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 is the study and use of data, information technology, and informatics methods to advance healthcare delivery and health outcomes for individuals and communities. Clinical Informatics research and applications target the advancement of one or more areas from the Quintuple Aim1: 1) Enhancing health equity, 2) Promoting workforce well-being (with a particular focus on the reduction of clinician documentation burden), 3) Improving population health, 4) Enhancing the care experience, and 5) Reducing costs. Clinical Information Systems, such as the electronic health record (EHR), and mHealth Apps play a pivotal role in the field and are used by all types of health professionals, caregivers, and patients themselves.
Examples of Clinical Informatics activities include:
- Methods for collecting, storing, integrating, analyzing, and re-using health data
- Application of data science methods, including healthcare process modeling, predictive modeling, and machine learning
- Advancements in interoperability and standards
- Understanding of information needs and cognitive processes
- Workflow analyses, cognitive burden, and clinical decision making, often related to care quality, care coordination, and patient safety
- Optimization of Clinical Information Systems and mHealth Apps
- System evaluation and redesign to optimize use, usability, and workflow processes
- Advancements in Clinical Decision Support (CDS)
- Integration of clinician-generated and patient-generated health data
- System implementations and evaluations
- Leveraging Implementation Science methods
- Maintenance of information infrastructures, including for privacy and security
- Human factors analysis
- Analysis of workload, fatigue, situational awareness, usability, user interface, learnability, attention, vigilance, human performance, control and display design, stress, visualization of data, individual differences, aging, accessibility, shift work, work in extreme environments, and human error
- Policy and Advocacy work, including but not limited to:
- Interoperability, standards, privacy, and security
- Reduction of documentation burden
- Nundy S, Cooper LA, Mate KS. The Quintuple Aim for Health Care Improvement: A New Imperative to Advance Health Equity. JAMA. 2022;327(6):521-522. doi:10.1001/jama.2021.25181
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
Consumer Health Informatics
Consumer Health Informatics is a field that focuses on application of informatics methods and tools to understanding and promoting health of individuals, families, and communities. Consumer Health Informatics places particular focus on empowering individuals to manage their own health and to develop innovative approaches to promoting health literacy and providing decision support, and to reducing structural barriers to engaging in health management.
The field includes:
- Study of individuals’ health behaviors and decision-making, and shared decision-making between individuals and their providers
- Study of relevant theories of health behaviors, and principles of theory-driven design
- Study of social, cultural, and other contextual factors that influence health behaviors, with a particular focus on health disparities
- Methods to collect, analyze, and derive insights from patient generated and self-monitoring data
- Methods to create data-driven interventions for health consumers
- Methods for applying user-centered design process to design and evaluation of consumer health interventions
Clinical Research Informatics
Clinical research informatics (CRI) is a rapidly advancing sub-discipline within biomedical informatics. It focuses on developing new informatics theories, methods, and software to accelerate the full translational continuum covering basic research, clinical trials, and medical centers and community practice. Two recent factors accelerating CRI research and development efforts are the expansive and diverse informatics and data sciences needs of the NIH Clinical and Translational Sciences Awards (CTSA) community and the growing interest in sustainable and distributed Collaboratories for large-scale observational data sciences and informatics for the development of learning health systems.
This fields includes
• Methods for improving the semantic interoperability of clinical information systems
• Methods for bridging the sociotechnical gap in clinical and translational research
• Methods for improving the portability of phenotyping algorithms
• Methods for improving the generalizability of clinical trial results
• Methods for clinical evidence extraction, appraisal, and synthesis
• Methods for generating real world evidence using observational health data
• Methods for using electronic health records data for evidence appraisal and for supporting evidence-based medicine
• Methods for achieving learning health systems
• Methods for integrating multi-source and multimodal clinical data for disease knowledge discovery and application
Translational bioinformatics focuses on the technology that transforms basic science discoveries into clinically applicable knowledge. By leveraging a range of biomedical data, from single cell sequencing to billions of patient records, researchers apply artificial intelligence and data science algorithms to analyze and understand the human system.
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 (cell lines, mouse, fly, yeast) into knowledge about human disease
• Using data gathered on patients and human disease and relating it back to basic science principles
Computational biology is a field in which scientists search for quantitative laws that govern biological systems and develop predictive models in health and disease conditions. Computational biology has been driven by exponential increase of data generated by high-throughput experimental approaches, such as DNA sequencing and single cell transcriptomics. These large data sets enable systematic investigation of biological questions at multiple scales and inference based on probabilistic and mathematical models. The field is at the intersection of biology, medicine, mathematics, physics, statistics, and computer science.
At Columbia University, research in computational biology focuses on a broad range of areas, such as:
• Systems biology, study of biological systems with computational models rooted in how genes expression and protein activities are regulated mechanistically at the levels of cells, tissues and organs.
• Computational and statistical genetics, study of genetic causes and risk factors for human traits and diseases. Discoveries of genetic causes and risk factors can improve our understanding of disease mechanisms and provide targets for intervention.
• Modeling of protein sequence and structure. Modern deep learning methods and differentiable programming techniques have led to breakthroughs in ab initio prediction of protein structure. The question is still wide open for proteins that do not have deep homology. The same general approaches also hold promise for designing new proteins and predicting functional impact of mutations.
• Microbiome. In each person, there are more microbes than human cells. How these microbes coexist with human cells and how they contribute to human health and diseases is largely unknown.
• Modeling of infectious diseases. How viruses that cause pandemic emerge in nature and how do they evolve in adaptation to humans and vaccines?
Bioinformatics is sometimes used interchangeable with computational biology. In practice they usually are just two aspects of the same research: when the focus on the biological question, it is usually called computational biology; when the focus is on developing computational tools to answer these questions, it’s often called bioinformatics.