The inHealth Precision Medicine Initiative at Johns Hopkins is bringing together revolutions in measurement, connectivity, and data science to enable the discovery of clinically-relevant and biologically-anchored subgroups at scale.
Experienced clinicans see patterns in subgroups of patients that are similar in some way. They can adjust treatment based on what has worked on similar patients in the past, resulting in better outcomes.
We want to enable you to find and validate those subgroups faster, by providing more data, more efficiently, along with the tools to analyze it all. We give you the tools to find the underlying mechanisms. And we help you quickly put those discoveries back into clinical care.
We have worked closely with the Johns Hopkins Medicine Institutional Review Boards, the Data Trust, and the Institute for Clinical and Translational Research to ensure that your research data is secure and policy-compliant. We work with the Technology Innovation Center to ensure that your clinical delivery applications are professionally built, and part of your workflow.
The Discovery Platform brings your data and analytical tools together, for you to
accelerate the process of discovery and answer your scientific questions more
efficiently than ever before.
Clinical notes, labs, patient-reported outcomes, imaging, gene sequences, your own research data, and more are linked together, turned into limited data sets, and hosted in an approved, centrally-managed location.
Access the data sets through statistical analysis tools or machine learning environments, and apply computational horsepower from on-premise or commerical partners. The PMAP Discovery Platform will help you answer your scientific questions more efficiently than ever before.
When your discoveries are validated, we will partner with you to implement them in a clinical care setting throughout the Johns Hopkins Health System and beyond.
The PMAP Delivery Platform includes live data feeds, data visualizations, and predictors, built on a modern technology stack.