Background
Dr. Wang is an Associate Professor in the Division of Quantitative Sciences at the Sidney Kimmel Comprehensive Cancer Center (SKCCC), and jointly in the Department of Biostatistics and the Department of Applied Mathematics and Statistics at Johns Hopkins University. Dr. Wang focuses her research endeavor on both methodological development and its application to advancing diagnostic procedures in healthcare. She sees an accurate diagnosis as the first step toward successful treatment and prevention, and is therefore of utmost importance to patients, healthcare providers, and policymakers. Dr. Wang approaches this overarching goal from three research perspectives and has received recognition for her scholarship and leadership in all three areas.
Improving diagnostic accuracy when a gold standard is unavailable Dr. Wang is most widely known as an expert in statistical methodologies for diagnostic tests and biomarker evaluation in situations when a reference so-called gold standard is unavailable or subject to errors at the time a medical decision is needed. Evaluation of new diagnostic techniques against imperfect “less-than-gold” standards can provide unfair advantages to standard tests and mask the value of a novel approach. Dr. Wang has made a series of methodological development to use modern statistical techniques to evaluate test performance, and synthesize information from multiple biomarkers/tests to achieve more accuracy and/or timelier diagnoses in such situations. Her methodology has attracted great attention from medical researchers, with the most from the Alzheimer’s disease field. Dr. Wang was featured as one of the “10 Outstanding Medical School Professors under 40” by Career & Education.com due to the originality and impact of her work. She has received multiple grant funding as the Principle Investigator, with a recent R01 to tailor her method in particular for early detection of Alzheimer’s disease.
Reducing diagnostic errors with EHR data Dr. Wang sees reducing diagnostic errors and improving diagnostic accuracy as two sides of the same coin. She has made impactful contributions in this research area by utilizing her strong quantitative skills and exploiting the massive information that resides in electronic health records. Dr. Wang is the Biostatistics/Data Science Lead to develop statistical models and automatic algorithms that build on electronic health records and a Symptom-Disease Pair Analysis of Diagnostic Error (SPADE) framework in a large multidisciplinary research effort to track and reduce diagnostic errors. This research effort has now expanded beyond Johns Hopkins University and joined by Kaiser Permanente, IBM Watson Health, and the Society to Improve Diagnosis in Medicine, among others.
Promote appreciation and infrastructure for utilizing modern statistical methodologies in medical diagnosis and prevention Dr. Wang devotes a high level of effort to promoting the appreciation and enabling application of modern statistical methodologies in diagnostic and prevention. She is the leader of the SKCCC Innovation for Impact 2025: to Create a Cancer Prevention Clinical Data Core that centralizes the collection of clinical data across the Hopkins Healthcare System and as much of the catchment area as possible to inform cancer prevention research and deployment. She is also the Biostatistical Core Lead at the Armstrong Institute Center for Diagnostic Excellence at Johns Hopkins and oversees a large number of projects and grant applications as the primary statistician across disease areas, such as cancer, cardiovascular, infectious diseases, and others.
Patient Ratings & Comments
The Patient Rating score is an average of all responses to physician related questions on the national CG-CAHPS Medical Practice patient experience survey through Press Ganey. Responses are measured on a scale of 1 to 5, with 5 being the best score. Comments are also gathered from our CG-CAHPS Medical Practice Survey through Press Ganey and displayed in their entirety. Patients are de-identified for confidentiality and patient privacy.