Clinical Informatics is the application of information technology to better deliver healthcare services and to enhance clinician and patient decision making.
Clinical informatics addresses how patient data are acquired, structured, stored, processed, retrieved, analyzed, presented and communicated. Clinical Informatics at Stanford’s BMIR transforms data into usable, actionable information to improve both patient well being and the delivery of healthcare.
Dr. Jonathan Chen and his team develop data-driven algorithms and provider-facing interfaces that enhance clinical healthcare decision making. The goal of the Chen Lab is to anticipate what a physician needs without the doctor even having to ask for it, by automatically inferring patient context from electronic information.
The team develops and applies machine-learning algorithms, which, for example, are used to predict diagnostic results before they are even ordered by physicians and to optimize the use of laboratory tests.
The key focus of Dr. Boussard and her team of researchers is the application of novel methods and tools to large clinical datasets for hypothesis generation, comparative effectiveness research, and the evaluation of quality healthcare delivery. The Boussard team’s research involves managing and manipulating big data, which range from administrative claims data to electronic health records, and applying novel biostatistical techniques to innovatively assess clinical and policy related research questions at the population level. This research enables them to create formal, statistically rigid, evaluations of healthcare data using unique combinations of large datasets.
The Boussard team are scientists who study a broad range of clinical data and then generate insights, providing recommendations regarding the quality of healthcare delivery. They focus on areas such as:
- Health policy
- Patient-Centered Outcomes
- Population Health
Downing and team
Dr. Downing and his team focus on mobile AI applications to improve healthcare outcomes. The team is currently working to:
- improve end-of-life planning by empowering patients with mobile technology to voice their values
- improve stroke diagnosis through computer vision
- help control high blood pressure by making it easier for doctors and patients to adjust medications
Digital health has generated considerable excitement, but documenting an effect on clinical outcomes has lagged. Beginning with the largest problems in healthcare, those most amenable to technology intervention, Dr. Downing and his team are contributing to reducing this lag between the promise and the emergence of practical applications.
Dr. Nigam Shah’s team includes physicians, computer scientists, statisticians, and informatics experts who are experienced in analyzing multiple types of health data. These data include electronic health records (EHR), insurance claims, data from wearables, weblogs, and patient blogs. The Shah Lab answers critical clinical questions, generates insights, and builds predictive models for the academic health system where science, informatics incentives, and culture are aligned for continuous improvement in healthcare delivery.
Dr. Deendayal Dinakarpandian’s (“Dinakar”) research is focused on method development and insightful informatics. Based on his training as a physician, biochemist, and computer scientist, he is now concentrated on informatics research to help transform healthcare from where it is now to what it ought to be. He is interested in capturing and integrating emerging or expert biomedical knowledge to improve computational predictions of biological and clinical relevance, methods for adapting and evaluating machine learning, interventional and causal predictions, informatics research on problems in oncology, and the essential role of equity for the survival of humanity.