The mission of the Institute for Informatics (I2) focuses on the informatics landscape at Washington University School of Medicine in order to transform research, education and patient care by emphasizing precision medicine and efforts to improve the quality of health care and public health initiatives locally, nationally and worldwide.
Our vision at I2 is to serve as the academic and professional home for a preeminent interdisciplinary program of research, education and service in informatics at Washington University by enabling advances in biomedical research and improvements in the quality of health care.
The institute coordinates informatics efforts across the Medical Campus and the Danforth Campus while also developing partnerships with the Health Systems Innovation Laboratory at BJC HealthCare, the Cortex Innovation Community and other regional partners.
I2 offers a Master of Science (MS) and a certificate program in biomedical informatics. The purpose of the MS and certificate courses is to provide comprehensive and competency-based training in core biomedical informatics theories and methods for the following individuals:
- Recent college graduates with backgrounds in the biological and/or computational sciences
- In-career learners with a broad range of experiences in biomedicine/biosciences, mathematics, physical or computer information sciences or engineering, and cognitive and/or social sciences
Joanna Abraham, PhD, is focused on improving collaborative practices in health care using principles and techniques from informatics to promote patient safety, quality and care continuity.
Research interests: handoffs, care transitions, care coordination, decision making, health IT, medical errors, mixed methods, systematic reviews, evidence synthesis
Chih-Hung Chang, PhD, is focused on the integration of methodology and technology to advance clinical care, research and education.
Research interests: item response theory, Rasch measurement, computerized adaptive testing, psychometrics, informatics, smart testing and smart learning, health-related quality of life, patient-reported outcomes, clinical outcomes, shared decision making, quality improvement
Randi Foraker, PhD, is focused on applying epidemiology and informatics techniques to solve problems in the population health domain.
Research interests: approaches for the integration of socioeconomic and patient-reported outcome data with electronic health record data; interventional approaches to the use of electronic health records in order to address modifiable risk factors for disease and enable patient-centered decision making; study design methodology and data analysis
Thomas Kannampallil, PhD, is focused on integrating cognitive, behavioral and computational informatics techniques to develop health information technology solutions in the areas of clinical decision support, clinical reasoning and clinical workflow.
Research interests: clinical decision support applications for tracking, monitoring and evaluating electronic health record–based activities such as medication/lab orders, decision-making for chronic care, and opioid management; tracking and analysis of medical errors in a variety of situations (e.g., medication orders, transitions of care, clinical decision-making) and evaluating their impact on clinical outcomes and patient safety; use of cognitive and human factors approaches for identifying behavioral, collaborative and workflow challenges in the design and use of health information technology
Albert M. Lai, PhD, is focused on applying computer science and informatics techniques to solve problems in the clinical domain.
Research interests: clinical research informatics, clinical informatics, consumer health informatics, telemedicine, usability, natural language processing, mobile health
Fuhai Li, PhD, is focused on applying statistical, machine learning, deep learning and data mining approaches to diverse biomedical dataset integration and interpretation to solve the challenges of bioinformatics, systems biology and image informatics.
Research interests: integrative large-scale pharmacogenomics analysis for target, signaling network, drug and drug combination discovery; genomics data driven tumor-stromal communication discovery and modeling
Philip R.O. Payne, PhD, FACMI, is the founding director of I2 at Washington University in St. Louis, where he also serves as the Robert J. Terry Professor and Professor of Computer Science and Engineering. Previously, Dr. Payne was Professor and Chair of the Department of Biomedical Informatics at The Ohio State University.
Research interests: knowledge-based approaches to the discovery and analysis of biomolecular and clinical phenotypes and the ensuing identification of precision diagnostic and therapeutic strategies in cancer; interventional approaches to the use of electronic health records in order to address modifiable risk factors for disease and enable patient-centered decision making; the study of human factors and workflow issues surrounding the optimal use of health care information technology
Po-Yin Yen, PhD, RN, is focused on applied clinical informatics research to support clinicians adapting to health information technology.
