A5089176508- Cardiac, Anesthesia and Surgical Outcomes
- Sepsis Diagnosis and Treatment
- Healthcare Operations and Scheduling Optimization
- Opioid Use Disorder Treatment
- Healthcare Technology and Patient Monitoring
- Pain Management and Opioid Use
- Pediatric Pain Management Techniques
- Frailty in Older Adults
University of Virginia
2021-2024
. Very few predictive models have been externally validated in a prospective cohort following the implementation of an artificial intelligence analytic system. This type real-world validation is critically important due to risk data drift, or changes definitions clinical practices over time, that could impact model performance contemporaneous cohorts. In this work, we report analytics tool developed before COVID-19 and demonstrate during pandemic.
Background Patients in acute care wards who deteriorate and are emergently transferred to intensive units (ICUs) have poor outcomes. Early identification of patients decompensating might allow for earlier clinical intervention reduced morbidity mortality. Advances bedside continuous predictive analytics monitoring (ie, artificial intelligence [AI]–based risk prediction) made complex data easily available health providers provided early warning potentially catastrophic events. We present a...
Describe patient-, clinician-, system-, and community-level interventions for pain management developed employed by 9 healthcare systems across the United States report on lessons learned from implementation of these interventions.The high cost associated with coupled frequent use opioid analgesics as primary treatment options has made novel strategies a necessity. Interventions that target multiple levels within are needed to help combat epidemic improve manage chronic pain. Patient-level...
<sec> <title>BACKGROUND</title> Patients on acute care wards who deteriorate and are emergently transferred to intensive units have poor outcomes. Early identification of decompensating patients might allow for earlier clinical intervention reduced morbidity mortality. Advances in bedside continuous predictive analytics monitoring (i.e., artificial intelligence (AI)-based risk prediction) make complex data easily available healthcare providers, can provide early warning potentially...