The Johns Hopkins Individualized Health Initiative (Hopkins inHealth) is a University-wide, collaborative venture that is both visionary and pragmatic. The initiative builds on dramatic advances over recent years in biological research, in new technologies that afford an increasingly detailed view of disease, and in computational and data sciences.
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The inADM pilot project focuses on individualizing the management of autoimmune diseases, such as rheumatoid arthritis and scleroderma. The broad goal is to use clinical, immune, imaging, and patient self-reported data to monitor patient status and trajectory, and to thereby discover common disease trajectories among subgroups of patients for whom optimal treatment strategies are likely to differ.
The purpose of inCAR is to develop, test, and disseminate a patient-centered, data-driven, cost-conscious, and continuously improving model of interventional cardiology (IC) care among Johns Hopkins patient populations.
inCAR investigators are working to create new infrastructure that serves both clinical and research purposes. The team is planning to develop automated information technology systems that will harness complex data, drawn from multiple sources, display relevant indicators, and provide evidence to help physicians make precise, real-time IC decisions. The team seeks to identify subsets of patients into those who will benefit, and those who are less likely to benefit, from specific IC procedures. The inCAR investigators are also working to implement decision support tools which clinicians can conveniently use to apply the best current knowledge about IC procedures to the care of individual patients.
inTEL investigators seek to generate new knowledge about the fundamental biology of telomeres and to develop new telomere-based clinical strategies that allow better diagnosis and treatment of age-related diseases. The team has established and proposes to implement and evaluate a method for reliably measuring in the clinic the length of a patient’s telomeres. This test could be a powerful diagnostic and prognostic tool, potentially pointing to ways of improving the patient’s outcomes using currently available therapies.
The inCF team is working toward a new approach to cystic fibrosis (CF) patient care by collecting comprehensive study data on individuals with CF and using these data to customize care to each patient. They are also evaluating the impact of this data-driven, customized approach on patient outcomes. Sequencing whole genome data allows the inCF team to determine patients’ key CF genotypes, as well as their status with respect to all known genes and genomic regions related to CF. Clinical data are also captured, as well as data on environmental exposures and respiratory and gastrointestinal microbiomes.
The inCAS team is working to harness progress in the cancer biological sciences, leveraging the capacities of big data science, to develop (1) powerful tools for early cancer detection, and (2) overarching models of individualized cancer screening and prediction at Johns Hopkins, so that each patient’s risk is assessed and managed in a way that is optimally effective for preventing cancer and/or its recurrence.