Ihave a problem. I am a psychiatrist in the 21st century and yet I still evaluate patients the way Freud did a century ago: I sit with a patient and, by carefully observing how and what they say, I expect them to tell me what’s wrong.

The problem isn’t that I speak with and listen to my patients. Every doctor of every speciality does that. Rather, my problem is that I never measure the data I think are most important to my treatment of psychiatric diseases.

Consider how I evaluate a patient for psychosis in the emergency room. When I speak with them, I want to know what their life is like—what’s their day like? What’s on their mind? How social are they? How’s their sleep? These data depend on my patient’s ability to remember, accurately report, make sense of, and tell me about their experience—and further, my treatments depend on my own ability to listen to and make sense of what I’m hearing.

While we speak, I look for things like rapid or disorganized speech, somewhat incongruent facial expressions, or even recurrent ideas that might help me guage their mind’s function. I ask a series of finely-honed questions to poke and prod at their mind, creating a trove of essential clinical data. But my problem is that the only tool I use to gather and understand these data is my own brain. In other words, I leave the vast majority of that data unrecorded, unanalyzed and untapped. This is a problem. Consider what I’d do if this patient has chest pain.

Chest pain is a vague symptom that can be present in anything from heartburn to a panic attack to a heart attack. I would of course, ask them about their chest pain—when did it start? have they had it before? But I would dig deeper than conversation.

Heart rate is important in chest pain. I could put my fingers on their wrist and count out their heartbeats per minute, but I wouldn’t do that—I’d use a calibrated machine. I might carefully ascultate the lub-dub of their heart valves closing, but I would without question measure the flow of electricity through their heart each millisecond with an electrocardiogram. If I wasn’t reassured by these measurements, I’d probably draw some blood to check for protein SOS signals from their heart and call cardiology. Because I take chest pain seriously, in a few short minutes, I’d gather a host of measurements and would know whether their chest pain was caused by a heart attack.

Before decades of public-private partnerships developed the tools I use to evaluate chest pain, clinicians accepted that some data—in this case the essential data that defines the clinical problem of a heart attack—are invisible without technology and essential to provide good clinical care. Yet as a psychiatrist, I continue to ask questions without measuring the data I think are important to define my clinical problems like psychosis, even though the technology exists.

You probably have the most sophisticated behavioral measurement device ever created in your hand. The smartphone boasts a suite of technologies that might dramatically advance my ability to assess and treat my patients. Right now, our smartphones collect data that measure things I already believe are clinically important: what’s on our mind, how social we are, even how we sleep.

In addition to asking “what’s on your mind?” I might—with my patient’s consent and support, of course—analyze their online search history or social media profile, looking for subtle changes in the way they express themselves, changes that, studies have shown, might define an opportunity for us to work more closely together to improve their mental health. I could ask, but also measure.

Right now, I don’t use technology because, frankly, it’s not necessary. I diagnose and bill based on conversations, not measurements. Psychiatric diagnoses—organized before the advent of technology—are without exception based on patterns of symptoms and signs, or what a patient tells me and what I observe. Though psychiatry has tried to better define the diagnosis of, say, schizophrenia, this has backfired. The more we fiddle with our existing framework, the more muddled it becomes: I recently calculated that the latest diagnostic criteria (DSM-5) for schizophrenia describes ~7.6 trillion different patterns of symptoms and signs.

Notwithstanding these barriers, psychiatry has never been working more quickly or more effectively towards the goal of better defining the clinical problems we treat. The National Institute of Mental Health recently announced the Accelerating Medicines Partnership for Schizophrenia (AMP SCZ), an investment of over $82.5 million over five years and one of the largest private-public partnerships in the organization’s history.

For one of the first times at this scale, a band of psychiatrists and researchers from academic hospitals, pharmaceutical companies, and tech companies will combine traditional clinical conversation with measures of brain function, cutting-edge data from smartphones, personal measurement devices and audio-visual recordings.

For example, recording and analyzing a conversation might help clinicians detect subtle changes in the way people string ideas together or refer to themselves. Without technology, these changes would remain invisible even to a skilled clinician, yet studies have shown that they predict the onset of a psychosis episode in at risk patients. Such patients—previously in a grey zone—might have access to more and better treatments, thereby leading to better outcomes.

