How providers and payers can use AI to democratize healthcare

Updated on April 21, 2024
Healthcare costs and fees concept.Hand of smart doctor used a calculator for medical costs in modern hospital with VR icon diagram

Many of the inequities in our healthcare system can be traced back to the disparate circumstances facing individuals and population groups that aren’t obviously health related. People who struggle to find reliable and affordable transportation, for example, may not be able to access healthcare as easily as someone who owns a car and can drive to appointments with providers or pick up a prescription at a pharmacy across town. 

Transportation difficulties, along with low income and education levels, poor language and literacy skills, discrimination, and other factors that create barriers to accessing care are called social determinants of health (SDoH). According to research, SDoH – which include behavioral risks such as smoking and obesity – account for up to 80% of all health outcomes.

Compounding the challenge for disadvantaged patients is the fragmented, siloed nature of our healthcare system. From the exam room to the call center, healthcare is plagued by an inability to quickly match patients with their data, frustrating providers, patients, caregivers, payers, and support staff. 

Fortunately, advances in artificial intelligence (AI) are enabling healthcare organizations and their support operations to access and organize relevant information in real time, both at the point of care and during customer support agent interactions with patients and plan members. Providers and payers are beginning to use AI in partnership with humans – rather than replacing humans – to improve the patient/member experience and drive better outcomes.  

In the healthcare contact center, generative AI is deployed today to help direct patients and plan members to the most appropriate resource, whether it’s a self-service app that lets them make appointments by themselves or a support agent with knowledge on a clinical topic about which a patient/member is seeking information. 

One large hospital recently integrated a Gen AI chatbot into its Epic electronic health record (EHR) that assists patients in confirming, scheduling, and rescheduling appointments, finding physicians, and getting directions to the hospital on their phones. The chatbot had 10,000 patient interactions on its first day of operation, freeing up support agents to handle patient calls requiring human assistance, like complex access and navigation questions. 

Proactive and personalized support

These AI-driven customer support platforms allow healthcare organizations to be proactive in helping patients and plan members manage their health. This is particularly critical when dealing with populations heavily impacted by SDoH, such as low-income and elderly patients, many of whom are likely to have multiple chronic conditions both dangerous to their health and costly to our healthcare system if left untreated. 

Chronic conditions such as diabetes, heart disease, cancer, Alzheimer’s disease, and mental illness account for 90% of the $4.1 trillion in annual U.S. healthcare spending, the Centers for Disease Control and Prevention (CDC) estimates. It’s clear that the costs of treating chronic conditions could be lowered if patients and plan members could overcome SDoH barriers preventing them from scheduling screenings, regular checkups, and lab testing.  

For providers and payers, the best opportunity – the golden moment, so to speak – for proactively guiding a patient or plan member toward preventive health services and support is when that individual is reaching out to the contact center. Say a caller simply wants to refill a prescription. Gen AI can instantly gather and organize EHR information about the caller, along with customer interaction records across channels (phone, text, email, chatbot, etc.) and notes from those interactions. Critically, this happens while the agent is talking to the patient/member. 

By presenting a support agent with an easy-to-consume summary of that person’s contact center interactions and EHR records, the agent has enough information to suggest the “next best action” to the caller. In the case of a 60-year-old person whose EHR records show has never had a colonoscopy, the agent could provide some educational materials and offer to set up an appointment for an evaluation. This AI assistance takes the agent beyond handling the patient’s request as fast as possible and into a proactive and strategic role of driving better outcomes for the organization and the population it serves. 

It is impossible for healthcare organizations that lack AI capabilities in their contact centers to be as responsive in real time. Population health initiatives until now have had to rely on retrospective analytics and heavy calling campaigns to launch interventions with vulnerable patient/member populations. In the previous example, all the relevant information would have been accessible to the agent without AI assistance; but taking the time during a call or chat to read and analyze it across multiple systems would be very impractical and inefficient.

AI-powered technology provides agents with the information they need, when they need it, to inform recommended next best steps. As large language models (LLMs) improve, so, too, will the ability of Gen AI algorithms to select and organize the most relevant patient/member information for agents. Such capability enhances the sense in patients that they are recognized as individuals by the healthcare organization, that they are “seen.” When critical SDoH-related data like a patient or member’s mobility limitations or language preferences are accounted for when they call or chat with their healthcare organization, consumer engagement and loyalty improve while increasing the organization’s ability to drive positive health outcomes. 

Customer support agents also benefit from AI platforms. First, implementing Gen AI-based self-service eases the workload for agents because many routine and repetitive tasks are removed from their plates. Second, Gen AI allows agents to be more efficient and successful in assisting callers, which can boost job satisfaction and slow turnover. 

Additionally, an AI-powered contact center platform can sharply reduce how long it takes to train agents, a process that can take three months. Given that the average tenure of a contact center agent in some organizations is 18 months, that’s a poor return on investment for a healthcare organization. New agents supported by Gen AI in real time – to locate and present patient information, to suggest next best actions, and to enable self-service – can hit the ground running much faster. And they will be more effective – and happier – in their jobs.

Conclusion

Gen AI can be used by payer and provider organizations to better serve patients/members who face care barriers related to SDoH. By providing agents with real-time information, AI helps healthcare organizations proactively collaborate with patients on planning the next best steps for their health. Today, Gen AI is transforming healthcare contact centers into drivers of superior, personalized customer experiences that boost patient/member loyalty while increasing the efficiency and job satisfaction of customer support agents. 

Patty Hayward.Takdesk copy
Patty Hayward
General Manager of Healthcare and Life Sciences at Talkdesk

Patty Hayward is general manager of healthcare and life sciences at Talkdesk.