A photo of Judith Dexheimer.

Associate Professor, UC Department of Pediatrics

513-636-7966

Biography & Affiliation

Biography

The ultimate goal of my research is to harness technology to improve healthcare delivery and the quality of care for children. My research interests include machine learning (ML), decision support, personal health records and multi-center informatics implementations. During my National Library of Medicine (NLM) informatics training, I studied artificial intelligence and implemented reminder systems directly into clinical care in the adult and pediatric emergency departments. My goals are to improve disease detection, especially earlier disease detection, for patients with a long history of illness. I also want to provide technology and healthcare information to underserved populations, improve care delivery and help providers use the computer as a tool that incorporates ML directly into clinical care.

I see myself accomplishing this goal by designing ways to use ML to enhance the patient-clinician experience at the point of care and by improving patient interactions with health technology in and outside of the healthcare encounter.

I was drawn to this field of research after seeing well-designed ML algorithms created but not integrated into clinical care. I also noticed that patients frequently did not have access to their own healthcare data.

Working with a collaborative team, we want to improve the use of informatics. I have experience in designing, implementing and evaluating clinical information systems, including clinical decision support systems, computerized applications for emergency medicine, organizational and workflow aspects of informatics applications.

A few of my accomplishments include:

  • Collaborating with the Pestian lab to develop one of the first real-time integrations of machine learning (epilepsy classifier) into clinical care
  • Working with Dr. Yizhao Ni at Cincinnati Children’s to integrate an automated ML patient screening system into the pediatric emergency department
  • Collaborating with Mary Greiner (Mayerson Center for Safe and Healthy Children) and Sarah Beal (Behavioral Medicine and Clinical Psychology) to develop the IDENTITY platform to share data more efficiently between the hospital and Hamilton county job and family services caseworkers, including automated matching of patients in custody

I was nominated for the Presidential Early Career Awards for Scientists and Engineers (PECASE) in 2017 and invited as a presenter at the Amazon Web Services (AWS) Machine Learning Summit in 2019. I have been a researcher for over nine years and began my work at Cincinnati Children’s in 2011.

Research Interests

Learn more about the Decision Support Analytics Workgroup.

Academic Affiliation

Associate Professor, UC Department of Pediatrics

Research Divisions

Biomedical Informatics, Emergency Medicine



Blog Posts

Education

PhD: Biomedical Informatics, Vanderbilt University, Nashville, TN, 2011.

MS: Biomedical Informatics, Vanderbilt University, Nashville, TN, 2006.

Publications

Utilization of a Clinical Decision Support Tool to Reduce Child Tobacco Smoke Exposure in the Urgent Care Setting. Mahabee-Gittens, EM; Merianos, AL; Dexheimer, JW; Meyers, GT; Stone, L; Tabangin, M; Khoury, JC; Gordon, JS. Pediatric Emergency Care. 2020; Publish Ahead of Print:527-531.

Information Technology–Assisted Screening for Gonorrhea and Chlamydia in a Pediatric Emergency Department. Reed, JL; Dexheimer, JW; Kachelmeyer, AM; Macaluso, M; Alessandrini, EA; Kahn, JA. Journal of Adolescent Health. 2020; 67:186-193.

Feedback at the Point of Care to Decrease Medication Alert Rates in an Electronic Health Record. van Camp, PJ; Kirkendall, ES; Hagedorn, PA; Minich, T; Kouril, M; Spooner, SA; Gecili, E; Dexheimer, JW. Pediatric Emergency Care. 2020; 36:e417-e422.

An interactive online dashboard for tracking COVID-19 in U.S. counties, cities, and states in real time. Wissel, BD; Van Camp, PJ; Kouril, M; Weis, C; Glauser, TA; White, PS; Kohane, IS; Dexheimer, JW. Journal of the American Medical Informatics Association. 2020; 27:1121-1125.

Development of the Gender, Sex, and Sexual Orientation ontology: Evaluation and workflow. Kronk, CA; Dexheimer, JW. Journal of the American Medical Informatics Association. 2020; 27:1110-1115.

Technological Hindrances of Behavioural Medicine Patient Access: A Literature Review. Rounds, J; Dexheimer, J; Curtis, H; Studebaker, B. 2020; 11:1-8.

Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery. Wissel, BD; Greiner, HM; Glauser, TA; Holland-Bouley, KD; Mangano, FT; Santel, D; Faist, R; Zhang, N; Pestian, JP; Szczesniak, RD; et al. Epilepsia. 2020; 61:39-48.

A Time-and-Motion Study of Clinical Trial Eligibility Screening in a Pediatric Emergency Department. Dexheimer, JW; Tang, H; Kachelmeyer, A; Hounchell, M; Kennebeck, S; Solti, I; Ni, Y. Pediatric Emergency Care. 2019; 35:868-873.

Automated patient linking for electronic health record and child welfare databases. Dexheimer, JW; Beal, SJ; Divekar, P; Hall, ES; Patel, V; Greiner, MV. Journal of Technology in Human Services. 2019; 37:286-292.

Investigation of bias in an epilepsy machine learning algorithm trained on physician notes. Wissel, BD; Greiner, HM; Glauser, TA; Mangano, FT; Santel, D; Pestian, JP; Szczesniak, RD; Dexheimer, JW. Epilepsia. 2019; 60:e93-e98.