When you go to implement new technology into your organization, it’s important to look at all of the ways that it can go wrong so that way you can ensure you have safeguards and procedures in place. However, it is equally important to look at all of the ways that it can benefit your organization. After all, the ways that new technology improves the care of our patients and improves the work life of our staff is the sole reason we implement it. So while there are challenges and concerns with AI, let’s take a moment to look at its benefits and its potential.
We reached out to our talented Healthcare IT Today Community and asked them – what novel ways are you seeing AI being applied to healthcare? Where hasn’t it been applied yet that you think it should be? The following are their answers to these two questions.
Blake Richards, CEO at Elucid
When AI is applied to coronary computed tomography angiography (CCTA), an imaging test that looks at the blood vessels in the heart, it can provide valuable information that – until recently – was only available via invasive procedures in the cardiac cath lab, including measurements of the type and amount of plaque in the artery as well as whether there is a blockage limiting blood flow. Layering AI on top of CCTA can enable health systems to use cath labs for intervention – rather than diagnostics – which could dramatically reduce a backlog of procedures, optimize resources, and streamline efficiency in cardiac care. Additionally, there has been an increase in interest and clinical evidence supporting using this information to accurately plan interventional procedures, increasing efficiency and potentially reducing complications.
Conrad Coopersmith, General Manager of Coding Automation at AGS Health
From a revenue cycle management perspective, AI is becoming a lifeline for those challenged by evolving payer policies and scrutiny. Payers are, and have been, using AI to support their processing of claims and contracting, which I believe is one factor contributing to rising denial rates. Healthcare organizations that embrace AI in a similar way are in a better position to level the interaction model.
We’re seeing rapid advancements in AI-driven technologies throughout the revenue cycle. From financial clearance and prior authorizations to clinical documentation, coding, billing, and denials management, intelligent automation is helping healthcare providers combat payer policies and avoid regulatory penalties that can negatively impact their margins and ability to reinvest money toward the primary mission of care.
With this context, I believe novel applications of AI in RCM today are less about the technologies themselves and more about how traditional revenue cycle processes must evolve based on a technology-first mindset. Rather than simply plugging intelligent automation into existing processes, RCM and healthcare leaders should be asking themselves how these AI-driven capabilities can empower them to manage revenue operations in new and innovative ways. Over the next decade, I think we will start to see some very intriguing strategies bubble up including the real-time adjudication of claims at the point of care, and I’m excited to see where things go.
Joan Butters, Co-Founder and CEO at Xsolis
Operational efficiencies are increasingly important and achievable in areas where payers and providers must come together for decision-making, such as prior and current authorizations. These areas have been ripe for disruption for years, due to the administrative waste that data silos and friction perpetuate between providers and payers – a chief driver of the quarter-trillion dollars identified in administrative waste in the U.S. healthcare industry. AI-driven insights have been proven to save time, improve consistency and accuracy without bias, drive more efficient workflows, accelerate alignment on revenue-sensitive care decisions, and improve focus on patient or member care. These operational gains can be leveraged to drive organizational improvements across utilization review, case management, and revenue cycle teams, and can also transform relations between health providers and their payer partners.
Sunil Konda, Chief Product Officer at SYNERGEN Health
Many health systems have realized that you can’t afford to miss out on revenue cycle management optimization, and that’s an area where we’re seeing AI, machine learning and robotic process automation take off. Reducing denials is one of the top three priorities of every CFO we speak to, and by automating routine tasks such as filling forms, submitting appeals and denials pattern tracking, organizations can reallocate their skilled staff to more critical and high-value areas, enhancing the patient experience while managing operational costs effectively. By leveraging machine learning and predictive analytics, healthcare organizations can preemptively identify potential billing discrepancies and compliance risks before they escalate into financial losses. The result is a more efficient healthcare system with optimized resources and heightened patient satisfaction.
Bruce Springer, CEO at Prolucent
AI is changing the game in healthcare recruiting, with smart algorithms and machine learning bringing efficiency and precision to the entire hiring process by matching job seekers and healthcare employers based on their skills, experience, and preferences to find the perfect candidate for each job. Hospitals are getting savvy by using AI-powered platforms that let candidates explore job opportunities, chat with recruiters, and schedule interviews from their smartphones or computers. It’s like having an open house for talent, making the recruitment experience more engaging, and giving recruiters inside perspective on candidate preferences.
