A5053695863- Electronic Health Records Systems
- Nursing Diagnosis and Documentation
- Machine Learning in Healthcare
- Healthcare Systems and Technology
- Patient Safety and Medication Errors
- Healthcare Technology and Patient Monitoring
- Artificial Intelligence in Healthcare and Education
- Telemedicine and Telehealth Implementation
- Sepsis Diagnosis and Treatment
- Health Sciences Research and Education
- Biomedical Text Mining and Ontologies
- Patient Satisfaction in Healthcare
- Chronic Disease Management Strategies
- Mobile Health and mHealth Applications
- Clinical practice guidelines implementation
- Geriatric Care and Nursing Homes
- Emergency and Acute Care Studies
- Medical Malpractice and Liability Issues
- Frailty in Older Adults
- Clinical Reasoning and Diagnostic Skills
- Innovations in Medical Education
- Patient-Provider Communication in Healthcare
- Disaster Response and Management
- Health Policy Implementation Science
- Big Data and Business Intelligence
Columbia University
2019-2025
Columbia University Irving Medical Center
2019-2025
University of Rochester
2024
IPS Research (United States)
2023
Philips (United States)
2023
University of California, Irvine
2023
Emory University
2023
NXP (Netherlands)
2023
Philips (Netherlands)
2023
University of Pennsylvania
2023
Clinician trust in machine learning-based clinical decision support systems (CDSSs) for predicting in-hospital deterioration (a type of predictive CDSS) is essential adoption. Evidence shows that clinician CDSSs influenced by perceived understandability and accuracy.
Abstract Objective Understand the perceived role of electronic health records (EHR) and workflow fragmentation on clinician documentation burden in emergency department (ED). Methods From February to June 2022, we conducted semistructured interviews among a national sample US prescribing providers registered nurses who actively practice adult ED setting use Epic Systems’ EHR. We recruited participants through professional listservs, social media, email invitations sent healthcare...
Background About one in five patients receiving home healthcare (HHC) services are hospitalized or visit an emergency department (ED) during a care episode. Early identification of at-risk can prevent these negative outcomes. However, risk indicators, including language clinical notes that indicate concern about patient, often hidden narrative documentation throughout their HHC Objective The aim the study was to develop automated natural processing (NLP) algorithm identify concerning...
There are signals of clinicians' expert and knowledge-driven behaviors within clinical information systems (CIS) that can be exploited to support prediction. Describe development the Healthcare Process Modeling Framework Phenotype Clinician Behaviors for Exploiting Signal Gain Clinical Expertise (HPM-ExpertSignals).
Abstract Background The widespread adoption of electronic health records and a simultaneous increase in regulatory demands have led to an acceleration documentation requirements among clinicians. corresponding burden from is central contributor clinician burnout can lead increased risk suboptimal patient care. Objective To address the problem burden, 25 by 5: Symposium Reduce Documentation Burden on United States Clinicians 75% 2025 (Symposium) was organized provide forum for experts discuss...
Background Many health care organizations around the world have implemented information technologies (ITs) to enhance service efficiency, effectiveness, and safety. Studies demonstrated that promising outcomes of IT initiatives can be obtained when patients family members participate engage in adoption, use, evaluation these technologies. Despite knowing this, there is a lack using patient engagement strategies use adoption ITs, specifically. Objective This study aimed answer following three...
To identify the risk factors home healthcare (HHC) clinicians associate with patient deterioration and understand how respond to document these factors.
Abstract Background Narrative nursing notes are a valuable resource in informatics research with unique predictive signals about patient care. The open sharing of these data, however, is appropriately constrained by rigorous regulations set the Health Insurance Portability and Accountability Act (HIPAA) for protection privacy. Several models have been developed evaluated on open-source i2b2 dataset. A focus generalizability respect to remains understudied. Objectives study aims understand...
Abstract Background The impact of electronic health records (EHRs) in the emergency department (ED) remains mixed. Dynamic and unpredictable, ED is highly vulnerable to workflow interruptions. Objectives aim study understand multitasking task fragmentation clinical among clinicians using information systems (CIS) through time-motion (TMS) data, inform their applications more robust generalizable measures CIS-related documentation burden. Methods Using TMS data collected 15 ED, we...
Little is known about proactive risk assessment concerning emergency department (ED) visits and hospitalizations in patients with heart failure (HF) who receive home healthcare (HHC) services. This study developed a time series model for predicting ED HF using longitudinal electronic health record data. We also explored which data sources yield the best-performing models over various windows.
The primary objective of this study was to identify meaningful indicators patient portal use deemed important psychiatric consumers. secondary objectives were uncover: 1) barriers and facilitators use; and, 2) desired functionality the technology by consumers.A qualitative descriptive conducted using focus groups consisting consumers, their family members/caregivers, Peer Support Workers. Two members research team independently performed a content analysis, came agreement on identified...
In Brief Recently, there's been a dramatic rise in artificial intelligence (AI) research healthcare. This article explains the key concepts of AI and clinical decision support with examples real-world applications operational scenarios.
Every year, hundreds of thousands inpatients die from cardiac arrest and sepsis, which could be avoided if those patients' risk for deterioration were detected timely interventions initiated. Thus, a system is needed to convert real-time, raw patient data into consumable information that clinicians can utilize identify patients at thus prevent mortality improve health outcomes. The overarching goal the COmmunicating Narrative Concerns Entered by Registered Nurses (CONCERN) study implement...
Digital medical records have enabled us to employ clinical data in many new and innovative ways. However, these advances brought with them a complex set of demands for healthcare institutions regarding sharing topics such as ownership, the loss privacy, protection intellectual property. The lack clear guidance from government entities often creates conflicting messages about policy, leaving develop guidelines themselves. Through discussions multiple stakeholders at various institutions, we...