Transforming Health Outcomes
PMAP aims to revolutionize how health centers can continuously and more affordably improve their patient and population health outcomes through modern data science and rigorous systems engineering practices. PMAP is a dual nature resource, comprising a very large-scale data repository in the Microsoft Azure cloud, and a cloud-based analytics and knowledge delivery component.
Ingest: Multi-Modal Data Streams
Broad and expert driven use of data can be powerful. PMAP offers multiple data modalities of value in the practice of medicine, including clinical EMR data, underlying open notes (medical derived information), imaging data (radiology, pathology), genomic and molecular data (beyond routine lab values), wearable data, and operational and financial data (for value creation). For each of these data streams, the PMAP team is thinking across the entire lifecycle to identify the source, evaluate strengths and weaknesses, ingest, structure data in the right way with right annotations, and provide analytic tools to enable different use cases.
Unify: Engagement & Innovation
PMAP facilitates the delivery of safe, secure data streams to engage our JHM and JHU community through a centralized and comprehensive curation strategy, with the structure to distribute data from a central source of truth. We are continually enhancing PMAP’s data model to be more and more flexible, comprehensive, and capable of supporting various levels of data with differing identification risks.
Enrich: Data Client
The modern tools of cutting-edge AI analytics and data science are ever evolving. We work with our IT partners to continuously meet the challenge of harnessing the power of both artificial intelligence and human natural intelligence. This means building the right next-generation computing and data intensive computing environments to support research projects containing private health information (PHI), limited data sets (LDS), or de-identified data.
Translate: Patient Insight
Together, we’re creating virtuous cycles of clinical practice by coupling analytic tools with AI and machine learning opportunities that seamlessly incorporate Johns Hopkins’ world-renowned expertise in medicine and biomedical innovation. Real-world clinical data are collected, and a framework is in place for aggregating and curating this data. Analytics then generate new evidence and knowledge, which are fed back into medical practice.
