By Stephanie Jamison, Executive Committee Chair and Public Policy Leadership Workgroup Vice Chair, EHR Association.
In the months that have passed since the Office of the National Coordinator for Health Information Technology (ONC) issued the final Health Data, Technology, and Interoperability: Certification Program Updates, Algorithm Transparency, and Information Sharing (HTI-1) rule, the health IT sector has been working diligently to meet the earliest compliance timelines even as it continues an in-depth analysis of the regulatory impact on both developers and the providers who use certified technology.
For the EHR Association, that analysis has given rise to several concerns and ambiguities that need to be addressed to ensure HTI-1, which was published in the Federal Register on Jan. 9, 2024, achieves ONC’s stated goal of advancing patient access, interoperability, and standards.
The new regulations are an important step toward implementing key provisions of the Cures Act and enhancing ONC’s Certification Program. However, there are several aspects of HTI-1 that we believe may have unintended consequences for certified EHR technology (CEHRT) developers and users.
The first deadline is Dec. 31, 2024. That is when CEHRT developers must deliver DSI capabilities to maintain certification. Achieving compliance will necessitate substantial development efforts, including in novel areas for the program like AI/ML for predictive DSIs. Other areas of concern include requirements for:
Developing an end-user feedback function for evidence-based DSIs, including an export capability for machine-readable formatted feedback data.
Developing support for a significantly expanded set of data concepts for which selection of evidence-based DSIs must be available.
Developing support for enabling the selection of predictive DSIs using any data expressed in the USCDI.
Producing nine new source attribute data points for all evidence-based DSIs supplied by developers and more than 30 source attribute data points for all developer-supplied predictive DSIs.
Developing support for customer users to access and modify source attribute information provided by developers for those DSIs they supply.
Developing support for enabling customer users to record and modify their own source attribute entries in the system for DSIs they create or implement on their own.
Developing detailed intervention risk management policies and procedures for ongoing management of predictive DSIs supplied by developers.
Meeting these requirements within the 12-month timeframe presents a formidable challenge for CEHRT developers – a challenge amplified by the lack of a certified companion or other resource guide to support developers with compliant updates. Also coming into play are current CMS requirements governing providers’ use of CEHRT that would force developers to deliver updated technology to their customers well in advance of the ONC deadline.
To alleviate these challenges, we are urging ONC to consider implementing an enforcement discretion period of six to 12 months. This would provide much-needed relief for CEHRT developers and healthcare providers alike, while still ensuring that meaningful progress is made toward real-world implementation of DSI provisions by the 2024 deadline.
Over the last few years, artificial intelligence has become hugely prominent in society, and when it comes to healthcare that’s no different.
All parts of healthcare are now using it, and there are plenty of positives for it, whether it be personalised addiction treatment plans, spotting trends quicker and more efficiently, or improving diagnostics, there’s so much it’s improving.
However, are there concerns about AI taking such a prominent role in healthcare too?
Well, yes, there are. Mainly because there are some dangers that run alongside the positives too.
Erosion of patient privacy
That primarily lands with the lack of patient privacy. AI systems use huge amounts of sensitive data, which essentially means that the danger of misuse and unauthorised access is higher. While there are major security measures in place, patient records do become more vulnerable to hackers, which can lead to all manner of exploitation of such details.
Bias in algorithms
AI is always learning, but that also means that just like humans, there’s the opportunity of getting it wrong. For example, if the data used reflects existing societal biases then AI systems may inadvertantly discriminate against other groups, potentially leading to further inequality within the system.
Therefore, AI needs to be fed addressing such biases to ensure that how it operates reflects the entirety of society and not just one area.
Who’s accountable?
If AI becomes an increasingly bigger part in decision making, where does that leave accountability? Traditionally, whether it be addiction, cancer treatment, physio or anything else, there’s a clear chain of responsibility, with professionals accountable for their decisions.
While a human will likely still interpret the data given to them from AI, it still blurs the lines much more on accountability for diagnosis or course of treatment.
Misintepretation of data
AI is designed to enhance efficiency and accuracy, but there could be unforeseen circumstances where it doesn’t. Again, that falls into the accountability argument, but also could lead to incorrect diagnoses and courses of treatment that could prove dangerous.
