Conversational AI in Healthcare for Operational Efficiencies and Compliance

As the healthcare crisis around the world continues to impact both front-line workers and patients, finding ways to alleviate stressors in the system is becoming paramount. The World Health Organization (WHO) estimates the shortage of doctors, nurses and other health professionals will rise to 10 million worldwide by 2030. Emerging technologies, such as AI, will undoubtedly be an important component in delivering solutions.  

Given the rapid developments and escalating noise surrounding AI, it’s clear there’s operational interest paired with compliance concerns amongst healthcare organizations and professionals. The good news? There is a path forward for healthcare providers to benefit from the power of AI while remaining compliant and dedicated to positive patient outcomes.

AI or machine learning tools come in numerous forms, meaning its critical to understand their varying elements and appropriate applications by industry. While generative AI is becoming more widespread with tools like OpenAI’s ChatGTP and Google’s Bard, their “open” nature has sparked reasonable concerns over misinformation and unintended consequences across all industries – especially healthcare. The sentiment extends beyond healthcare professionals, with a recent Pew Research report finding 60% of Americans would be uncomfortable with their provider relying on AI to diagnose. 

Fortunately, there are machine learning tools available that encompass both the benefits and safeguards needed to propel healthcare forward. Enter conversational AI. 

Accuracy and Compliance

For professionals and caregivers contemplating the use of AI in healthcare, the biggest barrier to adoption lies in understanding its impact on data security and operational and medical accuracy. In no other industry are the stakes higher for identifying the appropriate applications for AI implementation. Conversational AI is already being utilized as a tool for many real-world consumer applications – think chatbots and automated messaging – and has a proven track record of benefitting users in a myriad of ways. 

When it comes to healthcare, the most important feature of conversational AI, which operates on a closed system, is that it learns only from an organization’s existing data and internal business expertise, meaning it does not utilize public internet sources that could compromise accuracy or present compliance issues with its outputs. While it presents generative AI capabilities, conversational AI provides critical safeguards and the opportunity for automated conversational flows to be tailored to organizations’ specific processes and needs. In addition, it handles conversations in a natural way, making the patient experience more seamless and enjoyable while leaving the expertise, medical advice and direction in the hands of healthcare professionals. 

Most importantly, what sets conversational AI apart is its capability to properly handle patient data, including protected health information (PHI), all in compliance with HIPAA regulations. 

Operational Efficiency

A recent report from Accenture showed healthcare consumers are increasingly comfortable switching providers when their current one isn’t meeting their needs, with Millennials and Gen Zers being six times more likely to change than older populations. While access and trust unsurprisingly topped the list of non-negotiables for health experiences, so did the ease of doing business and digital engagement, showcasing the expectation for technology-driven service approaches. 

Conversational AI empowers healthcare providers to streamline the patient experience from end-to-end like never before – at scale and via preferred means like mobile messaging. Examples of automated yet meaningful patient engagements through conversational AI include: 

• Sharing of relevant forms for the collection of patient information prior to planned visits.
• Ensuring all screening and expert/doctor-guided diagnostic steps are executed and recorded.
• Execution of patient follow-ups to make sure they are taking the proper steps to recover. 
• Tracking patient engagement with post-op or discharge paperwork. 
• Delivering appointment reminders and customized support and educational resources.

While vastly improving the patient experience, healthcare organizations stand to gain integral operational efficiencies with the proper AI tools that fuel more dedicated, patient-centered work and support.

Conclusion

While there may one day be a place for generative AI in healthcare, it’s much too early for widespread adoption. The technology is in its second inning, and there are still seven innings left. Until security and accuracy concerns are vetted and addressed, the healthcare industry will be slow to the widespread adoption of generative AI. With that said, AI is not to be feared and there are powerful, adjacent solutions available that allow organizations to walk and jog before they run.