Be Smart About Healthcare AI
Healthcare companies striving for improved human-centric care are rightfully exploring artificial intelligence (AI) and how it empowers providers to deliver smart(er) healthcare at scale.
This coming year will see the continued maturation of AI and adoption industry-wide, but companies should be careful when implementing and launching patient-facing applications. If done incorrectly, these technologies could deter usage and potentially harm brand relationships. We will look at two of the most common applications and their potential pitfalls.
Virtual Assistants (VAs)
While there is great potential for AI and natural language processing (NLP)-driven assistants to increase treatment adherence and facilitate patient care, the adoption of technology is still in relative infancy. When healthcare companies react prematurely, taking the off-the-shelf, simple automation approach to virtual assistant delivery, the results are—more often than not—counterproductive.
In 2018, patient trust of virtual assistants was low, one study reported 94% of patient respondents preferred to speak with a live person. Why? Because understanding patients is a prerequisite for truly human-centric healthcare service and basic virtual assistants do not offer the level of personalization needed to meet expectations. These VAs are not built on true artificial intelligence, but simplified automation. They do not understand or learn, and can, therefore, end up frustrating patients and wasting their time. In the absence of optimized AI learning, virtual assistants will only create more friction to what should be an increasingly smooth and personalized experience. This affords companies a window of opportunity to surpass their competitors, better engage customers and gain recognition with a truly exceptional VA experience.
Beyond voice learning, AI is empowering healthcare providers to competitively differentiate through facial recognition technology. From streamlined registration and check-ins, to verification of prescribed medication consumption, to remote pain level assessment—facial recognition has tremendous potential for streamlining patient-provider interactions.
Healthcare companies must be very careful when leveraging facial recognition for various legal and privacy reasons, but precautions are too often overlooked to the detriment of the companies who fail to heed prudence and transparency. Privacy compliance and regulatory requirements vary depending on geographic location, but they are present and must be accounted for. Additionally, there are related security requirements, and substantial legal ramifications for failing to ensure adherence.
Another area of caution is patient perception. Stating that your company is compliant is different than the patient knowing, understanding, and/or believing your security measures. Proactive transparency, education, and relevance need to be established and communicated beforehand for success.
2019 is going to be an exciting year for AI and other technologies in healthcare. Read more about it in SoftServe’s latest whitepaper, “Healthcare Trends in 2019”