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.

Readers Write: What We Can Learn about Mental Healthcare from a Cattle Farm

What We Can Learn about Mental Healthcare from a Cattle Farm
By Teira Gunlock

Teira Gunlock, MHA is CEO of First Stop Health.

What does mental healthcare in the United States have in common with a farm? As a healthcare executive who grew up on a cattle farm in Missouri, I can tell you there are more similarities than you might think.

Let’s start with what we know. Mental healthcare in the US is in crisis. One-third of Americans say they can’t get the help they need, and both individuals and employers face the same barriers to care of cost and access. Mercer reports that 94% of large employers have increased their investment in mental health coverage over the last three years, a trend we’ve also seen in small- and medium-sized businesses.

And yet, people aren’t getting the care they need because it’s too expensive and there aren’t enough providers to meet the demand. Costs will only continue to rise, making it increasingly more challenging for employers to provide adequate coverage.

Virtual care has the potential to fill this gap. For employers, virtual care offers the promise of low administrative costs, high utilization, ease of engagement, and a positive patient experience. For patients, virtual mental healthcare means that they can see providers on their own schedule, with fewer barriers to getting care.

Seems like virtual care is the silver bullet, right? Not exactly. A lot of virtual mental healthcare models have fallen short where it counts. With low engagement rates and poor patient satisfaction scores, the current model has proven unsustainable. Many providers are cutting out telehealth options altogether. 

Clearly, the system is broken.

This is where the farm analogy comes back in. On the farm where I grew up, things are constantly broken – fences, machinery, you name it. I learned that small fixes each day can make a big impact over time. A problem may seem overwhelming, and healthcare surely is, but big problems just don’t get solved overnight. They require a series of small, ongoing fixes rather than a one-and-done solution. I bring that mentality to my work in healthcare every day.

Revolutionizing the mental healthcare landscape is a lofty goal, and no one company can do it alone. It requires insights and innovative ideas from people with a wide variety of expertise and experience who are passionate about being part of the solution.

During the pandemic, when mental health services were desperately needed, we saw a proliferation of virtual mental health solutions enter the market. Those early solutions addressed some of the problems, but we learned there was more to fix.  

Effective care requires removing the barriers that prevent people from accessing it. In mental healthcare, high costs, difficulties in connecting with providers, and lack of long-term support all hinder patients from getting the care they need. Moreover, mental healthcare can’t be siloed from the rest of a patient’s care; it must be integrated to treat both the mind and body as a whole. 

The right virtual model can address many of these roadblocks. First, effective virtual care, particularly in rural areas, combined with on-demand access to licensed therapists and mental health coaches, can connect patients wherever they are. 

Second, a streamlined payment model allows for flexibility for providers and patients. It eliminates both out-of-pocket costs and the complicated and expensive reimbursement process.

Third, progress with mental health looks different for everyone, and care works best when it’s ongoing and sustainable. Long-term care models that also support provider selection allow patients to build a relationship with a provider they choose, making them more engaged and invested in their care journey. 

It’s unlikely that the demand for mental health services will decline any time soon, making it more important than ever to have sustainable models that can get patients the care they need. Virtual mental healthcare works best when patients have options that increase their access, are low-cost, and allow for relationships to build between patients and providers over time.

Just like on the cattle farm, fixing what’s broken requires constant problem-solving and resilience. To make meaningful change, we must leapfrog over the status quo and commit to reshaping mental healthcare into a system that emphasizes whole-person health, seamless access, and that puts patients first.

Readers Write: Healthcare’s Hidden Cost Crisis: How Middlemen and Outdated Tech are Bankrupting America

Healthcare’s Hidden Cost Crisis: How Middlemen and Outdated Tech are Bankrupting America
By Navin Nagiah

Navin Nagiah, MS is co-founder and CEO of Daffodil Health.

Recent articles, including those in The New York Times, have shone a spotlight on how middlemen contribute to rising healthcare costs, notably out-of-network (OON) pricing companies like MultiPlan and pharmacy benefit managers (PBMs), whose fees often obscure and inflate costs. While these analyses are thorough, they often focus on single facets of a sprawling, deeply rooted problem.

The truth is more intricate and defies simplistic solutions. High costs in US healthcare have accrued over decades, shaped by actions across the board, from government policy to insurer practices, provider pricing, and patient behavior.

The presence of intermediaries such as PBMs, OON re-pricing firms, and healthcare consultants reflects the US healthcare model’s structural complexity. As a hybrid of public, private, and even cash-based systems, it has produced a $4.1 trillion industry — 22% of the total economy — where $1 trillion alone goes toward administrative costs, with an estimated $500 billion of that deemed unnecessary or wasted.

For ordinary Americans, this complexity translates into hardship. Forty-one percent are burdened with medical debt; 46% forgo needed care due to cost; and 58% of debt collection involves medical bills. This financial strain is unsustainable for individuals, society, and the nation at large.

An underlying issue is healthcare pricing, which is inelastic, opaque, and tethered to outdated systems. Unlike typical markets, healthcare prices in the US do not respond to supply and demand. The pricing framework is labyrinthine, requiring deep domain expertise to navigate tens of thousands of procedural codes and varied pricing methods. Additionally, administrative systems used by both payers and providers often rely on outdated technology, exacerbating inefficiencies.

However, this does not make the primary actors — whether insurers, providers, or third-party entities — the villains of the story. In a capitalist framework, each stakeholder is incentivized to prioritize revenue and profits. Healthcare is no exception. It’s probable that any rational actor in similar roles would make comparable decisions.

The question we must address is: How do we move forward? What changes are necessary to begin mending this broken system?

The solution demands both regulatory and technological reform. First, let us take a closer look at regulation, where bipartisan consensus on the need for reform offers rare common ground. The No Surprises Act, for instance, was enacted under one administration and implemented by another, underscoring shared political will to mitigate healthcare’s impact on everyday Americans. Yet if we are to achieve genuine change, regulatory bodies need to adopt a more thoughtful and strategic approach.

Understand the market dynamics of payers and providers

Insurers and providers operate with the goals of revenue and profit growth, which regulators and regulations often fail to consider. Laws that don’t account for potential loopholes simply shift costs rather than reduce them, creating the illusion of progress. It is imperative to keep in mind that rising healthcare costs implies higher revenue for providers; a higher revenue for providers means higher premiums, i.e. revenue for payers.

