Hospice is a compassionate approach focusing on quality of life for terminally ill patients and their caregivers, with approximately 1.55 million Medicare beneficiaries enrolled in hospice care for at least one day during 2018 – 17% more than in 2014. However, at least 14% of Medicare beneficiaries enrolled in hospice stayed for more than 180 days, and hospice stays beyond six months can result in substantial excess costs to healthcare organizations under value-based care arrangements. David Klebonis, COO of Palm Beach Accountable Care Organization, has developed highly interpretable machine learning models that, because of the sensitivity of the clinical decision involved, cannot only accurately predict hospice overstays to drive appropriate hospice referrals, but also surface decision criteria that satisfy clinician scrutiny and promote adoption. "Artificial intelligence and machine learning have the potential to use data to predict patients with a high probability of expiring within the next six months, so that physicians can enter into conversations with these patients and their families about the possibility of referral to hospice," he said. Klebonis, who will address the topic this month at HIMSS22, said in Florida about 58% of Medicare decedents were in hospice at the time of death.

KenSci, a company that has developed a machine learning risk prediction platform for healthcare, recently presented a paper on predicting end-of-life mortality and improving care. The paper, which tackles a tricky topic with predictions for the last six to 12 months of life for patients, was accepted by the Association for the Advancement of Artificial Intelligence. At stake is $205 billion in cost spent on care for the last year of an individual's life. The number of Americans using palliative care services continues to grow and was estimated at 1.7 million, or about 46% of those who die (NHPCO 2016). Yet these services are being utilized too late: the median length of stay in hospice care in 2016 was only 23 days.