As healthcare moves, by necessity, towards greater digitization and automation, conversational AI has emerged as the connective tissue between providers and their patients.
Increasingly, health systems are implementing automated virtual assistants to help attract patients and streamline care access and navigation. Unlike rudimentary chatbots of old, which were designed to provide only basic information without the delays associated with human engagement, today’s virtual assistants are capable of much more than programmed responses to simple questions.
Conversational AI, which leverages machine learning and natural language processing, comprehends the context and intent of human language, and parses it to deliver adaptable responses. This capability is invaluable in an industry like health care, where clinical vernacular and medical jargon differ significantly from the vocabulary that patients may use to relay information. With the ability to recall preceding statements and apply context to ongoing dialog, conversational AI can identify the needs of individual users with greater precision, delivering personalized and actionable responses. Broadly speaking, conversational AI engages patients and provides important information with efficiency and empathy.
Advances in conversational AI virtual assistants deliver tremendous value both to patients and their providers. Leading solutions can answer routine questions, support symptom triage, help patients schedule appointments, provide education, facilitate intake and procedure prep activities, and engage patients post-discharge and during follow-up, all while reducing administrative burdens for care teams and support staff.
Consider this example…
For the patient: Finding care and the right provider
Chris comes to “Memorial Health’s” website, looking for information and care guidance because he is experiencing shortness of breath. He enters this information into the digital virtual assistant’s symptom checker. The conversational AI-powered assistant requests additional details: Chris’s age, the duration and severity of his symptoms, his medical history, and so on. The solution allows him to use everyday language to describe his symptoms, so it’s unlikely that Chris will become frustrated with obscure medical terminology or receive inaccurate information.
Drawing from approved knowledge bases, the virtual assistant conducts a hyper-personalized dialog to help Chris decide whether to seek care right away or follow up with his PCP or a specialist the next day. Throughout the exchange, the virtual assistant also obtains important information like insurance coverage, whether preauthorization may be required, Chris’s location and proximity to care, his language preferences, and which gender of provider he is most comfortable seeing.
At the end of the dialog, conducted solely through the virtual assistant, Chris will have more information about his symptoms and clinically sound guidance about available providers and care settings. He can take the action that is appropriate for him and, if necessary, schedule appointments directly through the virtual assistant.
It is worth noting that, not only is a virtual conversational AI experience more efficient and effective, it is also the preferred method for today’s patient. Research published in 2022 by ModMed found that 68% of patients get frustrated when they call and have to wait to be called back. In addition 67% say they are more likely to use chat over calling to make appointments or request lab results.
For the health system: Alleviating call volume and improving the employee experience
More than ever, hospitals and health systems struggle to balance the volume of inbound queries from patients against a workforce that is overextended and burned out.
Not only does Chris’s story illustrate how conversational AI streamlined his healthcare experience, but it also underscores how a virtual assistant can improve efficiency, employee performance and job satisfaction. Consider what did not occur, from the provider’s perspective:
- Call center operations did not have to answer Chris’s inquiry about his symptoms. This reduced the daily call volume, shortened the queue for other patients and eliminated the possibility that Chris would be placed on a lengthy hold or that his call would be dropped altogether. Staff members could instead prioritize calls from patients who truly needed human assistance.
- Chris did not visit a higher-cost setting of care unnecessarily or without the essential insurance coverage. This, too, helped ease the burden on both clinical and administrative staff, who did not need to explain bottlenecks to Chris, phone insurance companies, or juggle overextended resources in a busy care setting.
- The scheduling staff did not need to engage in a frustrating and time-consuming game of telephone tag with Chris while trying to reach him during business hours (which is when Chris is also at work). Instead, he could schedule his appointment on his own time, asynchronously, and conveniently.
The principles and promise of conversational AI virtual assistants can also be applied to unique challenges for specific practices or service lines. For example, consider the potential time and effort savings an AI virtual assistant delivers in guiding patients through surgical prerequisites and the procedure fully compliant and prepared. Patients receive all the necessary instructions and reminders, which frees up staff time, reduces preventable scheduling issues, and increases volume and throughput.
Likewise, conversational AI virtual assistants can be used at the enterprise level, triggering dialogs with post-discharge patients to monitor recovery and identify potential complications. These frequent touchpoints help improve outcomes and reduce avoidable readmissions while freeing up staff to connect one-on-one with patients who require additional intervention.
There is no doubt that virtual assistants will play an increasingly important role in successful hospital operations as technology and use cases continue to evolve. Conversational AI offers serious value for both patient and the provider and delivers scalable functionality that can address specific and unique challenges across the healthcare enterprise.
Patty Riskind is CEO of Orbita. Orbita uses conversational AI, generative AI and machine learning to automate both administrative and clinical workflows to make navigating healthcare easier. Patty was previously Head of Global Healthcare for Qualtrics, an experience management technology company. Prior to Qualtrics, she was CXO at Press Ganey, and before Press Ganey, she founded the first e-survey company in healthcare, PatientImpact, that Press Ganey later acquired.
Patty has served on several venture-backed private boards as well as a public (non-physician) board member of the Accreditation Council for Graduate Medical Education (ACGME) on its compensation, audit, and finance committees. She earned her Bachelor of Arts degree with honors from Brown University and her MBA from the Kellogg School of Management at Northwestern University.
