Tag: healthcare

Predictive Maintenance for People – the endgame for the Internet of Things (#IoT) and healthcare

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.

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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

Technology in healthcare, a post-Sapphirenow update

As noted here recently, technology is completely revolutionising the healthcare industry.

And that was brought home to us forcefully when we attended SAP’s 2014 Sapphirenow conference last week. I had fifteen meetings scheduled at the event, and while there wasn’t much mention of healthcare during the keynotes, seven of my fifteen meetings were healthcare related. In previous Sapphirenow conferences, there might have been one.

The meetings were with a range of organisations. Some were larger organisations like MKI, Stanford University (specifically their Center for Computational, Evolutionary and Human Genomics (CEHG)), and unsurprisingly SAP. MKI talked about their use of HANA, R, and Hadoop for genomic analysis. Stanford’s Carlos Bustamante talked about the research being done by the CEHG, in conjunction with SAP, on understanding different genomes and their health-related phenotypic consequences, while SAP discussed their Care Circles initiative, as well as their Genome Sciences projects.

One interesting data point that emerged from Prof Bustamante was that one dataset of 2534 individual genomes contained in excess of 20 billion records and it consumed 1.2 terabytes of RAM. This is big data. Especially when you consider you are interrogating it against matrices of other data points (such as age, nationality, gender, etc.).

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Three of the companies I met were part of the SAP Startup Focus program. This is a program aimed at start-up companies with offerings in the big data, realtime or predictive analytics spaces. The program helps them develop their product on SAP’s in-memory HANA database platform, and also helps them with go to market strategies.

The three healthcare startups were Convergence CT, Phemi, and Core Mobile. ConvergenceCT makes software for hospitals which can take in data from multiple data sources (EMR systems, labs, radiology, etc.) and produce insights via predictive analytics, and reporting dashboards. Phemi, similarly takes in healthcare info from the various disparate hospital data sources, and then has a number of apps sitting on top of the data delivering results and outcomes. While Core Mobile has mobile apps for doctors, patients, and carers to help optimise care processes, and share patient information with authorised recipients.

So lots of interesting things happening in this sector right now and much of the innovation is down to SAP’s decisions to 1) turn it’s HANA database into a platform, and 2) to initiate the Startup Focus program. Now that IBM is going the platform route with it’s Watson cognitive computing engine, we’re likely to see a lot of healthcare innovation emerging there too.

(Cross-posted @ GreenMonk: the blog)

Technology is completely revolutionising the healthcare industry

Healthcare is changing. Recent advances in technology are completely revolutionising how we approach the prevention, diagnosis and treatment of illness. And this is just the beginning of what will be a technological revolution in healthcare.

Smartphone use is growing at an enormous pace. They now account for 87% of the total mobile handsets in the US, for example. And with the smartphones has come hundreds of new apps related to health and fitness. These apps do everything from monitoring sleep and movement (steps), to keeping track of glucose levels, blood oxygen, and even ovulation.

Fitbit Dashboard

The relentless rise of wearable connected devices is also having a big effect on people tacking their health and fitness. These small devices (such as the Fitbit Force, the Jawbone Up, and the Withings Pulse) are light and easy to wear, and they communicate with apps on the smartphone to monitor and record health-related information.

The next evolution of wearables, where they are built-in to the clothes you wear, has already begun. If these devices become as ubiquitous as smartphones, they will help us make far better informed decisions about our health and fitness.

Then you have major players like Apple going on a hiring spree of medical technology executives to bolster its coming Healthbook application, as well as its rumoured iWatch wearable device. Samsung too have wearable fitness trackers and announced their own Healthcare platform “to track your every move” today.

Going further back the stack, and we see IBM using its artificial intelligence play Watson to make inroads into the health industry (see video above). IBM has been partnering with WellPoint Inc. and Memorial Sloan-Kettering Cancer Center to help clinicians better diagnose instances of cancer in patients.

And more recently IBM has announced that it is working with New York Genome Center to create a prototype that could suggest personalised treatment options for patients with glioblastoma, an aggressive brain cancer. From the announcement:

By analyzing gene sequence variations between normal and cancerous biopsies of brain tumors, Watson will then be used to review medical literature and clinical records to help clinicians consider a variety treatments options tailored to an individual’s specific type and personalized instance of the cancer.

And IBM aren’t stopping there. They announced last month that they were opening up Watson as a platform so developers can create apps that can utilise Watson’s cognitive computing engine to solve all kinds of difficult problems. And earlier this month IBM announced that several “powered by Watson” apps have been developed, including one to help dermatologists better diagnose skin cancer.

And IBM also announced the acquisition of Cognea. Cognea offers virtual assistants that relate to people using a wide variety of personalities—from suit-and-tie formal to kid-next-door friendly – think Siri, or better yet Cortana for Watson!

Then, newer in-memory database technologies such as SAP’s HANA, are being used to crunch through datasets so large they were previously to big to query. For example, SAP announced today a partnership with the Stanford School of Medicine to “achieve a better understanding of global human genome variation and its implications in disease, particularly cardiovascular disease”. From the release SAP goes on to say:

Researchers have already leveraged SAP HANA to corroborate the results of a study that discovered that the genetic risk of Type II Diabetes varies between populations. The study looked at 12 genetic variants previously associated with Type II Diabetes across 49 individuals. With SAP HANA, researchers in Dr. Butte’s lab were able to simultaneously query all 125 genetic variants previously associated with Type II Diabetes across 629 individuals. Using traditional methods, this analysis on this amount of data would have taken an unreasonable amount of time.

So, the changes which technology are bringing to the healthcare industry now are nothing short of revolutionary. And with the likes of SAP’s HANA, and IBM’s Watson, set up as platforms for 3rd party developers, the stage is set for far more innovation in the coming months and years. Exciting times for healthcare practitioners, patients and patients to-be.

 

(Cross-posted @ GreenMonk: the blog)

Things are changing fast in medicine, thanks to mobile.

This post was written by and was published at Things are changing fast in medicine, thanks to mobile.

Wow – this is an amazing video on just how much smartphones can help reduce costs, increase healthcare efficiency and improve patient well-being and outcomes.

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We may not be fully there yet in terms of the widespread availability of this hardware and software, and then there’s getting it accepted by the medical establishment, but this is certainly a big step in the right direction.

A search in the iPhone App store for the term ‘Glucose” returns 217 apps and a similar number for the term ‘ECG’, while a search for ‘Glucose” in the Google Play Android apps store returns over 1,000 results

Things are changing fast in medicine thanks to mobile – exciting times ahead.

(Cross-posted @ GreenMonk: the blog)