As I wrote on Monday, 2019 was a year in which federal payment and regulatory policy dominated the discourse in the U.S. healthcare system as never before. As I mentioned, the conflict between the Centers for Medicare & Medicaid Services (CMS), and its Administrator Seema Verma, on the one hand, and a number of national healthcare associations, encapsulated the complex landscape around alternative payment models specifically, and more broadly, the attempted shift from volume-based to value-based reimbursement, as it stands right now. There’s no question that federal reimbursement change, driven by the looming cost cliff (the Medicare actuaries are predicting that total annual U.S. healthcare system expenditures will explode from their current total of $3.6 trillion to $5.963 trillion, by 2027—a 60-percent increase over eight years. No policymakers or policy leaders from either major political party believe that that trajectory is sustainable.
So if 2019 was dominated at the policy level by discussions (and sometimes arguments) over how best to use reimbursement mechanisms to prod healthcare providers forward into two-sided risk and other payment mechanisms, the level of awareness of what it will take to help achieve that shift, also rose considerably this year. To wit, an awareness that value-based care delivery and payment can only succeed through the leveraging of data analytics—along with, of course, clinical transformation, continuous performance improvement, incentive alignment, and a number of other critical elements.
And though so many other elements will be vital as well to the success of value-based healthcare delivery and payment, all of those in the trenches—especially those who have achieved a level of advancement in that area—agree: it’s becoming incredibly clear that that the successful leveraging of data analytics will be absolutely essential going forward.
As documented in our November/December cover story, many hospital-based systems, medical groups, and health plans are forging ahead, cracking the code on the leveraging of data for value-based care. Among the numerous case-study examples I noted:
Ø The Indianapolis-based Anthem has been focusing on the highest-utilizing plan members in the health plan’s western region, to a level at which, as Antonio Linares, M.D., regional vice president and medical director for Anthem National Accounts, told me, “Our analytics process can identify the 3 percent of their panel membership that are responsible for 29 percent of the panel’s healthcare costs.”
Ø Meanwhile, Matthew Pirritano, Ph.D., director of population health analytics and quality improvement at L.A. Care, the contracted health plan for MediCal (the California term for Medicaid) enrollees in Los Angeles County, has been helping to lead a program that is leveraging a regression model that helps Pirritano and his colleagues to set targets for physician groups in terms of the number of patient encounters they should be involved in on a regular basis.
Ø And at Advocate Aurora Health, the 15-hospital integrated health system based in Downers Grove, Ill., and with care sites across northeastern Illinois and southeastern Wisconsin, that health system’s three accountable care organizations (ACOs) participating in the Medicare Shared Savings Program (MSSP) saved $61 million in costs in 2018, achieving the highest MSSP savings in Illinois and the second-highest in Wisconsin.
What’s the “secret sauce” here? “We have a lot of innovation in our care,” Gary Stuck, D.O., Advocate Aurora’s chief medical officer told me this autumn. “Some examples include that we invest in predictive analytics; we have outpatient care managers and care management teams that try to anticipate high risk of hospitalization or readmission.”
The word “predictive” is a key one here, with the leaders of many of the more advanced patient care organizations plunging headlong into the leveraging of predictive analytics, in order to do what needs to be done: achieve true clinical transformation. For example, at the St. Louis-based Mercy, Joseph Drozda, M.D., director of outcomes research at the 40-plus-hospital health system, has been leading a fascinating initiative to proactively improve the identification of congestive heart failure patients for whom the implantation of cardiac pacemakers is appropriate. As Dr. Drozda told me, “We created a robust data set with all of our patients with heart failure, around 120,000 patients.” Drozda and his colleagues have learned a great deal so far on the journey. “It gets into data curation. It’s apparent to us that with previous efforts to use electronic health record [EHR] data, when it’s raw data in data dumps, it hasn’t been very helpful,” he told me.
And of course, artificial intelligence (AI) reached a new level of adoption this year in patient care organizations nationwide. AI was the absolute talk of the annual RSNA Conference, sponsored by the Oak Brook, Ill.-based Radiological Society of North America, and held the first week of December at Chicago’s vast McCormick Place Convention Center. As I noted in a December 2 report, “On Monday, December 2, at Chicago’s McCormick Place Convention Center, the annual RSNA Conference included more sessions than ever that were focused on artificial intelligence (AI), and its application to radiological practice. Among numerous sessions today that involved AI was a session held at 3 PM local time, and entitled ‘Informatics (Artificial Intelligence: Triage, Screening, Quality).’ While the title of that session didn’t fully convey its content, much was shared by its first two presenters that was enlightening for radiologists and other radiology-related professionals, around controlled studies assessing the effectiveness of AI in specific clinical practice situations.”
The case studies presented during that educational session involved one major area in radiological practice in which AI is being applied—that of the development of AI-derived algorithms that can trigger alerts to advise radiologists that certain diagnostic studies require urgent attention; in other words, the AI-derived alerts help radiologists to optimize their worklists and workflow.
The other type of AI adoption is taking longer to emerge, Joe Marion, principal in the Waukesha, Wisconsin-based Healthcare Integration Strategies consulting firm, and a 40-year veteran of RSNA conferences, told me during RSNA 19. “There are really two phenomena taking place right now,” Marion told me. “What you’ve just mentioned—leveraging AI in order to improve radiological workflow process and prioritize important clinical phenomena—that’s one track, and that’s moving forward in place. The broader use of AI that’s been predicted for years—the use of algorithms for diagnosis—that is still lagging at this point; and that’s the use that had been widely predicted. [T]hat first use that you mentioned, is really about the development of smart worklists, and a more intuitive way of directing studies for reads. There’s more potential in that area, early on, than in investing in algorithms for diagnosis,” he said.
