It's already too late for healthcare leaders to start thinking about adding technology to the revenue cycle. The industry is forging ahead.
The healthcare industry is constantly changing in ways that revenue cycle leaders find it essential to keep up with. Changes in billing requirements, clinical criteria, payment models, and patient access can cause struggles for organizations with lagging processes.
A recent survey published by ModMed also adds urgency to the need for optimized technology. Sixty-one percent of patients surveyed placed importance on how easy it is to make payments when considering whether to continue with a health system. On top of this, 60% of patients surveyed were more likely to select one organization over another if appointments could be made online.
Successful organizations enhance their revenue cycles and create the bandwidth to address these changes with technology to streamline revenue cycle processes.
All areas of the revenue cycle must function at their best to achieve overall success. In this cover story, we hear from revenue cycle leaders on how they have used the best in technology to optimize their departments.
Tech to ease the good faith estimate burden
One of the biggest changes that has affected the front end of the revenue cycle is the implementation of the No Surprises Act on January 1, 2022. Within the No Surprises Act hides a new, burdensome regulation for healthcare organizations: the good faith estimate (GFE).
Under the law, healthcare organizations need to give patients who don't have certain types of healthcare coverage—or those who are paying out of pocket—an estimate of their bill before services are provided.
Not only do these GFEs need to be created, but they also need to be created quickly as patients have the right to receive a GFE for the total expected cost of items and services as soon as they schedule an appointment (the items can include costs of tests, drugs, equipment, hospital fees, and more).
The GFEs also need to be accurate since patients can dispute final medical bills if the charges are at least $400 more than what was presented on the GFE.
It's easy to see how much work this regulation is for front-end revenue cycle staff. The American Hospital Association (AHA) agrees.
According to the AHA's March letter to CMS, GFEs regularly take revenue cycle staff 10–15 minutes to produce.
Because of this time constraint, an AHA member hospital reports that their staff can only process 75 estimates per day, which is barely meeting the GFE demand. A member health system with several locations reports needing to do 1,500 per day across the system, the letter said.
It's clear that operationalizing processes to generate reliable, accurate GFEs is necessary and has pushed many organizations to enhance their technology to ease this burden.
Ochsner Health, a nonprofit health system based in New Orleans, had price transparency initiatives already in place for years prior to the No Surprises Act implementation—including an online estimator tool on its website. But when January 1 came around, enhancements in technology still needed to be made to streamline its GFE process.
Since the organization already had some programs in place to adhere to GFE requirements, when looking to optimize their front-end revenue cycle, Ochsner Health decided to look internally at their preexisting software while filling in any gaps with a third-party vendor.
Melissa Woods, CPC, assistant vice president of revenue cycle financial clearance at Ochsner Health, calls this the organization's hybrid approach to its GFE technology.
"Most of our estimates generate automatically. Within our EHR system there's real-time eligibility that runs behind the scenes to verify insurance and coverage. We have batch processes that run nightly and some that run a certain number of days in advance of a scheduled service," Woods says.
Pictured: Melissa Woods, CPC, is the assistant vice president of revenue cycle financial clearance at Ochsner Health. Photo by Jonathan Bachman/Getty Images.
"We can also manually trigger an eligibility query if we need to have the latest benefit information on the patient from the insurance company. From this we get a plethora of information including what deductible amount is left, how much they have left on their max out of pocket, and the coinsurance or copay for that particular service," Woods says.
This takes a lot of the burden off staff as roughly 85% of these estimates generate automatically through Ochsner's preexisting Epic EHR system. This is invaluable since the organization now runs about 50,000 estimates a month on average.
However, Ochsner found that not all estimates were auto-finalizing using Epic. Auto-finalizing was important for the team since it would replace the manual work of employees correcting service prices.
So the organization added automation to address gaps in the current technology.
The team worked with the AI vendor Olive to help auto-finalize the estimates. For Ochsner, its third-party vendor auto-finalizes about 20% of the estimates now, massively streamlining the process.
Additional AI was an important part of a streamlined GFE process for Ochsner's revenue cycle staff, but the entire GFE process was built and centered around the patient financial experience, and the added AI helped to improve this.
Since implementing this new technology, the moment a GFE is finalized, it automatically goes to the patient's portal, and the patient is notified that there is a new estimate available. The patient can then pay for the service immediately. Getting this information to the patients quicker and more accurately greatly improves Ochsner's patient financial experience.
