How Can Wearable Devices, Health Apps Improve Health Care Efficiency?

Artificial intelligence (AI) is important to value-based care by contributing to better outcomes, patient adherence, and lowering costs, says Liz Kwo, MD, MBA, MPH, chief commercial officer, Everly Health, and faculty lecturer, Harvard Medical School.

This transcript has been lightly edited.

Transcript

Given the ongoing debate about AI’s role in clinical settings, how do you foresee the balance between AI and physician decision-making evolving in the context of value-based care?

When AI acts as an augmentative tool rather than a replacement for human expertise, that's where this magic is made. So, decision support is one [way] where you can provide evidence-based recommendations to identify potential issues that a clinician may overlook. They can analyze imaging data or detect early signs of disease like cancer, either through pathology or radiology scans, complementing the physician's diagnosis.

Another thing is routine tasks. The eye can handle routine administrative and diagnostics tasks, allowing for clinicians to focus on complex cases, direct patient care, and take away a lot of that routine task work away. The division of labor can really improve job satisfaction both among health care providers and patients.

The third is continuous learning. AI systems can continuously learn from new data. So, they're providing clinicians with up-to-date information and best practices, in addition to ongoing education to help clinicians stay current with the latest medical advancements.

And then the fourth is patient engagement. So, AI can empower patients with personalized health insights, recommendations, promoting active participation, but also shifting towards more patient-centered care, aligning with the principles of value-based care, which emphasizes outcomes and patient satisfaction.

How does the quality and volume of data from digital health tools like wearables and health apps impact the effectiveness of AI in delivering personalized, value-based care?

The quality and volume of data from digital health tools are critical to the effectiveness of AI in health care. Data volume is a key area where you have large datasets from wearables or even apps, that enable AI to identify patterns, correlations, all sorts of things that might be missed in smaller datasets. For instance, continuous glucose monitors provide comprehensive data that can be used to manage diabetes more effectively.

There's also data quality. Accurate, reliable data is essential for AI algorithms to make precise predictions and recommendations. High-quality data ensures that AI outputs are clinically relevant and actionable. A lot of these efforts to improve sensor accuracy and data integrity are ongoing, and really important. We talked earlier about how real-time monitoring wearables with these apps allow AI to detect changes in patient conditions. Specifically looking at things that are more actionable, as we've seen with monitors with even looking at heart attack or predicting something happening with an EKG [electrocardiogram]. So, the capability is particularly valuable in a lot of chronic diseases, and interventions can prevent complications that lead to really high costs.

And then lastly, patient engagement. This engagement can really involve adherence to treatment plans, promote healthier behaviors, contributing to better outcomes, and ultimately lowering cost for value-based care.