Still, tremendous effort is now going into AI development in radiology. As I reported on December 21, “Back in April, the leaders of the Reston, Va.-based American College of Radiology (ACR) announced the launch of a specialized research unit to help promote and advance the use of artificial intelligence (AI) in diagnostic practice in the specialty of radiology. As the April 5 press release announcing the new venture explained it: ‘The new American College of Radiology (ACR) Data Science Institute® (DSI) ACR AI-LAB™, a groundbreaking free software platform, will empower local radiologists to participate in the creation, validation and use of health care artificial intelligence (AI). The ACR DSI is committed to unlocking the potential of AI and helping radiology advance this technology throughout health care. As part of that strategy, ACR AI-LAB™ will provide radiologists with tools to develop AI algorithms at their own facilities, using their own data, to meet their own clinical needs.’”
And as Mike Tilkin, the ACR’s CIO and executive vice president, told me about the initiative, “We recognized that we needed to get radiologists more engaged in AI, recognizing that some people were total novices, some had a moderate level of understanding, some were more advanced. And as we saw AI becoming more and more important to radiology, we had this concept of democratization. We wanted to create smart consumers and help radiologists to be able to navigate the world of vendor algorithms and work on problems. So our goal really was to engage ACR members. And we didn’t want to turn our members into programmers. So how could we facilitate their engagement? The first phase was educational: let’s provide a cloud-based environment that could help radiologists, including novices, with a combination of educational tools, in terms of tools that can help you build an algorithm—including with easy drag-and-drop tools,” he said. “And the next rung up was helping members to evaluate algorithms.”
AI-fueled initiatives of all kinds seen
Nor was radiology the sole medical specialty or area in which AI was being applied this year; indeed, far from it. As Senior Editor David Raths wrote on December 5, “Artificial intelligence (AI) has gotten a great deal of hype in healthcare circles for its clinical decision support potential, but there hasn’t been as much buzz around how the technology could be applied to other areas, such as improving patient communication and access. This has recently become a new focus area for leaders at Boston Children’s Hospital as they believe that while patient care will see benefits from AI in the clinical setting over a long period of time, it can be also used in other aspects of an organization,” Raths wrote. “For example, when implemented within patient-provider communication, AI and automation can eliminate the need for providers to ask routine questions, in turn fueling a more meaningful connection. “
And he quoted Kevin Pawl, senior director of patient access at Boston Children’s, as stating that “As we have grown over the years, our cost, volume and the variety of tasks that families are trying to accomplish over the phone have expanded, and even if we had all the physical space that we would like to have, is hiring fleets of people to man call centers the right decision? Or is there a way to empower our staff, patients and their families with some choice?” Pawl asked. The answer, clearly, is yes; and the Boston Children’s folks are moving forward with AI, as well as machine learning and robotics, to, among other things, “automize the process of obtaining managed care authorizations and approvals for different procedures and outpatient visits.”
Indeed, the possibilities seem endless—and very broad in scope. As Managing Editor Rajiv Leventhal reported on December 6, “The U.S. Department of Veterans Affairs (VA) has established the National Artificial Intelligence Institute (NAII), designed to advance the health and well-being of veterans. The new NAII,” Leventhal noted, “is incorporating input from veterans and its partners across federal agencies, industry, nonprofits and academia, to prioritize and realize artificial intelligence (AI) research and development that is meaningful to veterans and the public, according to VA officials. The institute is a joint initiative by the Office of Research and Development and the Office of the Secretary's Center for Strategic Partnerships in VA, and will design and collaborate on large-scale AI R&D initiatives, national AI policy, and partnerships across agencies, industries, and academia, officials noted. VA executives pointed out that the department ‘is the largest integrated healthcare system in the country, has the largest genomic knowledge base in the world linked to healthcare information, and trains the largest number of nurses and doctors in the United States.’” And, the VA officials added, “Nearly three-quarters of all U.S. doctors receive training in VA. Given this, VA is uniquely positioned to advance AI research and development to the frontiers of science and health for our nation's Veterans, and the population at large.”
All of this is leading to organizations like the Orem, Ut.-based KLAS Research and the Ann Arbor, Mich.-based College of Healthcare Information Management Executives (CHIME) to begin to study the broad potential of AI in U.S. healthcare. As Leventhal reported on November 4, those two organizations this autumn published a study describing the experience of patient care organizations adopting AI in clinical, financial and operational areas. Surveying the leaders of 57 organizations reporting KLAS-validated use cases across 11 categories, KLAS validated a total of more than 90 use cases in the following segments: population health, clinical decision support, clinical research, patient engagement, clinical education, value-based reimbursement, revenue cycle, financial health, waste/cost/fraud avoidance, employee experience and bed/patient and staffing management. The report found that it’s still “too early” to determine whether some of these early experiments, which have been collaborations between patient care organizations and vendors, can be scaled up significantly in scope. But the potential is clearly there.
And while the industry overall is early on in the journey around data analytics and AI, 2019 has turned out to be a breakthrough year for the leveraging of data in U.S. healthcare delivery—with every indication that 2020 will be an even bigger year for data in care delivery. And that is exciting indeed.
