– Researchers at the Johns Hopkins Applied Physics Laboratory (APL) and Johns Hopkins Medicine have launched a new data analytics tool to support precision medicine research and enhance care delivery.
The tool, called the Precision Medicine Analytics Platform (PMAP), will facilitate big data research across the JHU enterprise and enable providers to make discoveries that will improve patient care.
“This is a particularly promising moment for harnessing big data, because high-powered computers can analyze newly available troves of information, including data from genetic sequencing, heart monitors, images and electronic medical records,” said Antony Rosen, vice dean for research at Hopkins Medicine.
“New technologies make it possible for researchers to combine and analyze data that before was hard to quantify, such as text from clinic notes.”
With precision medicine and big data, health systems and providers can better understand subgroups of diseases, which can help target treatments for patients with complex conditions.
“Patients get the treatment that is right for them, avoiding unnecessary tests and therapies,” said APL’s Geoff Osier, PMAP project manager. “PMAP gives researchers access to data, a virtual collaborative workspace and tools to help develop new algorithms that can ultimately improve clinical decisions and patient care.”
APL sought to help providers leverage the massive amounts of data available to them to generate actionable insights. These sources of data include EHRs, research studies, and information gathered from patient-reported surveys. APL assembled a multi-disciplinary team, composed of experts from systems engineering, cloud platform construction, cybersecurity, and data and computer science.
The team created an IT system that pairs biomedical research and discovery with clinical decision-making. The analytics platform supports both precision medicine research and healthcare delivery as the basis of a healthcare learning ecosystem. Key elements of the platform include genomic and imaging data access, as well as transforming and loading this data into a “data commons.”
“We helped clinicians at the Center of Excellence for Prostate Cancer extract meaningful data from biopsy reports using a natural language processing machine learning model,” Osier said.
“We’ve changed clinical data representation in the Multiple Sclerosis clinic. We’ve also done some initial development with clinicians to create tools that will be useful for allowing data to drive the creation of subpopulations within their cohort.”
This tool builds on APL’s past efforts to enhance precision medicine research at Johns Hopkins. Previously, the organization launched precision medicine centers for multiple sclerosis and prostate cancer.
Researchers and providers across the JHU network will be able to use PMAP to deliver quality, tailored care. Going forward, the team hopes to expand this tool so that other health systems can do the same.
“The clinicians and researchers in the Precision Medicine Centers of Excellence have compelling, hard challenges associated with how to better care for their patients, and we intend to work with them to make discoveries in the treasure trove of data on PMAP,” Osier said.
“As we work on specific problems with specific researchers, we will work to create generalizable tools that others can leverage for their own challenges — the creation of a data science ecosystem on PMAP is a key goal for transforming the hospital.”