**This article was taken from The WIRED World in 2016 --_our fourth annual trends report, a standalone magazine in which our network of expert writers and influencers predicts what's coming next. Be the first to read WIRED's articles in print before they're posted online, and get your hands on loads of additional content by subscribing online._
When assessing a patient, medics look at snapshots of physiological data that are manually taken by doctors or nurses, and make decisions against patient history, family background and test results, as well as their own knowledge and experience. But what if this data was constantly being taken, every second of every day? And what if a system was clever enough to compare these readings to thousands of patients worldwide with a similar history and disorder, as well as all the current clinical guidelines and studies, and make clinical suggestions to doctors?
In 2016, this kind of data-led decision-making will come ever closer. Sentrian, a California-based early-stage machine learning and biosensor analytics company for remote patient management, has created a system that does just that, and it's currently being trialled on patients. "We actually don't monitor people very frequently," says Jack Kreindler, Sentrian's founder and chief medical officer. "If I see a patient once a year, I may spend one hour listening to them, and the rest of the year's 8,759 hours not listening to them. We are trying to build a system that will enable us to listen to the lives and bodies of patients all the time, so we can make better, earlier and more personalised decisions."
Currently, wireless biosensors can collect simple data such as body temperature and heart rate as well as more complex information like oxygen saturation of the blood and potassium levels. Remote patient monitoring is typically done with one or two sensors at a time and the data is usually assessed by clinicians. But if a patient could constantly wear several sensors at a time, the amount of data produced would be enormous.
Sentrian's approach collects data streams from biosensors and uses machine learning algorithms to detect subtle patterns based on general information within the system on chronic conditions. These can include heart disease, diabetes and chronic obstructive pulmonary disease (COPD). Data such as heart rate, blood pressure and oxygen saturation from wireless biosensors on the patient are pushed to a cloud-based engine that analyses this data and notifies doctors when needed.
Martin Kohn, chief medical scientist at Sentrian, who practised emergency medicine for 30 years, explains the value in this approach. "It's based on the premise that for many patients with diseases such as congestive heart failure and COPD, the processes that lead to severe illness start days before the patient actually becomes acutely ill," he says.
The system is currently being tested in clinical trials in the US and UK in patients with chronic heart failure, COPD, high risk of falls and cancer. Early unpublished evidence has already shown the possibility of being able to spot congestive heart failure exacerbations up to ten days in advance. "That is quite extraordinary - before you maybe only had hours," Kreindler says. "We are seeing subtle, personalised patterns and data where odd things, which we didn't really expect before, may end up having strong statistical significance in predicting whether someone is going to fall over many days in advance." Very early research is showing that, in some people, factors such as heart rate variability, sleep duration and body temperature may be indicators of an impending crisis, These differ from the currently accepted warning signs and evidence-based triggers for treatment.
But are we ready to hand over all decision making to a black box, particularly when it comes to healthcare? "At the moment, there is a barrier, even from the profession themselves, to trust the kind of outputs that machine learning can deliver," Kreindler says. Sentrian has tried to account for this mistrust by giving some control back to humans - for example, by allowing doctors to specify rules for their patients. So, a scenario may run: "If Mr Smith's heart rate rises significantly, but his activity is going down and his breathing rate is going up, send a text message to the patient and a family member. If there is no response from the caregiver or patient after a text message, one email, and one phone call, then make a call to their doctor."
These rules, which can be general or personalised, are the kind of complex event-processing and subtle pattern-recognition that is going on in an experienced team of clinician's heads when they are monitoring a patient in intensive care, explains Kriendler.
And Sentrian's system will continue to learn exactly which rules and interventions work best for which patients: if a false alarm is indicated, the doctor can report this. The more patients the system "sees" and the more feedback it gets, the more the system learns. "Normally, the human brain remembers the last 30 or so patients it looked at," says Kreindler. "With this we may have more than 300,000 patients in memory."
Another issue that machine learning is being applied to is the volume and rate at which new medical information is growing. Knowledge is expanding faster than doctors are able to assimilate and apply. It is estimated that in 1950, the time to double the volume of the world's medical knowledge was 50 years; in 2010 it took 3.5 years; and in 2020, it will take 73 days. In another study, researchers projected that it took, on average, 17 years for new evidence-based findings to find their way to the clinic.
What if a doctor was able bring up every single case study, clinical study and national guideline worldwide on a particular disorder to the forefront of their mind? The second part of Sentrian's project aims to do just this, and augment the system with the ability to read and learn from all current clinical evidence.
IBM Watson, the supercomputer that won a game of Jeopardy! against humans in 2011, has already demonstrated that this sort of learning is possible. Kohn was chief medical scientist at IBM research, where he led the company's Watson supercomputer initiative in healthcare. Watson has "read" 204 oncology textbooks, medical databases of journals (one of which, PubMed has 24 million citations of biomedical literature), thousands of patient records, and had 14,700 hours of "training" from clinicians. In a study published in 2014, scientists from Baylor College of medicine in Houston, Texas and IBM used Watson technology to analyse more than 70,000 scientific articles to identify proteins that can modify a tumour-suppressing protein. Over the past 30 years scientists have identified 28 similar target proteins - Watson identified six in a month. "Watson will assist your clinician by providing timely insights into the specific condition by analysing the patient's detailed medical records including genomic considerations," says Robert S Merkel of Watson Health. "Watson will then suggest potential treatment recommendations from a very large repository of knowledge spanning millions of pages of medical literature, research articles and 180,000 clinical trial protocols."
Merkel explains that Watson does not have to be used as just a tool for suggesting clinical management options to doctors - it could also be a benefit in clinical trials. "Consider clinical trial matching," he says. "A clinical trial for an experimental breast cancer treatment may require a hundred patients who meet a variety of criteria, like a specific genetic marker, age, current stage of the tumour, history of interventions and a response to treatments and medications. Today, physicians and nurses spend hours manually reviewing patient records and comparing patient data to the criteria for a trial. This process introduces the possibility of errors, delays and missed matches." Watson's computing power is able to help doctors by accurately matching their patients with clinical trials that could benefit their care.
Sentrian's system is currently undergoing several randomised controlled trials, which means that the data of thousands of patients will be added to the platform. While we wait on clinical evidence, it is just a matter of time before this sort of artificial intelligence becomes a regular occurrence in the doctor's office.
This article was originally published by WIRED UK
