Readers Write: AI to the Rescue: Revolutionizing Efficiency in Healthcare Workflows

AI to the Rescue: Revolutionizing Efficiency in Healthcare Workflows
By Caleb Manscill

Caleb Manscill, MBA is president of Vyne Medical.

The healthcare industry is at a tipping point. With rising demands for high-quality care, increasing financial pressures, and widespread staffing shortages, healthcare providers face an uphill battle to maintain efficiency while meeting patient needs.

Operational bottlenecks and administrative burdens have long weighed down progress, but a game-changing shift is underway: the rise of AI and machine learning. These cutting-edge technologies are not just tools. They are a jumping point for innovation, set to change healthcare workflows, optimize decision-making, and deliver better care outcomes.

The Role of AI and Machine Learning

The adoption of automation technologies in clinical and administrative workflows is accelerating at a fast pace. By 2029, the global workflow automation market is projected to surge to $34 billion, up from $21 billion in 2024, reflecting the pivotal role these technologies play in healthcare transformation. At the heart of this revolution, AI and machine learning are taking on the most pressing inefficiencies, reshaping operations to unlock productivity, accuracy, and cost savings.

Impact on Administrative Workflows and Resource Optimization

AI-powered solutions address some of healthcare’s most persistent challenges by automating time-intensive administrative tasks, allowing staff to focus on higher-value activities. For example, data transcription, a necessary yet manual process, can now be completed in just 30 seconds with over 90% accuracy, compared to the five minutes it once required. These gains drastically reduce errors and boost productivity without sacrificing quality.

Though the front-end processes are critical to getting things right, they’re only half the story. Beyond administrative tasks, AI also optimizes documentation, scheduling, and claims processing to ensure that back-end operations run smoothly. By streamlining these processes, organizations can eliminate redundancies, reduce operational overhead, and achieve greater financial stability. AI further enables leaders to strategically allocate resources, improving patient flow and enhancing revenue cycle management. Together, these improvements drive measurable efficiency and cost-effectiveness.

Enhancing Decision-Making and Clinical Workflows

AI also enhances clinical workflows by enabling smarter, data-driven decision-making. Through advanced algorithms, AI analyzes patient data to identify patterns, predict outcomes, and recommend treatment options, supporting clinicians in providing more personalized care. Process automation helps streamline clinical workflows by reducing manual processes, allowing care teams to spend more time with patients and less on administrative tasks.

For example, AI can prioritize urgent tasks, reduce delays in patient care, and foster collaboration across departments. The impact of these efficiencies includes improved patient experiences, reduced clinician burnout, and better overall care delivery.

Take a surgery order workflow as an example. When a hospital system receives a surgery order, teams traditionally need to extract key details manually and link them to the patient’s electronic medical record (EMR). By using AI and machine learning, much of this process is now automated. AI extracts data from the order, indexes it to the appropriate patient record, and forwards it seamlessly to the EMR system.

However, the next step, leveraging Generative AI, takes this automation to a higher level. Gen AI can resolve more complex challenges, such as identifying and associating the correct patient record when multiple entries exist in the EMR. Traditionally, humans spend significant time verifying patient information, such as matching dates, MRNs, or account numbers, across hundreds of transactions daily. Gen AI can take over this decision-making process for straightforward cases, sending the data directly to the EMR.

By tackling inefficiencies, reducing administrative burdens, and empowering smarter decision-making, this technology is setting a new standard for operational excellence. As healthcare systems continue to navigate workforce pressures and resource limitations, the integration of AI is no longer a luxury — it is an urgent necessity. With its potential to streamline workflows, enhance outcomes, and drive sustainability, AI is the key to building a more resilient and efficient healthcare ecosystem.

Readers Write: CMS TEAM: What Hospitals Need to Know to Succeed

CMS TEAM: What Hospitals Need to Know to Succeed
By Mary Sirois

Mary Sirois. MBA is managing director of clinical transformation with Nordic

Healthcare reimbursement is undergoing a significant transformation, with the Centers for Medicare & Medicaid Services (CMS) spearheading a decisive shift towards value-based care, cost reductions, and confidence in care quality. At the forefront of this evolution is Transforming Episode Accountability Model (TEAM), a mandatory, episode-based, alternative payment program that is designed to improve the patient experience from surgery through recovery.

With the January 1, 2026 launch date quickly approaching, I strongly encourage healthcare leaders to prioritize understanding and proactively preparing for TEAM now. Without a strategic and well-executed plan that addresses topics such as EHR integration, clinical and operational workflows across the continuum of care, data infrastructure, change management, governance, and more, organizations risk compromised patient outcomes, competitive disadvantage, and financial instability.

Patient-centered care and financial sustainability: Unlocking TEAM’s potential

TEAM will advance the CMS Innovation Center’s prior work on episode-based alternative payment models, including the Bundled Payments for Care Improvement Advanced and Comprehensive Care for Joint Replacement Models. TEAM is designed to improve care coordination and outcomes for Medicare beneficiaries undergoing any of the following five episodes of care, which begin with the “event” (admission or surgery), extend throughout 30 days, and include both hospital and ambulatory care:

• Lower extremity joint replacement.
• Surgical hip femur fracture treatment.
• Spinal fusion.
• Coronary artery bypass graft.
• Major bowel procedure.

The assessment and payment structure under TEAM is based on a retrospective analysis of the total cost of care for each episode. CMS sets target prices based on historical data and benchmarks, and providers are accountable for managing costs within these targets. If the actual cost of an episode is below the target, providers may share in the savings.

Conversely, if costs exceed the target, providers may face financial penalties. This risk-sharing arrangement incentivizes providers to optimize care pathways, reduce unnecessary services, and improve patient outcomes. Key opportunities for healthcare organizations include:

• Leveraging Intersocietal Accreditation Commission data.
• Mitigating financial penalties.
• Aligning with ongoing population health/value-based care work.
• Improving care coordination across the continuum of care and partnerships.
• Reducing unnecessary readmissions.

Navigating CMS TEAM: Assessment, collaboration, monitoring, and strategic partnership

To effectively prepare for CMS TEAM and strive under the program, healthcare leaders should focus on three core areas:

1. Comprehensive assessment and playbook development. Begin with a thorough current state assessment, evaluating financial projections, risk stratification, care setting optimization, provider alignment, discharge planning, care coordination, outcomes management, quality measures, and model readiness. This assessment will inform the development of a strategic playbook, outlining specific strategies to improve performance and ensure compliance with TEAM requirements.

2. Strategic collaboration and technology integration. Foster collaborations with providers across the continuum of care (many of whom are not directly aligned to the healthcare system, such as post-acute, skilled nursing facilities, and home care) and payers. Evaluate and implement technology solutions that enhance data sharing and care coordination. Prioritize patient engagement and education, empowering them within the episode-based care model.

3. Continuous monitoring and adaptation. Establish a robust monitoring system, tracking performance against key indicators and implementing continuous quality improvement initiatives. Proactively adapt to evolving CMS guidelines and industry best practices. Create alerts for early identification of and response to care pathway deviations.

Given the complexities of TEAM and the critical need for urgency, hospitals and health system leaders can benefit from partnering with experienced, healthcare-focused consultants who can help identify potential challenges and areas for improvements. Through high-level performance reviews, strategic recommendations, and implementation considerations, partnership enables hospitals and health systems to take a strategic and clinically driven approach to TEAM compliance that harnesses the power of data and technology to enhance patient and clinician journeys and optimize performance.

CMS TEAM: Seizing this pivotal moment for healthcare excellence

As our industry stands on the cusp of the TEAM launch, I see this as a pivotal shift towards a more efficient, cost effective, data-driven, and patient-centered healthcare system. By embracing the principles of value-based care, taking proactive steps to prepare, and engaging in meaningful partnerships, healthcare leaders can ensure their organizations comply with TEAM requirements, deliver the highest quality care, and thrive in the evolving healthcare landscape.

Readers Write: Payment Cost and Confusion Continue to Frustrate Patients. Why Is Healthcare So Late to the Game?

Payment Cost and Confusion Continue to Frustrate Patients. Why Is Healthcare So Late to the Game?
By Tom Furr

Tom Furr is founder and CEO of PatientPay.

More than two years after a Kaiser Family Foundation survey found that 100 million American adults wrestle with medical debt, cost and affordability of care remain top concerns for consumers. Half of adults say that it’s difficult to afford care, a 2024 KFF poll found, and one in four have skipped or delayed care due to cost concerns.

Could 2025 be the year when the healthcare industry takes bigger, bolder steps toward easing these concerns by running a more automated and patient-friendly operation? In a year when medical costs are expected to rise about 8% and commercial healthcare spending could rise to its highest level in 13 years, according to a PwC analysis, one would argue that it should be.