Of course, none of these technologies will replace the empathic charm and human touch of a skilled clinician. Some clinical data are necessarily bespoke, artfully gathered by a skilled clinician; but not all data are like this. Modern medicine has brought chest pain from heart attacks from routinely fatal to often survivable and even preventable. Progress in evaluating chest pain required decades of fastigious measurement and, crucially, novel treatments to pair with those measurements.

Though technology isn’t a magic bullet, history has shown that the more we harness technology, the better we can define our clinical problems and treat our patients.

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A novel machine learning algorithm improved patient selection for cardiac resynchronization therapy (CRT) in a study of nearly 1,000 heart failure patients in the U.S., representing an opportunity to optimize care and spare certain individuals from a pricey procedure that might not benefit them.

CRT is indicated in patients with medically refractory systolic HF and left ventricular dyssynchrony to improve left ventricular ejection fraction (LVEF), corresponding author Charlotta Lindvall, MD, PhD, and co-authors explained in PLOS ONE earlier this month. Improvement in LVEF after CRT implantation is associated with improved survival and reduced HF hospitalizations, but at least one-third of CRT patients don’t see that improvement by 18 months post-op.

Right now, medical guidelines suggest patient selection for CRT implantation should depend on a number of factors, including NYHA functional class, LVEF, QRS duration, type of bundle branch block, etiology of cardiomyopathy and atrial rhythm. Physicians are also urged to consider patients’ “general health status,” but consensus statements offer little guidance on how to go about evaluating these CRT candidates.

“Accurate patient selection is important to minimize morbidity and mortality related to the device, and to control healthcare costs,” Lindvall, of the Dana-Farber Cancer Institute in Boston, and colleagues said. “It will only become more important as the population of heart failure patients continues to grow. However, more than a decade of work has shown that it is not easy to identify new predictors of CRT response.”

The authors said advancements in AI and machine learning offer a new opportunity for patient selection for CRT. If integrated with natural language processing, machine learning could make use of both structured and unstructured EHR data to build a precise, usable prediction tool.

Lindvall et al. built their AI model by applying machine learning and natural language processing to EHRs of 990 patients who received CRT at two academic hospitals between 2004 and 2015. Demographics, lab values, medication status, clinical characteristics and medical history were extracted from EHRs available before the CRT procedure, and the researchers also extracted bigrams from patients’ clinical notes using natural language processing. Patients accrued, on average, 75 clinical notes.

The team built a machine learning model using 80% of the patient sample and tested the model on the remaining 20% of the study pool. The average age of participants was 72 years old, and mean baseline LVEF was 24.8%. The authors’ primary endpoint was reduced CRT benefit, defined as less than 0% improvement in LVEF at six to 18 months post-procedure or death at 18 months.

Of the 990 patients studied, 403—40.7%—saw a reduced benefit from their CRT device within a year and a half. Just over a quarter of patients saw no improvement in LVEF by 18 months, and 15.6% had died by then.

Lindvall and colleagues’ finalized model identified 26% of patients who wouldn’t benefit from CRT implantation at a positive predictive value of 79%.

“The amount of data available in the EHR is massive, and is rapidly expanding,” the authors wrote. “Analysis of these data using methods from computer science can allow for discovery of complex patterns that are clinically important, but difficult for the human mind to identify. Machine learning does not require prior assumptions about causative variables and allows for an exploration of all available data for non-linear patterns.”

The team said validated models that can run in the background of an EHR could enable recognition of many marginal risk factors which, on their own, might not seem significant. The approach could allow for more individualized risk assessment and, in time, might change our current guidelines-based approach to patient selection.

“Clinicians often encounter patients with demographic and clinical characteristics that differ from the patients who participated in the studies that formed the evidence basis for the guidelines,” Lindvall et al. wrote. “Thousands of CRT procedures are performed in the United States alone every month and so opportunities for refined decision support tools should be pursued.”

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The Mabu robot, with its small yellow body and friendly expression, serves, literally, as the face of the care management startup Catalia Health. The most innovative part of the company’s solution, however, lies behind Mabu’s large blue eyes.

