The Origin Story of the Rothman Index – Episode 5
Hospitals collect enormous amounts of clinical data, yet preventable patient decline remains a persistent challenge. Over the past two decades, hospitals have invested heavily in early warning scores and rapid response infrastructure, but translating data into timely, meaningful action has proven difficult. As clinicians contend with alert fatigue and increasing documentation burden, a more fundamental question has emerged: are we measuring the right signals, and are we presenting them in a way that supports clinical decision-making?
That tension sits at the heart of the fifth episode of The Michael Rothman Podcast. If early deterioration goes unnoticed, what kind of measurement is needed to truly understand how sick a patient is before it’s too late?
In the latest episode, Dr. Michael Rothman, an Advisory Data Scientist at Spacelabs Healthcare, tells the origin story behind the Rothman Index. Dr. Rothman walks listeners through the unlikely, deeply human, and data-driven journey that led to the creation of a real-time patient acuity score—one designed not just to predict crisis, but to surface subtle clinical decline when there is still time to intervene.
What you’ll learn…
- How early warning systems like vital-sign–based scores were never designed to detect gradual deterioration—and why that matters.
- Why nursing assessments, long treated as “background noise” in the EMR, turned out to be one of the most powerful predictors of patient risk.
- How combining clinical intuition with mathematical modeling led to a simple, visual score that aligns closely with real patient outcomes.
Dr. Michael Rothman is a mathematician and data scientist whose work has had a lasting impact on patient safety and clinical decision-making. Along with his brother Steven Rothman, he co-developed the Rothman Index, a pioneering patient acuity score that integrates vital signs, lab data, and nursing assessments into a continuously updated, color-coded graph. He is currently an Advisory Data Scientist at Spacelabs Healthcare, with deep expertise in clinical modeling and translating complex healthcare data into actionable bedside decision support.
Article written by MarketScale.
