I'm excited to see the results from Geisinger's algorithm as I believe it brings us one step closer in being able to predict patients' risk of short-term mortality—and to provide them the clinical support (as well as the palliative and other end-of-life services) that they may need.
While many organizations have made significant advancements in working with physicians to screen for short-term mortality risk, we know that this can sometimes be an imperfect screening method—especially as doctors tend to err on the side of optimism when projecting patients' lifespans.
Therefore, we are excited to see other organizations using machine learning and other automated methods to objectively predict short-term mortality. In addition to Geisinger, a number of other organizations have made significant advancements. For instance, at the Denver Veterans Administration Medical Center, researchers created prognostic criteria to identify patients with the highest mortality risk. The criteria rely on data that every patient could tell their provider or could easily be extracted from the patient's chart. Among a hospitalized veteran population, the tool demonstrated 79% sensitivity (rate of correct positive prediction) and 75% specificity (probability of correct negative prediction).
And, to make this process even more sophisticated, researchers at Stanford are partnering with Google to leverage AI for predicting patients' risk of mortality for a number of different conditions.
But just creating predictive tools is not enough. Importantly, these predictions must be communicated back to the patient—forcing difficult conversations which can often make physicians highly uncomfortable. It's not surprising that many physicians feel unprepared for these conversations, as many simply haven't been trained to have them. For example, just 2% of the oncology board certification exam relates to end-of-life issues.
To guide providers through these conversations, Gundersen Health System created the "Respecting Choices Person-Centered Care Model," which has since been implemented at organizations across the country. The curriculum includes modules to help team members learn how to start conversations about patient's wishes and manage communication in the advanced directive process.
Organizations can also leverage their EHR to support these conversations—and can take a page from many oncology programs leading the way. For instance, UAB Medicine, AtlantiCare, and the USA Mitchell Cancer Center are using treatment planning software called Carevive to flag when cancer patients do not have an advanced care plan documented. Using patient-reported and clinical data, the tool alerts providers when the patient should have a care plan but lacks one. Since using the tool, documentation of advance directives for breast cancer patients have increased from 19% to 81%.
Of course, these process changes can be uncomfortable for organizations as they force patients and providers to have difficult conversations. But as our ability to predict mortality using AI increases, we have to make sure our ability to communicate and act on that information advances in lockstep.