According to a recent William Blair report, Consumer-Centric Healthcare: 2025 Update, US healthcare spending continues to outpace that of comparable countries with $12,555 in healthcare spending per person, “$4,000 greater than any other high-income nation, yet the nation falls behind most developed countries when it comes to health outcomes. And while this spending gap continues to widen, we’re not seeing better outcomes for the money spent.

“If federal health spending accounted for the same share of GDP that it did in 1973, the budget would be balanced,” the report states. “If it were the same as in 2000, the deficit would be 2.5% of GDP, less than both the 1946-2023 and 1962-2023 averages.” I would be shocked if this fact were not on DOGE’s radar, since Elon Musk was the first to ask why the government uses “cost-plus contracts” for military and space projects. I guess the space challenge between SpaceX and Boeing shows that having more capabilities with less expense ultimately wins the race.

Yet even as federal requirements for hospital price transparency continue to be put into play, the types of information patients want most — their out-of-pocket costs after insurance and their options for payment — remain challenging to determine at some organizations. It’s an area where digital tools that offer automation plus reduced cost for patient billing and collection to help reduce administrative expenses. One organization that currently devotes eight people to payment processing found that it could reduce manpower for this task to one person with an automated solution.

A New Era for the Patient Financial Experience

The proportion of self-pay patients has risen sharply since the end of Medicaid continuous enrollment, including for emergency visits among patients in all age groups. Meanwhile, as healthcare costs increase, employee pay raises have slowed. These are signs that healthcare organizations should reexamine their approach to automation, in particular for the patient financial billing and payment process.

A Deloitte survey of healthcare leaders suggests some organizations are poised to do so. Most leaders surveyed believe automation will help with cost and affordability for the healthcare industry this year, with 53% saying their organization will focus on improving the consumer experience, engagement, and trust while reducing the cost to achieve efficiencies and increase productivity.

To truly make an impact, patient financial services teams should look to automation to communicate financial responsibility, resources, and payment options in ways that meet patients where they are. This means sharing information in ways that can be easily understood regardless of a person’s education level or their native language. It also means making sure information is available in a variety of formats, including via mobile phone, given that 98% of American adults own a mobile phone. One company only allows patient payments to be set up after a call is made, even though most patients want to set up their payment online while reviewing their bill. Limiting patients’ options is a dissatisfier in an era of consumer-driven convenience.

Making the Right Connections to Ease Payment Concerns

In the quest to cure payment confusion and strengthen consumer trust, how can healthcare revenue cycle teams most effectively communicate financial information to patients? There are three things healthcare revenue cycle teams should consider.

1. Broadly communicate options for patient financial assistance.

This includes one-to-one conversations at the point of registration, via a widely publicized toll-free number, through posters and brochures in patient waiting rooms, on the provider’s website, and via secure text. It may also consist of discussions at the point of care, so long as the patient has been stabilized and consents. Discussions around financial assistance options should take place as early in the patient encounter as possible, according to guidance from the Healthcare Financial Management Association. It should also incorporate language the patient can readily understand, both verbally and in written form. Some organizations suggest that print and digital communications be written at a fifth-grade level and available in more than one language. When written communications are not available in the patient’s native language, seek a translator or translator service to ensure clarity.

2. Explore mechanisms for digital communication and payment.

Leading healthcare organizations leverage the device most consumers own, their mobile phone, to send payment notifications via secure text. It’s an option consumers gravitate toward: A 2024 J.P. Morgan survey reveals 75% of consumers want to pay their medical bills online. Yet 71% of healthcare providers most often collect payment from consumers via paper and manual processes, the survey found. “The trends reveal a deep disconnect between the healthcare industry and consumers,” according to the analysis.

Keys to successfully rolling out a text-to-pay model that collects more payments while reducing cost, such as the number of paper statements sent: Use patient payment behavior to determine which patients are most likely to respond to this approach. Give digital communications time to breathe, typically, one week, before following up. While some individuals will pay within minutes or hours of receiving a text notification, some may wait longer, although typically not more than a week.

3. Integrate EOBs with digital payment.

Providing access to the patient’s explanation of benefits (EOB) statement with their bill offers an opportunity to clear up questions around the out-of-pocket amount that is due from the start of the patient financial encounter. It gives patients a chance to review how much their insurance company has paid and how the amount due was calculated. By providing consumers a mechanism for verifying the amount that is due at the point of payment, this increases the likelihood of payment.

As healthcare leaders express a desire to strengthen the patient financial experience while also reducing their cost to accomplish better collection results, they should deploy a thoughtful approach to automation around financial communications and payment remittance before being pushed to do so by outside sources.

Readers Write: A Revenue Cycle Disruptor Perspective and the Future of Healthcare Finance

A Revenue Cycle Disruptor Perspective and the Future of Healthcare Finance
By Heather Dunn

Heather Dunn, MBA is chief revenue officer of Novant Health.

I recently moderated a panel discussion at the HFMA Western Symposium with an amazing group of healthcare leaders and “disruptors” in the revenue cycle industry. We talked about what it takes to innovate in a very regulated environment, how to break out of the mold in revenue cycle, and how to succeed while facing great internal constraints. The lessons that we shared from this conversation have shaped my thinking of what it means to be a disruptor in any setting.

The healthcare finance landscape is evolving rapidly, and innovation is at the heart of this transformation. From the introduction of AI-driven tools to the resurgence of RPA (robotic process automation) and the focus on predictive analytics that help reduce costs and make revenue cycles more efficient, we are witnessing a fundamental shift in how healthcare finance operates.

But innovation isn’t just about adopting new technologies; it’s about rethinking our challenges and reimagining what’s possible. We’ve seen industry disruptors challenge the status quo and bring forward new solutions that fundamentally change how we manage claims processing, denial prevention, and payment integrity.

Game-Changing Innovations

Healthcare finance has long been weighed down by inefficiencies, whether it’s cumbersome claims processes, endless back-and-forth with payers, or the sheer administrative burden of staying compliant. But recent innovations are flipping the script:

• AI-powered claims analysis. Custom machine-learning technology is helping hospitals and providers analyze medical claims and remittance data to pinpoint the root causes of denials and underpayments. Instead of playing defense, healthcare organizations can now predict and prevent revenue loss before it happens.
• Rethinking cybersecurity preparedness. With cybersecurity threats on the rise, new solutions are stepping in to ensure that financial operations remain uninterrupted even during an outage. Given how interconnected revenue cycle management is with IT infrastructure, having a fail-safe plan in place is no longer optional, it’s essential.
• National payer scorecard. Transparency has always been a challenge in healthcare finance. With the creation of a national payer scorecard, organizations can now access critical insights into payer performance, helping them make more informed financial decisions.
• Business partner relationships. These relationships can help health systems keep up with how the industry is changing. Health systems should challenge their business partners to bring them solutions that will make them more efficient and effective.

Lessons from the Trenches

As exciting as these innovations are, they don’t come without challenges. Healthcare is notoriously slow to adopt new technology, often for good reason. The complexity of regulations, interoperability hurdles, and the ever-present concern over cybersecurity risks mean that even the best ideas can face roadblocks.

• Regulatory hurdles. States are introducing laws to regulate AI in healthcare. For example, California recently passed landmark legislation prohibiting health insurance companies from using AI to deny coverage. While AI holds immense promise, organizations must tread carefully and ensure compliance with emerging state and federal policies.
• Cross-industry inspiration. Unlike industries such as retail or finance, healthcare has been slow to embrace automation. But we don’t have to reinvent the wheel. Looking at how other sectors have successfully leveraged AI and automation can provide valuable lessons in accelerating our adoption curve.
• Balancing AI’s promise with reality. AI isn’t a magic wand. It requires the right data, ongoing monitoring, and a human-in-the-loop approach to be truly effective. The real question isn’t can we use AI, but how should we use it in a way that’s ethical, effective, and sustainable?

Actionable Takeaways

What can healthcare finance professionals do today to future-proof their revenue cycle strategies?

• Start small, scale smart. If AI or automation seems overwhelming, begin with pilot projects that address your most pressing pain points, whether it’s reducing denials, improving payment integrity, or streamlining workflows.
• Stay informed on legislation. The AI regulatory landscape is shifting quickly. Keeping up with state and federal guidelines will be critical in ensuring compliance and mitigating risk.
• Invest in cybersecurity resilience. Cyber threats aren’t a matter of if, but when. Having a solid financial continuity plan in place is just as important as preventing breaches in the first place.
• Look beyond healthcare for inspiration. Retail, banking, and even logistics have mastered AI-driven efficiencies. What lessons can healthcare borrow to accelerate adoption without falling into common pitfalls?

The Future is Now

The revenue cycle is no longer just about processing claims and getting paid. It’s about leveraging technology to create a smarter, more resilient, and ultimately more efficient system. Health systems rarely challenge the status quo. There is just a lot happening in their world every day. They need help to think about how tech and the future can change their revenue cycles work. The disruptors in this space are showing us that innovation isn’t just about new tech; it’s about new ways of thinking.

