watch our interview with Mark Galea from Willis Knighton Health and Ian Shakil from Commure for other valuable insights into a successful roll-out of ambient AI.

Learn more about Commure: https://www.commure.com/

Learn more about Willis Knighton Health: https://www.wkhs.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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Commure is a proud sponsor of Healthcare Scene.

< + > How Automation and AI are Quietly Rebuilding the Hospital Back Office

The following is a guest article by Paige Schwein, Vice President, Technology Services at Healthrise

Ask most people where AI is changing healthcare, and they’ll describe a surgical robot or an algorithm that reads scans faster than a radiologist. But after spending more than a decade in revenue cycle operations, the real revolution unfolding quietly in the administrative back office.

Health systems and hospitals that embed intelligent automation into their day-to-day operations can reduce the administrative burden created by endless claim queues, denial worklists, and prior authorization backlogs.

There is a compelling business case for this pivot. Administrative work consumes 15% to 30% of U.S. healthcare spending, and most hospitals operate on margins between 1% and 3%, leaving virtually no room for avoidable waste. First-pass denial rates still top 10% nationally, even at organizations that have spent heavily on billing software and staff.

The 2025 CAQH Index estimates that automation saved the industry $258 billion in administrative costs in 2024 and puts the savings still on the table at another $21 billion a year.

That makes the hospital back office one of the clearest opportunities for AI adoption: it is data-rich, highly repetitive, and directly connected to a health system’s financial performance. The next phase of healthcare AI isn’t about adding more tools; it’s about embedding intelligence into the work hospitals already do. Most AI efforts began as isolated pilots targeting a single payer or denial category. Now, the opportunity is to move beyond those pilots and integrate automation directly into daily worklists, embedding flagged claims into existing queues so staff don’t have to check separate dashboards.

What AI Tools Actually Do

For years, denial management meant someone exporting claims data into a spreadsheet and piecing together patterns long after the revenue had been written off. Predictive models with embedded large language models (LLMs) now read through that data and surface the trends a person might miss. Health systems using them have cut incoming denials by an average of 25%.

The same historical data (billing history, seasonal swings in patient volume, and the habits of individual payers) also lets revenue cycle leaders forecast cash flow and identify where to focus resources before a shortfall arrives.

Coding and audit teams work differently now as well. Random sampling used to decide which accounts got reviewed, so the costliest errors often went unexamined. Automated selection sends reviewers to the highest-risk accounts first. Systems using standardized audit-coach-measure workflows have seen underperforming team members progress to consistent top performers in fewer than 90 days.

Together, these tools shift revenue cycle work from reacting to problems after they happen to identifying where attention is needed before those problems become costly. The technology handles the volume and pattern recognition, while revenue cycle teams can spend more time interpreting what the data means, deciding what action to take, and improving the processes behind it.

Confronting Bureaucratic Hurdles

New federal mandates require health plans to enforce strict prior authorization turnarounds — 72 hours for urgent cases and seven days for standard requests — before moving to standardized electronic submissions by 2027.

Physicians and their staff already spend about 13 hours a week on prior authorization. Keeping pace with those deadlines without adding headcount or exhausting the team you have requires automation that connects clinical documentation to the billing system, backed by a denial prevention program that catches problems before a claim goes out.

Before Adding One More Tool

A typical revenue cycle department already juggles an EHR, a clearinghouse, a claims scrubber, a denial tracker, and multiple point solutions. Another AI tool can easily become one more place for work to hide.

Before introducing new technology, healthcare leaders should confirm it can embed into existing workflows, assign clear ownership for flagged issues, and avoid duplicating current functionality. To track progress and prove it works, it’s important to establish concrete baseline metrics before the tool goes live, such as first-pass denial rates, days in A/R, and authorization turnaround times.

The goal shouldn’t be another dashboard. It should be a better workflow.

