Sunny Lou

Has grant

Assistant Professor

Washington University in St. Louis

United States

Research Interests

Explore related searches

Professors in United States

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Dr. Sunny Lou is an Assistant Professor at Washington University in St. Louis, specializing in the integration of electronic health records (EHR) with clinical decision-making processes. His research areas include cognitive effort measurement in healthcare settings and the analysis of EHR usage patterns. He has contributed to studies on surgical transfusion risk prediction and the impact of EHR workload on clinician burnout and errors.

Recent Grants

Grant: Close

Intelligent Clinical Decision Support for Perioperative Blood Management

Open Date: 2022-10-01

Close Date: 2024-09-30

Articles (10)

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Collaborators (2)

Seunghwan Kim

Washington University in St. Louis

UNITED STATES

View details

Thomas Kannampallil

Associate Professor & Assoc. Chief Research Information Officer

Washington University in St. Louis

UNITED STATES

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Sunny Lou

Research Interests

Explore related searches

Contact this professor

About

Recent Grants

Articles (10)

Measuring cognitive effort using tabular transformer-based language models of electronic health record-based audit log action sequences

Measuring the cognitive effort associated with task switching in routine EHR-based tasks

Characterizing the macrostructure of electronic health record work using raw audit logs: an unsupervised action embeddings approach

Characterizing the Patterns of Electronic Health Record–Integrated Secure Messaging Use: Cross-Sectional Study

Classifying Clinical Work Settings Using EHR Audit Logs: A Machine Learning Approach

National Multi-Institutional Validation of a Surgical Transfusion Risk Prediction Model.

Anesthesia Clinical Workload Estimated From Electronic Health Record Documentation vs Billed Relative Value Units.

Reducing perioperative red blood cell unit issue orders, returns, and waste using failure modes and effects analysis

Personalized Surgical Transfusion Risk Prediction: Reply

Temporal Associations Between EHR-Derived Workload, Burnout, and Errors: a Prospective Cohort Study

Collaborators (2)

Seunghwan Kim

Thomas Kannampallil

Make your next move.