Everywhere you look today, artificial intelligence (AI) is popping up, giving you the opportunity to make the things you already do smarter, faster, or just a little more efficient. Or it enables new, exciting ways to look at old problems. Across healthcare, it opens up new opportunities to assess risks, improve diagnoses, personalize medicine and develop less invasive medical procedures.
In fact, AI is popping up almost everywhere in healthcare, from assessing patient susceptibility to genetic mutations to predicting lifespan. There is no area of specialized research that is not exploring the potential of AI to improve medicine.
The latecomer? Value-based care. Ironically, as we improve the ability to do more with advanced medicine through AI, the organizations charged with improving outcomes, costs, and health equity are left behind, relying on less critical tools.
While the possibilities in most areas of healthcare are transformative, AI for accountable care organizations (ACOs) remains stuck at the edge of value-based care.
The current state of AI in ACOs
The artificial intelligence being implemented in ACOs today primarily consists of patient monitoring bots, robotic assistants, and chat GPT patient communication. There is also a hint of the future of AI in predicting patient utilization. Essentially, most AI deployed in ACOs are tools for use in an already predetermined value-based care menu—a menu focused on limited gains through care coordination, utilization reduction, and meeting regulatory requirements .
We think too small.
ACOs could implement transformative AI that drives clinical improvement in patient outcomes and costs. Imagine if ACOs could research and develop AI algorithms that identify characteristics of people at risk of developing or progressing a disease to deliver more targeted interventions, rather than relying on retrospective claims data. However, these algorithms are not cost-oriented per se (As these are clinical goals), decoding the risk factors significantly increases the likelihood that savings can be achieved in the future by identifying high-risk patients and intervening before events such as emergencies and hospitalization and preventing or slowing disease progression becomes. That is added value.
So why aren’t we leveraging the benefits of this capability in our value-based care initiatives?
ACOs need clinical data to achieve their goals – and the promise that AI offers
We will never realize the value of clinical advances in health outcomes, reduced costs, or health equity if value-based care and the delivery system do not go hand in hand. Likewise, the specialties that have developed successful risk prediction algorithms are not tied to the ACO resources of population health or patient navigation that can transform the patient’s future.
The lack of data collected by ACOs makes it difficult for them to achieve their mission of improving outcomes and keeping costs within a benchmark while ensuring equitable delivery of healthcare services and a good patient experience. While all ACOs receive claims data from Medicare, many use claims data only for cost management. This retrospective rather than predictive approach, based on insights from clinical data, makes it difficult for ACOs to achieve major savings or significant improvements in patient outcomes.
Because of concerns about the cost of data aggregation across provider systems, ACOs as a group fought the need to collect electronic health record (EHR) data for quality reporting and continued to report quality to Medicare in only a small sample of patients. There is no doubt that many ACOs have their practices with uncertified EHRs, making aggregation more expensive. But ACOs will only be sustainable if they can adopt data-driven solutions to improve outcomes, costs and services for historically marginalized people.
ACOs that will lead the future of value-based care must do more, including using predictive tools to avoid avoidable admissions and other costs. They must bridge the gap between fragmented specialty care and social services and provide a holistic view and plan for each patient. This requires creating a patient-centered, value-based care database, sharing performance analytics, centrally coordinating improvement initiatives, and establishing referral networks to outside specialists and social services.
Artificial intelligence solutions can help improve patient health – but first, ACOs must create the value-based care data substrates required to deploy these solutions.
Three strategies for creating vibrant, value-based care records
Here are three strategies for building the data sets ACOs need to use artificial intelligence to manage costs and improve outcomes for everyone:
- Aggregate data from all provider EHRs in the ACO, including demographic, transactional and clinical data. Data fuels ACO performance efforts – and data content must continually improve.
- Build the data substrate to be clinically rich and include assessment using special risk algorithms. Although ACOs may lack data, many participating providers do not—and their data, which captures more clinical and social data, should be included. Likewise, risk assessment data from AI-powered algorithms used by participating groups to identify and assess patient risk and refer patients to services should also be part of ACO data collection.
- Share data with providers, including performance data, patient treatment episodes and general patient risk data. In episode analysis, ensure providers can see where their own patients are, the cost fluctuations for each episode, and how they compare to their other patients.
To be successful in the long term, ACOs today must develop concepts for using artificial intelligence to combine healthcare and value-based care activities. Combining the capabilities of electronic health records with advances in AI offers ACOs the opportunity to share their data to improve patient health outcomes – a win for everyone.
Photo: Ralf Hiemisch, Getty Images
Theresa (Terry) Hush is a healthcare strategist and change expert with experience across the healthcare spectrum. Terry’s broad healthcare experience includes leadership roles in the public, nonprofit and private sectors, on both the payer and provider sides of the organization, peppered with experience in public health policy and regulation. She is co-founder and CEO of Roji Health Intelligence, founded in 2002 to help providers implement value-based care with technology and data-driven services. As an expert in building consensus for desired change through education and collaboration, Terry helps companies take cost and outcome ownership to achieve growth.