The stock market rewards revenue growth way more than improved margins. This provides extensive incentive to payers and providers to be innovative in how they “shift costs” when regulations are passed.

Regulation must be crafted with an understanding of its potential impact on healthcare costs for ordinary people, avoiding the squeezed balloon effect, where costs shift without any overall cost reduction.

Recognize healthcare’s local monopolies

While other sectors, like technology, are subject to national antitrust scrutiny, healthcare operates across many local micro-markets with localized monopolies. Regulation should reflect this structure, addressing these micro-monopolies with tailored policies that account for regional market dynamics.

Stop adding to the middlemen problem

Regulations must be enacted with caution to avoid inadvertently inflating the healthcare sector’s administrative footprint. The Transparency in Coverage Act, for example, while intended to increase transparency, has spawned a cottage industry of compliance tools companies and consultants — more middlemen — with minimal impact on consumer costs. Future regulations should include clear expectations and mechanisms for affordable, effective compliance without adding new categories of middlemen to the already bloated system. Additionally, regulatory enforcement should be robust, ensuring that non-adherence results in significant penalties that deter cost-shifting practices.

Without these considerations, regulatory measures may perpetuate the inefficiencies they aim to resolve. Now more than ever, Americans need a healthcare system that prioritizes access, transparency, and genuine affordability. Legislative reform, combined with strategic enforcement, could be the first step toward this elusive goal.

Second, let us take a closer look at technology. Once a system, any system, reaches a certain level of complexity, simplifying it again becomes a near-impossible task. However, technology offers a pathway to managing complexity in ways that improve usability and efficiency. Consider the internet. It’s an enormous, convoluted system, yet search engines allow us to find information quickly and (usually) accurately.

In healthcare, however, technology has so far largely added to both complexity and the cost burden rather than easing it. Generative AI could mark a turning point. This technology is unique in its ability to emulate human skills like storytelling, a talent that was once thought exclusive to humans, which helps achieve shared understanding and collaboration. The potential is enormous. AI systems can now analyze, interpret, and convey information much like a human, which could impact healthcare administration, a sector valued at $1 trillion, half of which is estimated to be wasteful expenditure.

Take the process of claim re-pricing and payment as an example. After a doctor generates a bill for reimbursement, that claim may pass through as many as 10 companies and 12 software systems, each with its own requirements and procedures, before the doctor is paid. This labyrinthine process stems from decades of regulations, changing market dynamics, and piecemeal ad hoc solutions. Yet by deploying Generative AI and semi-autonomous agents, we could digitize and automate this entire process from end to end, significantly cutting down on time, costs, and redundancies.

Similar opportunities exist across other healthcare administration processes, whether in prior authorizations, member enrollment, or patient management. I am not suggesting that technology or Gen AI is a silver bullet. This is a long-term undertaking, demanding deep expertise in both healthcare and technology, a rigorous attention to detail, and considerable patience. Still, nothing in the nature of the problem makes it unsolvable.

Companies routinely embark on “moonshot” projects that demand decades to bear fruit, like Facebook’s Metaverse, Elon Musk’s SpaceX and Neuralink, and Google’s Waymo, Wing, and Loon. These projects capture public imagination and dominate media cycles, but moonshots in healthcare administration, though less glamorous, offer far greater potential for transforming lives.

We need to encourage visionary entrepreneurs to pursue these difficult challenges within healthcare. Initiatives that, though unglamorous, offer substantial benefits to consumers and society at large. Government support is also crucial. Legislation that promotes competition within local healthcare markets and policies that encourage innovative solutions for complex healthcare issues would drive meaningful progress.

Readers Write: Tackling Diabetes Distress in Dual Eligibles Requires Integrated Care Management

Tackling Diabetes Distress in Dual Eligibles Requires Integrated Care Management
By Barbara Greising

Barbara Greising, MBA is chief commercial officer at Podimetrics.

Diabetes is a demanding condition. Slipping up even a little can quickly lead to devastating outcomes, and there’s never, ever a day off. 

The constant stress can lead to feelings of discouragement, isolation, frustration, and exhaustion, especially when the consequences of suboptimal self-management can be so severe. For example, every 3.5 minutes, someone in the US loses a limb due to complications of type 2 diabetes (T2D), and up to 50%of those individuals may face death as a result within just two years.

For people living with diabetes and behavioral health challenges, such as a large number of the socioeconomically complex dual-eligible Medicare/Medicaid (DE) population, the outcomes could potentially be even more catastrophic, with mortality risks up tofour times higherthan people with either condition alone.

Up to 45% of mental health conditions and cases of severe psychological distress go undetected among people being treated for diabetes. And with nearly a third of DEs experiencing a serious mental health disorder of some kind, including major depression, that’s a potentially huge number of high-needs people who are not getting appropriate care.

Without proactive, personalized mental health support for these individuals, “diabetes distress” can take root, leaving up to half of people with diabetes feeling overwhelmed, defeated, disengaged, and less equipped to manage their everyday needs at some point in their health journey.

It is crucial to understand the root causes of diabetes distress, particularly in high-risk, highly complex DE populations, and develop proactive, personalized strategies rooted in integrated case management techniques that merge effective mental healthcare resources and socioeconomic support with more traditional approaches.

The first step for assisting people with diabetes is knowing if they need help. Standardized questionnaires like thePHQ-9 can be helpful, but these tools are not usually designed to uncover diabetes-specific concerns, nor are they always used at the most effective points in the diabetes management process.

Providers and health plans may consider augmenting data collection efforts with more targeted measurement tools for diabetes distress, such as the American Diabetes Association’s Problem Areas in Diabetes (PAID) Scale. This check sheet asks detailed questions, such as if the person feels scared, angry, or discouraged when thinking about living with diabetes, what their support system looks like, and how much energy diabetes care takes from them each day.

Providers should also look at patient barriers from every angle to reveal hidden challenges. For example, when one patient stopped engaging in daily self-monitoring for diabetic foot ulcers, it wasn’t because she didn’t understand the importance. It was because she couldn’t get to her doctor’s office to get a refill of her blood pressure medication. The frustrating situation and negative health effects from being off her meds meant she wasn’t feeling able to take care of herself fully.

When the patient received help to get connected with plan-based home care benefits to see a primary care provider for a refill, she reengaged with her foot care immediately, and at the same time, avoided an ED visit for potential hypertension complications.

