AI's Impact on Value-Based Care Outcomes

Jay Ackerman, CEO & President of Reveleer, discusses the transformative impact of artificial intelligence (AI) and natural language processing (NLP) on health care payer organizations. Through AI-driven proactive workflows and prospective analytics, health care payers can revolutionize risk management, personalize care interventions, and optimize cost-saving initiatives.

Please introduce yourself by stating your name, title, organization, and relevant professional experience.

Jay Ackerman: I'm Jay Ackerman, the CEO & President of Reveleer, a company focused on harnessing the power of artificial intelligence (AI) and natural language processing (NLP) to empower health plans and risk-bearing providers to manage their quality improvement, risk adjustment, clinical improvement, and member management programs. My journey with Reveleer began in 2016 when I assumed the role of CEO, succeeding the company's founder who established the company in 2012. With over 30 years of leadership experience, I've held various positions, including Chief Revenue Officer at Guidance Software, where I worked in cyber security, and Worldwide Head of Sales and Customer Success at ServiceSource. Before that, I served as President & CEO at WNS North America. I am a growth-oriented executive driving transformative technologies to rethink how problems are solved.

How do you see the integration of AI-driven proactive workflows and prospective analytics impacting proactive risk management within health care payer organizations? Can you provide examples of how this approach can help identify and mitigate risks before they escalate?

Jay Ackerman: AI is a powerful partner. It can significantly transform risk adjustment because of its ability to process, analyze, and synthesize massive amounts of data into clinical insights that can be used by providers to improve patient care. Traditionally, risk adjustment in value-based care has functioned as an audit mechanism, ensuring accurate reimbursement for health plans based on the risk profile of their members. However, some value-based care organizations are evolving to prospective risk adjustment programs to engage providers before member interactions. Most health plans are limited by the member data they have in-house, making it difficult to effectively support and help providers due to limited and outdated information.

Integrated with external, clinical data sources such as health exchanges, pharmacies, and out-of-network specialists, AI can scour disparate sources of clinical information to create a complete picture of a patient's health. When these insights are pushed to providers at the point of care, risk adjustment shifts from a retrospective, audit-centric function into a proactive workflow that can significantly impact care. AI-powered workflows help risk-bearing organizations become more proactive in delivering patient care. It can improve productivity and capacity by automating repetitive tasks, streamlining processes, and identifying anomalies or outliers that may indicate potential risks. For example, AI-powered predictive modeling can forecast which health plan members are at higher risk for certain health conditions or costly treatments, enabling payers to intervene early with targeted interventions such as preventive care programs or care management initiatives.

Integrating AI-driven workflows and prospective analytics empowers health care payer organizations to take a proactive approach to risk management, leading to improved patient outcomes, more accurate RAF scores, and a more sustainable health care system.

Personalized care interventions have become increasingly important in health care. How can prospective analytics and AI technologies contribute to tailoring interventions to individual patient needs? Could you share some insights into how this approach has been successfully implemented in real-world scenarios?

Jay Ackerman: Prospective analytics and AI offer indispensable tools for delivering personalized health care solutions by empowering providers and clinicians to leverage a more comprehensive view of the patient in real time. Through the aggregation of disparate data silos to build a more holistic patient view, these AI-powered functions enable the physician to anticipate of future health outcomes based on demographics, medical history, and lifestyle choices, allowing the determination of tailored risk profiles and interventions for each patient. Predictive modeling facilitates the early identification of high-risk patients, enabling proactive interventions to optimize outcomes and reduce costs.

In the real world, AI plays a pivotal role in aligning risk adjustment and quality improvement programs by providing a unified, longitudinal view of members and presenting clinical insights to providers at the point of care. For instance, AI can analyze data for a patient with known diagnoses and identify new potential diagnoses based on evidence from the health ecosystem, such as congestive heart failure, aortic atherosclerosis, and stage three chronic kidney disease. This information can be translated into easily understandable patient summaries linked to supporting clinical documentation, facilitating informed decision-making by providers regarding diagnosis and treatment options.

Cost savings and efficiency are critical considerations for health care payers. How do you envision AI-driven prospective analytics influencing cost-saving initiatives within payer organizations? Can you elaborate on specific strategies or techniques that can be employed to optimize resource allocation and streamline operations?

Jay Ackerman: AI-powered prospective analytics have the potential to maximize cost-saving efforts within payer organizations. Prospective analytics complement proactive workflows by providing insights into future trends and potential risks based on current data. By analyzing historical data alongside real-time information, payers can anticipate changes in health care utilization, identify emerging health trends, and adjust their strategies accordingly. This foresight enables payers to allocate resources more efficiently, tailor interventions to individual member needs, and proactively manage and measure risk factors before they escalate.

AI technologies can also optimize provider workflows by assessing performance and cost-effectiveness while automating manual administrative tasks to reduce overhead costs. Payer organizations harness AI-powered predictive modeling to optimize resources and streamline operations.

Strategic decision-making is fundamental for navigating the complexities of the health care ecosystem. How can prospective analytics empower payer organizations to make more informed and strategic decisions? Are there any notable examples where the implementation of prospective analytics has led to significant improvements in decision-making processes?

Jay Ackerman: The implementation of prospective analytics has led to significant improvements in decision-making processes, including the use of predictive modeling to manage chronic diseases. For instance, some payer organizations have employed predictive analytics to identify members with chronic conditions such as diabetes or heart disease who are at high risk of hospitalization or other costly complications. By leveraging these insights, payers can tailor interventions such as care management programs, medication adherence support, or lifestyle interventions to help manage these conditions more effectively to deliver better outcomes and prevent costly hospitalizations.

Overall, prospective analytics enable payer organizations to make data-driven decisions grounded in evidence and predictive insights, ultimately leading to more effective resource allocation, improved member outcomes, and reduced health care costs.

What is one key takeaway you hope the audience gains from this interview?

Jay Ackerman: Don’t be afraid of AI. AI technologies are still in their infancy stages, but they are more than capable in helping organizations improve their productivity, capacity, and accuracy through automation and their predictive capabilities. It helps us do more -- faster and more accurately. AI-powered workflows and prospective analytics are advancing value-based care by improving patient outcomes and reducing costs. Rather than scattered in disparate sources, patient data is centralized in a member data repository, enabling a longitudinal view of each individual's health journey. This approach not only enhances care for individual patients but also provides insights into broader trends within specific populations and health care providers. By combining traditional and digital data and leveraging AI tools for analysis, health care professionals can efficiently access and utilize this wealth of information to drive better outcomes.