The public has lost confidence in our public health agencies and officials—and also our political leaders—thanks to confusing and contradictory pronouncements and policies related to the coronavirus pandemic.
But don't think for a moment that leadership failures in health care are confined to COVID-19. Two other recent stories highlight how both government and industry are missing an opportunity to use new machine learning technology to deliver better health care at a lower cost.
In the government's case, Lina Khan's activist Federal Trade Commission (FTC) is opposing a merger between Illumina and GRAIL, two medical technology companies. The latter company, which spun off from Illumina in 2015, has developed a test capable of providing early detection of 50 different types of cancer. Bringing GRAIL back under the Illumina framework would get this life-saving technology to market faster and more efficiently—but the FTC couldn't let a little thing like saving lives get in the way of its anti-business agenda. To its credit, Illumina closed the deal in August without waiting for approval from the FTC or European regulators.
Unfortunately, the government isn't the only entity that too often makes decisions without fully understanding how disruptive innovation really works. The frustrating case of Epic Systems' Early Detection of Sepsis model shows the industry itself can fall prey to a Luddite mindset.
Epic's model is designed to detect and prevent sepsis, a leading cause of death that also accounts for 5 percent of U.S. hospitalization costs. Many of these deaths and the associated costs can be prevented with early diagnosis. Epic is so committed to helping hospitals reduce sepsis-related deaths that it developed and gives away—for free—an AI-powered early-warning model that helps alert doctors and nurses when a patient might need a second look.
The sepsis algorithm has produced encouraging results with customers, but is facing criticism in the health care industry press. In one peer-reviewed study, Prisma reported a 22 percent decrease in mortality—which could translate to millions of lives saved if it were implemented globally. More recently, in a controlled clinical trial, MetroHealth found a meaningful reduction in mortality and length of stay, and reduced the time to antibiotic treatment of septic patients in the ED by almost an hour.
Critics of Epic's algorithm have claimed that it is not yet good enough at detecting sepsis cases. But this accusation reveals a misunderstanding of the technology involved. Both the GRAIL and Epic models utilize machine learning—a form of artificial intelligence that compares information from a test or a patient's medical record against vast amounts of data about previous cases with known outcomes. By its nature, machine learning gets better as it acquires new information. Failure to account for this fact has led many in government and media to dismiss promising innovations.
Machine learning cannot replace the expertise of a human doctor or nurse, but it offers those human health care providers a powerful new tool to see patterns and evidence they could never notice on their own. These cutting-edge technologies have the potential to save millions of lives and drastically reduce the cost of health care, if we let them.
Getting these complex algorithms right takes time, but we cannot allow the perfect to be the enemy of the already excellent. In the GRAIL case, the government needs to do what it always needs to do, and just get out of the way. In Epic's case, the industry needs to develop a deeper appreciation for how disruptive innovation works and work closely with bold creators to bring revolutionary technologies to life.
Steve Forbes is Chairman and Editor-in-Chief of Forbes Media.
The views expressed in this article are the writer's own.
Uncommon Knowledge
Newsweek is committed to challenging conventional wisdom and finding connections in the search for common ground.
Newsweek is committed to challenging conventional wisdom and finding connections in the search for common ground.
