Detecting Health Problems Earlier

Multiple physical and disease morbidities are known to confer risk for early mortality, greater symptom burden, and higher healthcare costs. Innovative health information technologies can improve quality of life, decision-making, monitoring, and outcomes in the ambulatory home context. Using SensePRO, an innovative mobile technology platform (smartwatch + beacons) developed at the UCLA Center for SMART Health, physical and functional data collected from a combination of wearable and home sensors are continuously combined with contextual data such as patient-reported outcomes (PROs). The goal of the project is to risk-stratify, monitor, communicate daily and weekly wellness status, and to inform efforts to reduce the need for acute, costly, and distressing health services (emergency room visits and hospitalization). This population-based study is being conducted in conjunction with UCLA Health. Our system collects PROs derived using the NIH PROMIS question bank, capturing measures related to physical function, anxiety, depression, fatigue, sleep, social activities, and pain. These data are then automatically combined with context-aware system measures: energy, activity, position (lying, sitting, standing), mobility (walking, steps, walking speed), indoor localization, and key daily activities. Based on this comprehensive view, daily and weekly summaries of the data are being developed to improve the management of patients through communication with case workers, caregivers, and providers.

Predicting Chronic Kidney Disease

In partnership with UCLA’s Faculty Practice Group (FPG), we are looking to better identify individuals within our healthcare enterprise who are at risk for worsening kidney function. Chronic kidney disease (CKD) affects a significant number of Americans, and if left untreated results in the need for dialysis and/or kidney transplant. Proper clinical management of individuals early in the course of the disease can sometimes recover impaired kidney function, if not slow or stabilize disease progression. Unfortunately, a number of patients suffer from rapid decline of kidney function, and it is presently unclear who these individuals will be. As part of the UCLA CTSI’s Clinical Pathways program, we are using machine learning (ML) methods on EHR-derived data to better stratify UCLA patients by identifying early patterns indicative of CKD patients who may be at risk of rapid decline. By finding these patterns in a timely manner, more targeted preventive interventions can be put in place to help the patient and prevent disease progression.

Predicting Future Hospitalizations

Some patient visits, either to the emergency room (ER) or in-patient admissions, could be prevented through timely outpatient interventions by healthcare providers. By identifying such situations early, we can not only improve a patient’s end outcomes and quality of life, but significantly reduce healthcare resource utilization and the high costs associated with such visits. Working with UCLA’s Faculty Practice Group (FPG), we have developed new predictive models from the electronic health record (EHR) to identify those patients who are at high risk for admission within the next year. Based on this risk prediction, UCLA healthcare providers and staff reach out periodically to check on the individual and provide guidance and monitoring to prevent subsequent healthcare issues that will result in the need for admission. Being deployed across UCLA’s primary care clinics, this new risk assessment tool is being consider for broader adoption across the entire UC Health system.