Will health plans be able to get the most out of their member data using wearable technology?
It’s hard to think of anything good that came from the Covid-19 pandemic, but the growth of telemedicine use in healthcare delivery is a clear example of something positive. The same coronavirus that forced many hospitals, clinics, and doctors’ offices to temporarily close their doors also opened the door for telemedicine to cover much of the care delivery gap that followed.
Telemedicine is now supplying health plans, healthcare providers, and plan members a serendipitous benefit of its own: large quantities of member health data coming from the remote monitoring devices they are using. AI-enabled smart medical devices are now available for home use in tracking chronic conditions like diabetes, heart disease, and even skin cancer. Many of these devices are small and lightweight enough for members to wear them.
Two issues threaten to hinder how thoroughly AI can incorporate member health data from AI-capable home monitoring devices. The first is how to make best use of the unstructured data that is the most valuable part of the information they supply. The second is encouraging members to make more use of AI-enabled monitoring. We’ll consider each of these issues below.
Deep Learning AI Unlocks Unstructured Member Data
We can compare unstructured and structured healthcare data to an iceberg. Structured data is the 20 percent we see on the surface, and hidden underneath is the 80 percent that is unstructured. Unstructured includes print and electronic text like medical journals, as well as physician notes, audio files, member behavioral data, telephone call logs, and images and videos. Artificial intelligence software excels at obtaining searching and analyzing unstructured data and generating actionable recommendations from the analysis.
If we take the iceberg comparison one step further, the significance of facility-based care in deciding member health outcomes is only about 20 percent. The other 80 percent comes from other environmental and behavioral factors outside of the care facility. One advantage of telemedicine is that in addition to the electronic vital sign data that patient monitoring devices provide, a physician’s recorded observations of members in their home environments can provide meaningful insights into how those surroundings influence their health.
Without AI, providers or health plans that want to comb through unstructured member health data to glean these non-facility factors must do it manually. Healthcare risk adjustment and quality improvement software with built-in natural language processing, like Reveleer’s, makes gathering and analyzing unstructured data practical by making it faster, more accurate, and more thorough.
Member Trust in AI Monitoring Devices Still Needs Work
The beauty of intelligence software in any setting is that for it there is no such thing as “too much data.” The accuracy of AI deep learning algorithms is in analyzing and predicting member health behaviors and patterns only gets better as it analyzes more information. It’s in our interests, therefore, to encourage members receiving care at home to use AI-enabled monitoring devices.
Not all members, however, yet see the benefit of using AI in their homes. According to one study of more than 1,000 individuals receiving telemedicine treatment including AI-enabled monitoring devices, less than half thought it was important to them, more than a third refused to use at least one device, and one in ten thought artificial intelligence posed a threat to them. Among their misgivings was concern about loss of privacy and the risk of data security breaches or misuse of gathered information. These concerns lead can lead to high drop-out rates in the use of home health monitoring devices by members, with a corresponding reduction in AI’s effectiveness in their treatment.
How Can AI Make Better Use of Wearable Device Data?
Companies that develop artificial intelligence software cannot directly ease the concerns of people at home about the safety of the AI-enabled monitoring devices they will increasingly be using. Member unease with new technology, especially tech they think could threaten their access to healthcare professionals or will make decisions without human input, takes time to overcome.
One thing that AI software companies can do to foster more member use of AI-enabled home monitoring is assure health plans and their members that member data is being securely gathered and stored. One way to do this is to make sure that they have HITRUST CSF certification for information security, the benchmark standard for healthcare contractors to safeguard member protected health information.
Even after the Covid-19 public health emergency ends, we can count on telemedicine remaining a significant hybrid healthcare delivery tool, and for remote monitoring devices to keep getting smaller, more powerful, increasingly AI-enabled, and ultimately something members receiving care at home will use routinely. By giving our customers AI-enabled software that is safe, secure, and provides member health insights that no manual review method can emulate, Reveleer is your partner in this ongoing helpful development.
