A5061929978- Electronic Health Records Systems
- Machine Learning in Healthcare
- Data Quality and Management
- Artificial Intelligence in Healthcare and Education
- Health Systems, Economic Evaluations, Quality of Life
- Chronic Disease Management Strategies
- Context-Aware Activity Recognition Systems
- Emergency and Acute Care Studies
- Healthcare Technology and Patient Monitoring
- Ethics in Clinical Research
University of Wisconsin Health
2021-2025
UW Health University Hospital
2025
University of Wisconsin–Madison
2022-2023
Wisconsin Department of Health Services
2022
One of the key challenges in successful deployment and meaningful adoption AI healthcare is health system-level governance applications. Such critical not only for patient safety accountability by a system, but to foster clinician trust improve facilitate outcomes. In this case study, we describe development such structure at University Wisconsin Health (UWH) that provides oversight applications from assessment validity user acceptability through safe with continuous monitoring...
Background The clinical narrative in electronic health records (EHRs) carries valuable information for predictive analytics; however, its free-text form is difficult to mine and analyze decision support (CDS). Large-scale natural language processing (NLP) pipelines have focused on data warehouse applications retrospective research efforts. There remains a paucity of evidence implementing NLP at the bedside care delivery. Objective We aimed detail hospital-wide, operational pipeline implement...
Emergency department (ED) admissions are one of the most critical decisions made in health care, with 40% ED visits resulting inpatient hospitalization for Medicare patients. A main challenge process is inability to move patients from an unit quickly. Identifying hospital discharge volume advance may be valuable helping hospitals determine capacity management mechanisms reduce boarding, such as transferring low-complexity neighboring hospitals. Although previous research has studied...
SummaryThe growth in the use of predictive models health care continues as systems adopt electronic records and gain access to real-time digitized clinical data. Although often have substantial experience quality improvement related interventions, they limited implementing part process. University Wisconsin (UW) Health’s goal was systematize process selecting, validating, implementing, evaluating a solution maximize potential benefits minimize harms using guide actions interventions learning...
Predictive models are increasingly being developed and implemented to improve patient care across a variety of clinical scenarios. While body literature exists on the development using existing data, less focus has been placed practical operationalization these for deployment in real-time production environments. This case-study describes challenges barriers identified overcome such an model aimed at predicting risk outpatient falls after Emergency Department (ED) visits among older adults....
<sec> <title>BACKGROUND</title> Emergency department (ED) admissions are one of the most critical decisions made in health care, with 40% ED visits resulting inpatient hospitalization for Medicare patients. A main challenge process is inability to move patients from an unit quickly. Identifying hospital discharge volume advance may be valuable helping hospitals determine capacity management mechanisms reduce boarding, such as transferring low-complexity neighboring hospitals. Although...
ABSTRACT The clinical narrative in the electronic health record (EHR) carries valuable information for predictive analytics, but its free-text form is difficult to mine and analyze decision support (CDS). Large-scale natural language processing (NLP) pipelines have focused on data warehouse applications retrospective research efforts. There remains a paucity of evidence implementing open-source NLP engines provide interoperable standardized CDS at bedside. This protocol describes...
<sec> <title>BACKGROUND</title> The clinical narrative in electronic health records (EHRs) carries valuable information for predictive analytics; however, its free-text form is difficult to mine and analyze decision support (CDS). Large-scale natural language processing (NLP) pipelines have focused on data warehouse applications retrospective research efforts. There remains a paucity of evidence implementing NLP at the bedside care delivery. </sec> <title>OBJECTIVE</title> We aimed detail...