Take fever as an example. For 150 years, doctors have routinely prescribed antipyretics like ibuprofen to help reduce fever. But in 2005, researchers at the University of Miami, Florida, ran a study of 82 intensive care patients. The patients were randomly assigned to receive antipyretics either if their temperature rose beyond 101.3°F (“standard treatment”) or only if their temperature reached 104°F. As the trial progressed, seven people getting the standard treatment died, while there was only one death in the group of patients allowed to have a higher fever. At this point, the trial was stopped because the team felt it would be unethical to allow any more patients to get the standard treatment.
So when something as basic as fever reduction is a hallmark of the “practice of medicine” and hasn’t been challenged for 100+ years, we have to ask: What else might be practiced due to tradition rather than science?
Today’s diagnoses are partially informed by patients’ medical histories and partially by symptoms (but patients are bad at communicating what’s really going on). They are mostly informed by advertising and the doctor’s half-remembered and potentially obsolete lessons from medical school (which are laden with cognitive biases, recency biases, and other human errors). Many times, if you ask three doctors to look at the same problem, you’ll get three different diagnoses and three different treatment plans.
The net effect is patient outcomes that are inferior to and more expensive than what they should be. A Johns Hopkins study found that as many as 40,500 patients die in an ICU in the U.S. each year due to misdiagnosis, rivaling the number of deaths from breast cancer. Yet another study found that ‘system-related factors’, e.g. poor processes, teamwork, and communication, were involved in 65% of studied diagnostic error cases. ‘Cognitive factors’ were involved in 75%, with ‘premature closure’ (sticking with the initial diagnosis and ignoring reasonable alternatives) as the most common cause. These types of diagnostic errors also add to rising healthcare expenditures, costing $300,000 per malpractice claim.
Healthcare should become more about data-driven deduction and less about trial-and-error. That’s hard to pull off without technology, because of the increasing amount of data and research available. Next-generation medicine will utilize more complex models of physiology, and more sensor data than a human MD could comprehend, to suggest personalized diagnosis. Thousands of baseline and multi-omic data points, more integrative history, and demeanor will inform each diagnosis. Ever-improving dialog manager systems will help make data capture and exploration from patients more accurate and comprehensive. Data science will be key to this. In the end, it will reduce costs, reduce physician workloads, and improve patient care.
Replacing 80% of what doctors do?
Much of what physicians do (checkups, testing, diagnosis, prescription, behavior modification, etc.) can be done better by sensors, passive and active data collection, and analytics. But, doctors aren’t supposed to just measure. They’re supposed to consume all that data, consider it in context of the latest medical findings and the patient’s history, and figure out if something’s wrong. Computers can take on much of that diagnosis and treatment and even do these functions better than the average doctor (while considering more options and making fewer errors). Most doctors couldn’t possibly read and digest all of the latest 5,000 research articles on heart disease. And, most of the average doctor’s medical knowledge is from when they were in medical school, while cognitive limitations prevent them from remembering the 10,000+ diseases humans can get.
Computers are better at organizing and recalling complex information than a hotshot Harvard MD. They’re also better at integrating and balancing considerations of patient symptoms, history, demeanor, environmental factors, and population management guidelines than the average physician. Besides, 50% of MDs are below average! Computers also have much lower error rates. Shouldn’t we take advantage of that when it comes to our health?!
Technology compensates for human deficiencies and amplifies our strengths – MDs and less-trained medical professionals can do more. Eventually, computers will replace 80% of what doctors do and amplify their capabilities. Lifecom showed in clinical trials that medical assistants using a diagnostic knowledge engine were 91% accurate without using labs, imaging, or exams. Another clinical study by the same company demonstrated that 75% of cases can be safely triaged to be treated by RNs, with the remainder handled by doctors. A MassGen study found that 25% of the time, a medical record for patients who wound up with ‘high risk diagnoses’ had ‘high information clinical findings’ before a physician finally made the diagnosis — in other words, there was a significant delay that might have been avoided had a clinical decision support system been used to parse the notes!
New technologies will make the receptive doctors better at their jobs – quicker, more accurate, and more fact-based. There is a tremendous opportunity in the influx of data that has never before been available. Once we have a large enough dataset, and an addressable database of research studies, we’ll be able to identify patterns and physiological interactions in ways that weren’t possible before.
Over time, doctors will increase their reliance on technology for triage, diagnosis, and decision-making. Eventually, we’ll need fewer doctors, and every patient will receive the best care. Diagnosis and treatment planning will be done by a computer, used in concert with empathetic support from medical personnel selected more for their caring personalities than for their diagnostic abilities. No brilliant diagnostician with bad manners, a la “Dr. House,” will be needed in direct patient contact. Instead, we’ll use “Dr. Algorithm” to provide the diagnosis, while the most humane humans provide the care.
Systems will start as clumsy toddlers and develop to maturity and efficiency
Don’t expect ace diagnosis systems overnight. They may start as seemingly minor point innovations or as clumsy-sounding systems not ready for prime time.