Healthcare Still Needs a Human Touch

Physicians have good reason to be wary. Six in ten say they worry payers will use AI to deny more claims, and nothing in the technology itself prevents that. A model that flags a questionable claim or an audit risk has done half the job. The other half belongs to a trained specialist who reviews the flag, understands the context around it, and has the authority to correct what’s wrong.

Governance decides whether these tools make the work easier for the people doing it or harder, and that requires clear ownership, visible oversight and a defined process for acting on what the software finds.

When those pieces are missing, automation just makes a broken process run faster. When they’re in place, the billing office finally catches up to its own workload. For a hospital operating on a 2% margin, the claims it stops losing can matter as much as anything new in the operating room.

About Paige Schwein

Paige Schwein is Vice President of Technology Services at Healthrise, where she leads the technology behind the company’s revenue cycle work for health systems. She has spent more than a decade in revenue cycle operations and builds teams that use data to improve efficiency and financial performance.

< + > This Week’s Health IT Jobs – November 4, 2026

It can be very overwhelming scrolling through job board after job board in search of a position that fits your wants and needs. Let us take that stress away by finding a mix of great health IT jobs for you! We hope you enjoy this look at some of the health IT jobs we saw healthcare organizations trying to fill this week.

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

• Information Security Officer (Remote) – Optum
• Chief Executive Officer (CEO) – Quorum Health
• Clinical AI Researcher – Ambience Healthcare
• Cybersecurity Incident Response Analyst II – Summa Health
• Associate Cyber Engineer – HealthEquity
• Appeals Specialist – Devoted Health
• Data Coordinator I- Environmental Medicine – Mount Sinai Health System
• Chief Information Security Officer – Qualified Health
• AI Product Manager – CareSource
• Cybersecurity Architect – Fresenius Medical Care
• Virtual Chief Information Officer (vCIO) – Healthcare – Meriplex
• Business Intelligence Analyst – Allegheny Health Network
• Chief Operating Officer – AMN Healthcare Leadership Solutions | B.E. Smith
• Head of IT & Cybersecurity – Eko Health
• AI Engineer – Oxbridge Health
• Clinical Data Analyst – Ascension
• Cybersecurity Research Assistant – Axle
• Chief Executive Officer – Prime Healthcare
• AI Product Manager – St. Jude Children’s Research Hospital
• Chief Medical Information Officer (CMIO) – Information Solutions – MUSC Health

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.

< + > East Georgia Healthcare Center Shares Its Experience with the New eClinicalWorks Pharmacy Solution and AI

East Georgia Healthcare Center is taking an innovative approach to healthcare technology by working with eClinicalWorks to bring new pharmacy capabilities and AI tools into its existing workflows. In our recent interview at the eClinicalWork National Conference, Peyton Frye, COO at East Georgia Healthcare Center, shares how the organization helped shape eClinicalWorks’ new pharmacy module, why having pharmacy and EHR workflows integrated makes a difference, and what the implementation has meant for its staff.

One of the biggest advantages of the new pharmacy module is how it connects pharmacy operations with the existing eClinicalWorks EHR. Frye explains how information such as patient contact details is now synchronized between the medical and pharmacy sides, eliminating the need to update multiple systems.  This may seem like a small thing, but it’s made a big difference in their organization.

Clinicians can also see whether medications are in stock or available for next-day delivery. On the pharmacy side, real-time updates to patient sliding fee scales help ensure pharmacists have the right information when patients arrive to pick up prescriptions. Plus, for an organization managing both an open-door pharmacy and its 340B program, having these workflows connected makes a meaningful difference.

We also discuss with Frye the AI solutions East Georgia Healthcare Center is using, including healow Genie and AI-powered analytics. Frye shares how the organization initially implemented Genie at its largest location before expanding it to two locations that had some of its lowest patient satisfaction scores for telephone access. The results were encouraging: within the second month, patient satisfaction sentiment at those locations increased from the low 80s to the low 90s. Genie gives patients 24/7 access to answers about things like lab results, appointments, and prescription status, helping address missed calls and the heavy volume of calls that often comes at the beginning and end of the workday.

