Death is never an easy topic. Hemingway addressed it eloquently in his novel “For Whom the Bell Tolls,” the title of which was taken from meditations on health, pain and sickness by poet John Donne. 

Donne illustrates that death has a direct impact not only on the person going through the process, but also on everyone who surrounds that person. His phrase “no man is an island entire of itself” is the perfect analogy for how providers can offer rational, compassionate end-of-life care to residents by accurately predicting mortality. 

Even though residents are eligible for the Medicare hospice benefit when they’re expected to live six months or less, most are not enrolled until their last month of life. 

One main impediment to delivering timely hospice care is not recognizing terminal status that isn’t based on a single disease like an incurable cancer, but rather on a combination of multiple diseases or conditions. For these, how can you accurately predict mortality? 

Turn to your MDS. It’s a rich source that predicts a range of clinical outcomes, including mental status, functional dependencies, continence, mobility, nutrition, skin integrity and subjective symptoms — covering diagnoses and treatments. 

Add in predictive models that incorporate dozens of variables from the MDS, and you now have a complete mortality picture. We’ve developed a highly specific MDS-based predictive model for mortality risk. In a test, 69% of the 7.2 million residents who scored highest on this scale expired within six months. This same scale was equally successful in predicting mortality post-SNF discharge to the community.

Armed with this knowledge, facilities can be proactive in developing a standard procedure for end-of-life planning. The early prognosis made possible by the MDS gives everyone time to understand and thoughtfully consider the hospice option.

When used with predictive analytics, the MDS enables you to guide residents and families through a compassionate end-of-life journey. 

Steven Littlehale is a gerontological clinical nurse specialist and former university instructor.