Predictive analytics
AI and machine learning can be used for mining data to predict adverse events. Applying algorithms to electronic health records (EHR) data can enable providers to create a more stratified scoring method for predicting patient falls that is more accurate than bedside assessments, the traditional method of assessing fall risks.

Conversational AI

Patient discharge is a critical time in a person’s health journey. This is when providers are giving patients vital information and education about their care plans, including medication instructions, recommendations and referrals for physical rehab and therapy, activities and hazards to avoid, etc. Discharge is one of the best opportunities for providers to minimize a readmission (for which the hospital doesn't get paid).

Just as conversational AI is used by online retailers, banks, and other businesses in the form of chatbots to interact with customers in a way that is human-like, this technology can be used to support assessments to determine if a patient is ready to go home. Do they have a ride? Are there stairs in their home? Do they understand the medications they’re going to be taking? At what point should they return to the hospital should something go wrong? Conversational AI can interact with patients to obtain this information as part of the discharge process without consuming nurses’ time.

Conclusion
AI has continued to evolve in recent years. So too have recording technologies such as cameras, microphones, speakers, and other equipment used in patients’ rooms. Indeed, these devices are more prevalent in healthcare settings since Covid-19 pushed healthcare to embrace telehealth.

This will make it easier for providers to leverage technologies such as computer vision, ambient listening, predictive analytics, and conversational AI.

However, the prevalence of these devices also raises ethical use and logistical questions. If cameras and speakers now are the equivalent of a door to the patient room, who decides when the door should be open or closed? What happens when multiple virtual care team members try to open that door and enter at the same time?

While these and other issues will have to be worked out, it’s clear that several different types of AI will be essential to creating virtual care networks that ease the pressure on understaffed healthcare workers and allow them to focus on helping patients.

About the author: Jacob Hansen is chief product officer of AvaSure, the inventor of the TeleSitter solution, recognized as the Top Solution to Reduce the Cost of Care in the KLAS 2022 Emerging Solutions Top 20 Report. As a trusted partner of more than 1,000 hospitals, AvaSure combines remote patient monitors, virtual nurses and other providers on a single platform to enhance clinical care.

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