Beyond patient calls, East Georgia Healthcare Center is also using eClinicalWorks’ AI-powered analytics capabilities to make it easier for its quality team to query data using natural language rather than building complex SQL queries. Frye also discusses the importance of having the right staff oversee AI implementations.  He shares why human intervention still matters and the additional AI capabilities he’s excited to see coming to healthcare.

Watch the full interview to hear how East Georgia Healthcare Center is leveraging the new eClinicalWork Pharmacy solution in their FQHC along with a number of other AI tools.

Learn more about East Georgia Healthcare Center: https://eghc.org/

Learn more about eClinicalWorks: 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.

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eClinicalWorks is a proud sponsor of Healthcare Scene.

< + > Augusta Health Automates Document Routing With Forward Advantage AI

AI is making contributions in many health care settings by creating doctors’ notes, but an equally urgent task in many organizations is processing, storing, and routing incoming documents such as referrals and prescription requests. Augusta Health, a community health system in central Virginia with a 255-bed hospital, four urgent care clinics, and many other sites, has to deal each year with 750,000 paper documents—a total of 2.4 million pages—submitted by scanning or fax.

In our recent interview, Leigh Williams, CIO at Augusta Health, and Audrey Brislin, Vice President of Product and Marketing at Forward Advantage, talk about the AI-based solution for document recognition and scanning from Forward Advantage that is relieving staff at Augusta Health of the tedious and error-prone job of redirecting the documents.

Augusta Health has had a relationship with Forward Advantage since 1999 and so it made sense to extend this relationship with Forward Advantage’s new document routing solution. Prior to implementing this new solution, Augusta Health would present each document to a human agent on a screen. The agent would determine what kind of document it was, where it should be stored, and which clinician should receive it for action. This was preferable to paper handling, but still delayed both treatment and billing. Williams said they needed “a really slick process.”

Luckily, Forward Advantage has recently added AI-driven categorizations that can potentially automate the entire process of identifying, storing, and routing documents. According to Brislin, the key is to understand the provider’s workflows. Some are more consistent than others, and automation should be applied to those that are consistent and predictable. One of the benefits of Forward Advantage is that it does leave room for a human in the loop.

Augusta Health has been deploying the AI automation for a few months and is finding it a great solution that fixes a “long-standing, intractable problem.” Success metrics they’re tracking include labor costs, and the time required to get documents to the clinicians to act on.  Clinicians love getting the documents in their native MEDITECH workflow faster than when the documents were routed by a human. Williams expects the automation to help with staff retention, because categorizing documents by hand was so boring and unfulfilling, “no value add.”

Automation, along with centralizing the categorization process, allows Augusta Health to run analytics and derive insights such as where prescriptions and referrals are coming from. Williams summarizes their experience as a safe and high-impact deployment of AI. Brislin expects that in the future, AI will also route documents outside the organization and enable work with third parties.

Watch our interview with Leigh Williams from Augusta Health and Audrey Brislin from Forward Advantage to learn more about this AI solution which is solving real world healthcare challenges.

Learn more about Forward Advantage: https://www.forwardadvantage.com/

Learn more about Augusta Health: https://www.augustahealth.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.

Forward Advantage is a proud sponsor of Healthcare Scene.

< + > Clinical AI Needs Version Control, Not Just Validation

The following is a guest article by Dr. Michael T. Neely

Healthcare AI is often governed as though validation were a one-time gate: test the system, approve it, deploy it, and move on. That model breaks down when the software changes.

AI tools can be updated far more frequently than traditional clinical technology. A vendor may replace an underlying model, modify a prompt or retrieval layer, change thresholds, retrain on new data, alter workflow logic, or release a new version without changing the product name clinicians see. When that happens, an organization can still have a “validated” system on paper while a materially different system is operating in practice.

The governance question is therefore not only, “Was this AI validated?” It is, “Which version was validated, which version is running now, what changed, and what evidence tells us the change is still safe for this use?”