"Our financial clearance call center will contact patients up to three weeks prior to their appointment to verify demographics, insurance, and communicate the upcoming expected out-of-pocket amount. Patients can pay in full right then or we can talk to them about payment arrangement options. We have internal payment plans, external payment plans—all interest-free to our patients. We work with our patients to give them plenty of options to try and pay for their care."
Adding a third-party vendor for automation on top of its existing software was the key to success for Ochsner and is the reason why this is the best in technology for the organization right now. Since being able to streamline the process and autofinalize more GFEs, the patient financial experience has greatly improved, and front-end staff are less burdened.
"My advice is to take a comprehensive look at your overall revenue cycle needs. For us, the hybrid approach worked best. We have gaps to fill with the No Surprises Act and pricing transparency requirements which are expanding faster than we can keep up with current technology and resources. That's why evolving our technology through a variety of vendors works for us right now," Woods says.
For Ochsner, having a good pricing transparency model and GFE process in place has played a large part in its preservice collections, which is no small feat as Ochsner is consistently considered one of the top performers of Epic preservice collections in the nation, says Woods.
A lot of the organization's success with preservice collections comes from their hybrid use of technology for GFEs as well as patient education.
"We have so many resources available to our patients on our website regarding the estimate process, understanding their insurance, and what to expect on their financial journey. And auto-finalizing our GFEs allows us to provide so many estimates to our patients up front so that they can ask questions and get help understanding what their costs are going to be before they even have services," says Woods.
Tech to improve CDI and physician workflows
At the heart of the middle revenue cycle you'll find the CDI and coding departments. This area of the revenue cycle is not new to technology as it is generally seen as an area within an organization that is closely tied to reimbursement.
In fact, according to the Association of Clinical Documentation Improvement Specialists' 2022 Industry Overview Survey, 74.82% of respondents said their CDI departments are directly involved with reviewing clinical validation denials, proving the CDI departments' direct link to reimbursement.
That's why it was so important for Tami McMasters Gomez, director of coding and CDI services at UC Davis Health, to implement the best technology for her CDI and physician teams to improve CDI and physician workflows to ensure maximum reimbursement for the organization's middle revenue cycle.
Photo credit: Tami McMasters Gomez is the director of coding and CDI services at UC Davis Health. Photo taken by: Don Feria/Getty Images.
The first step, even before implementing technology in these areas, was to build what they considered to be the perfect organizational chart to support the mid–revenue cycle.
After reworking departments and adding to various revenue cycle teams, McMasters Gomez says UC Davis Health was able to get the teams to a place where they were performing optimally.
"We were demonstrating a return on our investment with staffing and physician education. Once that was in place, I thought, ‘Well, we've accomplished what we set out to do by increasing our staffing, touching every patient, educating our providers. What can we do now?' And the next step was bettering technology," she says.
At the time, UC Davis Health was using computer-assisted coding with natural language understanding (NLU), but McMasters Gomez wanted to take its technology to the next level.
"We then embarked on a journey of investigating where we wanted to go and researching what products existed out there," she says.
For the CDI team, UC Davis Health decided to deploy the 3M™ M*Modal CDI Engage One™ software, which uses advanced AI and NLU technology to embed proactive clinical intelligence into front-end and back-end CDI workflows.
This has been the best in technology for UC Davis Health's CDI team, and according to McMasters Gomez, "it has what I like to call all the bells and whistles for enhancements and workflows. It has a prioritization list that we've been able to customize to ask, ‘What are the cases we want to prioritize for review?'"
For example, if a case has already been optimized from an MS-DRG perspective or severity of illness and risk of mortality and has accurate documentation for reimbursement, UC Davis Health is not interested in having its CDI team continue to follow that case and look for enhancement opportunities.
"We want a case like that to be on the CDI team's radar in case there are any unforeseen events or quality outcomes that may occur during the hospitalization, but our CDI teams shouldn't be spending their time on cases like that," she says.
This is why UC Davis Health created specific and deliberate prioritization lists for the CDI team to work from. Each team member has their own prioritization list, and those cases pop up with an established priority number (one through four) next to them—one being the highest priority for review and four being lowest in priority for review. This helps the CDI team to utilize their review time more efficiently.
"We also have what's called ‘evidence sheets.' As the CDI teams are reviewing their cases, there is artificial intelligence that will pop up and say, ‘There's evidence in the record that this patient may need further specificity on a diagnosis, or there's evidence in the record that the patient may have a diagnosis that hasn't been documented.' This prompts the team to further investigate," she says.
When it came to enhancing the technology for the physician teams, McMasters Gomez looked toward 3M's computer-assisted physician documentation.
