Wei-Hsuan "Jenny" Lo-Ciganic, Ph.D., M.S., M.S.Pharm.

Wei-Hsuan “Jenny” Lo-Ciganic, M.S.Pharm, M.S., Ph.D. is an associate professor in the University of Florida College of Pharmacy, Department of Pharmaceutical Outcomes and Policy and Center for Drug Evaluation and Safety. As a pharmacist and pharmacoepidemiologist and pharmaceutical health services researcher, she applies well-thought-out pharmacoepidemiological approaches to address relevant issues in clinical practice, to make appropriate recommendations to health care professionals, and to enhance drug safety. Her research focuses on improving drug safety, medication adherence, pain management, quality of prescribing, and treatment for substance use disorders, especially among vulnerable populations (e.g., Medicaid beneficiaries). Dr. Lo-Ciganic has extensive experience applying advanced predictive analytics including machine learning and group-based trajectory modeling with large healthcare datasets.

Since her faculty appointment in 2015, Lo-Ciganic served as Principal Investigator (PI) and Co-Investigator (Co-I) on more than 13 extramurally-funded grants and contracts. For example, she was the PI of an R21 grant, entitled “Developing a Real-Time Trajectory Tool to Identify Potentially Unsafe Concurrent Opioid and Benzodiazepine Use among Older Adults” funded by the National Institute on Aging. She is currently the PI for the R01 study entitled “Developing and Evaluating a Machine-Learning Opioid Prediction & Risk-Stratification E-Platform (DEMONSTRATE)” funded by NIDA. Recently, she became a Site PI for the R01 grant, entitled “Machine-Learning Prediction and Reducing Overdoses with EHR Nudges (mPROVEN)” funded by NIDA. She has published more than 75 peer-reviewed manuscripts with an h-index of 21. Since 2015, she has been a member and co-chair of the Pharmacy Quality Alliance’s (PQA) workgroups and Quality Metrics Expert Panel to develop and improve quality measures of prescription medications.

Pharmacoepidemiology, pharmaceutical health service research, patient safety, quality of prescribing and care, program and policy evaluation, medication adherence, prescription drug abuse and substance use disorders and advanced analytics including trajectory analysis, machine learning and spatial pattern mining

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