DBMI embraces a broad view of biomedical informatics which includes all computational aspects of health care, prevention, and public health as well as biology and medicine.
The field of biomedical informatics can be grouped according to methods used and by domain area of application. At DBMI, our researchers are involved in all categories, working on scales that range from single molecules to world populations.
Our team works along the scientific pipeline, from discovery (with genomics, proteomics, simulation of biological systems, and more) all the way to analysis (including natural language processing, knowledge management, and telemedicine, among others). We are deeply engaged in human and organizational factors as well, covering technology evaluation, decision support, electronic health records, and more.
From an applications perspective, this broad portfolio of work has our researchers embedded in some of the most challenging informatics needs of our time.
Clinical informatics, also known as healthcare informatics, is the study and use of data and information technology to deliver health care services and to improve patients’ ability to monitor and maintain their own health. The data and clinical decision support involved in this field are developed for and used by clinicians, patients, and caregivers.
The field includes:
- Methods to collect, store, and analyze health care data
- The study of information needs and cognitive processes, and optimal ways to meet those needs
- Methods to support clinical decisions, including summarization, visualization, provision of evidence, and active decision support
- Optimizing the flow of information and coordinating it with care providers’ and patients’ workflows to maximize patient safety and care quality
- Methods and policies for information infrastructure, including privacy and security
Public Health Informatics
Major challenges faced by the public health system demand informatics solutions — whether to provide essential public health services in the context of demographic change, protect against both biological terrorism and natural infectious disease threats, and tackle lifestyle-related epidemics such as obesity and tobacco use. Public health informatics is a critical component of new models of care designed to promote health, wellness, shared decision-making and consumer engagement.
The Institute of Medicine, Centers for Disease Control and Prevention, and other health organizations have cited the need for training in public health informatics. Career paths for people with these skills include teaching and research within academic and R&D organizations; public health practice at state and local public health agencies or positions in public health institutes, the CDC and other DHHS agencies; or private sector industry, including software vendors.
Our collaboration with the Mailman School of Public Health offers students a solid foundation in utilizing informatics for public health needs.
The field includes:
- Using in-depth computational and statistical analysis to parse epidemiological data sets
- The application of bioinformatics to public health data and challenges
- Studying real-world public health issues and performing complex analysis of multiple factors involved, from genotype and phenotype to population-level information
- Developing tools to aid patients in shared clinical decision-making and engaging with their health data and related research
Clinical Research Informatics
Clinical research informatics (CRI) is a rapidly evolving sub-discipline within biomedical informatics. It focuses on developing new informatics theories, tools, and solutions to accelerate the full translational continuum: basic research, clinical trials, and medical centers and community practice. Two recent factors accelerating CRI research and development efforts are the extensive and diverse informatics needs of the NIH Clinical and Translational Sciences Awards and the growing interest in sustainable, large-scale, multi-institutional distributed research networks for comparative effectiveness research.
Given the broad landscape that comprises translational science, CRI scientists conceive of innovative solutions that span biological, clinical, and population-based research. The field has simultaneously borrowed from and contributed to many related informatics disciplines. CRI integrates clinical and translational research workflows in addition to core informatics methodologies and principles into a framework that reflects the unique informatics needs of translational investigators. The conceptual framework for CRI is organized around three conceptual components: workflows; data sources and platforms; and informatics core methods and topics.
Translational bioinformatics transforms basic science discoveries into clinically applicable knowledge. Leveraging a wide range of biomedical data, from next-generation sequencing to millions of electronic healthcare records, researchers develop predictive models to analyze and understand the human system.
Translational bioinformatics focuses on taking biological discoveries made in the lab and finding ways to incorporate those findings in a clinical setting. Researchers in this area work closely with data from patient studies and disease biology to build predictive models and perform extensive analytical functions.
The field includes:
- Using computational and statistical analysis to bridge the gap between basic science and clinical practice
- The application of bioinformatics and computational biology methods to clinical data
- Developing models to translate knowledge generated in model systems (mouse, fly, yeast) into knowledge about human disease
- Using data gathered on patients and human disease and relating it back to basic science principles
In recent years, the increasing availability of technologies such as next-generation DNA sequencing, high-throughput experimentation, and high-performance computing has led to an explosion in the amount of biological data that are now available. This presents a unique opportunity: scientists believe that the data hold clues that could improve our understanding of how life works at the molecular level, as well as the causes of human disease. This new reality also presents great challenges, as new methods are needed for organizing, integrating, and interpreting this overwhelming amount of information.
In this context, computational biology and bioinformatics have become important disciplines in the modern study of biology. Computational biology is a science that uses advanced methods from mathematics, physics, statistics, and computer science to develop statistical and analytical models capable of predicting biological activity.
At Columbia University, research in computational biology focuses on a number of areas, including:
• modeling of molecular interaction networks that give rise to physiological and pathological phenotypes
• prediction of protein structure, function, and localization
• study of protein-protein and protein-DNA interactions
• gene expression analysis and prediction of regulatory network structure
• study of complex inherited traits.
Bioinformatics is a related field that focuses on the development of the computer technologies for studying biology in this way. Bioinformaticians design algorithms, software, databases, websites, and other tools for analyzing large collections of biological data. This work can also involve activities such as designing methods for integrating multiple data sets, developing standardized biomedical ontologies for organizing data, and creating automated methods for extracting knowledge from scientific literature and medical reports. An important part of research in computational biology and bioinformatics is the validation of computer-predicted models. Researchers in this field typically work closely with experimental scientists who test computational models in the laboratory to confirm that they correspond with the way cells and organisms actually behave.