Regularly fielding holistic questions about self-care competencies in the routine primary care environment is important, but plans and providers should also consider refreshing their data at other key points, such as during specialty visits for associated complications and before discharge from a hospital due to a diabetes-related event. This can ensure that individuals get the help they need when they need it, before diabetes distress becomes overwhelming.

Case managers can assist with this process by spearheading the development of compassionate, informed patient-provider and/or member-health plan relationships. These care team “quarterbacks” can help connect individuals with social workers, psychologists, psychiatrists, substance abuse counselors, and other behavioral health professionals to augment clinical care. 

Case managers, especially those with nursing backgrounds, often have the training, intuition, and experience to identify people who may be struggling with a variety of non-clinical concerns and can successfully pair these insights with their clinical knowledge of diabetes management to support and guide people with diabetes to better glycemic control and improved overall mental health and well-being.

To be effective, however, case managers must be equipped with the tools and resources to perform this work appropriately. For example, health plans and provider networks will need to ensure that high-quality mental health resources, such as patient support programs, social workers, and counseling options, are consistently available for referral in a timely and affordable manner. 

Case managers also need digital infrastructure to make referrals to socioeconomic support organizations, monitor the use of personal medical devices like continuous glucose monitors, and interact with individuals according to their preferred communication channels.

Diabetes distress is not a condition that can be wholly cured by a single pill or one-and-done injection. Instead, it requires ongoing attention and flexible degrees of management to establish and maintain emotional and mental equilibrium in the face of prolonged stress.

That means Medicare and Medicaid health plans, providers, case managers, patients, and unpaid caregivers must collaborate closely at all times to build a scaffolding of support around every individual.

Care team leaders should ensure that people with diabetes understand how, when, and why to use their medications and personal devices, especially when adding new technologies to the mix. Regular follow-ups around socioeconomic concerns and mental health status will be essential to success, including periodic refreshes of questionnaires and other patient-provided data. Health plans, health networks, and other industry stakeholders will need to remain dedicated to expanding access to mental and behavioral healthcare resources, especially in communities with a higher prevalence of diabetes.

By collecting the right information and getting people connected to the most appropriate resources for their needs, case managers can reduce the impact of diabetes distress on dual-eligible individuals and create the conditions for success for the tens of millions of people living with diabetes.

Readers Write: Collaboration, Trust Remain Essential to Connecting the Last Mile for Healthcare Interoperability

Collaboration, Trust Remain Essential to Connecting the Last Mile for Healthcare Interoperability
By Matt Koehler

Matt Koehler is vice president of product innovation for Surescripts.

Successful collaboration in healthcare, while easier said than done, almost always results in meaningful improvements, such as better quality, safer, and less-costly care for patients. Collaboration is essential to innovation because it reinforces the trust that is needed between stakeholders. It’s especially critical when the safety and lives of patients are at stake. 

Stakeholder collaboration and industry input have been key in the development and implementation of the policy changes that are reflected in Common Agreement Version 2.0 that was released in July 2024 by the Assistant Secretary for Technology Policy / Office of the National Coordinator for Health Information Technology (ASTP/ONC) and The Sequoia Project or the Recognized Coordinating Entity (RCE). 

Beyond the technical aspects of this work, it’s worth emphasizing that ASTP/ONC and the RCE purposefully did not go it alone. They brought together the very healthcare technology stakeholders who would be directly impacted to weigh-in and develop the Standard Operating Procedures (SOPs) or the guidelines their organizations would ultimately be required to follow. They are the same guidelines that future healthcare technology innovations that are aimed at advancing interoperability would be built upon.  

The stakeholder-developed SOPs introduce new exchange purposes (XPs) to reflect the need to be more specific and intentional about why patient information is requested and exchanged. For example, TEFCA Required Treatment was introduced to clarify when Participants and Subparticipants must respond to a request. Additionally, three new Health Care Operations Level 2 XPs were introduced to require future exchange for Care Coordination/Case Management, HEDIS Reporting, and Quality Measure Reporting.

These changes provide a framework to illustrate the scenarios where future use cases will be required: 1) scenarios that fall under existing HIPAA definitions for use of healthcare information; and 2) have well-defined requirements for what data must be exchanged. The new XPs will be required in February 2026, marking an exciting evolution of information exchange. 

This new guidance is widely supported by industry experts who agree that it will deliver on its promise to advance interoperability, better enabling clinicians to provide safer, quality, and less-costly care for patients.

Another recent example of collaboration driving innovation is the Sequoia Project’s new Pharmacy Workgroup. As part of their Interoperability Matters program, this work looks to advance clinical interoperability for pharmacies. Specifically, to address barriers that they experience today related to the exchange of clinical data to provide clinical services by developing practical guidance to prepare and adopt these new standards. 

At a time when the challenges facing healthcare seem insurmountable, every example of cross-industry collaboration that led to a successful outcome, like developing the new SOPs, should be a hopeful reminder that together we can make meaningful progress towards improving care for patients and clinicians.   

We should remain committed to this work because of what it means for patients and the future of healthcare across the country: an exciting new framework for safe and effective interoperability with trust at the center of every new healthcare innovation. 

Readers Write: Rethinking Specialty Care in the Shift to Value-Based Care: Getting the Orchestra in Tune

Rethinking Specialty Care in the Shift to Value-Based Care: Getting the Orchestra in Tune
By Najib Jai, MD

Najib Jai, MD, MBA is co-founder and CEO of Conduce Health.

In a value-based care world, the healthcare system operates like an orchestra that is attempting to perform a symphony. Primary care has led this effort — taking on risk, coordinating care, and striving to keep patients healthier in a more efficient way. Specialty care, however, has historically been left to play its own tune, disconnected from the overarching melody that defines value-oriented models.

This paradigm leaves patients navigating a seemingly divided ecosystem that often struggles to align around what’s best for them. Most visibly, individuals who are living with complex chronic conditions — especially those who are from historically marginalized and underserved communities — are left deserted somewhere in the middle.

As these models mature and we enter into the next frontier of value-based care, specialty care must come into alignment, which will allow every part of the system to contribute to the same score.

One of the biggest challenges that I have experienced, both as a physician and through my work with value-based care organizations, is the misalignment between specialty care and the core value-based ethos that has increasingly guided primary care. Primary care physicians (PCPs) are rewarded for keeping patients healthier and out of the hospital, while specialists often remain in a fee-for-service model, incentivized by the volume of care provided rather than quality and patient outcomes. Akin to a talented violinist who performs a beautiful and isolated solo, this fragmented care may be technically impressive, but is ultimately disconnected from the collective performance.