"The computer-assisted physician documentation is a physician-facing tool that we were very specific about," she says. "We did a lot of physician engagement, socialization, training videos, face-to-face meetings, you name it, to get physicians familiar with this technology. We didn't want this to replace the CDI team—we wanted it to enhance the overall program."
Adding computer-assisted physician documentation had several benefits for UC Davis Health. First and foremost, McMasters Gomez says, it allowed physicians to engage in real time as they were documenting during the patient care episode and allowed physicians to put their focus back on patient care. Its physicians were able to deliver more efficient care without being pinged after the fact or after discharge.
"As the physicians are engaging and documenting the patient's encounter, there's also NLU that's reading the documentation in real time and is asking for things like specificity of the documentation, such as, ‘Why are we treating this patient with this medication?' " McMasters Gomez says.
When it came to choosing technology and a vendor, McMasters Gomez says customization was key. UC Davis Health has since embarked on customization with various nudges with the vendor, which McMasters Gomez says is the key to successful adoption of technology.
UC Davis Health needed to be more sophisticated and deliberate about its technology enhancements, as it didn't want to lose physician engagement by pinging physicians for things that they already knew and documented well.
"When implementing technology, we wanted to be very deliberate about how we engaged with our teams. They are the customers and their voices matter to us. Listening to your revenue cycle teams' needs and putting patient care as the priority makes all the difference," she says.
To that point, McMasters Gomez says that technology deployment and enhancements are great, but technology can only be successful if your revenue cycle team is using it.
This is why UC Davis Health conducts extensive piloting and onboarding practices when implementing technology.
"Getting a large-enough pilot group and representation from various types of service lines is the key to success. Make sure you get a full pilot group that can provide you candid feedback, listen to what they have to say, and make sure that you actually hear them. Your revenue cycle staff and providers are your customers and if they don't engage with the technology, it's useless," McMasters Gomez says.
Since implementing these new technologies for CDI and physician teams, UC Davis has seen overall improvement of physician workflows, improved documentation accuracy, and an almost 5% increase in comorbidity capture rates.
"When it comes to implementing technology, making sure you stay flexible and willing to pivot and change is a huge takeaway," she says.
"You also have to make sure to hold the vendors accountable to product development. There may be technology that you need that the vendor is not equipped to give you, but if you had it, it would make all the difference for your organization," McMasters Gomez says. "Never stop asking them for more."
The federal oversight agency recommends CMS adopt new coding procedures to compare care quality to in-person visits.
With pandemic-fueled temporary waivers on telehealth leading to a surge in telehealth visits in 2020, especially on audio-only platforms, the practice is overdue for its own exam for effectiveness and privacy, according to a new Government Accounting Office (GAO) report.
The use of telehealth services topped 53 million visits in the period between April and December 2020. During the same period in 2019, only 5 million such visits occurred. Many of those were conducted by phone or non-video telehealth, which was rarely allowed prior to the pandemic.
The Centers for Medicare & Medicaid Services has monitored some risks to program integrity related to these telehealth waivers, but the GAO report found that CMS "lacks complete data on the use of audio-only technology and telehealth visits furnished in beneficiaries' homes," in part because no billing mechanism exists to identify all these telehealth visits.
"Providers are not required to use available codes to identify all instances of audio-only visits," the GAO reported. "Moreover, providers are not required to use available codes to identify visits furnished in beneficiaries' homes."
The GAO said this coding is important to monitor the quality of these telehealth services as compared to equivalent in-person services.
"CMS has not comprehensively assessed the quality of telehealth services delivered under the waivers and has no plans to do so, which is inconsistent with CMS' quality strategy," the GAO said. "Without an assessment of the quality of telehealth services, CMS may not be able to fully ensure that services lead to improved health outcomes."
The GAO offered three recommendations for CMS going forward:
Develop a new billing modifier or make clearer how to bill audio-only office visits for better tracking;
Require providers to use existing site of service codes when beneficiaries receive Medicare telehealth services at home; and
Assess the quality of telehealth services delivered during the public health emergency.
Finally, the GAO urged the Health and Human Services Department's Office of Civil Rights to offer additional education, outreach, and other resources to providers to help them explain risks to privacy and security that patients may face during telehealth visits.
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Healthcare organizations are using augmented and virtual reality technology to give doctors and nurses better insight on challenging treatments.
While augmented and virtual reality is seeing success as a clinical treatment for issues like pain management, some healthcare organizations are using the technology to improve training and education for doctors and nurses.