The real question isn’t whether the revenue cycle will evolve. It’s whether we will lead that change or struggle to keep up.

In every organization I’ve served, I’ve taken the approach of being a disruptor who is willing to embrace change. As I make my own career transition back to a patient care delivery organization, I am energized by the opportunities to be a disruptor yet again, to innovate, and to make a difference in the lives of patients and employees.

Readers Write: Why Healthcare Providers Need AI That Thinks, Not Just Repeats

Why Healthcare Providers Need AI That Thinks, Not Just Repeats
By Jaideep Tandon

Jaideep Tandon, MS is CEO of Infinx.

For years, automation has been the go-to fix for revenue cycle inefficiencies. Healthcare providers rolled out robotic process automation (RPA) to handle tedious tasks like eligibility checks, claim submissions, and payment posting. It was a game-changer — until it wasn’t.

RPA works like a hyper-efficient intern. It’s great at following instructions, but completely lost when something unexpected happens. Need to reprocess a claim after a payer changes the rules? Sorry, that’s not in the bot’s programming.

With payer guidelines constantly shifting, denials on the rise, and administrative burdens growing, healthcare providers need more than automation. They need intelligence.

Why Traditional Automation Falls Short

RPA has its place, but it’s not built for the complexity of modern revenue cycle management (RCM). Its biggest weaknesses?

• Zero adaptability. If a payer updates claim submission requirements, RPA bots don’t adjust — they just fail.
• No contextual awareness. RPA doesn’t know why a claim was denied or what’s likely to happen next. It just moves data from one place to another.
• No learning curve. AI improves over time, but RPA remains frozen in time unless someone reprograms it.
• Can’t problem-solve. RPA won’t notice payer trends, optimize claim prioritization, or proactively prevent denials.

In short, RPA does what it’s told. AI figures out what needs to be done.

AI, the Next Step in Revenue Cycle Management

AI takes automation a step further. It doesn’t just complete tasks, it makes smarter decisions. Here’s how AI is reshaping revenue cycle management:

• Accurate patient demographics. Patient name, date of birth, and insurance details must be correct from the start to prevent denials. AI-powered document capture extracts and validates this data automatically, reducing errors and ensuring that claims are submitted with accurate information.
• Smarter prior authorizations. Prior auth delays are the worst. RPA can submit requests faster, but it can’t anticipate what payers need or adjust to shifting criteria. AI detects patterns, flags missing information in advance, and even suggests the best way to avoid follow-ups.
• AI-powered coding audits. Billing rules are a moving target. AI-driven audits ensure claims are coded correctly the first time, preventing costly denials and compliance issues.
• Intelligent A/R prioritization. Most revenue cycle teams treat all outstanding claims equally or assign rules arbitrarily, but not all claims have the same likelihood of getting paid. AI predicts which claims should be prioritized based on payer behavior, patient payment history, and contract terms, helping providers maximize revenue with less effort.
• Denial prevention: catch issues before they happen. Instead of reacting to denials, AI predicts them. By analyzing payer trends and historical data, AI can flag risky claims before submission, reducing rework and accelerating reimbursements.

What Healthcare IT Leaders Should Consider

AI is only as good as its implementation. Before rolling out AI-powered RCM, healthcare CIOs should focus on:

• Seamless integration. AI should complement, not replace, existing EHR and RCM systems.
• Meaningful success metrics. AI’s impact should be measured by claim accuracy, denial reductions, and A/R improvements, not just automation rates.
• AI + human collaboration. AI isn’t here to replace revenue cycle teams. It’s here to free them from repetitive tasks so they can focus on complex problem-solving.

Final Thought: The Future is AI (But Not the AI You’re Thinking Of)

Healthcare doesn’t need AI that just automates. It needs AI that thinks.

The future of revenue cycle management won’t be about simply working faster. It will be about working smarter. AI-powered decision-making will reshape how healthcare providers manage revenue, shifting from reactive firefighting to proactive optimization.

The question isn’t whether AI will transform RCM. The question is,: will you be ahead of the curve, or struggling to catch up?

Readers Write: From “Make It Work” to “It Actually Works”: App Rationalization as a Bridge to the Technologies of Tomorrow

From “Make It Work” to “It Actually Works”: App Rationalization as a Bridge to the Technologies of Tomorrow
By  Wes Gattis, RN

Wes Gattis, RN is director of health informatics solutions at Cordea Consulting.

Healthcare IT has long been a patchwork of legacy systems, quick fixes, and digital duct tape. Over time, hospitals and health systems accumulate an overwhelming number of applications, each added with the best intentions but rarely assessed holistically. The result? Bloated tech stacks, hidden security risks, and skyrocketing maintenance costs.

App rationalization isn’t just a cleanup exercise. It’s a strategic approach to aligning IT investments with healthcare organizations’ business and clinical goals. By evaluating, consolidating, and modernizing applications, CIOs can unlock efficiencies, enhance security, and redirect budgets toward innovation.

Why App Rationalization Matters in Healthcare

Hospitals and health systems often inherit an unwieldy IT environment through years of incremental purchases, mergers, and regulatory shifts. This creates significant challenges:

•  Excessive IT costs. Licensing, maintenance, and support costs add up quickly when hospitals run redundant or outdated applications.
• Cybersecurity risks. Legacy systems often lack modern security protocols, making them prime targets for ransomware and data breaches.
• Operational inefficiencies. Poorly integrated applications lead to fragmented workflows, duplicated efforts, and user frustration.
• Lack of interoperability. When systems can’t communicate, it hinders data sharing and coordinated patient care.
• Regulatory compliance risks. Outdated applications may not comply with evolving HIPAA, CMS, and ONC requirements.

App rationalization addresses these pain points by eliminating redundancy, improving system performance, and ensuring that T investments align with clinical and operational priorities.

Key Benefits of App Rationalization

Hospitals and health systems can realize several critical advantages through a five-step structured app rationalization effort:

• Cost savings. Reducing redundant applications lowers licensing fees, support costs, and infrastructure expenses.
• Improved performance. Optimized IT environments improve response times, uptime, and overall system reliability.
• Stronger security and compliance. Eliminating obsolete applications minimizes vulnerabilities and enhances regulatory adherence.
• Better user experience. Clinicians and administrative staff benefit from streamlined workflows, reducing frustration and inefficiencies.
• Scalability and innovation. Freeing up budget and IT resources allows organizations to invest in forward-looking initiatives such as AI, cloud computing, and population health analytics.

A Step-by-Step Guide to App Rationalization

A successful app rationalization effort follows a structured approach:

1. Inventory and categorize applications. Start by creating a comprehensive inventory of all applications used across the organization. Document key details such as application owner, user base, licensing costs, usage frequency, and integration dependencies.
2. Assess business and clinical value. Evaluate each application based on its contribution to clinical workflows, operational efficiency, and alignment with organizational goals. Rank applications using a simple framework. Keep high-value applications that are essential to operations. Replace outdated but necessary applications requiring upgrades. Consolidate redundant applications that can be merged. Retire obsolete applications that no longer provide value.
3. Analyze costs and security risks. Perform a total cost of ownership (TCO) analysis, factoring in licensing, maintenance, and infrastructure costs. Assess security risks that are associated with legacy applications, especially those that are no longer receiving vendor support.
4. Develop a future state architecture. Map out a streamlined IT environment that eliminates redundancies, enhances interoperability, and aligns with strategic objectives. Establish technology standards, cloud strategies, and integration frameworks.
5. Implement and optimize. Execute the rationalization plan in phases to minimize disruption. Prioritize applications that pose the highest security risks or yield the greatest cost savings. Continuously monitor system performance and user satisfaction.

Best Practices for App Rationalization Success

App rationalization best practices include:

• Engage key stakeholders early. Seek input from clinicians, administrators, and IT leaders to ensure that rationalization efforts support real-world workflows.
• Leverage data-driven decision-making. Use analytics to assess application utilization, costs, and user feedback.
• Prioritize interoperability. Ensure that remaining applications integrate seamlessly to support coordinated care and data exchange.
• Review regularly. Reassess the IT environment at least annually to prevent future system bloat and inefficiencies.

A Special Note About Organizational Change Management

Organizational change management (OCM) is often overlooked in an application rationalization effort, but its impact on the effort’s success can’t be overstated. A well-planned OCM strategy ensures that key stakeholders, from IT teams to clinicians and administrative staff, are engaged from the outset, understand the rationale behind changes, and receive necessary support throughout the transition.

Resistance to change is a major hurdle in any IT initiative, and proactive communication, training, and leadership alignment are essential to overcoming it. By embedding OCM practices early in the process, organizations can increase adoption, minimize disruptions, and maximize the benefits of their rationalization efforts.