Essentially, AI will benefit all parts of healthcare, but at the same time it needs to be monitored carefully by humans to get the best out of it. Artificial intelligence can’t replace medical professionals, but it can complement the practice of them, creating a much more streamlined process and ensuring patients are treated more efficiently and effectively.
In today’s dynamic healthcare landscape, the integration of artificial intelligence (AI) solutions is becoming increasingly crucial. Healthcare organizations need to balance superior patient care with operational complexities. The adoption of AI presents a transformative opportunity, empowering your organization to enhance various facets of your healthcare services.
You may be wondering, “Why should I embrace AI solutions for my healthcare organization?” The answer lies in the numerous benefits AI brings to the table. In this blog, we’ll shed light on a few reasons why healthcare organizations, just like yours, are wholeheartedly embracing AI.
Enhance Diagnostics and Precision Medicine
In the healthcare sector, AI technologies are actively advancing diagnostic accuracy, particularly concerning dangerous diseases. A study published in the National Library of Medicine highlights the extensive utilization of AI in enhancing medical diagnostics. These technologies, proficient in interacting with medical image data, contribute significantly to disease diagnosis and prediction.
For instance, AI’s capability to detect tumors in medical images stands out, providing a crucial advantage in early-stage diagnosis and subsequent treatment. The study emphasizes the pivotal role played by AI-based algorithms in identifying patients who might otherwise go undiagnosed, including rare diseases. This effectiveness opens up abundant opportunities for early intervention and improved patient outcomes.
Personalize Patient Care with Predictive Analytics
Harnessing predictive analytics through AI is revolutionizing patient care, offering tailored interventions, and enhancing healthcare outcomes. Grand View Research reveals that the global healthcare predictive analytics market reached $11.7 billion in 2022, underscoring the widespread adoption of this transformative approach.
Predictive analytics allows healthcare organizations to proactively anticipate patient needs, facilitating personalized treatment plans. This proactive approach enables timely interventions and preventive measures based on individual patient data, contributing to a more patient-centric healthcare model. The insights derived from analyzing vast datasets enable healthcare professionals to optimize resource allocation, reduce unnecessary procedures, and streamline patient care pathways.
Enable Clean Water for Healthcare Facilities
Ensuring clean water for healthcare facilities is a paramount objective embraced by healthcare organizations leveraging AI solutions. AI technologies facilitate the efficient management of water resources, optimizing usage and minimizing wastage within healthcare infrastructure. By actively monitoring water quality and consumption patterns, AI-driven systems enable healthcare facilities to identify potential issues promptly.
Access to clean water is crucial for healthcare organizations, as a lack of it can lead to disease outbreaks, challenging the healthcare industry’s ethos. Your organization should learn from past incidents, such as the Camp Lejeune water contamination, which resulted in severe chronic conditions. According to TorHoerman Law, the incident affected veterans, family members, and workers, causing them to suffer from diseases like cancer and Parkinson’s disease.
There is no question that artificial intelligence (AI) has tremendous implications for the world of healthcare. The list of applications is long. From using machine learning to analyze medical images to facilitating patient communication through chatbots and even using predictive tools to identify high-risk patients, AI has the potential to enhance healthcare systems.
And that’s not to mention all the time AI can save on the backend by automating things like data entry and appointment scheduling, thereby granting healthcare professionals more time to focus on actually diagnosing and treating their patients.
Still, many in the industry have approached this new technology with trepidation. Potential violations of medical privacy laws are a perennial concern for healthcare organizations, and AI—with its seemingly opaque algorithms and its potential susceptibility to breach—can seem like more trouble than it’s worth on this front.
The reality is more complicated. Yes, generative AI does present a risk to healthcare organizations when handled without the proper precautions, as any technology does. In fact, nearly 60 percent of healthcare industry respondents to a recent survey conducted by Portal26 cited at least one GenAI-related security or misuse incident in the preceding six months. But with the right security mechanisms in place, the benefits of AI significantly outweigh the possible downsides.
The problem is that—as the same survey revealed—almost 30 percent of healthcare respondents said their employers lack any guidelines or policies at all surrounding AI usage. Building those guidelines—implementing AI as carefully, and cautiously, as possible—is essential to realizing the true possibility of this technology.
