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Informatics Research Seminar: A Direct-to-Patient Alert for Glycated Hemoglobin Screening

February 13 @ 4:00 pm - 5:00 pm EST

Speaker: Brian J. Wells, MD, PhD

Presented from Wake Forest

Broadcast Link: Seminar

Abstract: 

The creation of statistical models for risk prediction has rapidly increased over the past 20 years as electronic health records (EHRs) and other electronic data have become ubiquitous in health care. Unfortunately, these tools have had minimal impact on clinical practice and patient outcomes.  There are many reasons for the ineffectiveness of clinical decision support tools including: “alert-fatigue”, unsuccessful implementation of the tools into existing clinical workflows, clinician workloads, and lack of physician confidence in the tools. Many of these tools might have more impact if they were targeted at other members of the care team, including patients. Dr. Wells has proposed the use of “direct-to-patient alerts” and will give an example of an ongoing research project that will target high risk patients with text messages to suggest hemoglobin A1c (HbA1c) screening. He will describe the creation of a previously published HbA1c prediction tool being used to identify high risk patients as well as the research strategy for the upcoming project.

Biosketch: 

Dr. Wells is an Associate Professor in the Department of Biostatistics and Data Science at the Wake Forest School of Medicine where he also serves as the Associate Program lead for the Biomedical Informatics Program in the Clinical and Translational Science Institute. He is board certified in both Family Medicine and Clinical informatics and has extensive experience in the extraction and analyses of EHR data both locally and for multicenter projects like the CDC funded  SEARCH for Diabetes in Youth. Much of his research has focused on the creation of risk prediction models built from EHR data and the evaluation of outcomes in patients with diabetes. Dr. Wells is passionate about improving the creation and implementation of clinical decision support tools for better medical decision-making.

Details

Date:
February 13
Time:
4:00 pm - 5:00 pm
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