The momentum of value-based care (VBC) is poised to accelerate. The Centers for Medicare and Medicaid Services (CMS) has outlined an ambitious objective: to transition all traditional Medicare beneficiaries into a VBC arrangement by 2030, a notable increase from the mere 7% recorded in 2021 by Bain Research. As more plans, providers and members enter VBC arrangements, substantial volumes of clinical data will need to be managed effectively to oversee patient risk and care quality.
The transition to VBC is a complex path. Common obstacles include changing regulations and policies, trouble collecting and reporting patient information, such as care gaps, unpredictable revenue, complex financial risk, lack of resources to implement and manage VBC programs, and interoperability gaps within and outside the organization, according to a Definitive Healthcare survey.
These barriers exacerbate an increasingly complex system. The industry generates more patient data to be shared with more entities, preferably in time to impact patient care. Yet, the processes are currently manual, inefficient, and error-prone. Data and process fragmentation throughout the U.S. healthcare system contributes to administrative waste and $265 billion in unnecessary costs, according to Drug Topics.
AI-powered technologies have already demonstrated their worth in advancing VBC.
AI-enabled technologies are being employed across the industry, helping accelerate the transition to VBC. These technologies, including machine learning (ML), natural language processing (NLP), and optical character recognition (OCR), are in widespread use, while the use of generative AI, such as ChatGPT and Google Bard, is on the rise. Given the vast amounts of data, the complexity of the processes, and the decentralized nature of the U.S. healthcare system, AI brings unique capabilities. First, these technologies enable aggregating and synthesizing structured and unstructured patient claims and clinical data from electronic health record systems (EHRs), national and regional health information exchanges (HIEs), community providers, specialists, labs, prescriptions, etc.
Beyond aggregating and synthesizing data, AI then makes myriad data worthwhile. AI is unmatched in its abilities to sort and aggregate data, discern patterns, highlight relevant information, automate tasks, and streamline processes. As payers and providers face increasing pressure to enhance quality care outcomes while lowering costs, leveraging data both prospectively and retrospectively is critical – and AI makes that possible at scale. With the correct data in the hands of the right resource at the right time, it becomes possible to profile and manage member risk proactively. With pertinent information, payers and providers can employ evidence-based interventions to manage patient conditions and the health of at-risk populations. Here are three high-value use cases where AI improves payer operations in VBC.
Redefining risk adjustment programs
AI-enabled technology can expand and improve risk management by making both retrospective and prospective risk adjustment possible. By aggregating extensive clinical and claims data, AI can synthesize and prioritize suspected diagnoses, including links to clinical source documentation, and deliver that information to providers at the point of care. With this information in hand, providers can make evidence-based decisions to address gaps in care when they are seeing the patient rather than after the fact. Arming providers with a longitudinal patient summary for conducting comprehensive risk assessments improves patient outcomes while lowering the cost of care.
Driving better quality improvement programs
For quality improvement, AI analyzes data and summarizes actionable insights to predict disease progression, manages at-risk populations, and suggests appropriate interventions, which reduces costs associated with advanced disease management. AI-enabled technology can deliver personalized treatment plans and medication regimens, leading to better adherence and outcomes while avoiding costly adjustments and hospitalizations. AI can help providers monitor and analyze healthcare quality indicators for continuous improvement, driving quality of care, better patient experiences, and lower costs associated with avoidable errors.
Improving provider adoption of VBC contracts and processes
Putting accurate, relevant information in the hands of providers directly within their workflows is vital to building clinician trust and adoption. AI-enabled technology can summarize the insights providers need at the point of care to assess suggested diagnoses and make informed care decisions that mitigate risks by closing gaps in care. Offering accurate, timely information that providers can apply immediately builds clinician confidence in the technology while reducing common provider abrasion points. In addition, AI can automate menial tasks to use resources better. For example, AI-assisted documentation, which can tap enormous content libraries of industry-standard synonyms, acronyms, and abbreviations, helps clinicians document patient encounters quickly and accurately, freeing them to focus on patient care.
Conclusion
AI is demonstrating its transformative potential to accelerate VBC. It rapidly extracts valuable insights from various unconnected data sources and presents healthcare providers with a comprehensive view of member risk before and during patient encounters. Equipping providers to assess member risk, increase diagnosis accuracy, and close care gaps takes risk adjustment and quality improvement to a new level. By harnessing AI in these capacities, at-risk healthcare organizations can give providers the tools they need to fully embrace VBC, along with its potential to improve member outcomes, lower costs, and make the U.S. healthcare system better for all.
About Jay Ackerman
Jay is an Enterprise Software executive responsible for setting the vision, strategy, and objectives for Reveleer. As a leader, he is also keenly focused on shaping and stewarding the culture at Reveleer to attract a robust collaborative team, while driving an innovation mandate to execute our mission to accelerate value-based care.He is a seasoned software and services executive with over 30 years of experience in various leadership capacities. While at Reveleer, he established the company as a leader in SaaS solutions to enable our customer set to take control of these critical value-based care programs. Before Reveleer, Jay was the Chief Revenue Officer at Guidance Software, a publicly traded software security company. He is equally proud of his contribution to the success of ServiceSource, where he was the Worldwide Head of Sales and Customer Success at ServiceSource and WNS North America. WNS, where he was the President & CEO. Both organizations grew rapidly and joined the public markets.