Catalia Health’s software incorporates expertise in psychology, artificial intelligence, and medical treatment plans to help patients manage their chronic conditions. The result is a sophisticated robot companion that uses daily conversations to give patients tips, medication reminders, and information on their condition while relaying relevant data to care providers. The information exchange can also take place on patients’ mobile phones.

“Ultimately, what we’re building are care management programs to help patients in particular disease states,” says Catalia Health founder and CEO Cory Kidd SM ’03, PhD ’08. “A lot of that is getting information back to the people providing care. We’re helping them scale up their efforts to interact with every patient more frequently.”

Heart failure patients first brought Mabu into their homes about a year and a half ago as part of a partnership with the health care provider Kaiser Permanente, who pays for the service. Since then, Catalia Health has also partnered with health care systems and pharmaceutical companies to help patients dealing with conditions including rheumatoid arthritis and kidney cancer.

Treatment plans for chronic diseases can be challenging for patients to manage consistently, and many people don’t follow them as prescribed. Kidd says Mabu’s daily conversations help not only patients, but also human care givers as they make treatment decisions using data collected by their robot counterpart.

Robotics for change

Kidd was a student and faculty member at Georgia Tech before coming to MIT for his master’s degree in 2001. His work focused on addressing problems in health care caused by an aging population and an increase in the number of people managing chronic diseases.

“The way we deliver health care doesn’t scale to the needs we have, so I was looking for technologies that might help with that,” Kidd says.

Many studies have found that communicating with someone in person, as opposed to over the phone or online, makes that person appear more trustworthy, engaging, and likeable. At MIT, Kidd conducted studies aimed at understanding if those findings translated to robots.

“What I found was when we used an interactive robot that you could look in the eye and share the same physical space with, you got the same psychological effects as face-to-face interaction,” Kidd says.

As part of his PhD in the Media Lab’s Media Arts and Sciences program, Kidd tested that finding in a randomized, controlled trial with patients in a diabetes and weight management program at the Boston University Medical Center. A portion of the patients were given a robotic weight-loss coach to take home, while another group used a computer running the same software. The tabletop robot conducted regular check ups and offered tips on maintaining a healthy diet and lifestyle. Patients who received the robot were much more likely to stick with the weight loss program.

Upon finishing his PhD in 2007, Kidd immediately sought to apply his research by starting the company Intuitive Automata to help people manage their diabetes using robot coaches. Even as he pursued the idea, though, Kidd says he knew it was too early to be introducing such sophisticated technology to a health care industry that, at the time, was still adjusting to electronic health records.

Intuitive Automata ultimately wasn’t a major commercial success, but it did help Kidd understand the health care sector at a much deeper level as he worked to sell the diabetes and weight management programs to providers, pharmaceutical companies, insurers, and patients.

“I was able to build a big network across the industry and understand how these people think about challenges in health care,” Kidd says. “It let me see how different entities think about how they fit in the health care ecosystem.”

Since then, Kidd has watched the costs associated with robotics and computing plummet. Many people have also enthusiastically adopted computer assistance like Amazon’s Alexa and Apple’s Siri. Finally, Kidd says members of the health care industry have developed an appreciation for technology’s potential to complement traditional methods of care.

“The common ways [care is delivered] on the provider side is by bringing patients to the doctor’s office or hospital,” Kidd explains. “Then on the pharma side, it’s call center-based. In the middle of these is the home visitation model. They’re all very human powered. If you want to help twice as many patients, you hire twice as many people. There’s no way around that.”

In the summer of 2014, he founded Catalia Health to help patients with chronic conditions at scale.

“It’s very exciting because I’ve seen how well this can work with patients,” Kidd says of the company’s potential. “The biggest challenge with the early studies was that, in the end, the patients didn’t want to give the robots back. From my perspective, that’s one of the things that shows this really does work.”

Mabu makes friends

Catalia Health uses artificial intelligence to help Mabu learn about each patient through daily conversations, which vary in length depending on the patient’s answers.

“A lot of conversations start off with ‘How are you feeling?’ similar to what a doctor or nurse might ask,” Kidd explains. “From there, it might go off in many directions. There are a few things doctors or nurses would ask if they could talk to these patients every day.”

For example, Mabu would ask heart failure patients how they are feeling, if they have shortness of breath, and about their weight.