Today’s AI-powered talent marketplaces and job platforms not only connect job candidates and employers but can serve as the technical backbone and “digital front door” for recruitment initiatives. AI-powered attribute matching, easy application, and fast response times can be a big differentiator in attracting and onboarding talent in an increasingly competitive industry. When integrated with applicant tracking systems, a digital front door strategy modernizes and streamlines the entire recruitment process in support of strategic talent acquisition goals.
Dr. Nele Jessel, Chief Medical Officer at athenahealth
It’s important not to overlook AI and ML’s capabilities when it comes to automation and “small but mighty” use cases that have huge potential to make workflows and other clinical operations more efficient, accurate, and effective. For example, one key way that AI is reducing administrative burden for front-desk staff is by enabling insurance selection, which reduces the frequency of claim holds and thereby saves significant time. For providers, AI can help prioritize what a clinician sees in their inbox and predict response actions to take with those inbox items. Streamlining these tasks improves efficiency for clinicians and other healthcare staff, helping to reduce burnout. By using AI and ML for select automation, providers can focus more time on building high-impact patient connections and ultimately improve patient outcomes and the quality of care they deliver.
Dipak Patel, CEO at GLOBO Language Solutions
As use of artificial intelligence and machine learning becomes more prevalent in healthcare, we expect to see wider use of AI-driven predictive analytics to analyze and act on linguistic needs. Healthcare organizations can leverage insights from internal and industry data to better segment non-English-speaking patients by their language of choice, ensuring that adequate translation and interpretation services are available to support their needs.
Clinicians can drill down on specific populations to identify related health issues and social determinants of health to better manage care. We also expect greater use of AI-enabled translation and interpreting services across the patient journey to increase understanding and empathy, improving patient-provider trust to increase equity and inclusion.
From an operational standpoint, AI-assisted communications support at every touch point should help to circumvent unnecessary healthcare spending and misuse of resources; for example, frequent visits to the hospital emergency department, avoidable tests, and duplicate or unnecessary procedures. This improved patient and physician experience and streamlining of processes translates to reducing the financial burden on society.
Jim Flatt, Co-Founder and Chief Executive Officer at Brightseed
The health industry is on the cusp of a data and insights explosion that will advance a credible, preventative healthcare modality and our understanding of Food Is Medicine. Through rapid in silico predictions, AI is bridging the gap between healthcare and nutrition for the first time by discovering and mapping previously unknown bioactives in plants, fungi, and gut bacteria to validated human biological targets. Before these recent technological advances, bioactives in nature, and those uniquely created within the gut microbiome, have been vaguely understood by modern science. Nature historically has been a wellspring for health-promoting bioactives, representing the majority of approved small molecule drugs. Yet, the vast majority of bioactives remain undiscovered and many carry untapped potential in therapeutic and preventative nutritional approaches. With the help and application of advanced AI models, the illumination of these compounds will drive a new standard of nutrition, education, and cost-effective healthcare.
Hayley Dezendorf, Senior Vice President at Experity
Teleradiology serves as a virtual resource and a vital supplement for provider organizations, given the shortage of radiologists across the country, and the integration of AI helps radiologists uplevel their ability to deliver virtual care. AI helps accelerate scan reading time by pointing radiologists to areas of concern, allowing for faster turnaround times for providers and patients throughout the process. For radiologists, who may view hundreds of scans per day and often battle fatigue in the workplace, adopting a tool that provides this support reduces the chance of misreads during evaluation and alleviates some of their daily burden. Accuracy and quality of care should be top of mind in every practice, and embracing AI tools that can elevate care, help providers, and improve outcomes will be essential as the healthcare and telemedicine landscapes continue to evolve.