The technology gives providers an immersive experience, allowing them to see and even act in typical—and not so typical—situations, learning how to act under normal circumstances as well as in an emergency. According to one study profiled in the Harvard Business Review, providers using a VR platform to train on a surgical procedure saw a 230% improvement on the Global Assessment Five-Point Rating Scale when compared to providers receiving traditional training.
"Today’s rapidly evolving surgical landscape requires new ways to provide access to experiential surgical education," Gideon Blumstein, an orthopedic surgery resident at UCLA's David Geffen School of Medicine and the author of the HBR story, concluded. "In addition, we must formalize our approach to technical assessment in order to more objectively measure surgeons’ capabilities to ensure a consistent level of quality and standardized skill set of our surgical workforce."
At the Johns Hopkins University School of Medicine, AR and VR are used to give future clinicians a better idea of what they'll be facing when they begin their healthcare career.
"As part of our resident education curriculum, virtual reality, used in conjunction with physical models, provides our junior residents an immersive training environment to learn a variety of procedures," says Dawn Laporte, MD, vice chairman of education and a professor of orthopedic surgery. "Our residents can practice and assess their learnings both collaboratively and independently."
"From a residency program perspective, reporting and analysis from surgical VR platforms can be an outstanding tool to benchmark individual performance, proficiency and progression of residents across various programs, and can also detect areas of weakness or improvement in the curriculum," she adds. "Any time you can decrease the learning curve and increase the opportunity for residents and fellows to learn, train, and repeatedly practice outside of the operating room, will lead to improved procedural competence and performance–translating directly to better care quality and outcomes."
Laporte says the technology platform is quite different from the traditional routine of working with cadavers or Sawbones simulation training models.
"There was a learning curve for those unfamiliar with the technology," she says. "As with the integration of any new technology there are going to be challenges, so apart from the unexpected technical issues, [there were a few problems with] encouraging utilization of VR and making sure there are enough headsets available."
"It’s important to note that virtual reality is not a replacement for hands-on training, but rather an enhancement," Laporte adds. "Particularly, VR gives nuanced and aspiring surgeons the unique ability to practice both independently and repeatedly, for continuous skills training, with minimal utilization of risk or resources."
She says Johns Hopkins will be analyzing how the platform compares to other training methods in ease of use, comfortability, and performance, as well as confidence in a simulated VR environment.
"As we continue to integrate more VR training modules into the curriculum, we’ll explore offering tailor-made courses that meet the individual and residency program requirements," she adds. "We also look forward to the ability to introduce variability through VR modules to see how residents think on their feet or adapt when faced with unexpected events to develop the skills to anticipate and react to intraoperative complications.
At Texas A&M University's College of Nursing in Corpus Christi, administrators are using a combination of VR and patient simulation technology developed by Gaumard to help nursing students learns the nuances of assisting in childbirth and post-partum care.
"It's very difficult for students to visualize what's happening," says Lisa Snell, the school's nursing simulation laboratory supervisor. Students use a VR headset and holograms to not only virtually experience the delivery of care, but to also see what goes on inside a woman's body when she gives birth.
"Textbooks are flat, one-dimensional and often revised," says Catherine Harrel, an assistant clinical professor at the school. "This gives [students] an opportunity to see what actually happens in a normal birth as well as in an emergency. They learn how to think and respond quickly [to emergencies] they might not see that often" but which might save lives.
Snell says the program has proven its value in preparing nursing students for the real world and will soon be used in local hospitals to help nurses there improve their capabilities and stay up to date on the latest treatments.
"Teaching tends to be technical, and that can lead to some bad habits," Harrel adds. Nursing students "not only learn how to deal with different types of situations [through VR], they also learn how to communicate with patients. Sometimes that's the hardest thing to do when you walk into a [patient's] room."
In the second of a two-part interview, Dennis Chornenky, Optum's senior vice president and chief AI officer, looks for inspiration from finance and institutional review boards to steer AI toward maturity.
In early 2022, Dennis Chornenky, MPH, became chief AI officer and senior vice president at Optum Health, a subsidiary of the UnitedHealth Group health plan. In part 1 of his conversation with HealthLeaders, posted on Wednesday, he addressed the role of AI in creating data-driven insights to prevent disease and personalize care; how to apply governance frameworks to AI, and his prior role serving Presidents Trump and Biden in crafting national strategies on AI and telehealth.
HeathLeaders: Where are we on the maturity cycle of AI? Where are the limits of AI, particularly with the eye toward healthcare?
