Biomedical Informatics is the discipline that applies principles of computer and information science to the advancement of life sciences research, health professions education, and patient care. A major strength of Biomedical Informatics at Vanderbilt is the development and evaluation of real-world systems in each of these mission areas. Enterprise strategies reflect “the art of the possible” through Biomedical Informatics. The clinical enterprise is committed to delivering documented quality at an affordable cost through systematic evidence-based personalized care. Research is targeted on breakthroughs in population health, diagnostic and therapeutic discovery, and personalized medicine. Education programs are evolving to spiral curricula leveraging just-in-time information access, simulation and continuous competency assessment. The Department of Biomedical Informatics is home to an interdisciplinary faculty of over 65. Its training programs include research-oriented MS and PhD degrees, as well as non-degree postdoctoral training, and were awarded an institutional training grant from the NLM in 2002.
Medical education informatics, personalized learning technologies, natural language processing, information retrieval, adaptive user interfaces, and mHealth.
The intersection of natural language processing, biomedical informatics, and applied machine learning. Current research efforts focus on developing text mining algorithms for identifying clinical phenotypes from big collections of narrative reports and for facilitating translational studies of large patient cohorts.
The development and application of controlled terminologies and informatics-enabled approaches to quality improvement. Active participant in national standards efforts, in particular those related to the pharmaceutical coding system RxNorm.
Evidence based clinical decision support links healthcare, data management, biostatistics, and machine learning. Current research involves data management and risk modeling for healthcare resource utilization and disease prediction in a large patient cohort.
Phenotyping in the EHR and analysis of linked genetic data. Current research involves working with electronic health record data and genetics data to study complex disease and drug response/effects, and participating in projects to facilitate such research, from local resources including the synthetic derivative and BioVU to research networks including eMERGE and the PGRN, to the nationwide Precision Medicine Cohort Program initiative.
Health data analytics, healthcare organization modeling, healthcare workflow modeling, transfer learning of phenotypes and healthcare security and privacy, machine learning of collaborative care structures and phenotypes to optimize the management of care providers and patients. Interested in developing big data analytics tools to improve quality and efficiency of care
Consumer informatics and mobile health technologies. Currently working in how to improve patient engagement and activation in hospitalized patients and in individuals with sickle cell disease and their families; precision medicine, in particular participant provided information for the precision medicine cohort; and mobile health technologies and how consumers can use these technologies for their health.
Improving understanding of clinical records and information resources through natural language processing, concept identification, and clinical terminologies. Leading key aspects of NIH’s Precision Medicine Initiative.
Clinical research informatics in global health settings. Current work revolves around data quality assessment and improvement, audit tool development, informatics support for observational research (specifically HIV and TB studies), and the implementation of data coordinating centers at Vanderbilt.
Computer science, machine learning and data management, and healthcare. Current research involves applying and developing computational methods to a wide range of healthcare issues from discharge prediction to cancer recovery times.
Neuropsychology and informatics. Currently in VA Central Office managing national mental health projects, and at Vanderbilt DBMI developing software in the Personalized Medicine Initiative.
The adoption of clinical information innovations, incorporation of reimbursement information in workflow and decision support, health risk assessment in defined patient populations, and disease-specific patient registries.
The intersection of medicine, informatics and business. Primary research directed toward an understanding of economic sustainability and the development of technical and administrative measures to enable affordable chronic care management as part of a broader learning healthcare system.
Development and evaluation of innovative approaches for biomedical informatics education and training to meet the needs of health care. Also interested in studies of the implementation and evaluation of integrated clinical information systems in large health care delivery networks.
Clinical information systems and architectures that support interrupt-driven health care workflows; high availability in EHRs; and, the interaction between technology, organization, and people that goes into successful clinical informatics implementations.
Medical knowledgebase acquisition, innovative approaches to information provision, development of skills that promote true integration of the library in key informatics and medical center initiatives, patient communication, and health literacy research on personalized education strategies for health information and precision medicine.
Real-time natural language processing for clinical data analytics, automated enhancement of documentation quality, and statistical characterization of clinical free text.
Disease-neutral informatics tools supporting clinical and translational research; novel strategies for data collection, analysis and dissemination in patient-oriented and translational research; large-scale software collaboration and dissemination models (e.g. REDCap, ResearchMatch, IRBChoice); and global health technology transfer and training initiatives.
Dynamics and variability in biological systems, machine learning, multi-scale analysis. Current research involves developing and applying methods to improve our understanding of dynamic systems related to human health, including gastrointestinal disease and circadian rhythms.
Development of advanced informatics tools to support clinical workflows such as point of care documentation and medication management, and improvement of the adoption of Health Information technology.
Optimization of clinical communications about health and disease between providers and patients. Current research involves data capture of patient interval history to improve clinical interactions through documentation.
