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Inpatient nurses’ preferences and decisions with risk information visualization

J Am Med Inform Assoc. 2023 Dec 22;31(1):61-69. doi: 10.1093/jamia/ocad209.

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Alvin D. Jeffery, PhD, RN-BCM CCRB-K, FNP-BC
Vanderbilt School of Nursing

Dr. Alvin Jeffery is an Assistant Professor of Nursing and Biomedical Informatics at Vanderbilt University. He completed his PhD (Nursing Science & Health Services Research) at Vanderbilt University’s School of Nursing in 2017 and a Medical Informatics Post-Doctoral Fellowship with the U.S. Department of Veterans Affairs and Vanderbilt University’s Department of Biomedical Informatics in 2019. Dr. Jeffery previously held an AHRQ/PCORI K12 focused on Learning Healthcare Systems and implementation science. He is currently funded by an NIH/NIDA Avenir DP1 to develop precision phenotypes for substance use disorders with the aim of accelerating genetics studies as well as the Betty Irene Moore Fellowship for Nurse Leaders and Innovators focused on customizing electronic health record systems for diverse users and settings. He has a background in pediatric critical care nursing and as a staff educator at Cincinnati Children’s Hospital Medical Center. He holds board certifications in pediatric critical care nursing and as a Family Nurse Practitioner. He is a former Emerging Leader with the Alliance of Nursing Informatics.

Dr. Jeffery focuses on the design, development, and evaluation of probability-based clinical decision support tools. He leverages machine learning and data science techniques for developing prediction models with an emphasis on predicting outcomes that are incompletely ascertainable. He also incorporates qualitative methods for exploring how to implement CDS tools within clinicians’ cognitive and physical workflows. In addition to his scientific talks, he has delivered numerous presentations on pediatric critical care topics as well as leadership and professional development skills. He has written 3 books and more than 30 journal publications. For examples of Dr. Jeffery’s research products or to hear him talk about some of his work, you can explore Jeffrey's personal website. To discover resources for learning more about data science, you can explore his nursing data science website.

Statement of Purpose

Many hospitals have implemented sophisticated predictive models in hopes of improving care delivery; however, these systems’ effectiveness have yielded mixed results, with a primary reason being ineffective user interfaces contributing to implementation challenges. We conducted a mixed-methods descriptive study in which participants provided qualitative and quantitative responses to several user interface options in a simulated setting. We found a strong preference for, and greater inter-participant agreement with, the probability format.

Learning Objective

At the conclusion of the webinar the learner will be able to describe possible formatting options for sharing risk information from predictive models.


  • 35-minute presentation by article author(s) considering salient features of the published study and its potential impact on practice
  • 25-minute discussion of questions submitted by listeners via the webinar tools and moderated by JAMIA Student Editorial Board members. 

Accreditation Statement

The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

Credit Designation Statement

The American Medical Informatics Association designates this live activity for a maximum of 1.0 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Dates and Times: -
Type: Webinar
Course Format(s): Live Virtual
Price: Free
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