“Based on patients’ answers, Mabu might say ‘You might want to call your doctor,’ or ‘I’ll send them this information,’ or ‘Let’s check in tomorrow,’” Kidd says.

Last year, Catalia Health announced a collaboration with the American Heart Association that has allowed Mabu to deliver the association’s guidelines for patients living with heart failure.

“A patient might say ‘I’m feeling terrible today’ and Mabu might ask ‘Is it one of these symptoms a lot of people with your condition deal with?’ We’re trying to get down to whether it’s the disease or the drug. When that happens, we do two things: Mabu has a lot of information about problems a patient might be dealing with, so she’s able to give quick feedback. Simultaneously, she’s sending that information to a clinician — a doctor, nurse, or pharmacists — whoever’s providing care.”

In addition to health care providers, Catalia also partners with pharmaceutical companies. In each case, patients pay nothing out of pocket for their robot companions. Although the data Catalia Health sends pharmaceutical companies is completely anonymized, it can help them follow their treatment’s effects on patients in real time and better understand the patient experience.

Details about many of Catalia Health’s partnerships have not been disclosed, but the company did announce a collaboration with Pfizer last month to test the impact of Mabu on patient treatment plans.

Over the next year, Kidd hopes to add to the company’s list of partnerships and help patients dealing a wider swath of diseases. Regardless of how fast Catalia Health scales, he says the service it provides will not diminish as Mabu brings its trademark attentiveness and growing knowledge base to every conversation.

“In a clinical setting, if we talk about a doctor with good bedside manner, we don’t mean that he or she has more clinical knowledge than the next person, we simply mean they’re better at connecting with patients,” Kidd says. “I’ve looked at the psychology behind that — what does it mean to be able to do that? — and turned that into the algorithms we use to help create conversations with patients.”

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To understand the benefits that artificial intelligence can bring to the world of human medicine, consider the case of Ayako Yamashita, a 60-year-old Japanese woman, whose condition befuddled doctors in 2015.

Yamashita was thought to be suffering from acute myeloid leukemia. But after several unsuccessful treatment attempts, her doctors decided to search for another answer to her condition. They turned to IBM’s Watson, an AI system capable of analyzing vast amounts of data.

The computer reviewed nearly 20 million previously-published oncological research studies and cross-referenced data points. Watson’s analysis suggested the woman had a rare form of leukemia not detected through conventional methods. This led to a change in treatment and doctors crediting Watson for saving  the woman’s life.

The analysis of such huge amount of data is next to impossible for a human mind, but it’s like a walk in a digital park for AI. And it shows what may be one of the valuable things that AI can do for us. It is “the most practical application in the field of medical and healthcare for artificial intelligence,” said Seiji Yamada, of the National Institute of Informatics and chairman of the Japanese Society for Artificial Intelligence.

The global artificial intelligence market is expected to reach $19.47 billion by 2022, according to the research firm Allied Market Research. As AI is marking its presence, tech giants are working to capitalize on new opportunities. The healthcare sector is a natural fit, according to Sanjay Gupta, managing director, South Asia and Middle East for NICE, a technology firm based in Israel, in an interview with ETHealthworld:

“The development of automation enabled by technologies including robotics and artificial intelligence in healthcare sector brings the promise of higher productivity with increased safety.”

Saving lives and time

Among Google’s many AI ventures is an effort to develop new products targeting the health sector. The company is focusing on applications for life preservation, preventive care and improving health care services.

The company plans to launch a trial in India to test an AI system that scans a person’s eyes to look for signs of diabetic retinopathy. The company aims to license the technology to clinics. The system already has proven itself adept at detecting high blood pressure, or risk of heart disease or stroke, according to a study published in early 2018.

From a story published in the Washington Post:

“This may be a rapid way for people to screen for risk,” Harlan Krumholz, a cardiologist at Yale University who was not involved in the study, wrote in an email. “Diagnosis is about to get turbo-charged by technology. And one avenue is to empower people with rapid ways to get useful information about their health.”

Jeff Dean, the Chief at Google AI, outlined for Boss Magazine how this system will enable doctors to better diagnose and treat patients for a range of diseases. Moreover, this system will also track key events in the patient’s past (including hospital stays) to help doctors more effectively.

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