Moving Forward: Beyond “Make It Work”

Healthcare IT can no longer afford to operate under the “just make it work” mentality. The shift toward value-based care, digital transformation, and patient-centric models requires IT environments that are lean, secure, and adaptable.

Through application rationalization, hospitals and health systems can shed unnecessary complexity, enhance security, and redirect valuable resources toward technologies that drive better patient outcomes. It’s time to build IT ecosystems that actually work.

Readers Write: Narrow Your Focus: Amplify Your Impact

Narrow Your Focus: Amplify Your Impact
By Steve Shihadeh

Steve Shihadeh is founder of Get-to-Market Health of Malvern, PA.

Recently, we have observed the healthcare technology market and evolving companies that are feeling pressure to be all things to all people by offering broad solutions and application suites. This product approach is tempting in today’s healthcare technology field as health systems lean toward buying from fewer vendors.

Based on recent trade shows and our read of the market, too many companies end up with a murky product strategy and struggle to land a differentiated message. Our clear recommendation is to:

Focus and then focus 10 times harder.

When you look at some of the biggest success stories in our field, they all started with a very narrow problem set and got great at solving it. Here are three relevant examples:

• Epic’s first application was built to address ambulatory clinic scheduling in large academic medical centers. It was tricky and complicated, but they mastered it and gained credibility with market leaders who then looked to Epic for more.
• Nvidia saw graphics-based processing as the best trajectory for tackling challenges that had eluded general-purpose computing methods. They were obsessed about how to make their graphics processing units increasingly powerful. Subsequently, Wall Street has anointed them with a massive market cap, and Nvidia has a significant order backlog.
• The Livongo team did not try to be awesome at 10 things. They focused squarely on helping employers manage a single disease, diabetes. The market rewarded them with a $18 billion buyout.

Where are you trying to excel? If you are looking to solve real healthcare challenges, you need to be completely dialed into your customers’ needs and issues. To use a cliché, what is really keeping your customers up at night and stressing their metrics? Deeply understand a narrow set of their pain points and work obsessively to make their business function better.

For example, be great at connecting payers and providers, or be the best at the back third of the rev cycle, or deliver amazing tools to help radiologists get the full patient care picture of the images they are viewing. Whatever you prioritize, first be fantastic at one important function that solves a key customer priority.

Being magnificent at one thing can solve key challenges, especially for emerging health tech companies:

• It helps employees, clients and investors get your “why.”
• Focusing intently on narrow greatness builds a natural moat that defends against competitors and protects your growing business
• It leads you in the right direction for key product investment decisions.
• It helps potential buyers secure funding for your solution by giving them superlatives by which to remember you.
• Concentrating in one area allows you to demonstrate a straightforward ROI.
• It makes you an attractive partner for other healthcare tech solutions, as you may not be able to go it alone forever.
• It enables investors to clearly see your path to profits.

Once you are excellent at one thing, options multiply for your company. Become the brand that KLAS and others praise. Then build adjacent apps that amplify your presence and/or make you attractive to potential buyers.

With some buyers looking for suite solutions, our advice might seem contrarian. However, most great companies buck the trend to break through the noise, and there is a lot of noise in the health tech landscape. It ranges from claims of “AI-everywhere” to changing regulatory impacts from the new administration. Our current environment is extremely confusing and distracting to healthcare buyers, and it will take your focus, obsession, and amazing solutions to stand out in the crowd.

Give important stakeholders a reason to understand what you do by going for narrow greatness that will drive dividends down the road.

Readers Write: Unlocking Hidden Gems: Leveraging RCMTAM for Revenue Cycle Excellence

Unlocking Hidden Gems: Leveraging RCMTAM for Revenue Cycle Excellence
By Kim Waters

Kim Waters, MBA is principal consultant, advisory services with CereCore.

Revenue cycle management (RCM) has become more challenging than ever. With increasing denials, evolving payer policies, and growing patient financial responsibility, healthcare organizations need innovative solutions to optimize their revenue cycles. Enter the Revenue Cycle Management Technology Adoption Model (RCMTAM), a game-changing framework that’s revolutionizing how we approach RCM technology.

As a leader in healthcare IT solutions, I’ve seen first hand how RCMTAM can transform revenue cycle operations. Let’s dive into the hidden gems this model offers and explore how it can drive operational efficiency, improve financial outcomes, and uncover cost-saving opportunities.

The RCMTAM Advantage: A Data-Driven Approach to RCM Excellence

RCMTAM is more than just another acronym in the healthcare alphabet soup. It’s a peer-reviewed, five-stage framework endorsed by Healthcare Financial Management Association (HFMA) that assesses operational performance and the maturity of revenue cycle technology within healthcare organizations. What sets RCMTAM apart is its data-driven approach, linking technology adoption with financial outcomes to create customizable roadmaps for RCM optimization.

Key features of RCMTAM include:

• A five-stage model for assessing RCM technology maturity.
• Financial benchmarks for performance comparison.
• Correlation of RCM technology adoption to financial performance.
• Personalized organizational roadmaps.

Uncovering Hidden Gems: RCMTAM in Action

Let’s explore some specific examples of how RCMTAM can enhance operational efficiency and improve financial outcomes:

• Streamlining eligibility checks. Experian’s recent state of claims survey revealed that 43% of respondents spend 10 to 20+ minutes on secondary eligibility checks. By leveraging RCMTAM to assess and optimize eligibility verification technology, organizations can significantly reduce this time, improving staff productivity and accelerating the revenue cycle.
• Tackling denials head-on. With 38% of respondents reporting claim denial rates of 10% or higher, denials management is a critical area for improvement. RCMTAM can help organizations identify and implement advanced denial prevention and management technologies, potentially saving millions in denied claims.
• Automating manual processes. Nearly 50% of providers still review denials manually. RCMTAM can guide the adoption of AI and automation technologies in the denial management process, freeing up staff for more complex tasks and reducing errors.
• Enhancing data accuracy. Bad data is a leading cause of denials, with 46% of respondents citing missing or inaccurate data as a top reason. RCMTAM can help organizations assess and improve their data management technologies, reducing errors and improving clean claim rates.

The Path Forward: Embracing RCMTAM for Continuous Improvement

As the 2024 State of Claims survey shows, the healthcare landscape is constantly evolving. Payer policy changes are occurring with more frequency (77% agree), and 66% of respondents find submitting “clean” claims more challenging now than before the pandemic. In this dynamic environment, RCMTAM provides a structured approach to continuous improvement.

By regularly reassessing your organization’s position on the RCMTAM scale, you can:

• Identify emerging technologies that address your specific pain points.
• Benchmark your performance against industry leaders.
• Create data-driven strategies for ongoing RCM optimization,

Navigating the complexities of revenue cycle management can be tough. An RCMTAM assessment can provide the performance readout that your organization may need to help uncover the hidden gems in your revenue cycle so you can drive meaningful improvements in your financial performance.

As you embark on your RCMTAM journey, remember that technology is just one piece of the puzzle. Success lies in the seamless integration of people, processes, and technology. By taking a holistic approach and leveraging the insights provided by RCMTAM, you can transform your revenue cycle from a source of frustration to a driver of organizational success.

Readers Write: ViVE 2025 Recap

ViVE 2025 Nashville – The Tale of AI and the Snowpocalypse!
By Mike Silverstein

Mike Silverstein is managing partner of healthcare IT and life sciences at Direct Recruiters, Inc.

Tell me about your agents! No, not one from the CIA, FBI, or your record label. Tell me about your AI agents!

That was the theme of ViVE 2025 in Nashville. Music City was frigid outside, but inside the Nashville Convention Center, the agentic AI blaze was on. 

Two weeks ago, I gave up and finally asked a client on a Zoom call, ”Can you tell me what the heck agentic AI is?” My head is still spinning from large language models (LLMs), GPTs, and generative AI (still not entirely sure what this is). Now if you don’t have AI agents, you are an HIT artifact.

Big tech is coming to healthcare and this time I think it is REALLY here to stay. With the labor crunch and margin pressures being ubiquitous and in full force, an AI agent that can make a phone call to a patient / member / health plan and converse like a real person with whatever tone and accent you prefer might just be a game changer.

The tools are really getting smart, borderline scary smart. I had several people demo products for me that I literally couldn’t tell with certainty if I was listening to a real person or an AI agent. Even more, these agents are being trained on serious healthcare data and workflows, and I believe the dominoes are going to start falling.

At HLTH 2024 in Las Vegas, AI co-pilots were all the rage. Now those firms are racing to stay up the value stream and provide even higher value clinical impact as AI note-taking feels like it will be table stakes very soon. Call centers and one-way SMS texts both seem like they could be on the chopping block, and it feels like we could be on the precipice of patients actually having a real consumer experience like in every other industry.

