By Matt Clare
The use of artificial intelligence (AI) in the healthcare industry is rapidly increasing — and for good reason. AI comes with promises of better patient outcomes, reduced operational cost and increased efficiency; but it’s not a perfect world, especially as healthcare organizations are struggling with implementation and use of AI and machine learning technologies. And what’s more, with industry executives predicting that AI will be ubiquitous in the field by 2025, healthcare organizations are also grappling with limiting the use of technology in instances where a human touch is more appropriate.
At its roots, healthcare is still an incredibly personal industry that deals with the most important factor to everyone — health. When it comes to integrating technology into certain aspects of healthcare like communications, there will inevitably be a time when it makes sense for patients to speak with a live person face-to-face. Consider an instance where a patient is repeatedly using telemedicine (a field increasingly adopting AI to improve upon patient interactions) to interact with their physician. There are foreseeable instances where a doctor may have to see their patient face-to-face. Finding the right balance of knowing when to use technology and when to supplement with human interactions is essential.
So how can institutions execute a successful “man-needs-machine and vice versa” outlook? Organizations need to consider these two new approaches as part of their AI implementation.
An IoT approach
Wearables and health applications are much more widely adopted as they are driving patients to be more active participants in their own healthcare and giving them greater access to their medical data. IoT technology, like sleep monitoring devices or fitness trackers, allow them to monitor their health, connect with their electronic medical records and share key health information with their doctor, all from their own devices. AI then takes this IoT-driven information and aggregates it for healthcare organizations to analyze and provide more personalized care.
This approach, centered around IoT, is about moving from a reactive business to a proactive business by leveraging new AI and machine learning capabilities to better predict outcomes. In the case of a sleep monitor for example, the device monitors a patient’s sleep habits and an adjacent interactive application coaches the patient by sending reminders and adapting to the patient’s individual habits. This is great for those looking to monitor their health on an ongoing basis. However, once the technology shares predictive analytics and recommendations daily, it’s best that a physician monitors that data as well, to provide more effective treatment (perhaps once a year or more depending on the patient’s condition).