Chornenky: Maturity in the AI space is certainly an evolving concept. The further out something is, the less defined it becomes, and the more variability you're going to run into. My view is that we're early in the maturity stages, in terms of development potential.
Dennis Chornenky, MPH, senior vice president and chief AI officer at Optum Health. Photo courtesy Optum Health.
Some of the work that I’m privileged to be advancing, that creates governance and a portfolio management model for our organization, is quite innovative and I would say ahead of the curve. I'm doing that by leveraging a combination of experiences and emerging frameworks.
With regard to managing a portfolio of AI investments, we can pull in some insights from the financial sector. We can look at institutional investment policy statements, for example, and approaches to outlining which risks you may be likely to encounter as a portfolio manager. What are you doing to mitigate those risks? What's the strategic goal of the portfolio? What level of risk are you willing to accept to achieve your target return? These kinds of established approaches can be helpful to keep in mind.
I also draw on my epidemiology experience with institutional review boards, which are designed to ensure ethics and safety in clinical trials. There is an emerging recognition in healthcare that we can do something very similar for the AI space, so we're seeing the emergence of “AI review boards,” loosely modeled on institutional review boards, that can screen AI projects, models, and applications for various types of risks in operational and clinical environments. We want to make sure that we have consistent and reliable processes that help ensure our models are safe, ethical, and as fair as they can be for any particular use case.
As for the limits of AI, we can frame this from the perspective of the trade-offs between performance when doing precise, clearly defined tasks against the ability to perform a broad range of tasks and solve a broad range of cognitive problems creatively. Machines excel at doing repetitive tasks with precision and humans excel at navigating dynamic environments and solving new problems. What business leaders should really be thinking about in this context is how to drive the kind of digital transformation in their enterprise that optimizes the collaboration of humans and machines in a way the amplifies the strengths of both and minimizes the limitations of both.
HL: The fact that you have this title of chief AI officer at Optum suggests that the day is not far off where AI becomes part of the standard of care. Is that something you ever hear discussed?
Chornenky: It's an interesting question. For me, this conversation started in telehealth, where the question was, could telehealth be the standard of care in the sense that there were concerns many years ago around the risks of doing telehealth? A health system that's providing care delivery through telehealth may be exposing itself to legal action if some harm comes from offering virtual care. The other side of that argument was that there may come a day where health systems that don't provide telehealth as a modality may be the ones exposing themselves to lawsuits, because they’re the ones providing less access to care, or restricting care, because they’re forcing people to only get care in person, potentially disadvantaging certain populations.
It's the same question with AI. Does using AI introduce legal risks, or will there come a day when it's expected and not using AI is what introduces legal risks? Because if you're not using more advanced technologies, you might be relying on imperfect and more variable human decision making in diagnosis and care delivery, without the use of more precise machine recommendations. We're pretty far away from something like that, maybe decades, because of the very limited maturity of the use of these technologies and the lack of scale. And in some ways, we may never entirely get there.
There's always going to be some sort of downstream human decision making. It's unlikely that we're going to get to a point where machines will completely control all aspects of the diagnostic or care delivery process. There's always a requirement, a natural need, to have humans make the broader decisions around whatever machine insights may be derived from data, and the kinds of actions we may want to take as a consequence. That said, over time we will certainly see a growing automation of tasks and more care decisions being informed by data-driven insights.
HL: Before the pandemic, Vinod Khosla was going on and on about how we don't need doctors. The pandemic changed that dialogue.
Chornenky: The pandemic forced a recognition of the importance of investing in technology. But I don't think It did much to support Khosla's case, which, if it can be described as doctors inevitably being replaced by computers one day, is a poor formulation. There are many tasks currently performed by physicians and medical technicians that will indeed be replaced with AI, i.e., computational methods and machines that simulate or surpass human cognitive capabilities. But the overall role of the physician is much too broad and requires the kind of big picture thinking, creativity, and bedside manner that machines are unlikely to achieve in the coming centuries, if ever.
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CHIME and others say hospitals and other providers are not ready and need more help from HHS.
Despite assertions from the Office of the National Coordinator for Health IT (ONC) that it has gone the extra mile to help health systems abide by new information blocking rules that begin to take effect on October 6, several are lobbying for a significant extension of the deadline.
In a statement, CHIME officials asserted the organization has been an ardent supporter of information sharing and continues to advocate for patients’ ability to access their healthcare information in a digital format. But members have been hampered by scarce resources in trying to meet the deadline, the organization said.