This issue isn’t just about efficiency or cost control, it’s about equity. Patients from underserved communities, who often face the highest barriers to specialty care access, are disproportionately affected. These patients, who may be burdened by multiple chronic conditions such as kidney and heart disease, require coordinated efforts from primary and specialty care providers. They shouldn’t have to experience the vertigo of moving among different worlds when visiting various physicians. This disconnect can lead to conflicting advice, confusion, and missed opportunities for early and effective intervention.

If we truly want to begin addressing disparities in health outcomes, building bridges between primary and specialty care is a critical effort. Excellence in isolation, much like a disjointed symphony, is jarringly insufficient. Different components must work together seamlessly to create a beautiful melody.

To bring specialty care into the fold of value-based care, we must address the incentive structures that keep it siloed. Specialists need to be rewarded for their contributions to patient outcomes. Bundled payments, shared savings, and capitation agreements are all imperfect tools standalone, but can make a significant difference if implemented effectively.

When incentives align, specialists are more likely to collaborate with primary care teams, contributing holistically to patient outcomes. Much like a symphony, physicians are a part of one group that has the single aim of taking care of patients. Aligned incentive structures solidify this implied connectivity.

Lack of integrated data has become a ubiquitous healthcare challenge and impediment to the symphony of value-based care. Too often, specialists must make decisions without access to the full picture of a patient’s longitudinal medical history and lived experiences. These data silos make it difficult for well-intended physicians to deliver coordinated care. Interoperability remains a buzzword, but true integration is paramount for specialists and PCPs to operate from the same “sheet music.” A cardiologist who is treating a patient without seeing their recent visit notes from their PCP is like playing a solo without knowing the key of the piece. At best, it creates dissonance, and at worst, it causes harm.

While data may present a challenge, it also represents an opportunity. Leveraging data-driven insights to personalize specialty care can ensure that patients are connected with the right specialist at the right time. Instead of generic referrals, predictive models and frictionless workflows can help identify which specialists are best suited for each patient’s nuanced needs while maintaining the clinical autonomy of providers.

Understanding performance through the lens of a patient’s’ comorbidities, social drivers, care preferences, and the specialist’s experience with similar patients unlocks a novel, personalized approach to integrated specialty care. Analogous to an esteemed flutist not playing the cello, specialists have skills and training that positions them to serve certain patient populations better than others. This approach ensures that referrals are not only timely, but also meaningful, leading to better outcomes and a more efficient care journey.

The cultural shift that is required to bring specialists into value-based care is perhaps the most challenging part of this transition. Specialists are highly trained and adroit experts who are focused on their specific area of practice. However, creating a value-driven system means that all physicians must think beyond their individual role and understand how they fit into the broader picture of a population’s health.

This requires fostering a culture of collaboration, where specialists are enabled to see themselves as part of a broader healthcare orchestra rather than isolated soloists. It’s about creating an infrastructure in which their expertise is more clearly seen as one critical component of a larger effort to provide patient-centered care.

If we get this right, the potential for positive change is immense. By aligning incentives, improving data access, and fostering a culture of integration, we can create a coordinated and cohesive healthcare chassis. While simple in theory and challenging in practice, a meaningful opportunity remains – ameliorating a system of incongruous care that so many patients experience, particularly those who are already struggling due to systemic inequities.

It’s time to do away with the solos and grow towards a unified symphony, one that prioritizes the patient’s experience and ensures that every provider is working from the same score. It’s time for “value-based care 2.0” where specialists have a seat in the orchestra.

Readers Write: HLTH 2024 Did It Again

HLTH 2024 Did It Again
By Mike Silverstein

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

Once again, HLTH 2024 delivered. In my opinion, HLTH has become the most important healthcare conference on the calendar, and this week’s event in Las Vegas did not disappoint. While sales teams manning booths may have found it less fruitful for direct lead generation, that’s not the true purpose of this conference. HLTH and its sister conference ViVE are where healthcare’s biggest strategic moves are set into motion.

No other event except perhaps the JP Morgan Healthcare Conference brings together such a diverse mix of healthcare investors and vendors from around the world under one roof. HLTH plays a crucial role in shaping the industry’s three- to five-year outlook, and I would argue that it’s even more impactful than JPM since it fosters face-to-face connections in one concentrated venue.

Despite ongoing political uncertainties, the market flywheel is starting to spin again. After a year and a half of valuation struggles, investors and companies are finding common ground. Investment bankers who I spoke with mentioned that deals are once again flowing, and I expect a wave of health tech and healthcare services companies to announce successful funding rounds in the coming months.

Interest rates are beginning to tick down, and HLTH serves as a prime meeting point for key players across the ecosystem — vendors, payers, providers, life sciences, and employers. As healthcare costs continue to rise, software designed to reduce expenses and drive system-wide efficiency is becoming indispensable. Unlike HIMSS, which is more narrowly focused on health systems, HLTH brings together the entire healthcare economy, providing early-stage investors with access to companies on the cutting edge of innovation.

AI was the dominant theme at HLTH, and its influence is only expanding. The companies that are making the most traction attracted significant attention from investors who are eager to deploy capital from the funds raised in 2022, which remained largely untapped in 2023 and early 2024. These companies are focusing not only on cost reduction, but also on addressing the looming clinician shortage that will hit the healthcare system over the next decade.

Solutions that reduce time spent by doctors and nurses on administrative tasks, allowing them to focus more on patient care, are in high demand. Technologies like ambient scribing and workflow tools that augment Epic are gaining traction, helping clinicians operate at the top of their licenses. Additionally, AI is finally showing real potential to address healthcare’s persistent interoperability challenges, a problem that has long frustrated the industry.

While the upcoming election could reshape parts of the healthcare landscape, HLTH 2024 reaffirmed a more immediate truth: the healthcare industry is primed for growth and innovation, with investors ready to fuel the next wave of transformation.

Readers Write: Primary Care Mental Health Support Requires a Whole-Person Care Approach

Primary Care Mental Health Support Requires a Whole-Person Care Approach
By Cynthia Horner, MD

Cynthia Horner, MD is chief medical officer of Amwell. 

