HealthTech for the Healthy Middle
The range of HealthTech devices is wide and of varying utility. Apps for Apple Watches can track many things, like heart and respiratory rates. But it’s just more data points that aren’t really put to use meaningfully. Activity tracker devices are popular, but don’t seem to have much impact in helping people lose weight.
At the other end of the spectrum are tech tools originally designed to help people manage their chronic illness, like continuous glucose monitoring (CGM) for people with diabetes. They’re built to help people manage living with a disease they already have, but are these tools the healthy middle can use to learn more about themselves?
Right now, we don’t use the HealthTech tools that collect medical grade data as diagnostic tools for the healthy middle. If understanding patterns of glucose fluctuations in our bodies could identify triggers and benchmarks that show when someone might slide towards a state of ill health - don’t we want to have that data and analytical understanding to prevent disease?
Healthcare Artificial intelligence (AI) used to analyze medical data sets is filling in these benchmark gaps between pre-diagnosis, diagnosis, and treatment. It’s building our knowledge of how to identify inflection points on the journey from healthy to sick. Because continuous glucose monitoring (CGM) has been around for a while, the industry has already had more than a decade to develop benchmarks on how to understand glucose fluctuations. For example, we now know that the amount of time and time of day that a person's blood glucose is too high means something; it’s not just a point-in-time blood glucose unit number that’s important.
The more we use AI analysis on these data sets, the better it gets at predicting when someone’s glucose might spike. We now know that the amount of time a person spends each day with healthy blood glucose levels is critical, which makes being able to prevent a predicted spike incredibly important to maintaining long-term health.
AI’s help in identifying triggers and benchmarks is a necessary piece to convert personal health devices from tools that regurgitate simple data to tools that provide timely, actionable guidance. This idea is trickling down to some devices connected to our home networks. Mattresses that can self-adjust if the sleeper’s body temperature gets too high or low. Smart mirrors that track our skin and facial characteristics to offer nutritional recommendations.
This trend should lead to a state where we can each monitor our body’s well-being with the same precision that our cars can monitor their systems. Really, think about how much we rely on our cars’ ability to gauge for itself the earliest moment when one of its systems starts to function sub-optimally. That little red light on the dashboard lights up and we’re on it. We’re on it because we want to keep our cars running smoothly. We’d rather take small, reasonable action now to avoid catastrophic failure and paying astronomical prices for a major system overhaul in the future.