On the investment front, deal activity seems to be up. The firms I talked to are living less hand to mouth (as opposed to only focused on sales in the next six months) and are back investing in products and technology out of fear of being left in the dust. However, what stood out as interesting to me is that access to this cutting-edge technology seems to be far wider than various technological breakthroughs of the past.

Most of the vendors I talked to are layering their healthcare workflows on top of off-the shelf AI agent tools and platforms, so the speed-to-market has been pretty blinding. New healthcare tools are being developed using publicly available foundational tech and low code development. ViVE 2025 felt a bit like healthcare’s version of a big-time arms race. Everyone is working diligently to stay out in front of, or totally away from, Epic’s roadmap. It feels like this could be the start of a major leap in how we experience healthcare as consumers.

Unfortunately, the other major thing I learned this week is that Nashville is woefully unprepared for snowstorms. At the end of every conversation I had in the past 48 hours, everyone told me, “You had better move up your flight. It’s going to snow on Wednesday and this whole place is going to shut down.” As a result, there was a mass exodus from Nashville Tuesday evening, and the airlines cashed in big on change fees.

Even so, Music City was a great host, and 2025 feels like it’s going to be a pivotal year in the adoption of AI in healthcare.

Readers Write: Solving Healthcare’s $125 Billion Fax Problem

Solving Healthcare’s $125 Billion Fax Problem
By Thomas Thatapudi

Thomas Thatapudi, MBA is CIO of AGS Health.

In the six years since Centers for Medicare and Medicaid Services called for an end to faxing in healthcare, the industry remains stubbornly attached to fax communications. Fax-led communications solutions are prevalent today, as organizations continue exchanging more than 9 billion fax pages each year, representing about $125 billion in annual costs to the healthcare system.

It is a problematic practice in terms of data integrity, productivity, and efficiency. According to information compiled by DirectTrust, 30% of tests must be re-ordered due to lost faxes and 25% of faxes fail to arrive before a patient’s first visit. Faxes also often require manual indexing for integration into the EHR and other health information systems, a time-consuming process that requires human and financial resources that are hard to come by for many healthcare organizations.

Luckily, fax indexing lends itself to automation. In particular, generative AI (GenAI) and agentic AI excel at automating mundane and repetitive tasks. However, it is unrealistic to expect digital workers, such as AI agents that mimic human actions, to accurately index 100% of the faxes from the outset. Thus the best outcomes are realized when digital workers are paired with human counterparts who manage exceptions and handle specialized information and requests, at least until the digital workers have accrued enough on-the-job training via machine learning and deep learning models to take over higher complexity tasks.

Implementing a hybrid fax indexing model accelerates processing and eliminates the care delays that are caused by improperly managed faxes. It also alleviates the strain on increasingly scarce resources, reducing costs and freeing internal staff to focus on higher-value tasks.

However, achieving these outcomes requires careful orchestration of a workflow that seamlessly integrates digital workers (AI agents) and their human counterparts delivering on quality, timeliness, and accuracy.

Designing the Digital Workforce

The heart of a successful hybrid fax indexing strategy is a well-designed digital workflow model that helps orchestrate workflows between humans and digital workers. It starts with mapping the necessary technologies, a step that is best informed by shadowing human indexers to fully understand the process and map any unique needs. This information is also used to plan the implementation and conduct feasibility testing.

Like their human counterparts, digital workers are armed with an array of intelligence and automation tools, including optical character recognition (OCR), to analyze faxed documents and convert them into machine-readable text. They use natural language processing (NLP) models to interpret and manipulate the data contained within. GenAI is then leveraged to classify faxes based on the sender’s documentation format, determine its confidence threshold, and either index it into a documentation management system or EHR or divert it to the validation workflow for manual processing.

Machine learning allows digital workers to adapt to new document formats and categorize data according to providers’ templates and styles. Further, each processed fax enhances accuracy, efficiency, and capabilities while reducing exceptions.

Monitoring effectiveness is crucial to success. Establish clear KPIs, such as the volume of faxes indexed per day, indexing accuracy, turnaround times, and productivity levels to assess progress over time.

AI Grounded in Reality

While automated fax indexing is a relatively new entry into the burgeoning field of healthcare AI, it is quickly making an impact. One health system’s implementation of automated fax indexing has put it on track to save approximately $2 million in annual expenses. Automation has reduced the number of manual indexers that are required to process the health system’s fax volume, which allows key team members to focus on higher-value tasks while achieving a near-perfect accuracy rate and 24-hour turnaround time. As digital workers “learn” over time, the automation rate will increase, while the need for human intervention decreases, adding to the anticipated cost savings.

While it is unlikely that we will see a fax-free healthcare system in the near-term future, leveraging readily available automation and AI tools makes it possible to digitize the process and alleviate its associated cost, productivity, and patient safety burdens.

Automated fax indexing is yet another example of a thoughtful AI application that solves an age-old problem that, until now, has been stubbornly resistant to change.

Readers Write: Are Your Patient Access Metrics Ready for Healthcare Consolidation?

Are Your Patient Access Metrics Ready for Healthcare Consolidation?
By Emily Tyson

Emily Tyson, MBA is COO of Relatient.

The healthcare industry witnessed a surge in merger and acquisition (M&A) activity in 2024, a trend that is expected to gain momentum in 2025. While organizational growth can unlock new potential for patient care and financial performance, it often brings operational inefficiencies that, if left unchecked, can strain staff, frustrate patients, and hinder sustainable success.

One of the biggest hurdles is managing the influx of data from fragmented sources. Adding new providers and locations introduces complexity, making it difficult to maintain consistency in scheduling, patient flow, and care delivery. Without the right tools to consolidate and standardize this data, organizations risk creating bottlenecks that impact the patient experience, disrupt operations, and negatively affect financial outcomes.

To ensure sustainable growth, healthcare organizations need proactive, data-driven strategies that are focused on four key aspects of patient access — provider scheduling performance, call center efficiency, patient experience, and financial outcomes — to not only identify inefficiencies, but also address them before they escalate.

Provider scheduling performance

During periods of growth, whether through acquisition or organic expansion, the last thing healthcare organizations need are scheduling disruptions. Scheduling is a cornerstone of operational success, directly impacting patient acquisition, physician satisfaction, and financial performance.

Accurate data insight is key to identifying gaps and uncovering opportunities for improvement. For example, high patient no-show rates might signal ineffective communication about appointment details, leading to missed visits. Another critical metric is appointment wait time, as patients overwhelmingly identified this as a top frustration in a 2024 survey.

Other essential metrics such as provider utilization rates, rule optimization, and scheduling accuracy reveal how effectively an organization accommodates provider preferences while balancing patient demand. Monitoring these indicators helps prevent overbooking, supports efficient patient throughput, and ensures that providers’ schedules are sustainable.

Metrics like the third next available appointment and waitlist conversion further illuminate the balance between patient access and appointment availability. These insights empower organizations to fill open slots more efficiently, expanding patient access while maximizing financial performance.

Contact center efficiency

During periods of growth and M&A activity, ensuring a seamless transition at the first touchpoint of the patient journey, the contact center, is critical. As new organizations integrate, call volumes naturally increase and require careful management to maintain care quality and consistency.

Proactively measuring and analyzing key performance indicators (KPIs) for both efficiency and patient satisfaction allows healthcare organizations to identify potential gaps. For instance, tracking peak call times allows administrators to allocate resources strategically, ensuring that patients receive timely support during high demand periods. The right metrics provide insights to reduce bottlenecks, streamline scheduling, and lower operational costs. Other KPIs like appointment booking efficiency, minutes per call, and staff training time offer a comprehensive view of contact center performance and areas for improvement.

Patient experience

Today’s healthcare consumers demand convenience, and if scheduling processes are complicated or time-consuming, particularly during periods of growth or consolidation, patients are likely to seek care elsewhere. To meet these expectations and enhance both patient experience and contact center efficiency, organizations must monitor patient experience metrics that reflect engagement and satisfaction.

Given that research consistently shows that patients prefer self-scheduling options, empowering patients to take more control of the scheduling process can lead to better outcomes for both patients and staff.

With the right systems in place, providers can track self-scheduling rates and after-hours scheduling activity to gain deeper insights into patient behavior. Other essential patient experience metrics include patient acquisition, referral conversation rates, and appointment abandonment rates, all of which provide a clearer picture of how well the organization is meeting patient needs.

Financial outcomes

Effective scheduling and data management play a vital role in driving financial performance. During periods of rapid growth, healthcare organizations should closely monitor key financial metrics that are tied to scheduling and payment processes. Doing so enables them to identify opportunities to streamline operations, reduce costs, and maximize revenue.

Crucial metrics that provide insight into a practice’s financial health include payment collection percentages, reductions in accounts receivable, balances collected through payment plans, and the speed of patient balance collection. By tracking these indicators, organizations can pinpoint inefficiencies, implement targeted improvements, and ensure financial stability during times of expansion.