Primary care physicians started seeing a dramatic uptick in the number of patients with mental health concerns even before COVID took a toll on the country’s mental health. Now, as the nation struggles with record-high rates of distress and a lack of access to mental health support, there’s a critical need for the healthcare industry to embrace an integrated, whole-person approach to care.

Nearly one out of four adults experienced a mental, behavioral or emotional illness of some type in the past year, according to the latest report from the Substance Abuse and Mental Health Services Administration. For primary care physicians, the swell in the need for mental health support reflects a pattern they have seen during the past two decades:

• From 2006 to 2018, primary care visits that addressed mental health concerns grew 50%, from 10.7% to 15.9%, according to a study in Health Affairs.
• Between 2016 and 2018, about 40% of patients who were diagnosed with anxiety, depression, or any mental illness saw their primary care physician for treatment.
• The percentage of people suffering from anxiety and depression has doubled since before the pandemic. Medicaid data illustrates the enduring impact of COVID, with prescriptions for mental health-related conditions outpacing prescriptions for other conditions in 2022.

To help as many people as possible, we must initially reach patients where they are most likely to be seen: by their primary care providers.

The shortage in the behavioral health workforce may be why more people are turning to primary care physicians for support. The National Center for Health Workforce Analysis reports that as of December 2023, more than half the U.S. population—169 million Americans—lives in a mental health professional shortage area. Compounding the issue is a lack of primary care physicians to meet patients’ health needs.

Given the shortfall of mental health and primary care professionals, virtual care is vital to ensuring that patients have access to the right resources for a whole-person, integrated approach to care. Adopting hybrid care models that include telehealth is crucial to closing care gaps and enabling continuity and access for all.

Primary care physicians have a foundational understanding of mental health conditions. However, a whole-person approach to care — including comprehensive and ongoing mental healthcare from digital programs and behavioral health specialists — is vital to positive outcomes.

That’s one reason why it’s important to continue managing patients even after referring them to a specialist for support. This integrated approach can effectively bridge the gap between physical and mental health.

When it comes to which mental health conditions primary care providers should treat, the acuity matters more than the diagnosis. For example, earlier in my career as a family medicine physician, I managed a patient who was living with schizophrenia. His condition was well controlled and he complied with his regimen and his follow-up. For these reasons, I could continue to treat him. But had his disorder been more acute, or if he had been a new patient and the severity of his schizophrenia was unclear, I would have referred him to a behavioral health specialist.

Ideally, even after that referral, I would have remained part of his care team, received progress updates, and helped manage his other care needs. That’s the best scenario for patients and their primary care providers when they begin working with a mental health professional and receiving care through digital programs.

Whole-person care—delivered in-person, virtually, and through automated care—facilitates collaborative care. It removes the challenges of geography at a time when nearly 80% of U.S. counties are considered healthcare deserts. Whole-person care also offsets the challenges that patients face when they need support from a behavioral health specialist but can’t find one.

As the industry looks for ways to integrate mental healthcare into the primary care setting, here are ways providers can foster whole-person care for overall patient well-being.

• Lean into virtual technologies for support. With virtual primary care, network providers can manage referrals and care across digital behavioral health, urgent care, specialty care programs, and digital companions. This facilitates personalized care and optimal health outcomes by giving providers medical and mental health updates, helping to inform clinical decisions. Embracing virtual technology also minimizes instances where underserved communities can’t access the support they need. Today, 60% of psychologists report that they do not have openings for new patients.
• Establish stronger relationships between primary care providers and behavioral health specialists. Care teams that share assessments, treatment plans, and test results support an integrated model for healthcare. The adoption of health tech solutions nurtures this collective approach to care. It also improves the patient experience and helps align specialty referrals and digital care program enrollments, which empower patients to take an active role in improving their health.
• Partner with health plans to provide the right support for digital populations. This may include investment in a platform that blends in-person care with digital health tools. Evidence shows that patients who are receiving primary care services regularly see 33% lower healthcare costs. In 14 studies that examined the relationship between engagement and efficacy, 64% found that increased engagement with digital interventions was significantly associated with improved patient outcomes.

The movement of patients who are seeking care for mental health conditions from trusted primary care physicians isn’t going to slow or reverse. The industry can strengthen health outcomes by embracing a whole-person care approach, in-person and virtually. We can also keep primary care providers close to a patient’s physical and mental health care, offering the complete, integrated, and personalized support that patients want and need.

Readers Write: Harnessing the Full Potential of AI in Healthcare Requires Carefully Prepared and Clean Data

Harnessing the Full Potential of AI in Healthcare Requires Carefully Prepared and Clean Data
By Brian Laberge

Brian Laberge is solutions engineer at Wolters Kluwer Health.

Artificial intelligence (AI) implementation in healthcare is gaining more and more traction. However, messy data can lead to challenges in training these platforms and helping uncover bias to ensure they offer the most impact. With 80% of healthcare data existing in unstructured formats, there’s often an extra step required to map these insights to more structured standards, enabling AI algorithms or large language models to parse through the information and distill takeaways in a clear and comprehensive way.

As the saying goes, garbage in means garbage out with these platforms. To fully embrace large language models in healthcare and capitalize on the opportunities for AI, it’s important to acknowledge the data quality challenges to overcome and tips for maintaining clean data for optimal use of advanced technologies.

When considering the use of AI in healthcare, there are two phases to consider — the training of the technology and the implementation and insights that will ultimately be delivered. When thinking about training the technology, one of the biggest challenges with healthcare data in particular is consistent data quality and accuracy. With multiple standards across healthcare, and valuable information stored in unstructured fields, it can be difficult to map insights from one care setting to another and ensure that data doesn’t lose meaning amid these bridges.

Additionally, lab or medical data often comes back with portions incomplete, inaccurate, or lacking validity, which skews the data from showing AI models the full picture. Adding further complexity, physicians often use different clinical verbiage to mean the same medical term. All of these data quality issues can result in a hallucination, where the model perceives a pattern that doesn’t exist, which results in made-up, incorrect, or misleading results. Knowing what those synonymous phrases are and being able to address them when training new models or tuning an existing large language model can help increase accuracy.

Another challenge comes from deciphering clinical notes. When you get a mix of data, these notes need to be extracted and properly codified to an industry standard. If this process cannot be completed, it’s often recommended to exclude them, as the data will lead to noise and bias within the AI models. This gap could represent a huge loss of insights that could be incredibly impactful for patient care and outcomes reporting.

