Death and data science: How machine learning can improve end-of-life care Posted on : Feb 27 - 2018

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. But it's not just about costs. Here's an excerpt from the paper Death vs. Data Science: Predicting End of 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. Additionally, 28% ofhospice patients were discharged or died within 7 days ofhospice enrollment (NHPCO 2016). In work by Christakis and colleagues, they suggest that hospice clinicians consider 80-90 days of hospice care as optimal for the needs of patients and their families (Christakis 1997). Surveys of family members of decedents indicate that satisfaction with end of life care is correlated with their perception of timeliness of hospice referral (Teno et al. 2007). Finally, providers that commonly encounter in-hospital patient death, like intensivistsand critical care nurses, have high rates of professional burnout (Embriaco et al. 2007). It follows to conclude, therefore, that timely and appropriate end of life care impacts all aspects of the Quadruple Aim in healthcare (quality,satisfaction, cost savings, and provider satisfaction).

As part of our ongoing series on data scientists and their approaches, we caught up with Ankur Teredesai, CTO of KenSci and one of the authors of the paper, which was recognized in the emerging technologies category.

What data sets did you use to model?

The challenge of predicting 6-12 month mortality risk is fairly complex. It's a $205 billion problem just in the U.S. At KenSci we have a platform that is designed for scale and operational effectiveness of machine learning to solve societal problems such as these with such a large impact. In this particular setting, we had existing machine learning models for 6-12 month mortality prediction from prior efforts. We partnered with two major health systems in the Pacific Northwest and then re-trained our models and created additional ones with new data.

The data from Health System A came from a patient population with a history of heart failure (HF), and included 4,888 patients with a variety of electronic medical records data including:

• demographic features
• patient length of stay
• overall cost related features
• specific cost related features (in-patient, out-patient, home health, hospice, skilled nursing facility) readmissions information
• counts of procedures performed, tracked through the Healthcare Common Procedure Coding System (including things like ambulance rides, equipment and prosthetics)
• The data from Health System B consists of patients with any type of illness and includes 48,365 patients. Only claims data was available for Health System B. View More