Many illnesses are easy to treat if caught in time. New EHR studies show how optimizing data can spot problems clinicians might overlook.
Early detection is a mantra in hospitals: Find a nascent condition before it can morph into something serious and you can alleviate suffering, improve care and save lives.
Symptoms can be hidden in plain sight or can be masked by other known illnesses. In these manners, thousands of patients a year fall through the cracks and become seriously ill.
Two new early detection algorithms that integrate with electronic health record data are looking to make the odds more favorable.
Johns Hopkins has developed an algorithm called TREWS that can detect sepsis in patients far more reliably than a clinician alone, and the American Medical Association has published guidelines on optimizing EHRs to identify patients at risk of diabetes.
WHY IT MATTERS
In the case of both type 2 diabetes and sepsis, the at-risk population is broad and the conditions for both diseases are widespread: Accurately targeting those most likely to be affected takes more detailed hunting than many clinicians can do.
Johns Hopkins researchers note that one in 10 sepsis alerts from a “dumb” EHR system are true – their algorithm brings that down to one in two. Similarly, the AMA says that while many Americans are at risk of type 2 diabetes, optimizing an EHR to monitor relevant test results and make it easy for clinicians to order additional testing – both steps that empower providers to better manage their population health.
“One of the challenges traditionally with sepsis has been making sure the patient gets all of the interventions within the first three hours,” says computer science graduate student Katharine Henry, who worked on the Johns Hopkins TREWS program.
THE LARGER TREND
Artificial intelligence-assisted detection technologies are helping clinicians find needles in haystacks in every specialty.
When a disease like sepsis can present through very common symptoms like elevated heart rate or temperature, a doctor can’t be sure of a false positive based on a few metrics. Relying on an optimized EHR algorithm that monitors more data points than a clinician alone could, however, means the EHR does the heavy lifting on the data end and frees a provider up to take action when needed.
Similarly, much like a disease like type 2 diabetes can be brought under control through management of lifestyle, a healthcare provider can nip many instances in the bud when they manage the health of their patient population well. Providing monitoring tools and predefined courses of action through an EHR makes identifying patients and taking appropriate action that much easier.