In general, human error, or simply the large amount of disparate verbiage used in healthcare, doesn’t always translate easily for a uniform standard to train AI. In order to avoid this, healthcare organizations should make sure they have tools or processes in place to assess the quality of their data, clean their data, and standardize it before implementing LLMs.

Though it can be challenging to fully prepare data before training an AI model, it’s imperative to ensure that future AI use and insights are purposeful and accurate. It can be dangerous to train an AI with messy data for a number of reasons. Missing, incomplete, or incorrect information can reduce the accuracy and insert bias, which could lead it to infer incorrect assumptions that are then built into the core of the model.

Additionally, low quality or overly simplified data for minority populations could cause a bias to be built into the model. In data, race and ethnicity often are jumbled together. Sometimes, because of biases within the healthcare system itself, there is not as much data for certain groups compared to another. While addressing those care gaps is a much larger discussion, staying ignorant about the fact that the data gaps exist is also dangerous.

For example, if you are building a model to predict the most effective drug for a patient based on historical administration of various drugs, and the data used to train the model has data quality issues with race, then it is more likely not to detect a situation where a drug is more effective for a particular race and would result in a bad recommendation.

Maintaining the data, including knowing where the gaps are, and evaluating training data to address these gaps is a challenge. However, it’s essential to address from the get-go as bias or inaccuracy in the model will make the system harder to use, and ultimately, these biases will then be intrinsic to the AI platform and future insights.

Integrating data, particularly high-quality data, is proven to save hospitals money and reduce risks to compliance and industry standards. There are six core elements to maintaining data quality that organizations should consider when preparing to implement AI tools:

• Accuracy is important in reflecting the true outcomes of healthcare.
• Validity assesses the appropriateness of the data to support conclusions.
• Data integrity ensures the reliability of the data.
• Having complete data helps to identify any possible gaps within the data set.
• Consistency is important to maintain uniformity across the set.
• Timely data helps to harness the full potential of the data for meaningful actions.

All of these qualities will strengthen the data and create an easier AI implementation with less room for error.

While maintaining clean data for use by advanced analytic platforms can be challenging, there are steps that organizations should take to keep data ready for use in AI models. First, it’s important to have a strong data governance process to ensure accurate data, and to decipher good versus bad data before feeding it to an AI model. It’s also important to verify lab results against the appropriate codes to eliminate errors and incorrect codes being built into the model. We have found in one data set that the data quality was as low as 30% accurate as it contained invalid codes and incorrect codes for the labs.

Ensuring alignment of data, and validating codes to an industry standard, will help to streamline the process. The richer the data used to train the AI, the better the outcome will be. Normalizing and mapping the data can help to streamline data from multiple sources and authors. Mapping the information ensures accuracy in the data and helps break down any discrepancies between sources.

Lastly, constantly assessing and ensuring an understanding of data from the team that is responsible for training the model will help to identify gaps or potentials for biases within the data itself. It’s important for the team that is training the model to work with their data governance colleagues to ensure that they are aware of any missing data, such as gaps in lab results and member data, to remedy these gaps for more complete quality measure reporting.

By implementing these best practices, data can be properly utilized to its full potential to inform decision-making, increase quality, and enhance patient care.

Healthcare data can be messy, but creating a process where the data is properly assessed and cleaned can be beneficial in so many ways beyond AI. It’s encouraging to see an industry that has historically moved slowly be so eager to adopt new technologies. While the opportunity for AI use in healthcare is great, we can’t forget the basics of data quality that are essential in determining the future success of these platforms. With this process, organizations can make better use of AI and ensure the most accuracy in their models to help better serve patients.

Readers Write: The Uncomfortable Truth About Healthcare Data

The Uncomfortable Truth About Healthcare Data
By Mike Green

Mike Green, MBA is chief information security officer of Availity.

Cyberattacks have become an all-too-common occurrence, with no industry immune from their effects. In healthcare, the stakes have reached unprecedented levels, with the FBI recently identifying the sector as the top ransomware target.

Consider that in 2023, healthcare data breaches that impacted 500 or more records were reported to the Department of Health and Human Services (HHS) Office for Civil Rights (OCR) at a rate of 1.99 per day. The results of that equate to a whopping 364,571 healthcare record breaches every day and 133 million records exposed or impermissibly disclosed in 2023 alone.

Data like this, combined with lessons learned from previous cyberattacks, reveal the uncomfortable truth that healthcare data is increasingly vulnerable. Hardware, software, and the information that runs through it are more interconnected than ever. The vital nature of healthcare’s digital infrastructure, combined with increased cyber threats, magnify the vulnerability of this connectedness further.

Reflect on this year’s example in which a major clearinghouse experienced one of the worst cyberattacks in the history of the US healthcare sector, affecting up to one-third of the U.S. population. What makes this incident stand out is the company’s crucial role as a healthcare clearinghouse.

As digital super-highways, healthcare clearinghouses connect the healthcare ecosystem, routing billions of electronic transactions between health plans and providers and streamlining administrative processes that are associated with claims, prior authorizations, and provider payments. Yet today, under HIPAA, the closest thing to an information security standard is a catch-all “reasonable efforts” expectation. Such a standard, or lack thereof, was wholly inadequate to protect hundreds of thousands of providers and millions of patients across the interconnected healthcare landscape from this unprecedented cyberattack.

Members of Congress have caught on, announcing the proposed Health Infrastructure Security and Accountability Act in late September, which aims to direct HHS to craft a new set of minimum cybersecurity standards for healthcare providers, health plans, clearinghouses, and business associates. As calls for change such as this highlight, to truly improve cybersecurity across the US healthcare system and prevent this from happening again, the industry—and clearinghouses in particular—must do more to safeguard and swiftly recover with minimal disruptions.

The following best practices can help bolster cybersecurity posture and speed recovery time for healthcare organizations that are impacted by attacks.

