Mazur, Lukasz

Development and Assessment of Artificial Intelligence (AI)-Enhanced Pretreatment Peer-review Process to Improve Patient Safety in Radiation Oncology

Description

This research develops and evaluates an artificial intelligence-enhanced pretreatment peer-review process in radiation oncology, aiming to improve patient safety by reducing variability among providers in treatment planning, minimizing clinical errors, and enhancing overall treatment outcomes.

Grant Number
R18 HS029474
Principal Investigator(s)
Mazur, Lukasz
Marks, Lawrence B

Association of the usability of electronic health records with cognitive workload and performance levels among physicians.

Citation
Mazur LM, Mosaly PR, Moore C, Marks L. Association of the usability of electronic health records with cognitive workload and performance levels among physicians. JAMA Netw Open. 2019 Apr 5;2(4):e191709. doi:
10.1001/jamanetworkopen.2019.1709. PMID: 30951160.
Principal Investigator
Mazur, Lukasz
Project Name
Enhancing Providers' Ability to Follow Up on Abnormal Test Results

Relating task demand, mental effort and task difficulty with physicians’ performance during interactions with electronic health records (EHRs).

Citation
Mosaly P, Mazur LM, Fei Y, et al. Relating task demand, mental effort and task difficulty with physicians’ performance during interactions with electronic health records (EHRs). Int J Hum Comput Interact 2018;34(5):
467-75, DOI: 10.1080/10447318.2017.1365459
Principal Investigator
Mazur, Lukasz
Project Name
Enhancing Providers' Ability to Follow Up on Abnormal Test Results

Reducing Provider Burden through Better Health IT Design

Event Date
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Presenters discussed their research findings around how clinical work is accomplished using electronic health records and how to optimally design key functionalities such as prevention alerts and abnormal results followup.