Best Practices for Implementing AI Into Value-Based Care


Liz Kwo, MD, MBA, MPH, chief commercial officer, Everly Health, and faculty lecturer, Harvard Medical School, shares important tips on how artificial intelligence (AI) can best be implemented into value-based care.

The benefits of artificial intelligence (AI) include predictive analytics, personalized treatment plans, operational efficiency, and and remote patient monitoring, says Liz Kwo, MD, MBA, MPH, chief commercial officer, Everly Health; faculty lecturer, Harvard Medical School, and author of the book, DigitalMD.

This transcript has been lightly edited.


How can AI effectively contribute to value-based care by improving patient outcomes and reducing health care costs?

AI can significantly enhance value-based care by optimizing various aspects of patient management and health care delivery. First, predictive analytics. Algorithms can analyze vast amounts of data to predict disease outbreaks, patient deterioration, and even readmissions. For instance, DeepMind worked with the VA to predict acute kidney injury, and they demonstrated the ability to not only look at the outcomes, but suggest personalized treatment plans, improve patient care, and reduce unnecessary interventions.

There's also personalized treatment plans that AI can tailor to help patients based off their patient data, genetic, work, lifestyle, and environmental factors. The customization can lead to more effective treatments, improve patient adherence. An example is even AI-driven precision medicine in terms of recommending the most effective treatments for cancer patients, even for drug holidays. So, at times when a very expensive cancer drug needs to be ongoing given, you can potentially look at when they can be on a drug holiday, taken off it, still do well, and have to only be placed back on it when the cancer returns.

There's also operational efficiency. AI can streamline administrative tasks such as scheduling, billing, managing patient records, reducing the burden on providers, and lowering the operational costs.

Lastly, I'll mention remote patient monitoring. There are various ways that AI can track patient health metrics, allowing for early intervention, reducing hospital admissions, and be integrated with the health care providers' work.

What are the main limitations and challenges of integrating AI into clinical settings, particularly in terms of data quality and the need for clearly defined clinical questions?

Integrating AI into clinical settings presents several different challenges. The first is data quality. AI systems require high-quality, standardized data to function effectively. Sometimes it is unstructured; it could also be structured, but it has to be correct and not be inconsistent data formats, incomplete records, errors in data—they can compromise the accuracy of error predictions. Ensuring data interoperability and standardization across different health information systems is crucial.

The second is clinical relevance. AI algorithms generally need clearly defined clinical questions to create meaningful results. Without precise objectives, AI may generate irrelevant or misleading results or information. I definitely recommend collaborating with clinicians to identify and refine questions that are essential.

The third is ethical and regulatory concerns. The use of AI in health care raises ethical issues such as patient privacy, data security, and the potential for bias in AI algorithms. So, regulatory frameworks need to evolve to address these concerns, ensuring that AI applications comply with ethical standards and legal requirements.

Last but not least [is] acceptance and trust. Clinicians sometimes may be hesitant to adopt AI due to concerns about its reliability, so, building trust through rigorous validation studies and demonstrating tangible benefits will be key.

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