• There is a pressing need to establish mandatory cybersecurity standards for all clearinghouses. The days of “please see attached HITRUST certification” are gone. That is simply not enough, and the false sense of security provided by these certifications is dangerous. These standards should be updated regularly to address evolving threats. Clearinghouses should be required to disclose the scope of their information security programs and demonstrate compliance with highly specific security standards, such as the US Defense Information Systems Agency Provisional Authorization Impact Level 2 (DISA IL2), which maintains cloud computing security requirements and the National Institute of Standards and Technology SP 800-171, a standard for safeguarding sensitive information on federal contractors’ IT systems and networks.
• Clearinghouses should also comply with SOC-2, a security framework that was developed by the American Institute of Certified Public Accountants (AICPA). SOC-2 specifies how organizations should protect customer data from unauthorized access and is built around five Trust Services Criteria: security, availability, processing integrity, confidentiality, and privacy. Not all healthcare organizations comply with SOC-2 criteria. Clearinghouses should be required to fully implement these cybersecurity standards, adjusting criteria over time to keep pace with evolving threats.
• It is crucial to implement stringent disaster recovery and business continuity standards. These standards should include annual reviews by boards of directors and mock cyberattack exercises to ensure preparedness. Clearinghouses must demonstrate the capability to recover from disruptions swiftly, with recovery times measured in hours and days, not weeks and months. Moreover, Recovery Time Objectives and Recovery Point Objectives should be shared with clients annually, with these metrics audited by credible third parties.
• Streamlining the administrative processes for providers is also essential. Simplifying and standardizing the enrollment process for electronic data interchange (EDI) with Medicare and Medicaid will reduce redundant requirements and enhance efficiency. Establishing a unified, automated EDI enrollment system across all Medicaid and Medicare programs will further ease the administrative burden on healthcare providers, saving time and money while ensuring the ability to run practices through a disruption of service to the primary clearinghouse.

While there’s no one-size-fits-all solution to addressing cyber threats in healthcare, the establishment of such clear standards and accountability measures can help better ensure the resiliency and security of the entire digital infrastructure. Strengthened cybersecurity practices can also instill confidence in the integrity of the healthcare ecosystem, which connects patients, providers, payers, and other stakeholders alike.

Readers Write: Healthcare Knows Everything About Patients, But Can’t Keep Them Engaged

Healthcare Knows Everything About Patients, But Can’t Keep Them Engaged
By Carrie Kozlowski

Carrie Kozlowski, OT, MBA, is co-founder and COO of Upfront Healthcare.

Part of what I love about leading a growth-stage health tech company is the chance to jump between worlds. From big-picture “how might we” brainstorming with innovators to in-the-trenches problem-solving with the health system leaders responsible for delivering patient care — I get to see healthcare from both sides.

The problem is, they often remain siloed. While there’s no shortage of ideas about how to solve healthcare’s biggest problems, teams on the ground are barely staying afloat in managing the day to day, let alone implementing big fixes. The industry as a whole gets stuck operating the same way it did 20 years ago.

That’s the paradox that has been on my mind since I spoke at South by Southwest earlier this year, when I addressed an audience of innovators about the future of data-driven care. That is an area of healthcare where the disparity between what we could do and what we do is striking. My co-presenter and I explored why 97% of all data produced by hospitals each year goes unused, even at the expense of transforming healthcare for the better.

Think about how much healthcare providers know about us. Our doctors know our kids’ names, when they were born, what we do for a living, when our schedule is usually free for appointments, and the likelihood that we’ll cancel last-minute. With this much information, healthcare should be creating incredibly personalized patient experiences, but is falling behind.

Healthcare leaders have an incredible amount of data at their fingertips. As an industry, it’s uniquely positioned to understand who consumers are, how they behave, and what services they still need.

I use the word “consumer” intentionally. No matter how healthcare is perceived, patients are consumers and healthcare enterprises are competing for their business. Patients are making consumer decisions, and these decisions hinge on factors like marketing, convenience, and personalization.

If healthcare made the most of its data, health systems could be running tailored engagement programs that are capable of predicting patients’ actions, speaking directly to their needs, and driving better outcomes across the entire healthcare industry to deliver on the promise of patient-centered care. That’s what’s at risk when it comes to data-driven care. Not just efficiency, but long-term success for patients and enterprises alike.

The average hospital produces 25 trillion pages of data each year. Healthcare’s data collection is growing at a staggering annual rate of 36%. That’s 11% faster than media and entertainment.

Not only is the data vast, it’s also accessible. Health systems already have patient information, collected safely and stored securely with no new data collection processes needed. They know about patients’ jobs, families, and modes of transportation. They know if they need translation services and if they have a history of canceling appointments at the last minute. In other words, they have the exact kind of consumer data to make healthcare more convenient, accessible, and effective.

While Netflix and TikTok use their consumer insights to engage viewers for hours each day, healthcare has so far failed to capitalize on patient data. The industry is sitting on a treasure trove of consumer insights, but they’re going unused. As a result, only 8% of patients complete all the screenings they need in a given year. Ignoring healthcare data isn’t just inefficient, it’s reckless.

It’s easy to point to the healthcare industry’s resistance to change as the problem, but we can be more specific. Let’s look at the challenges one by one.

• Privacy concerns. The words “patient data” often carry with them the fear that a health system will somehow violate a patient’s privacy. Patients might worry that their data will be used against them, preventing them from getting insurance, causing issues with their employer, or otherwise introducing bias into their care. The truth is that HIPAA already forbids this kind of unethical data use. When I talk about leveraging patient data, what I mean is taking the information patients have already willingly handed over and using it to improve their experience dynamically and securely.
• Fear of litigation. We’ve all heard healthcare described as a risk-averse industry. This makes it sound like individual healthcare leaders aren’t open to new ideas. What it really means is that healthcare lives and dies by compliance, sometimes to a fault. It’s worth a conversation about the difference between reasonable precautions and completely overblown fears. The concept of leveraging patient data might feel new, but the data itself is not. It’s already been collected and is being stored securely by health systems. The next step is as simple as using what’s already known about patients to make more practical decisions.
• Deficient tools. Patient data is available now, but that doesn’t mean that it’s easy to access or interpret. Health systems are burning money and human capital on often redundant or cumbersome software platforms. If these platforms don’t play well together, there’s no guarantee that they will produce useful insights on demand.

In many cases, these tools could be stripped back and replaced by one or two patient engagement solutions that integrate with the rest of a robust software suite. We don’t need a separate platform for every point of data collection. Instead, look to HIPAA-compliant engagement tools that speak directly to market-leading EHRs, which allow a bidirectional flow of patient data to empower truly personalized outreach.

Healthcare already has the ingredients to change how patients access and experience care. The challenge is actually making that happen, with data at the forefront. In an industry that is understandably reluctant to change, healthcare pioneers will be looked toward to lead adoption. Once processes are built around patients instead of bureaucratic restrictions, the foundation will be laid for a whole new era of healthcare, one in which care is personalized, patients are engaged, and data leads the way.

