According to the recently released NPR article, medical errors are the number three cause of death in the U.S. Medical errors kill more than 250,000 Americans each year just behind heart disease and cancer. The problem is that many of these instances go undetected because the coding systems used by the CDC don’t effectively capture medical error related deaths.
Experts agree that there is room for improvement and acknowledge that no collection or documentation solution is foolproof. But there is recognition that new requirements could help. They also site clinician education as a key component to documenting, understanding, and addressing the problem.
But putting all of the responsibility for reporting medical errors on clinicians creates an interesting dilemma. First, we are asking the same people who may have made the error to report on it. While clinicians work under some of the highest ethical standards, there is the human inclination to hide mistakes. There are also quality measures that penalize for underperformance, the rise of consumerism within healthcare, and patient satisfaction requirements that all create incentives for underreporting.
New reporting requirements are definitely a step in the right direction, but they can’t be the entire solution. As with just about any challenge in healthcare, the solution has to be multipronged. And an important prong in the approach is machine learning.
New cognitive computing capabilities including the emergence of technologies such as Clinical Patient Pods, can help predict and point to areas where there is an increased risk of an error. This could include the type of patient, clinical situation, time of day, staff mix, and even the individual clinician. Advanced machine learning technologies can look across operations and account for missing data to deliver risk information. And this information could be applied to pin point efforts and lower the occurrence of medical errors.
No doubt, medical errors have been around as long as medicine. But the renewed conversation about how to identify and stop instances from happening is coming at a time when innovation and machine learning technologies are starting to be a boon for healthcare. How we apply these capabilities and combine what we know with effective regulation will create the foundation that will lead to a drop in medical errors across US hospitals.
- By Jvion Health