AI Case Study
Kaiser Permanente predicts probability of rapid deterioration of patient condition and alerts physicians using machine learning
Using real-time monitoring and historical data like the condition during admission, the system calculates a score indicative of the risk of developing a critical condition periodically and alerts care providers.
Industry
Healthcare
Healthcare Providers And Services
Project Overview
"Challenges
• Working with real patient data in a highly regulated
healthcare industry
• Operationalizing the data model
• Combining data analytics with clinicians’ opinions
and conclusions to deliver best outcomes for the
patient
• Near zero tolerance for failure
• Infrequent occurrence of critical deterioration
events among patients
Solution
• Using analytics to calculate the probability of a
rapid deterioration in a patient requiring unplanned
transfer to ICU
• Flexible, configurable open source tools like H2O to
help build and continuously improve models"
To analyse risk and calculate a score that provides the warning, they used bed history data: where the patient had been in the past few days since the patient was admitted to the hospital; chemistry: which includes all of the lab work lab work; the vital signs or any vital sign information; any comorbidities: presence of one or more additional disorders (or diseases) co-occurring with a primary disease or disorder by bringing in all their past information into the models; and demographic information: so age, gender, etc. All of this information was used to estimate either the probability of a late
transferring to the ICU that is a sudden crash or mortality in the hospital.past information into the models; and demographic information: so age, gender, etc. All of this information was used to estimate either the probability of a late transferring to the ICU that is a sudden crash or mortality in the hospital.
The Kaiser team built models to try and predict the probability of a patient experiencing a sudden deterioration based on available electronic records, demographic information and vital signs and lab results collected during recent admissions."
Reported Results
Implemented Early Warning System
Technology
Function
Strategy
Data Science
Background
"Multiple studies showed that patients in the surgical ward who get an emergency transfer to an ICU as a result of sudden deterioration have significantly worse outcomes than patients who are admitted directly to the ICU or get moved to the ICU before they “crash”. These patients account for only five percent of all hospital admissions, yet, they represent about a fourth of all Kaiser ICU admissions, a fifth of all deaths in the hospital, and about an eighth of all of the hospital days. In fact, patients who experience an unplanned transfer to the ICU experience two to five times the mortality of patients who are directly admitted to the ICU, and they
would stay in the hospital an average of 8 to 12 days more than patients who are directly admitted to the ICU."
Benefits
Data
"Predictors included physiologic data (laboratory tests and vital signs); neurological status; severity of illness and longitudinal comorbidity indices; care directives; and health services indicators (e.g. elapsed length of stay)"