The Boston Children's Hospital Computational Health Informatics Program (CHIP), founded in 1994, is a multidisciplinary applied research and education program. Our faculty advance the science of biomedical informatics for molecular characterization of the patient, gene discovery, medical decision making, diagnosis, therapeutic selection, care redesign, public health management, population health, and re-imagined clinical trials. Biomedical informatics has become a major theme and methodology for biomedical science, health care delivery, and population health, involving high-dimensional modeling and understanding of patients from the molecular to the population levels. The field is inherently interdisciplinary, drawing on traditional biomedical disciplines, the science and technology of computing, data science, biostatistics, epidemiology, decision theory, omics, implementation science, and health care policy and management. CHIP faculty are trained in medicine, data science, computer science, mathematics and epidemiology. Though CHIP has a robust pediatric research agenda, our contributions span across all ages.
CHIP innovations have transformed the landscape of big data in health care, the use of AI in clinical decision making, and the IT strategies of commercial enterprises. Our discoveries and inventions have been widely adopted by technology companies such as: Apple, Microsoft, and Google.
Our software is reading millions of doctors’ notes across sites of care and surfacing key insights about adverse medication effects. Our leading-edge precision medicine clinics are personalizing choices of therapies for children, many of whom had thought they were out of options. Our biosurveillance computational platforms have found the earliest signals of Ebola outbreaks. For the work of CHIP, Health 2.0 voted Boston Children's Hospital the 10 Year Global Retrospective Top Influencer among all health care organizations.
Our recent publications
Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study.
Experiences implementing scalable, containerized, cloud-based NLP for extracting biobank participant phenotypes at scale.
Rethinking domain adaptation for machine learning over clinical language.
Real-time forecasting of the COVID-19 outbreak in Chinese provinces: Machine learning approach using novel digital data and estimates from mechanistic models.
EHRtemporalVariability: delineating temporal data-set shifts in electronic health records.
In the press