AI that improves healthcare efficiency also threatens profits | Artificial Intelligence
Healthcare is a world of perverse incentives. One person’s waste is another person’s annual bonus. If you have shares in Novo Nordisk, you don’t really want people to eat more fruits and vegetables, because then sales of diabetes meds might drop. The same is true on a larger scale for hospital systems that get paid to conduct tests and perform surgeries instead of keeping people healthy.
It doesn’t pay—yet—to keep people healthy. Many entrepreneurs and government leaders are dragging the American healthcare system away from the fee-for-service model, but the road to pay for performance medical care is long and winding.
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HeartFlow is the perfect example of how a positive change for patients could mean a drop in health system revenues. The company’s goal is to improve care for heart disease by avoiding both under- and over-treatment. HeartFlow takes data from a CT scan and turns it into a 3D model of the veins and arteries that supply blood to the heart.
“If our system is adopted, the net annual savings would be $2 billion for one commercial payer,” said John Stevens, MD and president of HeartFlow. “We could avoid 250,000 unnecessary procedures and even save 30,000 lives.” Stevens shared these shocking numbers at HLTH 2018 in early May in Las Vegas.
Currently when a doctor is treating a person with heart trouble, he has to make an educated guess about the severity and location of a blockage that may be causing blood flow problems. The most common diagnostic test is a heart catheterization, and this is where the waste and inefficiency enters the picture. In many cases, the doctor who recommends this procedure also gets paid for conducting it.
A 2013 study published in JAMA compared two groups of people who had heart caths: One in New York City and one in Ontario, Canada. More people in the New York group received heart caths than the Ontario group, even though the New Yorkers were younger and showed fewer symptoms of heart disease. According to these findings, “one might reasonably conclude that a more selective use of cardiac catheterization should be implemented to reduce its associated costs and to improve its diagnostic efficiency.”
A US News & World Report analysis studied Medicare data on heart catheterizations. The results showed that doctors in rural areas were more likely to prescribe the test, despite the fact that heart attack rates were not higher in those areas. A small study of doctor-patient conversations showed that doctors don’t often mention alternatives to catheterization or discuss the test’s limitations.
The HeartFlow software combines computational fluid dynamics with an analysis of a patient’s anatomy and physiology to create a 3D model of the patient’s heart. Coronary segmentation algorithms are trained using data collected from clinical trials and refined by HeartFlow cardiologists. The algorithms that build the color-coded map of the coronary arteries take into account the many factors affecting blood flow, including total blood flow and flow to different parts of the heart. The software also solves millions of equations to assess the impact that blockages have on blood flow; this helps doctors understand if a blockage in an artery is serious enough to require surgery. In addition to the 3D model of the patient’s heart, HeartFlow analysis recommends treatment options to the physician to help make the decision between more invasive tests and possibly surgery or medication.
The company is making significant progress in expanding access to its diagnostic services. UnitedHealthcare just announced that it will cover the HeartFlow analysis for its 45 million customers.
Earlier this year, HeartFlow scored a big win recently in the UK. The NHS will start using HeartFlow this year to treat the 2.3 million people in England with heart disease. HeartFlow is one of four new technologies that will be fast-tracked into use through the health system’s Innovation and Technology Payment program. Simon Stevens, chief executive of NHS England, said that these technologies will improve patient safety and potentially reduce the need for invasive and expensive tests. The Innovation and Technology Payment program speeds up the adoption process for new technologies by making reimbursement easier—that is a huge barrier in America for entrepreneurs working in healthcare. If there is no ICD-10 code for a device or a procedure, insurance companies won’t pay for doctors to use a new method.
Just as a lack of reimbursement slows down tech adoption rates, fee-for-service health systems are also skeptical of new technologies that would change current revenue sources, like emergency room visits.
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Many analyses of promising AI technology focus on applications with the most money-saving promise without connecting those savings to a hospital’s bottom line. Another HLTH speaker—Leonard D’Avolio, PhD, CEO of Cyft—did make the connection during a panel discussion and in an excellent blog post. Cyft’s goal is to keep people out of the hospital by analyzing data from multiple sources—call center transcripts, EHR, care management notes—to identify people most likely to benefit from a specific intervention. D’Avolio said that between 5% and 12% of the total healthcare delivery market is value-based, meaning that doctors and hospitals get paid for keeping a person healthy. “The other 90% gets paid only when someone lands in the hospital,” he said. “What’s the incentive to protect the older persons from a fall when that’s $12,000 out of my pocket? It doesn’t pay to keep people healthy.”
Healthcare has always been slow to adopt new technology. The current worry around artificial intelligence is that robots will replace doctors. If AI represents a threat to a hospital’s bottom line, it may be even harder to get widespread adoption. Stevens of HeartFlow says the challenge is to strike the right balance between high tech and high touch.
“Personal interaction is vital to healing,” he said. “If we can we optimize the encounters, we can do more with a lot less money.”