Predictive maintenance is one of the oldest and most tested uses cases for the Internet of Things (IoT).  For years now, we’ve been able to analyze incoming data from sensors embedded in machines and make decisions about whether or not maintenance activities should be executed.

Typical scenarios have historically focused on things like wind farms, oil rigs, and fleets of trains. And while there’s plenty of excitement and new developments in these areas, what’s grabbing a lot of attention today is how predictive maintenance can be applied to new scenarios.

For example, in an earlier blog, I talked about predictive maintenance for autonomous vehicles – how sensors can send out data on the status of parts and components, allowing manufacturers to analyse this data to predict part failure and, thus, avoid breakdowns.

Yet, even this scenario keeps us in the realm of machines – because, sophisticated as it may be, an autonomous vehicle is still a machine. But what if we could now take the same general idea of predictive maintenance for machines and apply it to our bodies? Call it preventative maintenance for people – or just predictive healthcare. The reality is that in many ways, we’re already there.

Understanding in context

One of the advantages to predictive maintenance for machines is that incoming data about what’s going on in the moment can be analyzed in the context of historical data about the same machine. Let’s say an HVAC machine on the top of a hotel in Seville – where I live – sends out a high-temperature alert.

In and of itself, yes, this may be cause for concern. But when you realize that the machine sends out the same alert every month at the same time – well, maybe it’s not so concerning. Maybe the HVAC unit runs continuously for 8 hours on the first Monday of every month to help cool a large conference room on the used for the packed monthly meeting of the Seville Dog Walker’s Association.

Or maybe there’s another reason. The point is that in such a scenario, the high temperature alert is understandable and predictable in context – and thus of little concern. It would be nice if we had something similar for healthcare.

More than a snapshot

Here’s the problem: On a typical trip to the doctor you wait in the waiting room for 10-15 minutes, with other people, some of whom are likely sick. When you finally see the doctor, you’re thinking of the next appointment you have across town in 30 minutes so your anxiety levels go up.

My sister Mary was recently diagnosed with high blood pressure because when she was in the doctor’s clinic her blood pressure measured 150/89. The doctor advised her to get a connected blood pressure cuff, and to take regular measurements. When she did, it turned out her blood pressure was 108/75 – completely normal. She was suffering from what doctors call White Coat Syndrome.

But as with the HVAC machine, the high blood pressure reading was understandable in the context of her being in a doctor’s office. Wouldn’t it be great if the doctor evaluating your blood pressure had more than a snapshot of (often misleading) data to work from? Wouldn’t a whole bunch of relevant historical data be better?

With the smartwatch on my wrist, I can now share 3 years of data with my doctor. Now s/he can see things in context and treat me more effectively. I think it’s only a matter time before their office can take my sensor data in automatically – over the cloud. This will make my yearly check-up more productive. Instead of figuring out what the problem is (if there is one, hopefully not) we’ll be able to focus on what to do about it.

A business network for health  

As with so many things IoT, this is only the beginning. But let’s step back for a moment.

One of our offerings here at SAP is the SAP Asset Intelligence Network (SAP AIN). Think of it as a business network application. With SAP AIN, all of the data (metadata, specifications, bills of materials, whatever) that goes into the creation of a device (a compressor, coffee machine, car, whatever) can be stored in a central location.

When connected to the asset intelligence network, the device can push out real-time data that describes its state at any given moment. When the device owner allows access to this data, the manufacturer can then analyse it in conjunction with other data from other devices – making product improvements that can then be pushed out by way of the same asset intelligence network.

In fact, nothing is stopping device owners from sharing their data with whoever they wish – like maybe a service vendor, or insurance company. If a device goes out of tolerance for some reason, the service vendor could receive a notification and schedule an appointment to service the device automatically. Or in the case of an insurance company – they could then set rates according to actual device usage data.

Returning to the theme of health – what if we took this idea of an asset intelligence network and applied it to our own bodies? What if we had a “people’s intelligence network” – where a device like my smartwatch publishes my health data into a trusted cloud application?  When my device senses high blood sugar, for example, this data gets analyzed not only in the context of the unique moment mixed with my own personal health history – but also in the context of similar data from potentially millions of people.

Based on this much larger dataset, the network could then contact my service vendor – in this case, my doctor – and make an appointment if necessary. Yes, this would be convenient. But more importantly, it would move us away from making medical decisions based on poor data and the intuition of physicians, toward something often heralded but seldom achieved – real evidence based medicine.

Photo credit Chelsea Stirlen