JeffreyWajdaDOMS, FACEP

Emergency Medicine • Sacramento, CA

Clinical Informatics

Chief Health Information Officer, University of California Davis - Clinical Informatics Fellowship Director, Clinical Professor - Emergency Medicine

Join to view full profile

Dr. Wajda is on Doximity

As a Doximity member you'll join over two million verified healthcare professionals in a private, secure network.

Gain access to free telehealth tools, such as our “call shielding” and one-way patient texting.
Connect with colleagues in the same hospital or clinic.
Read the latest clinical news, personalized to your specialty.

See Dr. Wajda's full profile

Already have an account?

Office
2315 Stockton Blvd
Sacramento, CA 95817
Phone+1 916-734-2011

Fax+1 916-734-8080

Summary

Dr. Jeff Wajda serves the University of California, Davis Health System as the Chief Health Information Officer. He received his medical degree from Chicago College of Osteopathic Medicine and completed Residency training at Rush University / Cook County Hospital. He is experienced in ambulatory / inpatient population health, health technology/informatics, healthcare administration, clinical and biomedical informatics, and clinical transformation.

Education & Training

Cook County Health and Hospitals SystemResidency, Emergency Medicine, 1990 - 1993
Chicago College of Osteopathic Medicine at Midwestern UniversityClass of 1989
Oregon Health & Science University (OHSU Health)Fellowship, Clinical Informatics (Internal Medicine)
University of Michigan MS

Certifications & Licensure

OR State License 2010 - Present
CA State License 2014 - 2025
WA State License 1993 - 2012
IL State License 1989 - 1993
American Board of Emergency Medicine Emergency Medicine
American Board of Preventive Medicine Clinical Informatics

Publications & Presentations

PubMed

3 citations
Prediction of Tuberculosis Using an Automated Machine Learning Platform for Models Trained on Synthetic Data.
Hooman H Rashidi, Imran H Khan, Luke T Dang, Samer Albahra, Ujjwal Ratan
Journal of Pathology Informatics. 2022-01-01
26 citations
Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept.
Nam K. Tran, Samer Albahra, Tam N. Pham, James H. Holmes, David G. Greenhalgh
Scientific Reports. 2020-07-23
40 citations
Early Recognition of Burn- and Trauma-Related Acute Kidney Injury: A Pilot Comparison of Machine Learning Techniques.
Hooman H. Rashidi, Soman Sen, Tina L Palmieri, Thomas Blackmon, Jeffery Wajda
Scientific Reports. 2020-01-14

Journal Articles

Artificial intelligence and machine learning for predicting acute kidney injury in severely burned patients: A proof of concept. Burns. 2019 Jun 20.
Tran NK, Sen S, Palmieri TL, Lima K, Falwell S, Wajda J, Rashidi HH., pii: S0305-4179(18)31129-X. doi: 10.1016/j.burns.2019.03.021., 6/2019