Secure full visibility into your AI systems
At the center of any concern around AI and medical privacy violations is protected health information (PHI). PHI encompasses any information relating to:
The past, present, or future physical or mental health/condition of an individual.
The provision of health care to that individual.
The individual’s payment history.
Feeding PHI into the large language models (LLMs) that are at the foundation of GenAI can pay massive dividends to healthcare organizations looking to optimize their day-to-day operations. But successfully achieving this objective, with a minimum of risk, requires taking an extremely proactive attitude towards precisely how this data is being used.
The key word here is “visibility.” If you are going to be feeding massive quantities of sensitive PHI into your systems, you need to ensure that you are aware of what it is, who is using it, and for what purpose. The need for this is especially acute given the rise of “shadow AI”—i.e., AI-related activities occurring out of sight of those tasked with overseeing it.
Unsurprisingly, 67% of healthcare industry respondents to the Portal26 survey are concerned about shadow AI at their companies. It is a problem that is growing daily—and one that can only be curtailed through increased visibility.
Healthcare has a vibrant startup and innovation ecosystem, but that doesn’t mean everyone shares the perks that come with technological developments. Historically, payors have often been ahead of the game in adopting and benefiting from new tech, forcing providers to play catch up.
But artificial intelligence (AI) is changing the game. A persistent trend I’ve witnessed is the steady rise of providers prioritizing technology – especially AI – to inform strategic priorities and address chronic headwinds such as staff shortages, increasing cost pressures, and slow reimbursement times, to name a few.
As healthcare leaders catch on to the enormous potential of AI to combat thorny issues, AI will take center stage next year, reshape the larger healthcare ecosystem, and begin to even the playing field between payor and provider.
As the end of the year approaches, here’s how I see this playing out in 2024:
Autonomous medical coding will be widespread — if not the norm.
The latest health IT report from Bain & Company and KLAS Research highlights the increasing importance of software and technology. Per the report, 70% of providers think AI will have a more significant impact on their organizations this year vs. last year, and an impressive 56% of those surveyed view software and technology as one of their top three strategic priorities, with revenue cycle management (RCM) coming in at a resounding first place. With many health systems focused on reducing administrative burdens for clinicians and a continued shortage of medical coders, autonomous coding adoption will surge.
Large language models (LLMs) like ChatGPT won’t work as advertised.
There’s plenty of commotion about the capabilities of language models, but they will likely disappoint when functioning as the core of autonomous coding engines. However, they will be enormously valuable in solving smaller pieces and edge cases, pushing coding automation rates to 100% for all the high-volume outpatient specialties.
If you were to believe all the headlines you read about AI in healthcare, you’d probably think that AI will be curing cancer and replacing doctors within the year. I mean, there have certainly been some exciting advancements. For instance, medical teams at MIT and Mass General Cancer Center recently developed and tested an AI tool that was able to look at an image and accurately predict the risk of a patient developing lung cancer within six years.
On the other hand, Elizabeth Holmes, the founder of Theranos, stands as a prominent example of what happens when people blindly believe the hype about healthcare and technology. Her fraudulent claims about a supposedly revolutionary blood testing technology raised concerns about the oversight and regulation of AI and healthcare innovations, and ultimately ended with her being sentenced to eleven-years in prison.
To make the most of AI without getting blinded by the hype, I recommend treating it like any other new technology: subject it to rigorous scrutiny, demand transparency, and emphasize responsible implementation. AI isn’t a magic wand that will instantly cure all ailments or replace the expertise of medical professionals. It’s a tool – a potentially powerful one – but it’s still just a tool.
Which medical fields benefit most?
Some fields of medicine will benefit from using AI more than others. For instance, the field of medical imaging and diagnostics has already seen the benefits of AI. Again, radiology departments can now utilize AI algorithms to analyze medical images such as X-rays, MRIs, and CT scans. These algorithms can identify abnormalities and assist radiologists in making more accurate and timely diagnoses.
