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Real-World Evidence Generation and Evaluation of Therapeutics Proceedings of a Workshop (2017) / Chapter Skim
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3 Opportunities for Real-World Data
Pages 17-26

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From page 17...
... . Greater investment in data science could support the health industry in realizing the potential of big data for health care and clinical research purposes.
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From page 18...
... . for an appropriate purpose that drives us to where we want to get." He reminded the audience that "real-world evidence is good evidence and people are using it every day to make decisions." LEVERAGING ELECTRONIC HEALTH RECORDS The promise of a learning health system is dependent on the ability to digitally capture, aggregate, and analyze health data for research and q ­ uality improvement purposes.
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From page 19...
... ONC is actively working on many of the barriers that are frequently noted, he said, including lack of standards and interoperability issues. Certified EHR technology is now required for participation in the Medicare incentive program and the newly released quality payment program, and in October 2015, ONC released the final version of its interoperability roadmap, Connecting Health and Care for the Nation: A Shared Nationwide Interoperability Roadmap Version 1.0 (ONC, 2015)
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From page 20...
... Although this is an area of significant interest and some work has been done in the private sector, federal efforts to implement unique patient identifiers are currently prohibited by law,1 explained White. Other efforts to facilitate data linking and aggregation include the use of claims data to link patient records across EHR systems and the development of common data models, which map concepts from different data sources into a common format with common definitions.
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From page 21...
... Because it incorporates standardized data from different sources using a common data model, the PCORnet infrastructure can now be used to identify potentially thousands of patients across the networks with particular conditions, to conduct observational studies that follow patient cohorts over time, and for interventional clinical research, including comparative effectiveness trials. Rothman also described tools that have been developed for PCORnet to support clinical trials, including electronic processes for patient identification and recruitment, consenting, and collecting patientreported outcomes.
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From page 22...
... To address interoperability issues across different record systems within provider networks, Optum uses an intensive manual process to extract information; validates, maps, and normalizes it; and iterates it to get to a standardized data format. Following a series of data quality checks at the end of the process, the company has generated a centralized repository containing data for those patients within a particular provider network.
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From page 23...
... Collectively, he estimated, wearables and other consumer devices can now measure physiological parameters at a level that is approaching what might be seen in a hospital intensive care unit. Because many mobile health devices are commonly worn throughout the day and sometimes even during sleep, excitement regarding their potential stems from the ability to capture data from the 99 percent of patient and consumer activity that occurs outside the health care setting.
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From page 24...
... A lot of scientific work is needed to validate results from wearables and define wearable-oriented endpoints that will support regulatory approval, cautioned John Hernandez, head of Health Economics, Value, and Access, Verily Life Sciences. CONSIDERATIONS FOR REALIZING THE POTENTIAL OF REAL-WORLD DATA The ability to use real-world data to answer research questions regarding effectiveness and value is contingent on access to the full spectrum of health data and capability to transform the data into evidence using analytic tools.
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From page 25...
... Several individual workshop participants discussed the creation of a culture of data science within organizations and the importance of investment in data science experts to transform health care data into meaningful information. The health care industry is lagging behind others already adept at working with big data, like many of the dominant American corporations such as Amazon and Walmart, said Califf, who added that efforts are needed to recruit that talent into the health care industry.
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Key Terms

  • provider networks
  • digital health
  • clinical trials
  • health data
  • clinical data

  • electronic health
  • health information
  • health records
  • big data
  • data science

  • clinical setting
  • individual workshop
  • research networks
  • certified ehr
  • ehr technology

  • evidence generation
  • comparative effectiveness
  • data research
  • learning health
  • health tools

  • mobile health
  • standardized data
  • claims data
  • health devices
  • data quality

  • capture data
  • data outside
  • pragmatic clinical
  • care industry
  • participants discussed


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