Setting the state for growth

Tracking the right metrics enables teams to address inefficiencies and achieve sustainable growth by focusing on four critical areas:

• Optimizing scheduling workflows. Track scheduling KPIs like no-show rates, wait times, and provider utilization to balance patient demand and provider efficiency.
• Boost contact center performance. Analyze KPIs such as peak call times, appointment booking efficiency, and staff training to improve operations and care quality.
• Enhance patient engagement. Offer self-scheduling options, track patient satisfaction metrics, and resolve issues like appointment abandonment to align with consumer expectations.
• Improve financial health. Focus on payment collection rates, accounts receivable reductions, and patient payment speed to drive revenue stability.

Intelligent patient access tools such as multi-channel appointment scheduling, provider preference management, and automated appointment reminders allow organizations to accurately track the most relevant KPIs, enhancing operations and improving the patient experience. Combined with robust analytics, these tools enable data-driven strategies, optimize performance, and support sustainable growth.

Readers Write: HIPAA Security Rule Update: Why Should Every Practice and Hospital Have to Build Their Own Defenses?

HIPAA Security Rule Update: Why Should Every Practice and Hospital Have to Build Their Own Defenses?
By Joseph Schneider, MD, MBA

Joseph Schneider, MD, MBA is with the University of Texas Southwestern.

I read Jason Ward’s excellent Readers Write and agree that it’s important to have clear best practices for security and that the Security Rule needs an update. However, I strongly disagree that a “one size fits all” approach like the proposed HIPAA Security Rule update is the right approach. The impact on smaller physician practices is tremendous and is not commensurate with the “probability and criticality of potential risks to ePHI” as required by the wording of the original HIPAA legislation.

Here are some comments on the proposed rule that are being sent to HHS:

• HHS states (on page 1004 of the Federal Register) “if the proposed changes in the NPRM reduce the number of affected individuals by 7 to 16 percent, the rule would pay for itself. Alternatively, the same cost savings may be achieved by lowering the cost per affected individual’s ePHI by 7 percent ($35) and 16 percent ($82), respectively.” Logically, the changes should reduce breaches or reduce costs, but there’s no evidence cited (that I could find) that supports this level of improvement. How much will this impact things? We don’t know, and it’s going to cost a huge amount to find out. First-year costs are ~$9 billion, with years 2-5 costing $6 billion annually. The present value of the costs, if I’m reading it correctly, is $32 billion, which coincidentally was just a little less than the original estimated cost of the Meaningful Use program. 
• On page 1007, HHS estimates that “the cost for a one-establishment [location] firm is $1,235 …” That’s absolute nonsense. The cost of doing all these things could be WAY more than $1,235 per practice. The averaging methodology used to come up with this number is flawed. A detailed cost analysis by requirement should be done and published for review. 
• HHS goes on to say, “In the context of the RFA, HHS generally considers an economic impact exceeding 3 percent of annual revenue to be significant…” Three percent of revenue spent on this activity alone is enough to put some small practices and possibly some rural hospitals out of business. No practice has 3% of revenues just sitting around. 
• Finally, HHS says that “In the context of the RFA, HHS generally considers an economic impact exceeding … 5 percent or more of the affected small entities within an identified industry to represent a substantial number.” This is ridiculous and arbitrary. In essence, HHS is saying that it’s OK for up to 4.99% of small practices to be bankrupted or badly damaged. Additionally, it’s easy to say that practices can go out of business, and it’s not significant, but it ignores the impact on the community when the only practice in town or for miles may close. 

Two additional global thoughts:

This is a gross over-expansion of the original HIPAA legislation wording. As noted on the HHS website: “The Security Rule does not dictate the specific security measures that a regulated entity must use. Instead, it requires the regulated entity to consider the following factors when selecting security measures that meet the Security Rule’s requirements: 1) its size, complexity, and capabilities; 2) its technical infrastructure, hardware, and software security capabilities; 3) the costs of security measures; 4) the probability and criticality of potential risks to ePHI.” By defining specific measures that MUST be taken, HHS is going far beyond what the law says. If the proposed changes are put into place, I expect that they will be challenged based on the Lopez Bright Enterprise v. Raimondo decision that overturned the Chevron doctrine.

Most importantly, the approach that we’re taking regarding security protection requires each organization to do everything. That stems from our culture of individualism. A better alternative would be if we had national approaches to at least some elements of this national problem. 

Here are two examples. Instead of every hospital and practice having to develop its own training, why not have a single national training and re-education program that clinicians and staff need to complete just once? And instead of having every small practice / rural hospital bear the costs of developing their own security plans and pay for audits, why not have the equivalent of the Agricultural Extension Offices provide these? It would cost less than having everyone do everything. Security is a national priority and we need to start thinking about national solutions, just as we do with national defense.  

I’m not suggesting that we keep the status quo of security, but we have to have different thinking about how to approach this so that we don’t damage or destroy small practices and rural hospitals. And while I don’t agree with a lot that’s going on in Washington right now, if this proposed rule died in the transition, I wouldn’t be crying too hard.

Readers Write: HIPAA Security Rule Update: Why Basic Compliance Isn’t Enough

HIPAA Security Rule Update: Why Basic Compliance Isn’t Enough
By Jason Ward

Jason Ward is VP of IT and tech support at Collette Health.

The healthcare sector has become an increasingly attractive target for cybercriminals, with attacks growing in both frequency and sophistication. The scale of healthcare data breaches nearly tripled in 2023, with 140 million individuals affected compared to 51 million in 2022, highlighting the rapidly growing threat to patient privacy. 

In response to these escalating threats, the US Department of Health and Human Services (HHS) has proposed the first major update to the HIPAA Security Rule since 2013. This update reflects a growing recognition that current security measures are insufficient to protect modern healthcare systems. 

While these proposed changes represent a significant step forward, they should be viewed as minimum requirements rather than comprehensive security solutions. In today’s healthcare environment, where increasingly interconnected systems create multiple attack vectors and expand the potential attack surface, organizations need to think beyond basic compliance.

The current security landscape demands a more proactive and robust approach. Many of the proposed requirements — such as annual audits, basic encryption, and standard access controls — are practices that security-conscious organizations have already implemented, and in many cases, exceeded. As we examine these updates, it’s crucial to understand that they represent a foundation upon which to build more comprehensive security measures.

Key Changes and Why They Matter

• Mandatory security documentation and regular auditing. Previously optional security measures will now become mandatory, with few exceptions. Organizations must document all security policies and procedures. Annual compliance audits will be required to verify adherence to these requirements.
• Enhanced asset management and network visibility. Organizations must maintain and regularly update a technology asset inventory and network map. These must be reviewed at least annually and updated whenever there are changes that might affect protected health information.
• Strengthened access controls and authentication. Multi-factor authentication becomes mandatory for accessing systems containing protected health information. Organizations must notify relevant parties within 24 hours when workforce access changes or is terminated.
• Robust incident response and recovery. Organizations must establish documented incident response plans and procedures. Systems and data must be restorable within 72 hours, with clear procedures for reporting and responding to security incidents.
• Comprehensive technical controls. Organizations must implement encryption for data at rest and in transit, deploy anti-malware protection, establish network segmentation, and conduct vulnerability scanning every six months. Penetration testing must be performed annually.
• Enhanced business associate accountability. Business associates must verify their compliance annually through a written analysis by a subject matter expert. They must notify covered entities within 24 hours of activating contingency plans.

Beyond Compliance: Adopting a Shared Security Model

While these updates represent significant progress, healthcare organizations must recognize that meeting compliance requirements alone doesn’t ensure adequate security. True cybersecurity in healthcare requires a shared security model where:

• Everyone plays a role. Security isn’t just an IT problem. It requires active participation from every department and role within the organization. From clinical staff to administrative personnel, everyone must understand their part in protecting patient data.
• Continuous evolution. Cyber threats evolve faster than regulations. Organizations must stay ahead by continuously updating their security measures and adapting to new threats, rather than waiting for regulatory requirements to catch up.
• Cultural transformation. Building a security-first culture means making security considerations part of every decision and process. This includes fostering open communication about security concerns, celebrating security-conscious behaviors, and ensuring that security is viewed as an enabler rather than a barrier to healthcare delivery.

We’re only as secure as our weakest link. By working together and viewing these new requirements as a starting point rather than an end goal, we can build a stronger, more resilient healthcare security ecosystem that truly protects patient data and maintains trust in our healthcare system.

Readers Write: Improving the Healthcare System with Advancements in Data Science and AI

Improving the Healthcare System with Advancements in Data Science and AI
By Hugh Cassidy

Hugh Cassidy, PhD, MBA is head of artificial intelligence and chief data scientist at LeanTaaS.

