AI has the potential to help healthcare call centers—and it couldn’t come at a better time. Callers are frustrated and needing help more than ever, so this solution could make a big difference.
Thirteen percent of calls in the healthcare industry are disconnected before the caller is routed to an agent, and 67% of callers hang up the phone because they are frustrated at not being able to speak to a representative, according to a 2019 survey finding from 8×8, a unified communications vendor. In 2021, call center frustration persists for most healthcare customers.
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“The most common issues in healthcare call centers revolve around inefficient and expensive operations,” said Joe Hagan, chief product officer at LumenVox, a speech recognition vendor. “As a result of the rapid shift to remote work in early 2020, it became clear that more often than not, contact centers have disparate systems and incompatible software making it difficult to meet the increased call volumes and demands on live agents.”
Being in the midst of the COVID-19 pandemic hasn’t helped, either. Healthcare call centers must often reset patient and employee passwords, and the tedium of doing this when call volumes are high can slow down the process.
“Call centers have become a foundational element in customer service in many industries, and they play a central role in healthcare,” said Nick Kagal, vice president of marketing and business development at SpinSci, which specializes in customer engagement solutions. “Call management is critical to support patient needs, including scheduling, prescription refills, care questions, outbound communications and management of critical information.”
To meet high customer service demands, healthcare providers are turning to automation technologies like voice recognition to strengthen efficiencies, improve performance, reduce costs and improve the patient experience. One of the technologies they are implementing in their call centers is context artificial intelligence-based speech recognition.
“AI can’t replace everything that a human agent can do, but it is often sufficient to reach a satisfactory resolution for simple requests,” Kagal said. “Businesses can leave the routine, day-to-day questions (like password resets) to AI, freeing up human agents to respond to more complex calls and to deliver other operational efficiencies.”
There’s also a wealth of information in every customer interaction, and call center AI is the mechanism that can capture it automatically. Simple sentiment analysis of dialogue can provide hints as to how people feel about a brand, service or product. With features like natural language processing and voice recognition, call center agents can record and transcribe service interactions. Transcriptions make it simple for supervisors to review conversations at a glance, pick up necessary details and spot areas where agents can improve.
“One of the biggest ways that NLP assists with call center operations is by helping software programs to understand caller speech patterns and lines of thought,” Hagan said. “This understanding enables these programs to do more accurate work in serving patients. It also helps contact center technology teams create more natural-sounding interactions in automated chats and instant messages.”
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To implement NLP in AI, IT teams must first train their speech applications to properly interpret and learn how to process calls quickly and accurately. This means training the AI to correctly understand the language and intent of the caller, while also ensuring that the application supports a smooth customer experience.
“In the initial training step, the AI model is given a set of training data and asked to make decisions based on that information,” Hagan said. “As IT teams spot mistakes, they make adjustments that help the AI become more accurate. Once the AI has completed basic training, it can move to validation. In this phase, IT teams will validate assumptions about how well the AI will perform using a new set of data.”
After validation, IT conducts tests to see if the AI can make accurate decisions based on the unstructured conversational information it receives. The AI model continues to get refined until everyone testing it feels that it has arrived at a degree of dependability where it can field calls from users.
Will big data technologies like AI and NLP improve the call center experience in healthcare?
If the request of the system is simple, such as scheduling or canceling an appointment, yes. But for more complex issues, such as discussing the results of a lab test, callers should still be routed to a knowledgeable person.
Knowing where this handoff point is and then crafting workflows that run smoothly for employees and patients is the key to effective running of a call center. This is still a work in progress for healthcare institutions, but the addition of AI technologies certainly helps.