Sarah Reilly, SVP, Product and Strategy at Lucet
From a strategy and product development perspective, I am optimistic about the potential of AI and, more specifically, in behavioral health. Given our country’s widespread provider shortages, the technology is well-positioned to scale efforts to create digital front doors for members to enter and remain on their behavioral health journey, including care assessments, triage, provider matching at scale, and longitudinal care plan engagement. AI has also proven valuable when it comes to automating resource-intensive administrative tasks, freeing up providers to focus more on care delivery. The human element remains essential when it comes to providing behavioral care, which starts with building relationships based on trust and fostering long-term connections for a patient’s treatment journey, so AI must find a way to integrate seamlessly into this journey for it to have widespread adoption.
Tyler Wince, SVP and Chief Product Officer at Myndshft Technologies
As artificial intelligence, particularly generative AI, permeates healthcare, its disruptive—and game-changing—potential is undeniable. We’re particularly excited by AI’s potential to slice through the red tape of prior authorizations. Natural language processing and generative AI can help us turn the chaos of disparate payer guidelines into streamlined, structured data that supports a more automated process, allowing healthcare providers to shift their focus from the bureaucratic to therapeutic.
And the potential does not end there. On the payer side, machine learning algorithms make it possible to digitize the complex medical necessity criteria to automatically adjudicate future requests, unlocking a predictive pathway that accelerates approvals and redefines efficiency. That doesn’t mean that human expertise is sidelined. In fact, human judgment becomes even more critical to mitigate the risk of bias being baked into AI tools due to insufficient or poor quality data. Human oversight and usage guardrails inject empathy and ethics in AI-enabled processes, helping to ensure that every decision influenced by AI aligns with the highest standards of patient care.
This is a pivotal moment in healthcare—a blend of technology and human insight paving the way for a future where care is more accessible, equitable, and personalized. As we navigate this promising yet precarious landscape, we are constantly reminded that it’s not just about making healthcare faster; it’s about making it better for everyone.
Calum Yacoubian, MD, Director Healthcare NLP at IQVIA
The hype around AI in healthcare isn’t novel, but the current speed of innovation and accessibility is unprecedented. Over my years in clinical informatics, I’ve witnessed AI’s use in precision medicine, population health, and predictive analytics. However, operationalizing AI remains a challenge, with ethics, privacy, and data security at the forefront. If we can address these challenges, we can reduce the burden on clinicians. For example, we are seeing health systems applying AI to identify at-risk patients based on social determinants of health – reducing admin time by eighty percent and giving clinicians more time to spend with patients. This holistic approach is where AI can revolutionize healthcare.
Sulabh Agarwal, Chief Technology Officer at KeyCare
Ambient AI is revolutionizing healthcare, offering seamless integration into clinical settings, while generative AI in EMRs improves quality and decreases burden. Despite these advancements, the full potential of AI—to increase care access, reduce costs, and improve population health—remains untapped. The challenge lies not in technology but in systemic barriers: the lack of an industry-wide framework for safe and ethical use of AI, outdated compensation models, and the need for better coordination among healthcare entities. Overcoming these hurdles is crucial for harnessing AI’s transformative power, shifting from incremental improvements to widespread, meaningful change in healthcare.
Jeff Surges, CEO at RLDatix
AI isn’t new, but ChatGPT and large language models changed how we can engage with it. As hospitals and health systems continue to explore AI’s adoption and implementation, we can take a proactive, rather than reactive, approach by removing data silos and using AI to connect healthcare operations and deliver safer care. When thinking about AI transformation, the intention should be to enhance the human, drive accuracy and identify trends, rather than replace the human.
Karie Ryan, RN, BS, MSN, Chief Nursing Officer at Artisight
Facilitation of hospital operations and augmentation of the work of the clinician are areas where AI can be leveraged to support clinicians working at the top of their license. Ambient voice can be used to streamline short-form documentation, giving nurses time to provide direct patient care and minimizing administrative burden. Camera vision improves safety through continuous monitoring of patients and their environment. Implementing algorithms developed by clinicians for clinicians is key to realizing the potential of AI in the hospital setting.
Michael Rivers, PhD, Sr. Medical Director of Ophthalmology at ModMed
Claims processing needs AI applied at both ends of the transaction. Insurers quickly adopted AI to help determine which claims to process and which ones to reject, but the providers who run their claims through an AI “scrubbing” before submitting can minimize rejections and improve their efficiency. As more providers employ AI to vet their claims, it will create a more level playing field that limits patient involvement in the billing process and secures proper reimbursement.
