The University of Pennsylvania in Philadelphia has a large clinical enterprise, Penn Medicine, and a highly regarded School of Medicine. But integrating data from the two for research purposes had always been a challenge. Even though Penn Medicine has had a clinical data warehouse for several years, the research data resided in islands of siloed databases using myriad data formats.
But the 2014 launch of PennOmics, a research data warehouse serving the hospitals of the University of Pennsylvania Health System and the Perelman School of Medicine, has given physicians and academic researchers access in one platform to massive amounts of de-identified, aggregate patient data from electronic health records and cancer genomics data from six formerly stand-alone systems. For its vast potential to contribute to the field of translational medicine, the editors of Healthcare Informatics have selected Penn Medicine as the co-third-place winning team in the 2016 Innovator Awards program. As with all such projects, there were critical decisions and cooperative efforts required to make this broad, enterprise-wide initiative happen. Here is Penn Medicine’s story of innovation.
In 2011, Michael Restuccia, the vice president and CIO of Penn Medicine, and Brian Wells, the health system’s associate vice president of health technology and academic computing, held a series of meetings with clinical researchers to understand their challenges in terms of technology. “What we heard about was a lack of access to integrated data,” says Wells. “They were unable to join their research data with the already consolidated health system data. As we talked, it crystalized in our minds that we needed a tool that could bring it all together.”
One of the driving forces was the team that managed the tumor registry. Although they had a robust registry, they had no way to query it, analyze it, and discover things within it. “That was a big driver,” Wells adds. “We brought in the tumor registry, a lot of genetic data, biobank data and clinical trials data. We needed a platform where we could join that data together, de-identify it and offer self-service access.”
Restuccia calls the PennOmics effort a good example of the information systems group enabling research and patient care capabilities based on what researchers tell them is important. “We heard loud and clear there was a void in having a repository to store the data in an integrated manner.”
(From L to R:) Brian Wells; Michael Feldman, M.D., Ph.D.; Mike Restuccia
Once the need was identified, the next step was to approach a senior IT council made up of six representatives from the health system and six from the school of medicine. “We bring recommendations to this group, and they determine funding, priorities and pace,” Restuccia explains. One of the first key decisions that had to be made was whether to build the data warehouse themselves or work with a vendor’s solution. “Our preference here at Penn is to build. We like to have control,” Restuccia says, “but in this instance, building it would have required significant investment in bioinformaticians’ time, energy and know-how, and that excess capacity just didn’t exist. Meanwhile, Oracle had a pretty strong data model for what our identified need was. So we decided to buy the solution vs. developing it in-house.”
The next step was to form a PennOmics governance committee made up of researchers working in fields such as genetic sequencing and high-performance computing. “We met monthly and they helped us clarify important data sources and data types and gave feedback to Oracle about their product and what it needs to do,” Wells explains. A few researchers let the project team load their genetic data to do a proof-of-concept to prove that the data-loading process worked and that researchers could query it on the back end.
Penn Medicine has a new Center for Personalized Diagnostics, a clinical laboratory that does somatic tumor testing of targeted genes that are clinically significant and actionable. A big task was loading the center’s data into the warehouse and making sure it flows correctly. Now data about all the patients sequenced flows into the PennOmics warehouse every two weeks.
Today PennOmics is proving of critical value to researchers studying clinical effectiveness by doing retrospective analytics on aggregated data. For example, a researcher studying a population of patients with the BRCA1 gene mutation can study treatment regimes and outcomes. Or by evaluating individuals with similar medical profiles, a physician might conclude that one blood pressure medication would be more effective than another. Using PennOmics, physicians can ask questions of the data, such as “find all lung cancer cases with EGFR-activating mutations that have failed primary EGFR therapy, with disease-progression presenting as new metastatic disease.”
“That all feeds back into the decisions you make in the clinical area about what is the best drug or treatment,” Wells says. “It all ties together. That is translational medicine at its heart.”