Areas of Research

Biomedical Informatics research covers a broad spectrum of inquiry - from the analysis of genomic microarray datasets to the evaluation of hospital organizations during the adoption of new technology. This spectrum reflects the many facets of Biomedical Informatics, which can be defined as “the scientific field that deals with biomedical information, data, and knowledge – their storage, retrieval, and optimal use for problem-solving and decision-making.” (Shortliffe & Blois, 2001). Below we present our faculty’s major funded research areas. We welcome your ideas for collaboration and invite individuals interested in training with us to contact us for further information.


Biostatistics

Over many years, Roger S. Day, ScD has studied how computational and modeling tools could help people create better biological understanding, then apply it to better individual treatment decisions.

Clinical Informatics

Steven Handler, MD, PhD, Assistant Professor with a primary appointment in the Department of Biomedical Informatics and secondary appointments in Geriatric Medicine, and Clinical and Translational Research focuses on clinical and translational informatics in the long-term care setting.  His interests include developing and testing active medication monitoring systems to enhance the detection and response to potential adverse drug events, improving medication safety and adherence during care transitions, and the application of information tech

Clinical Predictive Modeling

Clinical care involves making many predictions under uncertainty, including risk assessment, diagnosis, prognosis and therapeutic management. The better those predictions can be made, the better clinical care is likely to be. The increasing availability and richness of electronic health records (EHRs) are increasing the opportunities for developing and deploying computer-based clinical prediction methods. Such methods can serve as key components of computer-based decision support systems. The data in EHRs can be used to construct prediction models using machine learning methods.

Comparative Effectiveness Research (CER)

The IOM defines CER as the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat and monitor a clinical condition, or to improve the delivery of care. The purpose of CER is to assist consumers, clinicians, purchasers, and policy makers to make informed decisions that will improve health care at both the individual and population levels.

Data-Driven Modeling of Usual Clinical Care

Clinical care is complex and often fast paced. Preventable medical errors can and do occur, as has been well documented in recent years. Clinical guidelines and rule-based alerts provide clinical decision support that is intended to reduce medical errors. These methods are driven by expert knowledge. As such, they tend to focus on high impact areas in which medical errors are either prevalent, serious, or both. However, the coverage of such methods is relatively narrow.

Datawarehouses and Repositories

Michael J. Becich MD, PhD, professor and chair of the Department of Biomedical Informatics, focuses on developing datawarehouses and data mining strategies for genomic and proteomic data derived from high throughput methodologies such as expression microarrays and tissue microarrays. His interests also include tissue bank information systems, clinical information systems and imaging repositories that are currently operating in the Pathology Department at University of Pittsburgh.

Dental Informatics

The Center for Dental Informatics (CDI) at the School of Dental Medicine is a world leader in the emerging discipline of dental informatics. Three faculty, Titus Schleyer, Thankam Thyvalikakath and Heiko Spallek, as well as several trainees and staff, are conducting research on informatics projects in dental practice, research, education and management.

Genomic and Proteomic Data: Analysis and Data Mining

Vanathi Gopalakrishnan, PhD is exploring the application of technology to the analysis of datasets from biological studies. She is fundamentally interested in technologies for data mining and discovery that allow incorporation of prior knowledge.

Health Informatics for the Underserved

Gerald Douglas, PhD is leading research efforts that will contribute to the elimination of health disparities, improve health care quality, encourage the adoption of personal wellness strategies, and provide support for the development and advancement of underserved communities. Dr. Douglas implemented several innovative approaches to using technology in health care, including the use of a low-power, robust and inexpensive touch screen workstation for clinicians at the point of care in Malawi, Africa.

Human Computer Interaction and Evaluation

Friedman's "fundamental theorem" of biomedical informatics states that the combination of human intelligence and information resources is more powerful than human intelligence alone. Realizing the potential benefits of this combination requires careful attention to the design and evaluation of user interfaces that will help users maximize the utility of information resources. Harry Hochheiser, PhD uses techniques of contextual inquiry and prototyping to develop bioinformatics interfaces for the FaceBase project.

Image Perception Research

Medical images contain a significant amount of useful information for the diagnosis of a given patient, but they also contain even greater amounts of distractions. How do physicians separate the signal from the noise? Which type of information do they use in their decision making process?

Intelligent Tutoring Systems and Simulations

Rebecca Crowley, MD, MS, also has received R01 funding and R25 funding to develop an Intelligent Tutoring System for teaching visual diagnostic skills in pathology. Crowley is collaborating on an AHRQ grant exploring whether the use of this tutoring system can reduce medical errors in melanoma diagnoses.

Natural Language Processing

Patient data often is electronically stored in free text fields resulting in relatively inaccessible information. Two DBMI faculty members, Rebecca Crowley, MD, MS, and Henk Harkema, PhD, have been exploring theapplication of Natural Language Processing (NLP) techniques to the identification and retrieval of relevant free text information. Harkema has been exploring the use of NLP for measuring the quality of clinical care and to support automatic case detection for biosurveillance.

Pharmacoepidemiology

Pharmacoepidemiology may be defined as the study of the utilization and effects of drugs in large numbers of people. To accomplish this study, pharmacoepidemiology borrows from both pharmacology and epidemiology. Thus, pharmacoepidemiology can be called a bridge science spanning both pharmacology and epidemiology. (ISPE Website)

Public Health Informatics and Biosurveillance

Several faculty members, including  Rich Tsui, PhD (funded by an U01 award) and Garrick Wallstrom, PhD (also funded by an R01 grant), investigate methods for real-time detection and assessment of disease outbreaks within the Realtime Outbreak and Disease Surveillance (RODS) Laboratory.

Systems Biology and Computational Biology

Understanding the molecular basis of diseases from a network perspective is a research focus of Panayiotis (Takis) Benos, PhD, and Madhavi Ganapathiraju, PhD.

Translational Bioinformatics

Translational bioinformatics is an emerging area in informatics focused on “the development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data, and genomic data in particular, into proactive, predictive, preventive, and participatory health” (from the American Medical Informatics Association Strategic Plan http://www.amia.org/inside/stratplan/).  Within DBMI,  Xinghua Lu, MD, PhDVanathi Gopalakrishnan, PhD,