Another field that will benefit from AI is drug development in pharmaceuticals. Scientists can use AI to analyze massive datasets of molecular structures and predict potential drug candidates. This is much more efficient than having organic chemists sift through datasets by hand. AI can also expedite clinical trial recruitment by matching eligible patients with suitable trials based on their medical records. So, AI can accelerate drug discovery, reduce research and development costs, and bring life-saving treatments to market more quickly.
Even more human-oriented tasks, such as patient engagement and remote monitoring, stand to benefit from AI. AI-powered healthcare CRM systems can enable personalized patient communication and remote health monitoring. These systems can send automated follow-up messages, answer patient queries, and detect potential issues based on patient-reported symptoms. AI enables enhanced patient engagement, improved adherence to treatment plans, and early detection of health issues. This frees up time for healthcare staff, allowing them to focus on more complex tasks.
As the healthcare industry continues to evolve, the integration of artificial intelligence (AI) has emerged as a powerful tool with the potential to revolutionize the way medical professionals work. Contrary to popular concerns, AI will not replace doctors and nurses; rather, it will complement their skills and make them more productive and effective. One significant area where AI will disrupt and bring about transformative change is the “back office” of hospital operations, where manual and outdated processes, along with fragmented systems, have been wasting countless hours of clinicians’ time.
The potential for AI to streamline and optimize various aspects of healthcare administration is vast. One key advantage lies in dramatically reducing the time physicians spend on researching and keeping up with their Continuing Medical Education (CME) and Maintenance of Certification (MoC) requirements.
Traditionally, physicians devote significant amounts of time to stay updated with the latest medical research and advancements, which can be an arduous and time-consuming task. AI-powered platforms can swiftly process vast volumes of medical literature, journals, and clinical trials, providing doctors with curated and relevant information tailored to their specific areas of expertise. This will not only save valuable time but also enhance the accuracy and quality of patient care.
Healthcare leaders can also leverage AI to offer more personalized employee experiences at scale. By analyzing vast amounts of data on individual clinicians’ preferences, work patterns, and career aspirations, AI can create tailored development plans and support systems. This personalized approach can boost job satisfaction and engagement, ultimately leading to higher retention rates among clinical staff.
Gone will be the days of a one-size-fits-all approach to workforce management, as AI empowers organizations to cater to the unique needs of each clinician, thereby fostering a more conducive and fulfilling work environment.
My father was diagnosed with diabetes when he was very young and lived his entire life maneuvering various healthcare systems. He had multiple eye surgeries, which is common for diabetes patients. He had pancreatic issues, open heart surgery, and two kidney transplants. It was a lot and, as his daughter, one of the most difficult parts of it all was watching my mother be his primary caregiver, managing doctor appointments, medication prescriptions, treatment plans and insurance claims.
At the time, there was little to no technology on the practice management side of healthcare. Until the internet connected us all, EHR systems were contained to inpatient and outpatient facilities, primarily used as data interchange platforms for claims processing with scanning capabilities to create images of documents. I remember my mother carrying a binder from doctor’s office to doctor’s office – the most effective way for her to keep an easily accessible list of my father’s ailments, healthcare conditions, medications, and all the other details needed by the countless doctors across my father’s continuum of care.
How AI is creating more meaningful patient experiences
Fast-forward two decades and we’re now in an entirely new reality with an ever-expanding healthcare technology ecosystem. Medical office software platforms do so much more than claims processing and digital document storage. Interoperable EHR systems let physicians share treatment plans and easily access medical histories and lab results anywhere and anytime. Patients can schedule appointments online and login to patient portals to get test results in real time. Telehealth has ushered in brand new healthcare delivery models and helped drive greater adoption of behavioral health services. Remote patient monitoring (RPM) technologies have been a game-changer for so many, helping patients better manage chronic illnesses.
And now, with the integration of artificial intelligence (AI), we’re seeing the next wave of healthtech – an advancement that is bringing our entire industry closer to the promise of fully connected or interoperable healthcare systems. AI is having a massive impact on medical practice workflow processes, empowering healthcare providers and medical office staff. AI applications are making it possible to automate tedious and redundant tasks so that medical professionals can focus their time on valuable work that greatly improves the patient experience and outcomes – and that’s just on the business side of things.
By Sarah M. Worthy, CEO and founder, 