Readers Write: EHR Due Diligence: Five Questions That Could Save Millions

EHR Due Diligence: Five Questions That Could Save Millions
By Kem Graham

Kem Graham, MS is VP of sales for CliniComp.

Electronic health records (EHRs) have become an indispensable tool in healthcare today. As hospitals and health systems navigate the complex process of selecting an EHR vendor, avoiding hidden costs and ensuring transparency are paramount. Here are five key questions to consider when evaluating EHR vendors to maximize ROI, maintain workflow continuity, and achieve overall success.

1. How transparent is the cost breakdown?

With budgetary constraints more challenging than ever, it’s crucial to identify all contract elements comprehensively. Seek a detailed account of software, hardware, and services components, including costs for data migration, staff augmentation, medical device interfaces, and interoperability. Validate the scope of implementation, configuration, and ongoing support services. Determine any third-party costs that are not covered by the EHR vendor and confirm if there are monthly service support limits. Identify whether your organization will be billed by volume or a fixed cost solution and determine the total cost of ownership from contract signing to renewal.

2. Is the system adaptable and interoperable?

Look for an architectural framework that addresses evolving challenges in interoperability, scalability, and real-time performance data. The system should provide a comprehensive longitudinal patient record that can seamlessly cross multiple sites and environments, adapting to changing data needs over time. Seek a solution that can normalize disparate data sources for seamless interoperability, meeting both current and future innovation requirements.

3. How will it impact staffing?

Organizations often underestimate the staff that is required for EHR implementation and ongoing system management. With persistent clinical and IT staffing challenges, it’s important to understand a vendor’s staffing requirements and support services. Consider whether the new system offers a robust, out-of-the-box solution that can be customized, and how it will affect current clinical, administrative, and financial workflows. Look for a reliable and integrated system that is intuitive and user-friendly, built by clinicians for clinicians, with 24/7 end-user support to minimize the burden on staff.

4. Is System and medical device integration included?

Data migration and integration among systems, devices, and other technologies are critical components that can sometimes be costly add-ons. Understand exactly what elements are included, whether there are limitations around the EHR system’s technology, and what additional costs may be incurred to bridge those gaps. Consider future integration costs as well, such as migrating to different medical devices or vendors. Confirm that the EHR vendor does not limit the hospital’s options.

5. How will operational disruptions be mitigated?

Not all EHRs require downtime for scheduled updates, security patches, and upgrades. Seek a solution that delivers 100% uptime for maintenance, upgrades, and unplanned incidents. Investigate the vendor’s history to understand their experience in avoiding clinical dissatisfaction, poor patient care, and financial losses due to system downtime.

Choosing an EHR system is a pivotal decision with far-reaching implications on both the clinical and operational fronts. Trust and transparency are essential in fostering a successful relationship between the vendor and the hospital system. Healthcare organizations with a complete understanding of both upfront and long-term investments, including impacts on staff satisfaction, workflows, and patient care, will have the most satisfactory outcomes throughout the EHR acquisition, implementation, and utilization process.

The vendor’s success should be defined by the hospital’s success, reflecting a true partnership where the vendor acts as an extension of and integrates seamlessly into the organizational team. Transparency from the outset, and exploring all options, such as the comprehensive system as a service model, will set the system up for success for years to come.

Readers Write: AI is Here to Stay, So Don’t Miss Out on the Opportunity

AI is Here to Stay, So Don’t Miss Out on the Opportunity
By Greg Miller

Greg Miller is VP of business development at Carta Healthcare.

AI is going to take all of our jobs. At least that’s the impression one would get today from far too many media outlets. Alas, blatant scare-mongering works and generates advertising revenue.

We’ve talked recently with dozens of health system technology decision-makers who acknowledge that artificial intelligence (AI) can make their organizations more efficient and cost-effective. Yet some worry that AI will replace their employees. This isn’t just another concern; it’s the top concern that we’ve been hearing.

The prospect of losing valued employees to technology is one kind of AI-related anxiety among healthcare professionals. There’s also fear of missing out (FOMO). Healthcare IT pros are under heavy pressure from leadership to do something with AI or risk being left behind. However, these healthcare veterans have heard it all before about why they must implement a certain technology to keep up with competitors or face imminent doom. No wonder many have become immune to marketing hype.

Whether you’re in the fear of AI or FOMO camp, AI is happening with or without you. Provider organizations that fail to implement an effective AI strategy will struggle as their understaffed workforces become deeply buried under a backlog of clinical administrative tasks. As more healthcare data is generated and jobs go unfilled, healthcare organizations that lack AI capabilities won’t be able to keep up with clinical documentation.

But while many provider organization leaders fear that AI will replace humans, healthcare workers are more likely to welcome the assistance that AI can provide. In a survey from earlier this year, 77% of responding healthcare workers said that emerging technologies like AI could be useful in combating the healthcare staffing shortage.

AI implementations can optimize the return on investment for hospitals and health systems while providing a blueprint for future successful AI initiatives. There are pragmatic and safe ways for provider organizations to apply AI today that are affordable and designed to ease the administrative burden for clinicians.

One good example is using ambient listening to perform clinical documentation tasks. Physicians typically spend between 30 and 90 minutes at home completing clinical administrative work that they couldn’t finish during office hours. Ambient listening functionality can perform these essential clinical documentation tasks, improving efficiency and accuracy while vastly reducing clinician workloads and burnout.

Another strong use case for AI in healthcare is abstracting data from electronic health records (EHRs). On average, it takes an abstractor one hour to finish abstraction work for a single case. That’s a lot of costly time. In contrast, the right AI technology can perform abstractions for thousands of cases in minutes. Can a hospital or health system afford to pass up this opportunity?

It’s important to know where AI fits into your provider organization. AI is a tool and part of a process. It’s also familiar since we use AI every day in our regular lives through computers, smartphones, and other connected devices.

AI is going to help clinicians do more with the time they have. It will help physicians, nurses, coders, and clinical data abstractors by automating simple but necessary tasks. It will also help provider organizations improve efficiency, reduce costs, and enhance care quality. What AI will not do is replace medical professionals.

The already disruptive shortage of physicians and nurses in the US is expected to get worse as the nation’s population ages and our need for care services increases. Hospitals and health systems should embrace the opportunity to use AI in ways that enable their clinical staff to optimize their care for patients.