Department of Medicine
Faculty Profiles by Division

Division of General Internal Medicine

Faculty Profiles
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photo Wei-Hsuan Jenny Lo-Ciganic, PhD, MS, MSPharm

General Internal Medicine

Professor of Medicine

Email: jenny.lociganic@pitt.edu
Contact
 
E-mail: jenny.lociganic@pitt.edu
Administrative Assistant:
Monica Loughran
Email: mhk31@pitt.edu
Education and Training
Education
BS, Pharmacy with a minor in Horticulture, National Taiwan University, 2003
MS, Clinical Pharmacy, National Cheng-Kung University, 2005
MS, Biostatistics, University of Pittsburgh, 2010
PhD, Epidemiology, University of Pittsburgh, 2013
Training
Geriatric Pharmaceutical Outcomes & Gero-Informatics Research & Training Program, University of Pittsburgh, 2013
Pharmaceutical Health Service Research Postdoctoral Fellowship, CP3, University of Pittsburgh, 2014
Research Interest
Dr. Lo-Ciganic’s research focuses on applying advanced predictive analytics, such as group-based trajectory modeling and machine learning, to improve opioid use safety, quality of prescribing, and adherence to medications for opioid use disorder (MOUD), especially among vulnerable populations. She has explored the complex issues of addiction and overdose from various angles, including developing and evaluating risk prediction algorithms using claims and electronic health records data, assessing the generalizability of the algorithm in data from another state, evaluating the stability of individuals’ risk over time, integrating human services and criminal justice data to improve prediction performance, and identifying high risk patterns of gabapentinoids and benzodiazepines used concurrently with opioids.

In the National Institute on Drug Abuse funded R01 entitled “Developing and Evaluating a Machine-Learning Opioid Prediction & Risk-Stratification E-Platform (DEMONSTRATE)”, she is successfully taking her prediction artificial intelligence algorithms that were developed in claims data to the next level—implementation—by developing a best practice alert that will be integrated into an electronic health records (EHR) system for assisting prescribers in clinical decision making related to a specific patient and based on a patient’s specific level of risk for an adverse outcome (opioid overdose or development of an opioid use disorder) in the next three months. Dr. Lo-Ciganic also applies her methodological expertise to deprescribing and studies of pharmacoepidemiology in cancer, geriatrics, and women’s health.
Clinical Interest
Dr. Lo-Ciganic possesses extensive expertise as a pharmacist, pharmacoepidemiologist, and biostatistician. Her clinical interests primarily focus on addressing the national opioid epidemic crisis and conducting drug safety evaluations through pharmacoepidemiology, health services, and outcome research.

One of Dr. Lo-Ciganic's significant contributions to science revolves around the development of machine learning algorithms for predicting individuals' subsequent opioid overdose risks. Her work has furthered our understanding of the national opioid crisis. Dr. Lo-Ciganic's dedication extends to the practical implementation of her research. She leads a team of experts in integrating her risk prediction algorithms into electronic health record systems, a significant step towards improving clinical decision-making and patient care in the context of opioid overdose and opioid use disorder.

Furthermore, her expertise in trajectory modeling has significantly influenced clinical practice and policy decision-making. Notably, her findings on buprenorphine adherence trajectories have informed medication-assisted therapy policies while her work on high-risk trajectories of concurrent opioid and gabapentinoid use has influenced regulatory considerations.
Educational Interest
Dr. Lo-Ciganic's teaching philosophy centers on the belief that passionate, knowledgeable, and compassionate mentors are essential for students' professional and career development. She cultivates a culture of lifelong learning in an interactive and engaging classroom where students are encouraged to actively participate, explore problem-solving, and confidently share their ideas.

She emphasizes hands-on learning, particularly in research projects requiring strong conceptualization and analytical skills. In her view, involving students from diverse backgrounds early in research fosters inclusivity, collaboration, and enhances their programming and analytical abilities.

Dr. Lo-Ciganic has coordinated required and elective courses for Doctor of Pharmacy programs and PhD courses in pharmaceutical outcomes research and applications of AI in healthcare settings. Since her faculty appointment in 2015, she has successfully chaired six doctoral dissertation committees, contributed to more than 10 additional committees, and mentored two post-doctoral fellows, seven independent study graduate students, one MPH intern, more than 10 PharmD research/senior projects, and three visiting international pharmacist scholars. Additionally, she has served as a primary mentor or co-mentor for five junior faculty members. Her students and mentees were from diverse backgrounds.

Her commitment to teaching extends to her active participation in publicly engaged education. Dr. Lo-Ciganic shares her wealth of knowledge and expertise through presentations, short courses, and workshops, nationally and internationally. Over the last four years, she has been an integral part of the International Society of Pharmaceutical Outcomes Research (ISPOR) annual international conference, where she has taught a highly regarded short course on "Introduction to Machine Learning."
Publications
For my complete bibliography, Click Here.
Selected Publications:
Lo-Ciganic, W., Huang, J. L., Zhang, H. H., Weiss, J. C., Wu, Y., Kwoh, C. K., Donohue, J. M., Cochran, G., Gordon, A. J., Malone, D. C., Kuza, C. C., Gellad, W. F. Evaluation of machine-learning algorithms for predicting opioid overdose risk among Medicare beneficiaries with opioid prescriptions. JAMA Netw Open. 2019; 2(3): e190968.
Lo-Ciganic, W., Donohue, J. M., Hulsey, E. G., Barnes, S., Li, Y., Kuza, C. C., Yang, Q., Buchanich, J., Huang, J. L., Mair, C., Wilson, D. L., Gellad, W. F. Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: a machine-learning approach. PLoS One. 2021; 16(3): e0248360.
Lo-Ciganic, W., Donohue, J. M., Yang, Q., Huang, J. L., Chang, C-Y(g)., Weiss, J. C., Guo, J., Zhang, H. H., Cochran, G., Gordon, A. J., Malone, D. C., Kwoh, C. K., Wilson, D. L., Kuza, C. C., Gellad, W. F. Developing and validating a machine-learning algorithm to predict opioid overdose among Medicaid beneficiaries in two US states: a prognostic modeling study. Lancet Digit Health. 2022; 4(6): e455-e465.
Lo-Ciganic, W., Hincapie-Castillo, J., Wang, T(p)., Ge, Y., Jones, B. L., Huang, J. L., Chang, C. Y(g)., Wilson, D. L., Lee, J. K., Resifield, G. M., Kwoh, C. K., Delcher, C., Nguyen, K. A., Zhou, L., Shorr, R. I., Guo, J., Marcum, Z. A., Harle, C. A., ..., Gellad, W. F. Dosing profiles of concurrent opioid and benzodiazepine use associated with overdose risk among US Medicare beneficiaries: Group-based multi-trajectory models. Addiction. 2022; 117(7): 1982-1997.
Notable Achievements
Excellence Awards for Assistant Professors, University of Florida, 2020
Young Outstanding Alumni Award, National Cheng-Kung University, 2021
College of Pharmacy Research Impact Award, University of Florida, 2022
Excellence in Health Economics & Outcomes Research Application Award, International Society for Pharmacoeconomics & Outcomes Research, 2023

Division Websites

Cardiology

Endocrinology and Metabolism

Gastroenterology, Hepatology and Nutrition

General Internal Medicine

Geriatric Medicine
Hematology/Oncology

Infectious Diseases

Pulmonary, Allergy and Critical Care Medicine

Renal-Electrolyte

Rheumatology and Clinical Immunology

Contact

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