TOM LASKO, MD, PhD
Computational Medicine - the art and science of using mathematics and large scale computing power to improve the precision of diagnosis, prognosis and treatment decisions. Current research uses data-driven machine learning methods to infer from population-scale EMR data the hundreds of similar but distinct conditions underlying what we used to think of as single diseases. Also a latent interest in computational disclosure control.
Clinical Decision support, safety, quality, and cost savings. Clinical Informatics Education. Current research effort focuses on knowledge management and clinical decision support to reduce cost of care, reduce length of stay, and improve clinical outcomes.
Precision cancer medicine and learning cancer systems, informatics methods to support the continuum of cancer care and cancer research. Specific research focus includes model driven computing for longitudinal cancer treatment management and decision support for genome directed cancer treatment selection.
The integration of omics data to address fundamental biological questions and study complex disease, carcinogenesis, and drug resistance mechanisms.
Change management related to introduction of information technology, including organizational and personnel issues related to change in clinical workflows.
Big data management, health data analytics, privacy and security. Current research interests are in the design and application data privacy risk assessment and protection methods, machine learning over clinical databases and social media platforms, and workflow discovery and anomaly detection in electronic medical record systems.
Predictive analytics, machine learning and data mining, medical device surveillance, and natural language processing. Involved in a variety of diabetes and acute kidney injury health services research, as well as statistical and informatics tool development related to medical product surveillance.
Clinical decision support, especially diagnostic decision support; computers as diagnostic consultants; large scale biomedical knowledge-base construction; the computer as a decision support partner to the practitioner; and automated knowledge acquisition through machine analysis of text and electronic health records.
Development of provider-facing tools to improve quality and efficiency of care delivery through appropriate clinical workflow integration. My current research involves the development of a prediction model for disease-specific 30-day readmission in patients with type 2 diabetes.
Medication reconciliation, clinical decision support, knowledge engineering and knowledge maintenance, medication ontologies, EHR design for pharmacists, CPCOE/e-prescribing, cost-effectiveness analysis, and all things pharmacy informatics.
Qualitative research and evaluation of the relationship between information systems and work in clinical settings, social construction of risk and safety among clinicians and patients, and strategies employed by clinicians to create safety and time during the implementation of new technology.
Using informatics methods to improve care across the cancer care continuum. Current research efforts focus on extracting detailed tobacco exposure from clinical notes and developing clinical decision support to promote lung cancer screening.
Value of broad genetic testing in populations and systems barriers to implementation, evaluation of drug safety, computerized prescribing, and clinical decision support systems for drug therapy. Recent projects include guided dosing for geriatric patients, drug protocols for sedation and analgesia of intensive care patients, and safety alert systems to identify high-risk prescribing for hospital inpatients.
Language modelling, temporal reasoning in medical narratives, automated inferencing techniques in medical informatics, clinical knowledge representation. Current research involves leveraging clinical data for maximum informativity, e.g., cross-validating information from divergent modalities and sources, such as radiographic imaging information in relation to textual description of the same phenomena.
Informatics evaluation; pediatric informatics, including automated growth charts, summer camp medical records and representing developmental milestones in a machine-computable way. Studying how healthcare providers, patients and caregivers interact with health information technologies when providing and documenting patient care, and when making clinical decisions.
Informatics tools to capture structured data during clinical workflow, mathematical modeling, management decision support, enterprise data modeling, and enterprise application services.
Primary research interest is in clinical informatics, specifically the use of Natural Language Processing (NLP) and Data Mining of clinical notes to provide decision support and improve quality of care. Current research focuses on the study of adverse drug effects (ADEs), including discovery, detection, and the development of an automated monitoring system to detect potential ADEs in hospitalized patients.
System-based care and research leading toward personalized care and population health management, complex adaptive systems, informatics architectures, and innovation life cycle management.
Electronic prescribing, clinical decision support, mobile health care applications, and natural language processing
Interaction between clinical workflow and health information technology and improving the fit between technology and work practices through the development of health information technology design and implementation strategies. Currently works on ways to expand and diversify the pipeline into the biomedical informatics field through outreach to undergraduate and high school students and by improving how communication with nontechnical audiences
Applying machine learning to predictive modeling problems for under-served/vulnerable populations (mental illness, suicide, adolescents, homelessness), value-based care, and implementation gaps.
Developing new informatics tools/resources to optimize phenotyping performance or enable deep phenotyping. Current research also focuses on making discoveries that may favorably affect a patient’s treatment outcome using big EHR data.
Immunization-related efforts including automated content management, clinical decision support using web services, bi-directional interfaces with state registries, data analytics, and 2-D barcoding. Also experienced with electronic whiteboards providing intuitive user interfaces to support outpatient care and the analysis of underlying workflows.
Sustainable and sharable decision support, knowledge engineering, clinical knowledge management processes. Current research effort focuses on clinical knowledge management best practices within the framework of migrating legacy clinicaldecision support knowledge to a commercial HER.
Global health informatics, computerized decision support, mHealth, and capacity building in Health Informatics. Current research involves the breath of application and implementation of health information technologies in resource-limited settings.