I worked in research on wearables for more than a decade, including research with medical doctors. The number one reason we got from doctors about not using wearable data was that they didn't know what to do with it, what it meant. If blood pressure is too high when they've had you sit still for a while and taken a measurement in their office with their machine, they know how to interpret that. If you show them a continuous blood pressure trace from a persons whole day, they have no idea what to do with it. How much should your BP go up when you are doing something strenuous? How much should it go down when you sleep? They don't know. The volume of data also dismayed them. Present a years worth of heart rate data and who has time to look through it all and understand what it means? You need to know the context of the readings, what the person was doing, what was going on in the environment, even what the person was thinking, and wearable data comes with little context (mainly just accelerometer and gyro data, but it can't tell if you are eating dinner or watching tv or typing on a keyboard). The UI's for looking at the long term data are terrible, and it requires a lot of processing power to get a reasonable response time, and there is no ability to query the data, such as looking for time-offset correlations between one type of reading and another. The data just is not very usable without context. A person looking at their own data for the day remembers the context, what they did that day, but how about a month ago? A year ago? Sensor accuracy still needs improvement also (e.g., did your body temperature go up today because you're getting sick or because you spent the day outside when it was hot instead of staying inside in the air conditioning? If the sensor is affected by ambient temperature there is no way to tell.) Wearable health sensing is good enough to be somewhat useful to an individual, but it needs to be developed a lot more to be useful to a doctor (though it can provide clues that are useful sometimes). There need to be a lot of RCT's to understand just what the data means, what is normal for a person, what indicates a problem. There is also the fear that if the doctors help develop this technology it will replace a lot of their diagnostic function. That could be good, but it could also result in everyone trusting the machine's decisions rather than looking at what is actually happening with the person.

To give an example, a wearable might indicate a sudden change in gait for an elderly person. Is that a sign of mental decline? Muscle deterioration due to age? A stroke? Or were they playing baseball two days ago with a grandchild and injured themselves? New shoes? It could be any of these and more besides. If the machine does not evaluate all the possibilities it can only choose from the ones it does implement, which raises the likelyhood it will be wrong.