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How ACOs Can Harness AI’s Transformative Potential

Strategies for building the datasets needed for ACOs to take advantage of artificial intelligence, manage costs and improve outcomes for all

Everywhere you look today, artificial intelligence (AI) is making an appearance and offering to do something you already do smarter, faster or just a little more efficiently. Or it’s allowing new, exciting ways to look at old problems. Across health care, it’s opening up new opportunities to weigh risks and improve diagnoses, personalize medicine and develop less-invasive medical procedures. 

Indeed, AI is popping up almost everywhere in the healthcare arena, from evaluating patient propensities for genetic mutations to lifespan prognosis. There is no area of specialty research that is not investigating the potential for AI to improve medicine. 

The laggard? Value-based care. Ironically, as we improve the ability to do more with advanced medicine through AI, the organizations charged with improving outcomes, cost, and health equity are left behind and dependent on lesser tools.

While the possibilities across most areas of healthcare are transformative, AI for accountable care organizations (ACOs) remains stuck on the outskirts of value-based care.

The current state of AI in ACOs

The artificial intelligence being implemented in ACOs today consists mostly of patient-checking bots, robotic assistants and Chat GPT patient communications. There’s also a nod to the future of AI in predicting patient utilization. Essentially, most of the AI deployed in ACOs are tools for use in an already predetermined menu for value-based care—a menu focused on limited gains through coordinating care, keeping utilization down and meeting regulatory requirements. 

We’re thinking too small.

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A Deep-dive Into Specialty Pharma

A specialty drug is a class of prescription medications used to treat complex, chronic or rare medical conditions. Although this classification was originally intended to define the treatment of rare, also termed “orphan” diseases, affecting fewer than 200,000 people in the US, more recently, specialty drugs have emerged as the cornerstone of treatment for chronic and complex diseases such as cancer, autoimmune conditions, diabetes, hepatitis C, and HIV/AIDS.

ACOs could implement transformational AI that advances clinical improvement in patient outcomes and costs.  Imagine that ACOs could explore and build AI algorithms that identify characteristics of people at risk for acquiring or progressing in disease, for better targeting interventions, rather than depending on retrospective claims data  While these algorithms are not cost-focused per se (because the goals are clinical), cracking the code on risk factors greatly increases the probability that future savings can be reaped by identifying patients at high risk and intervening prior to events like emergencies and inpatient care, and preventing or slowing progression of disease. That’s improved value.

So why aren’t we reaping the benefits of that capability in our value-based care initiatives? 

ACOs need clinical data to achieve their goals—and the promise AI offers 

We will never realize the value of clinical progress in health care outcomes, lower costs or health equity if value-based care and the delivery system don’t operate in tandem.  Likewise, the specialty areas that have created successful algorithms for predicting risk will not be tied into the ACO resources of population health or patient navigation that can change 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 health care services and a good patient experience. While all ACOs receive claims data from Medicare, many only use claims data for cost management.  That retrospective, rather than forward-thinking approach based on clinical data insights, makes it hard for ACOs to achieve higher levels of savings or significant improvements in patient outcomes.

Because of concerns about costs of data aggregation of various provider systems, ACOs as a group fought against the need to collect electronic health records (EHR) data for quality reporting and continued to only report quality to Medicare on a small sample of patients. There’s no doubt that many ACOs have practices with non-certified EHRs, making it more expensive to aggregate. But ACOs will only have future viability if they can adopt data-driven solutions to improve outcomes, costs, and services to historically marginalized people. 

ACOs that will lead in the future of value-based care must do more, including using predictive tools to avoid preventable admissions and other costs. They must fill the gap between fragmented specialty care and social services and provide a holistic view and plan for each patient. Doing so will require creating a patient-centric value-based-care database, sharing performance analytics, centrally coordinating improvement initiatives, and establishing referral networks to external specialists and social services.

Artificial intelligence solutions are capable of helping to improve patient health—but first ACOs need to create the value-based-care data substrates needed to put those solutions to use. 

Three strategies for creating vibrant value-based care datasets

Here are three strategies for building the datasets needed for ACOs to take advantage of artificial intelligence to manage costs and improve outcomes for all:

  • Aggregate data from all provider EHRs in the ACO, including demographic, transactional, and clinical data. Data is the fuel for ACO performance efforts—and data content must be continually improved.
  • Build the data substrate to be clinically rich and include scoring from specialty risk algorithms. While ACOs may be short on data, many participating providers are not—and their data that captures more clinical and social data should be included.  Likewise, risk score data from AI-powered algorithms being used by participating groups to identify and score patient risk and refer patients to services should also be part of ACO data collection.
  • Share data with providers, including performance data, patient episodes of care, and general patient risk data. Ensure providers can see where their own patients are in episode analytics, in cost variation for every episode and compared to their other patients. 

To thrive long-term, ACOs today must conceptualize how artificial intelligence can be used to bridge health care and value-based care activities. Coupling the capabilities of electronic health records with advances in AI offers ACOs opportunities to share their data to improve patient health outcomes—a win for all.

Photo: Ralf Hiemisch, Getty Images

Theresa (Terry) Hush is a health care strategist and change expert with experience across the health care spectrum. Terry’s broad range of health care experience includes executive positions in public, non-profit and private sectors, from both payer and provider sides of the business, peppered with health care public policy and regulation experience. She is co-founder and CEO of Roji Health Intelligence, formed in 2002 to help providers implement Value-Based Care with technology and data-guided services. An expert at creating consensus for desired change through education and collaboration, Terry helps organizations to move toward cost and outcome accountability to achieve growth.

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