Imagine using the AliveCor* iPhone case to take an ECG every day for less than $1/test. This device and others like it would capture a lot more information than the typical heart patient’s semiannual ECG check at the doctor’s office (it would also cost a lot less). What if you could send 500 “auto-diagnosed” ECGs to your doctor for less than it costs to get one ECG done in the hospital? Today, most heart disease is identified only after patients have heart attacks. But imagine having preventative cardiac care, enabled by machine-learning software that identifies abnormalities and predicts episodes. We could discover most heart disease before a heart attack or stroke and address it at a fraction of the cost of care that would be needed following such a trauma. But we need a decades-worth of data to be really good at it.
Dermatology appointments could be handled by CellScope*, which produces low-cost iPhone attachments for imaging skin moles, rashes, ear infections, and (in the future) your retina or throat. Those images could be processed by algorithms to detect patterns that warrant closer inspection. A device like the Eyenetra* could give you an eye test and fit you for glasses at little cost or hassle. Adamant* is attempting to produce a chip that can identify hundreds of gases in your breath, which could be used to detect and even identify different types of lung cancer, all for far less than a big CT scanner that’ll just tell you that you have a nodule. Ginger.io* monitors your rate of emailing, tweeting, texting, and calling to gauge your social activity. By watching for changes in your behavior, it can tell how you’re doing far better than a psychiatrist.
These point innovations will seem immaterial at first, but, when there are enough of them, they will integrate and start to feel like a revolution. The technologies of 2020 will be as different from today’s systems as the car floor-mounted, multi-pound cell phones with bulky handset cords of 1986 are from today’s iPhones!
The human element will survive
Some critics of more automated healthcare argue that medicine isn’t just about inputting symptoms and receiving a diagnosis; it’s about building relationships between providers and patients. Providing good bedside manner and answering certain questions can often be handled better by a person than a machine, but you generally don’t need a medical degree to do that. Nurses, nurse practitioners, social workers, and other less expensive, non-MD caregivers could do this just as well as doctors (if not better) and spend more time providing personal, compassionate care. I’m not advocating the removal of the human front-end here. I’m arguing that we should build robust back-end sensor technology and diagnostics through sophisticated machine learning and artificial intelligence operating on data in greater volumes than humans can handle.
A transition to automation has already happened in other areas where we once thought human judgment was required. Most commercial flying is now done by auto-pilot, not by the captain. Algorithmic trading now drives most stock market volume. Google’s (GOOG) self-driving car has had zero accidents driving 300,000 miles on normal streets. The same replacement of human involvement by computers will also happen in healthcare. This will create a more comprehensive understanding of patients and improve health outcomes with more personalized treatment. Physicians will have MORE time to spend talking to their patients, making sure they understand, socializing care, and finding out the harder-to-measure pieces of information because they’ll spend a less time gathering data and referring to old notes. And, they will be able to handle many more patients, reducing costs.
The source of healthcare innovation
Where will all this innovation come from? Some believe we have to work within the constraints of the medical establishment. I disagree.
Innovation seldom happens from the inside because existing incentives are usually set up to discourage disruption. Pharma companies push marginally different drugs instead of potentially better generic solutions because they want you to be a drug subscriber and generate recurring revenue for as long as possible. Medical device manufacturers don’t want to cannibalize sales of their expensive equipment by providing cheaper, more accessible monitoring devices. The traditional players will lobby/goad/pay/intimidate doctors and regulators to reject innovation. Expecting the medical establishment to do anything different is expecting them to reduce their own profits. Granted, these are generalizations and there are many great and ethical doctors and organizations.
Fortunately, it doesn’t matter if the establishment tries to do this or not, because it will happen regardless. And it may start at the periphery, e.g. with the 40 million uninsured Americans or the hundreds of millions of people in India with no access. This shift in healthcare delivery will allow for less money to be spent on capital equipment, cutting costs. It will allow us to provide care and basic service to those who can’t afford it now. It will help avoid errors. And, it will prevent simple things from getting worse before being addressed.
Entrepreneurs can come at these challenges and inject new insight. They can ask naïve questions that get at the heart of pervasive and sometimes unperceived assumptions. They can leverage the many insiders to provide real understanding of medicine at the right time. They can build smart computers to be objective cost minimizers while being care optimizers.
This evolution will take time, but it won’t take as long as people think. The move will happen in fits and starts along different pathways, with many course corrections, steps backward, and mistakes. Maybe we’ll start seeing disruption at the fringes. Many naïve innovators, maybe even 90% of them, will attempt this change and fail. But, a few will succeed and change the system. For those of us who support entrepreneurs and companies that create this change, most investments will be lost, but more money will be made than lost through the few successes. None of us knows how this space will turn out, but there’s a huge opportunity for technologists, entrepreneurs, and other forward-thinkers to reduce healthcare expenditures and improve patient care at the very same time.
Vinod Khosla is the founder of Khosla Ventures, a venture capital firm in Menlo Park, CA. A longer version of this story can be found here on its website.
* A Khosla Ventures investment