Steve Albert, Chief Product Officer at R1
As technology continues to reshape healthcare, AI and machine learning take center stage in driving innovation and the transformation of healthcare operations. By pursuing AI in previously untapped areas like the revenue cycle, it is streamlining workflows, boosting productivity and accelerating cash flows. Large Language Models (LLMs) are being used to analyze vast datasets like account notes and call records, providing real-time guidance and improving the patient experience. Machine learning is being used to analyze previously rejected claims, reducing denial rates and improving overturn rates, ultimately leading to increased revenue. By integrating AI and machine learning into revenue cycle management, healthcare providers are not only improving financial performance but also reallocating resources to deliver higher-quality patient care.
Michael Gao, Co-Founder and CEO at SmarterDx
We’ve only begun to tap into AI’s potential in healthcare, and I’m particularly enthusiastic about its role in transforming vast amounts of data into meaningful insights for improved decision-making. Much of healthcare data is still raw data, and the ability to make use of that data adds friction from care delivery to billing to physician burnout. AI has the capability to transform this data into evidence-based insights with comprehensive audit trails, benefiting both care teams and back-office operations. For example, AI can sit on the backend, akin to a spell checker, to translate clinical data points like labs, medications, orders and flowsheets into documentation opportunities for CDI teams, enhancing both their findings and efficiency.
Matt Duffy, Chief Product Officer at Lumeon
The operational management of healthcare is on the verge of being revolutionized by AI in 2024. Administrative and back-office tasks consume an inordinate amount of time from our front-line clinical teams. We can expect to see AI have a major impact on scheduling, claims management, patient communication, data entry from notes and more. The anticipated outcomes is that clinicians are unshackled in order to pursue patient wellness.
Jason Jones, Chief Analytics and Data Science Officer at Health Catalyst
Though the hope and promise have been present for years, the actual performance of large language models is enabling a massive, practical shift in everything from chart abstraction to inbox response. While there remain gaps to close both in autonomous and co-pilot application, the pace of progress and reduced technical barriers are encouraging. GenAI is showing signs of addressing two of the biggest challenges facing healthcare today: labor efficiency/cost and healthcare worker burnout. We need more people to study the impact and help us all improve.
MJ Stojak, Managing Director, Data, Analytics and AI Practice at Pivot Point Consulting
There are several different applications of how AI is providing value to healthcare including streamlining patient flow, reviewing medical images for signs of disease, and advancing personalized medicine. I was fortunate to contribute to the product design and initial implementation of a complimentary decision support tool for the NICU of a children’s hospital. The product leveraged ML (machine learning) and NLP (natural language processing) – both branches of AI – to extract salient data from otherwise untouchable data sets to help improve care and outcomes for critically ill children.
The development of this product is still evolving but the ultimate goal is to provide a 360-degree view of the patient using disparate data sources (medical record, waveform bedside data, social determinants of health, social media, etc.), then aggregate that data to identify new information that could have contributed to the patient’s current condition. In parallel, we also built out a cohort creation tool that enabled the physician to select key values of their patient’s data to compare it with other similar patients as a way of collecting additional insights to further aid their decision making and improve outcomes. The first implementation of this product was specific to the NICU but imagine expanding it for any ill patient. AI has tremendous potential to be used for good in so many different ways in our industry. Because healthcare is heavily regulated, the level of trust in the data that feeds the AI is higher than in other less-regulated industries.
We recommend any healthcare organization embarking upon AI have a clear strategy and to ask yourself: what are you trying to accomplish with AI? Having data governance married to that strategy is also foundational to success to ensure the use of data, and outcomes, are ethical and equitable, and the full benefits are able to be leveraged. While not a sexy aspect of AI, this foundational work is critical. It comes down to doing the work to improve data quality and literacy so that an organization can accelerate their data maturity and drive innovation through more advanced and diverse AI applications.
So many good insights here! Huge thank you to everyone who took the time out of their day to submit a quote and thank you to all of you for taking the time to read this article! We could not do this without your support. What novel ways do you see AI being applied? What areas do you think AI needs to be applied to? Let us know either in the comments down below or over on social media! We’d love to hear from all of you.
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