"CHIME members remain steadfast in their dedication to be a trusted partner for patients and safeguard their ability to access their healthcare records, but it’s clear that more time is needed to ensure that providers have a thorough understanding of these important policies," said Russ Branzell, CHIME's president and CEO. "There has not been enough guidance on best practices and potential enforcement."
Citing what it said was overwhelming feedback from CHIME members representing a broad range of providers from across the healthcare continuum, including electronic health record (EHR) vendors, CHIME leadership maintained that these stakeholders are not fully prepared for the deadline.
With clinician burnout rates increasing, CHIME officials said HHS' unclear and inconsistent information sharing about data requirements could further strain healthcare providers and their support staff and may inadvertently undermine HHS’ goals to reduce provider burden, improve interoperability, and empower patients with their information.
Branzell said the “need for clear guidance is so important, and we need to make sure that all healthcare providers fully understand the many nuances of these complex policies.”
Small and rural providers, who more heavily rely on their EHR vendors for regulatory compliance support, are particularly unready, CHIME officials said.
Postponing compliance was not CHIME's only request. The organization also requested that HHS use corrective action warning communication to providers-- especially before they impose any financial penalties or begin formal investigations.
CHIME urged the HHS to ensure that providers and clinicians have the guidance, education, and technology to support these new policies before full implementation and enforcement of information sharing regulations.
In a statement, CHIME officials said, "We believe strongly in information sharing and want to see these policies succeed. A successful implementation of information sharing necessitates that all stakeholders have the critical tools, knowledge, guidance, and systems required to comply. This is simply not the case for most of the providers represented by CHIME."
An ONC spokesperson noted that the agency has provided a wealth of information about information blocking, including fact sheets, FAQs, blogs, and webinar recordings.
The spokesperson said these resources have been continually updated since April 5, 2021, the date when the regulation became official, and pointed to a fact sheet posted at that time.
Starting October 6, "actors will be expected to avoid interfering with access, exchange, or use of the full scope of EHI, except when applicable law mandates the interference or an information blocking exception is met," the spokesman said.
The ONC also said it would post a related blog post ahead of October 6 on its Buzz Blog.
In 2016, the 21st Century Cures Act made sharing electronic health information the expected norm in healthcare by authorizing the HHS Secretary to identify "reasonable and necessary activities that do not constitute information blocking." The ONC's 2020 Cures Act Final Rule established information blocking exceptions to implement the law.
The CHIME letter was also signed by the American Hospital Association, American Medical Association, Federation of American Hospitals, and Medical Group Management Association.
In the first of a two-part interview, Dennis Chornenky, Optum's senior vice president and chief AI officer, describes how he is driving AI tech for 15 million members of the United Health subsidiary.
At the start of 2022, Dennis Chornenky, MPH, became chief artificial intelligence officer and senior vice president at UnitedHealth Group health plan subsidiary Optum Health, after having served as a senior advisor and presidential innovation fellow in the White House in both the Trump and Biden Administrations.
The chief AI officer is one of the newest titles in the C-suite. Only a handful exist, in places such as the US Department of Health and Human Services and at technology companies like IBM, Elevance Health, and eBay.
In this two-part HealthLeaders interview, Chornenky describes just what a chief AI officer does, how it dovetails with pressing needs in Optum and all of healthcare, and what AI means for the future of healthcare.
HealthLeaders: Optum has pushed for value-based healthcare. What role is AI playing in driving that?
Dennis Chornenky: At Optum and UnitedHealth Group we’re driving healthcare transformation toward comprehensive value-based care, and AI is playing a big role in that. It’s a key focus of our growth strategy, helping more patients and care providers transition from fee-for-service to value-based approaches. We’re applying advanced technologies to drive better and more consistent care outcomes at lower overall cost.
Dennis Chornenky, MPH, senior vice president and chief AI officer at Optum Health. Photo courtesy Optum Health.
We have around 15 million members participating in value-based arrangements with over 1,000 hospitals and over 100,000 providers. Through our OptumCare delivery organizations we’re leading the industry in terms of the proportion of the patients we serve participating in value-based arrangements. I think we're expanding that at the highest rate out of any other care delivery organization in the US as well.
The way AI can help us accelerate this expansion into value-based care is by leveraging data to identify patients and members best fit for value-based care models and the clinical innovations and operational efficiencies that are most important in driving that transformation. There is a spectrum of data-driven insights that help us to better understand which patients may benefit most from which types of interventions and which types of care plans. That ends up getting broken down into a whole lot of different things, whether we're looking at disease prevention or surveillance, or integrating telehealth and virtual encounters into care modalities.