Validation Without Version Identity is Incomplete

Healthcare organizations already understand version control in other high-stakes domains. Medication formularies, clinical protocols, cybersecurity patches, laboratory methods, and medical devices are not managed as timeless objects. Changes are documented because a change can alter performance, risk, or downstream workflow.

AI should be treated the same way.

The FDA’s final guidance on predetermined change control plans for AI-enabled device software functions offers a useful regulatory example. It asks manufacturers to describe planned modifications, the methods used to develop and validate those modifications, and the impact of those changes. Not every healthcare AI tool is an FDA-regulated medical device, but the operational principle travels well: change should be anticipated, documented, assessed, and tied to evidence.

Health systems need an internal version record that identifies, at minimum, the vendor and product, deployed model or build, material configuration, approved use, implementation date, validation evidence, and responsible owner. For generative AI, the record may also need to capture prompt templates, retrieval sources, orchestration logic, and other components that meaningfully shape output.

Material Changes Need a Trigger

Not every software update requires a full revalidation. A cosmetic interface change is different from replacing the model that drafts a clinical summary. The goal is not bureaucracy for its own sake; it is a defensible material-change process.

Organizations should define in advance which changes trigger review. Examples include a new model family, altered training or tuning, changes to decision thresholds, new data sources, expanded intended use, changes in clinical workflow, a new autonomous capability, or modifications that affect how outputs are presented to clinicians or patients.

Vendor contracts should support this process. A health system cannot govern a system it is not told has changed. Material-change notification should therefore be treated as an operational requirement, not simply a courtesy from the vendor.

Revalidation Should Match the Risk

Once a material change is identified, the next question is how much testing is enough. The answer should depend on what the system does and the consequences of error.

A documentation assistant may require testing for factual accuracy, omission, workflow fit, and clinician review burden. A predictive decision-support tool may require renewed performance assessment, subgroup analysis, calibration checks, or workflow-specific validation. A patient-facing system may also need testing for clarity, escalation behavior, unsafe advice, and whether human review remains available when needed.

The NIST AI Risk Management Framework is useful here because it treats risk management as a lifecycle activity rather than a single approval event. That lifecycle view is especially important for AI systems that can drift, be reconfigured, or change through vendor updates.

Keep the Ability to Stop and Roll Back

Version control is not meaningful if an organization cannot act when a problem appears.

Every consequential healthcare AI implementation should have a defined process for suspension, rollback, or isolation. Governance teams should know who can make that decision, what evidence triggers it, what version can be restored, and how affected users will be notified.

Incident review also depends on traceability. If a problematic output contributed to a patient-facing communication or clinical workflow, the organization should be able to reconstruct which system version produced it, what data or context were used, what the output was, and what human review occurred.

Without that record, post-incident analysis becomes guesswork.

Transparency Has to Survive the Update Cycle

ONC’s HTI-1 final rule established algorithm-transparency requirements for predictive decision-support interventions in certified health IT. That transparency is most useful when it stays current. Documentation that accurately described a model six months ago may be misleading after a material update.

Hospitals and health systems therefore need a simple rule: every meaningful AI change should force a question about whether prior documentation, validation, disclosures, training, and monitoring still describe the system that is actually in use.

That is the core discipline of AI version control.

Validation tells you that a particular system met a standard at a particular moment. Version control tells you whether that is still the system affecting care today. In healthcare, where software changes can influence clinical judgment, patient communication, workflow priority, and organizational accountability, that distinction is not administrative detail. It is part of safe AI operations.

About Dr. Michael T. Neely

Dr. Michael T. Neely is an independent researcher and AI educator whose work focuses on responsible AI, governance, accountability, patient protection, and human oversight. He is CEO of Global Music Conference and XS10 Magazine and brings more than four decades of leadership in the creative industries. His healthcare-AI commentary has appeared in KevinMD and Healthcare Business Today. His work examines how organizations translate responsible-AI principles into operational controls.