Healthcare has historically been slow to adopt modern technologies, but recent advances in AI have propelled it into the mainstream, allowing AI to be confidently used in critical systems like healthcare. These advancements, in both computational power and sophisticated algorithms, have made AI not only more popular, but also more reliable for complex, high-stakes environments.

According to a recent survey from the Berkeley Research Group, 75% of healthcare professionals anticipate that AI technologies will be widespread throughout the industry within the next three years. This optimistic outlook highlights the potential of AI to transform healthcare operations and to keep pushing forward with advancements in data-informed technology and predictive healthcare solutions.

However, healthcare’s history of slow technology adoption emphasizes the need for a strategic approach. Without a clear implementation plan, organizations may fail to harness the full potential of AI to improve operational efficiency and outcomes and to meet broader organizational goals.

To fully appreciate AI’s transformative potential, it’s essential to delve into its specific applications across various healthcare challenges.

One of the most pressing issues in healthcare is excessive patient wait times. Many of us have experienced long hospital delays, and the ongoing staffing crisis coupled with rising patient volumes has only made the situation worse. AI can play a pivotal role in streamlining patient flow, helping ensure timely care while reducing operational inefficiencies.

Predictive analytics can sift through historical data to forecast patient inflow, going beyond current conditions to anticipate future trends. However, predictive insights alone aren’t enough. Prescriptive systems are essential to translate those forecasts into actionable schedules. By combining AI-driven predictions with prescriptive analytics, healthcare facilities can generate optimized schedules that not only forecast patient demand, but also suggest the best staffing and resource allocation to handle peak hours. These prescriptive systems are necessary to minimize bottlenecks, reduce wait times, and ultimately enhance the overall patient experience.

Another pain point that healthcare professionals face daily is an overwhelming number of administrative tasks, which inundates staff members and ultimately detracts from patient care. Staff members often work overtime to give patients the best care, yet still have mountains of paperwork to complete once their assigned shift is complete.

AI can make a major impact and alleviate this burden through automating routine tasks such as data entry and billing. Optical character recognition (OCR) and natural language processing (NLP) tools can read and organize clinical notes, reducing the time that doctors and nurses spend on paperwork. AI-powered conversational assistants can handle common patient inquiries and triage less-critical cases, freeing medical staff to focus on more complex and urgent patient needs. By using AI to their advantage, healthcare teams can streamline processes like appointment scheduling and build schedules that are tailored to each facility’s unique demand and capacity.

By streamlining administrative tasks and automating certain aspects of patient care, AI can contribute to increased efficiency in the healthcare system, leading to cost savings and better resource allocation. Tools such as automated billing systems reduce errors and streamline the billing process, reducing administrative overhead. Scheduling tools fill unused time and unlock the full potential of the OR and infusion centers. All of this helps create more revenue and lower costs for health systems and patients alike.

One of the best ways health systems can reduce costs is by accurately allocating their resources and serving their communities by providing consistent and timely access to care for every patient who is in need. This not only improves patient outcomes, but also drives higher revenue and keeps costs low.

The potential of AI and data science to revolutionize healthcare is immense, but it requires a thoughtful and strategic approach to implementation. Health systems should work towards overall workforce adaptation and train and prepare hospital staff to effectively work with AI tools. This will likely require changes to existing education and training programs, as well as require ongoing support to ensure the integration of AI-driven tools into everyday workflows, but it will also help shorten patient wait times, ensure that patients are getting better care, and guarantee that healthcare workers aren’t overworked.

Considering AI’s immense popularity these days, hospitals should capitalize on staff members’ excitement about new tools. The future of healthcare lies in the intelligent use of data and AI, and these technologies are already helping many healthcare systems overcome current limitations and deliver superior care. Along with better patient care, hospitals are also maximizing revenue and improving overall hospital operations, leading to happier staff and hospitals that are more capable at handling growing patient volumes.

Readers Write: Social Care Data: The Key to Unlocking Community Health

Social Care Data: The Key to Unlocking Community Health
By Carla Nelson

Carla Nelson, MBA is senior director of healthcare policy at Findhelp.

Rising healthcare costs in the US demand innovative solutions, and social care data is emerging as a critical tool for driving informed decisions and improving community health. Policies that are promoting high-value care and funding for social services like transportation and medically-tailored meals show promise but face significant hurdles, including a lack of standardized data. Without a clear picture of community needs and resources, decision-makers struggle to optimize investments and implement effective strategies.

Social care data – such as health-related social needs (HRSNs), referrals, services received, and program availability — fills critical gaps in understanding community health. Technology can play a pivotal role in collecting, analyzing, and sharing this data, enabling its integration with datasets like healthcare claims, Medicaid member files, and public data sources such as Census data or CDC indices. These combined datasets provide actionable insights, empowering organizations to identify unmet needs, allocate resources efficiently, and improve service delivery. By integrating social care data with healthcare and other datasets, technology can enable more effective policies, investments, and service delivery strategies.

Analyzing patterns in social care searches or service usage can uncover gaps in available programs. For example, if a region shows high demand for food assistance but limited service availability, this insight can guide resource allocation and program expansion. Similarly, aggregated data on social care needs can help measure the capacity of community organizations and inform targeted investments.

As social care systems become increasingly digitized, ensuring the privacy of sensitive data is essential. Unlike healthcare data, which is protected by HIPAA, social care data lacks comparable safeguards. Organizations and governments must prioritize stringent privacy measures, ensure consent-driven data collection, and adopt policies to protect individuals’ sensitive information as they seek assistance.

To harness the potential of social care data, readers can take these key steps:

1. Invest in Data Infrastructure

• Advocate for and allocate funding to modernize data collection and sharing systems.
• Support community organizations in adopting technology that enables real-time data sharing and analytics.

2. Promote Cross-Sector Collaboration

• Build partnerships between healthcare providers, community organizations, and government agencies to share data and insights.
• Facilitate the integration of social care data with other datasets to create a comprehensive view of community needs.

3. Advance Data Standardization

• Participate in initiatives to develop and adopt standardized formats for social care data to enable consistent use and sharing.

4. Prioritize Privacy and Consent

• Implement robust privacy policies and ensure individuals provide informed consent for the use of their data.
• Stay informed about evolving regulations to protect sensitive information.

5. Leverage Data for Decision-Making

• Use data to identify gaps in resources, track outcomes, and guide investments in social care programs.
• Share insights with policymakers to advocate for targeted interventions and funding.

6. Educate Your Community

• Raise awareness of the importance of social care data among stakeholders, emphasizing its role in improving community health.
• Provide training on how to use data tools and analytics for effective decision-making.

Advancing the infrastructure for social care data is essential to make informed policy and investment decisions. Challenges remain, including limited technological capacity for many community organizations and early-stage standardization of social care data. However, progress is underway. States and organizations are leveraging new technologies to integrate health and social care, building seamless referral systems, and creating platforms for effective data sharing.

As social care data capabilities mature, they will unlock new opportunities to understand and address community needs, leading to more effective policies, smarter resource allocation, and improved health outcomes. Investments in data systems and technology today are paving the way for a healthier, more equitable future for all.

Readers Write: AI Meets the Front Lines: The Contact Center of the Future

AI Meets the Front Lines: The Contact Center of the Future
By Bill Smith

Bill Smith is director of Epic practice at Cordea Consulting.

Hospitals are always striving to deliver a better patient experience. Unfortunately for many health systems, the front line of patient interactions, the contact center, is often the weakest link in the chain. Burned-out agents, lengthy hold times, and frustrated patients are the norm.

What if AI and the cloud could turn the tide? What if health systems could reduce call volumes, capture valuable patient insights, and drive down operational costs by using AI-powered contact centers?

Practical, cloud-based AI tools are ready to make life easier for agents, patients, and healthcare execs alike. This is the low-hanging fruit of AI in healthcare, delivering results today while paving the way for tomorrow’s tech innovations. As it turns out, AI-powered contact centers are the low-risk, high-reward solution health systems need right now.

Every day, hospitals handle countless calls: appointment scheduling, prescription refills, billing questions, you name it. Patients expect quick, accurate, empathetic responses, but most contact center agents are working with outdated tools, incomplete patient data, and scattered knowledge bases.

Throw in staffing shortages and fluctuating call volumes and it’s no wonder long wait times and unresolved issues are the norm. Today’s patients also expect to connect through multiple channels — phone, chat, email — but many hospitals just don’t have the infrastructure to keep up. And those legacy systems? They’re buckling under the weight of modern demands.

