The Kaiser Permanente Northern California Advance Alert Monitor Program: An Automated Early Warning System for Adults at Risk for In-Hospital Clinical Deterioration

Abstract

Background In-hospital deterioration among ward patients is associated with substantially increased adverse outcome rates. In 2013 Kaiser Permanente Northern California (KPNC) developed and implemented a predictive analytics-driven program, Advance Alert Monitor (AAM), to improve early detection and intervention for in-hospital deterioration. The AAM predictive model is designed to give clinicians 12 hours of lead time before clinical deterioration, permitting early detection and a patient goals-concordant response to prevent worsening. Design of the aam intervention Across the 21 hospitals of the KPNC integrated health care delivery system, AAM analyzes electronic health record (EHR) data for patients in medical/surgical and telemetry units 24 hours a day, 7 days a week. Patients identified as high risk by the AAM algorithm trigger an alert for a regional team of experienced critical care virtual quality nurse consultants (VQNCs), who then cascade validated, actionable information to rapid response team (RRT) nurses at local hospitals. RRT nurses conduct bedside assessments of at-risk patients and formulate interdisciplinary clinical responses with hospital-based physicians, bedside nurses, and supportive care teams to ensure a well-defined escalation plan that includes clarification of the patients' goals of care. Success of the intervention Since 2019 the AAM program has been implemented at all 21 KPNC hospitals. The use of predictive modeling embedded within the EHR to identify high-risk patients has produced the standardization of monitoring workflows, clinical rescue protocols, and coordination to ensure that care is consistent with patients' individual goals of care. An evaluation of the program, using a staggered deployment sequence over 19 hospitals, demonstrates that the AAM program is associated with statistically significant decreases in mortality (9.8% vs. 14.4%), hospital length of stay, and ICU length of stay. Statistical analyses estimated that more than 500 deaths were prevented each year with the AAM program. Lessons learned Unlocking the potential of predictive modeling in the EHR is the first step toward realizing the promise of artificial intelligence/machine learning (AI/ML) to improve health outcomes. The AAM program leveraged predictive analytics to produce highly reliable care by identifying at-risk patients, preventing deterioration, and reducing adverse outcomes and can be used as a model for how clinical decision support and inpatient population management can effectively improve care.