We are applying supervised machine learning techniques to improve our ability to predict disease progression and enable earlier interventions and unsupervised techniques like clustering to help us better understand the natural cohorts in our patient and member populations to advance more personalized care models. Overall, we are looking at anything that can help us improve patient outcomes, advance clinical innovation, and reduce costs.
HL: How can healthcare audit the AI it's starting to consume and use that to drive improvement?
Chornenky: You're right to make the connection that the way we approach the risks involved in deploying AI applications can be an important opportunity to drive improvement. AI governance is an emerging field that can leverage industry frameworks like Responsible AI to facilitate auditability and mitigate regulatory and reputational risks. A more technical framework referred to as ML Ops can help mitigate technical and model lifecycle risks. When done correctly, AI governance in healthcare helps to improve access to care and advance health equity. Without it, AI applications can run the risk of actually amplifying existing healthcare disparities.
I’m really encouraged that healthcare leaders are starting to understand that AI governance is an important area of investment and that it can help enterprises identify and mitigate the technical, regulatory, and financial risks posed by AI.
It’s also fascinating how rapidly innovation has been evolving in this field, with more and more startups and AI enterprise companies launching new offerings for Responsible AI, ML Ops, and bias and fairness assessments. The more forward-thinking health systems are also making investments in internal processes. Mayo Clinic, for example, has recently stood up a governance model they refer to as the “AI Translation Assessment” process, led by a distinguished group of experts.
At the federal level, the FDA is developing regulatory guidelines for Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices. NIST is developing a new AI Risk Management Framework. There is a growing body of emerging legislation across the US and in the EU that will have a significant impact on how we develop and deploy AI. I participate in several industry groups focused on developing best practices and standards for AI governance in healthcare and strategies for regulatory engagement.
HL: What learnings did you take from your time at the White House?
Chornenky: Serving in a non-political role across Republican and Democrat administrations, especially during the pandemic, gave me a lot of perspective on how our federal government works and how to successfully formulate and advance national policy, particularly in the healthcare and technology sectors.
As a senior advisor and a Presidential Innovation Fellow I was initially focused on advising our US chief technology officer on national AI strategy and our federal chief information officer on federal AI strategy. National AI strategy is how we think about cultivating growth and innovation in the private sector and the markets regarding AI/ML technologies, scaling up investment in R&D, academic partnerships, and also building trust in these technologies among the American people. This is also where we start getting into AI ethics and Responsible AI, or trustworthy AI.
Federal AI strategy is how do we think about standing up better data science capabilities across the federal government. This is everything from vanilla IT cloud migration, to upskilling the existing workforce with data literacy and analytics curricula, to thinking about data science as a career path, creating new job codes with OPM, and new processes and programs for engaging data science talent, recruiting, and retention. I had a portfolio of agencies I worked with to advance innovation, AI governance models, and AI capability maturity roadmaps.
As part of this work, we launched a new federal AI community of practice that was meant to bring together leaders and practitioners from across federal agencies to share best practices and do the type of collaborative work that they might not always be able to do within their usual, perhaps more constrained, agency environments. I also helped manage a government coordination committee that produced the executive order promoting the use of trustworthy AI in the federal government. This was a very important initiative that was bipartisan in nature, and the government is implementing the provisions of that executive order today.
When the pandemic hit, I was able to apply my training as an epidemiologist to help coordinate response efforts across federal agencies and our private sector partners, including technology companies, health systems, and payers.
What turned out to be more consequential for me, however, was my background in telehealth. I previously had an AI-driven telehealth and smart-scheduling company out of Palo Alto. Through that work I got to know everybody in the industry, the CEOs of the larger telehealth companies, the different industry associations and who led them, and top law firms working on telehealth regulatory issues around the country. So I really ended up in a unique position to pull all of that together and very quickly formulate and advance a national strategy on telehealth and how we were going to work across federal agencies with our private sector partners to make telehealth accessible to as many Americans as quickly as possible.
I think probably the biggest silver lining, if you will, of the pandemic, was that it accelerated telehealth adoption and access to virtual care for Americans across the board. It wasn't only for mitigating the risk of the spread of infectious disease, but also helped to ensure continuity of care for non-COVID related cases.
There was a tremendous amount of work done from an administrative and a policy perspective. Within just a few weeks, we put out over 50 waivers to enable telehealth, a couple dozen new billing codes, and a new modern website, telehealth.hhs.gov, to help patients and providers adopt telehealth in safe ways. We also convened a telehealth innovation summit, which was a great way to celebrate a lot of the work that had been done, particularly out of the deputy secretary's office at HHS, and with our private sector partners, but more importantly, to align on what the next steps should be to continue to advance adoption of telehealth and expanding access to care for all Americans.