Now for the good news.  AI and cloud-based contact centers can tackle these problems head-on with minimal disruption and cost. These technologies aren’t pie-in-the-sky aspirations. They are operational game-changers that are already delivering these kinds of quick wins:

• Automating the everyday. AI-powered chatbots and voice assistants can handle routine tasks like appointment scheduling and FAQs, freeing up human agents for more complex cases. Interactive voice response systems (IVRs) use natural language processing to direct patients to the right department without the endless “Press 1 for…” menus.
• Smarter triage. AI can assess patient symptoms through virtual tools or integrate data from remote monitoring devices, alerting clinicians to potential red flags. Patients get quicker answers, and fewer calls are escalated to clinical teams unnecessarily.
• Personalized interactions. By analyzing patient data, AI can tailor responses to individual needs. It can even pick up on emotional cues, like frustration in a caller’s tone, and prompt agents to respond with extra empathy.
• Streamlined workflows. No more toggling between five systems to answer one question. AI unifies data and tools into a single interface, cutting down call times and improving first-call resolution rates.
• Data-driven insights. With AI monitoring of call trends and patient sentiment, managers can identify bottlenecks, predict call surges, and optimize staffing in real time. Agent training becomes more targeted and precise, with AI creating simulations based on actual patient scenarios.

Imagine this. A patient calls to reschedule an appointment. Instead of waiting on hold, they’re greeted by an AI assistant that offers new time slots in seconds. If the issue requires a live agent, the AI assistant hands it off to an agent with all the relevant information already on-screen, saving time and reducing stress. After the call, the system updates the EHR automatically, reducing admin work for clinicians.

Now multiply that scenario across thousands of interactions daily. Patients are happier, agents are less stressed, and hospitals save money. Everybody wins.

One standout solution is Amazon Connect, a cloud-based, AI-powered contact center platform. Its pay-as-you-go model appeals to cost-conscious health systems, and its integration capabilities make it a natural fit for EHR and ERP systems. Features like sentiment analysis, real-time agent guidance, and automated follow-ups are helping hospitals improve patient satisfaction scores, reduce costs, and boost agent productivity.

Healthcare organizations often approach AI with caution, fearing high costs and uncertain ROI. But contact centers offer a low-risk AI entry point. The stakes are manageable, the technology is already being used with great success in healthcare, and the benefits are immediate. In an era of tightening margins and growing patient expectations, AI-powered contact centers are the rare innovation that checks all the right boxes.

The contact center of the future isn’t just about answering calls. It’s a hub for patient engagement, seamlessly integrating with clinical and administrative workflows. It captures real-time insights to improve operations, outcomes, and experiences across the board.

Here’s the bottom line. Healthcare doesn’t need to wait for AI to revolutionize clinical care. The revolution can start today, in the contact center, with tools that deliver immediate, meaningful improvements for patients, providers, and staff alike.

Readers Write: The Future State of AI and Automation in the Revenue Cycle

The Future State of AI and Automation in the Revenue Cycle
By Patrice Wolfe

Patrice Wolfe, MBA is CEO of AGS Health.

Like many heavily regulated industries, healthcare has seen limited progress towards the use of artificial intelligence (AI) and automation, despite the enormous potential they hold for improving productivity, accuracy, care access, and the bottom line. Much of that promise comes from use cases that span the revenue cycle management (RCM) continuum, where legacy automation tools are already having a positive impact through activities like patient reminders, insurance verification, coding, and claims status transactions.

Today, generative AI (GenAI) is poised to upend, in a positive way, healthcare’s approach to front- and back-end financial operations. It has the potential to re-imagine the massive volumes of historical and real-time revenue-related data that is flowing through RCM departments and create entirely new approaches to optimize revenue and minimize financial risk.

AI is an advanced set of tools run by algorithms that use data to simulate human intelligence. GenAI takes things several steps further by leveraging that same data to not only tell the story of what it sees, but to also create entirely new, more effective approaches to RCM.

Already present in many RCM functions, AI and automation represent a continuum of capabilities that can be broken down into four major categories:

• Basic. Rules-based processes for repetitive tasks that typically follow pre-defined instructions without exceptions. Examples include a claim status or transaction query submitted by a provider using a basic bot or ANSI transaction that returns a response based on a predefined set of values.
• Advanced. Leverages more complex algorithms and machine learning to make predictions based on past performance, which allows for proactive intervention based on those probabilities. For example, a machine learning model may be able to identify claims that are likely to be denied and can be corrected before being submitted to the payer.
• Intelligent. Here is where AI enters the continuum with the addition of natural language processing (NLP) that uses unstructured data and human-like reasoning to process ambiguity. An example in RCM would be the use of machine learning, deep learning, and NLP models that recommend “next best actions” to prevent denials from even happening in the first place.
• GenAI. Uses neural networking and large language models (LLMs) with deep learning and other techniques to automate design and do complex problem solving, often aided by visual and written materials. An example would be a human-like chatbot that negotiates with payers to reverse claim denials using the clinician’s notes and imaging studies to develop an argument complete with appropriate medical terminology.

While healthcare remains in the early stages of the AI continuum, more complex and sophisticated Intelligent and GenAI use cases are on the horizon.

While all eyes are on GenAI, earlier-stage AI and automation is already impacting RCM outcomes and efficiencies. Meanwhile, ample opportunity exists to further influence RCM as capabilities grow. In fact, just as AI and automation fall on a continuum, so too do the RCM processes and workflows that can be boosted by their adoption.

Scheduling and Registration

Legacy automation has a stronghold in scheduling and registration with the use of basics like automated patient reminders now nearly ubiquitous among healthcare organizations. Looking toward the future, scheduling chat bots, integrated scheduling across care sites and clinical specialties, and comprehensive scheduling packages for patients that include cost estimates are high-priority investments for their potential to reduce patient friction, enhance the patient experience, and make a provider “stickier” by strengthening the provider-patient bond and improving patient retention.

Patient Access

Insurance and benefit verification are already close to fully automated. RCM’s holy grail of future automation use cases is prior authorization, particularly as payers build more complex and ever-changing policy requirements for prior auth. AI can help manage the prior auth process, maximize the probability of approval, and automate the appeals process if an authorization is denied. The challenge is the enormous amount of information that is required from both providers and payers who have little incentive to be transparent with those details.

Coding/HIM

Computer-assisted coding (CAC) enjoys broad adoption for inpatient coding and billing, delivering reported productivity gains of 10% to 30% for hospitals. Computer-assisted professional coding (CAPC) is beginning to make inroads on the professional side. Future use cases include autonomous coding, which has limited use in a handful of specialties due to the significant amounts of data needed to properly train the specialty-specific LLMs. Early work is also underway around ambient charting, which converts voice dictation into coding and promises to save physicians up to 4.5 minutes per chart by some estimates.

Patient Financial Services

As with prior authorization, AI and automation adoption in patient financial services is influenced by increasingly aggressive payer policies around denials, delays, and underpayments. There is enormous potential for streamlining collection workflows, including touchless A/R. Other promising areas are automated denials management and the movement to a reduced friction patient experience.

Clinical Services

Though farther behind other stops on the RCM continuum, future AI and automation use cases within clinical services include real-time patient status monitoring in utilization management (UM) to ensure accurate reimbursement. Other potential applications include professional fee UM and automated clinical documentation integrity (CDI) that uses NLP and other advanced tools.

Revenue Integrity

Also behind the adoption curve, revenue integrity AI and automation use cases include charge master maintenance, late charge identification, and coding/billing compliance audits. AI and automation are also used to proactively identify and resolve problem areas.

Healthcare has taken a cautious approach to adopting GenAI and other advanced forms of AI and automation within RCM, due in part to the industry’s necessarily risk-averse nature. Also at play are the complexities that are involved with adapting critical workflows to advanced AI and the need to balance the application of limited resources between multiple and sometimes conflicting strategic priorities.

For example, while advancements like ambient documentation are crowd pleasers that promise to deliver improvements in physician productivity and satisfaction, they won’t necessarily improve the completeness of clinical documentation. As such, CDI will remain a critical part of the RCM process.

The reality is that while GenAI and its AI peers hold great promise for optimizing RCM, these technologies can be expensive to use, staff, and support. Health systems and other provider organizations will have to place bets with scarce resources, and it’s more likely that AI use cases that improve physician and patient satisfaction will come out on top.

GenAI and advanced automation also require close collaboration between operating departments like RCM and their IT colleagues to create and test APIs, move/share data between systems, and access datasets to test predictive models and train LLMs and other advanced AI models. This collaboration may be hampered by information and data silos that were created by legacy technologies. This also impacts the opportunity to leverage AI and automation to create a seamless patient experience, which requires integration across multiple settings of care, systems of record, and data siloes.

As GenAI and other advanced automation solutions continue to deliver on their promise, the impact on healthcare RCM has the potential to be transformational. They also have the potential to reduce the challenges that are confronting providers across the RCM continuum, while streamlining patient access, increasing coding and billing accuracy, improving utilization management, and speeding the revenue cycle.

When the productivity and accuracy promises are fully realized, investing in GenAI and its predecessors becomes a true win for the entire healthcare industry.