Can data analytics aid in end-of-life care decisions?
When it comes to clinicians discussing end-of-life care with patients and their families, too often decisions must be made quickly. The reasons why are complex: Physicians often struggle to determine when and how to have these tough conversations.
Nearly 70% of physicians report that they have not been trained to discuss end-of-life care, and 73% of Medicare patients over the age of 65 have not discussed it with physicians, according to a JAMA study released in November 2016.
As data analytics plays a larger role in healthcare, some are wondering how it might help address these difficult decisions. Yet most health systems don’t have the technology capabilities and the corporate mindset to take a comprehensive look at end-of-life care that better serves patients, says Dan Hogan, founder and CEO of Medalogix.
“Most hospitals aren’t using data analysts or data-driven tools to look at end-of-life care and imminent decline,” says Hogan, whose company specializes in population health analytics-based solutions for end-of-life care. He says that factors including increased readmissions, multi-episode hospital stays, and falls are part of a landscape of data that can help health systems identify patients who may need end-of-life conversations.
“Many times, once a patient gets to the emergency department they are already imminent,” Hogan says. “At that point, the doctors may try to stabilize them and get them out the door, or they may have already missed their mark.”
Hogan adds that the data also allows systems to look at population statistics and make predictions on which patients in the future need hospice care.
“There’s the benefit of averted costs to hospitals,” Hogan says. “The real benefit lies in patients and families being satisfied with how they and their loved one is treated. They are very grateful for a higher level of care during that time.”