The Application of AI to Improve Employer-Sponsored Health Care
It’s well known that transforming our health care system can’t happen overnight – and that remains true when it comes to the application of Artificial Intelligence (AI). While innovators are increasingly testing new ways to leverage AI to improve care delivery and access, it hasn’t yet reached the scale needed to bring meaningful change to patients and providers.
There have been pockets of progress, backed by broad commitments to enhance the patient experience, reduce provider burnout and optimize the pharmaceutical research and development process. These present very real (and welcome) opportunities.
But one area that has been overlooked to date is the way AI can drive improvements in employer-sponsored health care. Approximately half of Americans (160 million people) receive their health coverage from the workplace – and employers take their role in providing robust, quality benefits very seriously. As interest in the role of AI in health care continues to grow, there are five key areas that employers should prioritize for future use and application.?
1. Helping patients navigate their care.
AI can help distill the complex, longitudinal data about a patient’s medical history and match it to the best possible health care and benefits offerings available to them – ensuring a “right time, right place” approach to care delivery. Ideally, these insights would be presented to patients in a way that facilitates more engaging conversations with their providers. For example, “Based on your history with diabetes, these benefit offerings may be helpful.” This could help improve patient engagement with personalized health benefits.?
Insights could be generated behind the scenes, while leveraging evidence-based clinical guidelines and validated eligibility for coverage, to confirm that recommendations presented to patients are clinically sound.
It’s worth noting that the market is still in the early phases here. This is due largely to underlying data fragmentation challenges, as it can be very difficult to wrangle the longitudinal data necessary about a population to make personalized navigation recommendations. To start, the industry needs to make improvements to its data infrastructure – starting with interoperability. More collaboration needs to take place across the entire health care system, including employers, insurance carriers, providers and vendors.
2. Identifying risks and opportunities for improvement with more precision.?
AI has the potential to offer employers more information about the future needs of the employees and dependents currently enrolled in health coverage. For example, AI can offer a more sophisticated model of how costly, complex diseases like diabetes and heart disease may impact their population in the long-term. It can also help employers choose programming and interventions that meet the needs of their population and prevent adverse outcomes, which reduces future costs and utilization.
While the promise is there, more progress needs to be made in this area.?Many aspects of an individual’s true health (e.g. physical, mental and social) are not accurately reflected in the data that are recorded due to the systemic disparities within our health care system. To this end, holistic data are needed to achieve meaningful risk prediction tools accurately for population health management. This will likely come from various medical and non-medical data sources – like the social determinants of health.
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3. Making billing and administrative work easier.
Billing and coding automation can eliminate errors in the system -- preventing both employers and patients from having to deal with the administrative stress and burden of manual remediation.?
AI also has the potential to drive more real-time transparency into a patient’s point-in-time insurance deductible status, which can help avoid unexpected costs and improve their experience overall. Making this type of information more readily available to patients could also help eliminate some of the friction between patients and their health plan.
4. Freeing up providers’ time to prioritize patient care.
Employers are increasingly focused on the quality of care, which is largely dependent on the relationship their employees and dependents have with their providers. Provider efficiency tools, including medical record summarization and ambient documentation, alleviate the administrative burden on providers, allowing them to spend more time with patients.
Frequently, patients say their providers do not spend enough time understanding their needs. Thus, it’s important that these AI-powered tools are coupled with value-based contracts that prioritize the quality of patient-provider interactions (versus number of patients seen). Leaving AI-driven efficiency unchecked has the potential to push providers to simply see more patients if AI allots them more time, instead of providing higher quality care to their patients.
5. Supporting clinical decision-making
Like provider efficiency tools, AI can support higher quality care by distilling large amounts of critical information and translating it to inform clinical decisions. Examples include the use of AI in medical imaging to identify subtle signals that are hard to see with the naked eye, or the use of AI to determine the best treatment regimen for a complex patient based on their clinical presentation. This not only enables providers to spend more time with patients, but also brings more information and speed into the decision-making process.
Some of these tools are already on the market and are being reimbursed at higher rates than human clinicians, which will increase costs in the short-term.?However, the widely held belief is that these tools could ultimately provide downstream cost savings in the future.
In summary, the rapid advancement of AI-driven solutions presents exciting opportunities to improve health care for employers, patients and providers. 2025 will be a critical year for the application of AI in health care, and we must continue to ensure the right foundation is in place so that patients, providers, payers and others benefit from what AI can do to improve the U.S. health care system.
Emily Lindemer, PhD is the Executive Director of Data for Morgan Health , the 摩根大通 business unit focused on improving the quality, equity and affordability of employer-sponsored health care.