Editor's note: Part 2 of this HealthLeaders interview with Optum SVP and chief AI officer Dennis Chornenky will be posted on Thursday, Sept. 29.
CIOs and other healthcare executives gathered in Boston this week for the HealthLeaders Innovation Exchange, where they talked about moving past the pandemic and into a new era of connected health.
As the healthcare industry seeks to regain its footing after the pandemic, those in charge of innovation strategy are looking to balance lessons learned from COVID-19 with the need to be on solid financial ground.
That's a challenging task, say health system CIOs and other executives attending the HealthLeaders Innovation Exchange this week in Boston. In many cases, health systems have adopted telehealth and digital health out of necessity, to deal with COVID-19, but they haven't really put the work into shaping a long-term strategy.
"For the better part of the last decade we've been paying lip service to digital transformation," said Saad Chaudhry, MSc, MPH, CHCIO, CDH-E, chief information officer at Annapolis, Maryland-based Luminis Health. The pandemic "was a splash of water in everyone's faces."
Chaudhry was one of about two-dozen healthcare executives attending this year's Innovation Exchange, an annual event designed to bring CIOs and others together to discuss innovation strategy. Today's event included a master class in human-centered design by Chris Waugh, vice president and chief innovation officer at San Francisco-based Sutter Health, along with round-table sessions aimed at defining innovation and discussing barriers and best practices.
In a poll conducted by HealthLeaders at the beginning of the event, about 70% said restoring their health system's operating margins was one of the top two priorities for the coming year, while advancing digital transformation followed right behind at about 64%.
Those results reflected a desire to move past the pandemic—in fact, only 18% listed as a priority coping with the fallout from COVID-19—and to apply lessons learned in the shift to virtual care to reimagine how healthcare is delivered. And they reinforced that digital transformation is at the top of the to-do list, as innovation was only listed among the top 2 priorities by 23% and strengthening cybersecurity—always a hot topic—didn't even get a vote.
"We should have done this a long time ago," Chaudhry pointed out.
When asked "who champions your causes most at the organization," 55% selected the CEO, indicating an emphasis on top-down support for innovation (10% selected the CMO or CNO, and 25% went with "someone else." But this question and the discussion around it highlighted the fact that innovation isn't necessarily channeled through one C-suite position or based in one department, and can and should be found in all areas and levels of the healthcare system.
James McElligott, MD, MSCR, executive medical director of telehealth and an associate professor at the Medical University of South Carolina's Children's Hospital in Charleston, echoed several comments in pointing out that innovation is best supported when many departments (and department heads) share in the process, and can be fostered as easily by one doctor with a unique idea or strategy as the head of a hospital.
That can also be a hindrance. When asked "who blocks your causes most at the organization," 25% selected the CFO, highlighting the challenge that innovation faces in securing financial backing, and 55% selected someone else, over the CEO (10%), the CMO (5%), and the CNO (5%).
This, and the discussion that followed, indicates innovative project face a wide array of challenges, including politics. A new technology or program might look great in a pilot, but it might run aground when several departments seek to take control and turn it into a political issue, or it might falter because no one wants to champion the project.
Bradley Crotty, MD, MPH, vice president and chief digital engagement officer at the Froedtert & Medical College of Wisconsin Health Network, as well as chief medical officer and chief product officer at Inception Health and an associate professor at the Medical College of Wisconsin, pointed out that the pandemic did give health systems a process that everyone followed to "get things done." That attention to one common goal worked, he noted, and showed healthcare organizations how to cut through the barriers to achieve a goal.
That should be a model for innovation, he and other said.
Finally, executives were asked where their organization stands in its digital journey. The results, as with the healthcare industry, were across the board. Some 44% were building out the technology and a process roadmap, while 33% were in the execution stage, 11% were conducting a needs assessment, almost 6% were either ensuring ongoing services and support or at the baseline.
The results speak to the various stages of digital health transformation, and point to the fact that each health system will travel its own path. But that doesn't mean they can't share advice on how to make that trip.
Pictured: Melissa Woods, CPC, is the assistant vice president of revenue cycle financial clearance at Ochsner Health. Photo by Jonathan Bachman/Getty Images.
Photo credit: Tami McMasters Gomez is the director of coding and CDI services at UC Davis Health. Photo taken by: Don Feria/Getty Images.