Research interests: clinical informatics, usability, technology acceptance, human–computer interaction, literature mining, data visualization, workflow analysis, time motion study
M18 BMI 5200 Biomedical Informatics Journal Club
Trainees will attend weekly one-hour seminars and student-led journal club discussions in which current peer-reviewed publications relevant to biomedical informatics will be reviewed and discussed.
Credit 1 unit.
M18 BMI 5201 Biomedical Informatics Rotation
Students will be responsible for arranging two rotations to identify a thesis lab or capstone project site. Each rotation will last approximately one month, with the goal being to expose students to research and practical biomedical informatics opportunities in both academic and industry settings.
Credit 1 unit.
M18 BMI 5204 Mixed Methods in Biomedical Informatics
Building on the fundamentals of biomedical informatics in BMI I & II, this course will introduce students to the various research methods and underlying theories used to conduct biomedical informatics research studies. This course will cover research methods, including the systematic review of published research as well as qualitative, quantitative, and mixed methods. Under each topic, we will focus on the formulation of research questions/hypotheses, the selection of appropriate study design, data collection and analysis methods, and methods to ensure rigor and reproducibility of research. The course will encompass several hands-on components for students to practice and apply their learned skills.
Credit 3 units.
M18 BMI 5302 Introduction to Biomedical Informatics I
This survey and methods course provides an overview of the theories and methods that comprise the field of biomedical informatics. Topics to be covered include the following: (1) information architecture as applied to the biomedical computing domain; (2) data and interoperability standards; (3) biological, clinical, and population health relevant data analytics; (4) healthcare information systems; (5) human factors and cognitive science; (6) evaluation of biomedical computing applications; and (7) ethical, legal, and social implications of technology solutions as applied to the field of biomedicine. The course will consist of both didactic lectures and experiential learning opportunities, including hands-on laboratory sessions and journal club-style discussions. The course will culminate with a capstone project requiring the in-depth examination, critique and presentation of a student-selected topic related to the broad field of biomedical informatics. Biomedical Informatics I is designed primarily for individuals with a background in the health and/or life sciences who have completed a course in introductory statistics (e.g., Math 1011). No assumptions are made about computer science or clinical background; however, some experience with computers and a high-level familiarity with health care will be useful. This course does not require any programming knowledge, and it will not teach students how to program.
Credit 3 units.
M18 BMI 5303 Introduction to Biomedical Informatics II
This course introduces students to the methods needed in order to apply the foundational theories covered in Biomedical Informatics I. The course will cover a broad spectrum of such methods -- including both computational and quantitative science techniques -- that can be employed in the design, conduct, and analysis of basic science, clinical, and translational research programs. This course is intended to enable individuals to critically select such methods and evaluate their results as part of both the design of new projects as well as the review of results available in the public domain (e.g., literature, public datasets). Core concepts to be reviewed during this course include basic computational skills, data modeling and integration, formal knowledge representation, in silico hypothesis generation, quantitative data analysis principles, and critical thinking skills surrounding the ability to ask and answer questions about complex and heterogeneous biomedical data. Prerequisite: M18 5302 or instructor permission.
Credit 3 units.
M18 BMI 5304 Introduction to Biomedical Data Science I
This course (formerly Biomedical Computing I) provides an introduction to fundamental principles of informatics tools and data analysis, and it is expected to fulfill the requirements of computer science prerequisites for suggested biomedical informatics electives. Competencies and concepts covered will include the following: (1) an overview of the Linux/Unix command line interface; (2) an introduction to programming using Python and R; (3) database models, management and querying using MySQL; (4) basic data manipulation, analysis and visualization using Excel, Python and R; and (5) an introduction to the development of web applications. Biomedical Data Science is designed primarily for individuals who wish to learn the basic skills required for biomedical informatics-based research and who have little or no computational experience in using command line shells, programming and databases. No assumptions are made about computer science or clinical background; however, some experience with computers and a high-level familiarity with the health and life sciences will be useful. The course will consist of both didactic lectures as well as experiential learning opportunities including hands-on laboratory sessions and a culminating project.
Credit 3 units.