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Digital Data Improvement Priorities for Continuous Learning in Health and Health Care Workshop Summary (2013) / Chapter Skim
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4 Issues and Opportunities in the Emergence of Large Health-Related Datasets
Pages 27-32

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From page 27...
... • Advances in mathematics, coupled with access to large datas ets, have the potential to allow researchers to discover cause effect relationships rather than correlations. • Research should focus on insights rather than analytics in order to come up with causal structure rather than static answers.
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From page 28...
... David Madigan, Professor and Chair of the Department of Statistics at Columbia University, discussed the challenge of bias in large datasets, and strategies and methods to more appropriately address bias in observational clinical outcomes research. Carol McCall, Chief Strategy Officer at GNS Healthcare, focused on new mathematical approaches that allow nuanced insights to be derived from large datasets.
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From page 29...
... OMOP researchers ran self-controlled case series analysis for a variety of drugs across each of the 10 component OMOP databases. The results demonstrated extreme heterogeneity.
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From page 30...
... MOVING FROM ANALYTICS TO INSIGHTS Carol McCall posed that the principal challenge in health care today is the ability to create a deep and dynamic understanding of what works best for whom. She noted that while there are currently many areas of redesign and improvement in health care -- aligning business models, transforming care models, building infrastructure -- all of these changes implicitly assume that there is access to evidence and an understanding of what works for whom.
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From page 31...
... With the mathematical methods in place, McCall noted, the priorities for big data analytics and evidence generation are shifting. Since mathematics can be scaled to any level and performed on any data set, the challenge now is finding data sources that are comprehensive and up to date.
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Key Terms

  • big data
  • mathematical methods
  • observational research
  • observational clinical
  • clinical outcomes

  • observational studies
  • confidence intervals
  • measurement error
  • sensitivity analysis
  • predictive models

  • carol mccall
  • percent confidence
  • benefit managers
  • omop researchers
  • david madigan

  • madigan noted
  • pharmaceutical companies
  • pairs studies
  • cal decision
  • efforts demands

  • generate actionable
  • data investments
  • currently exist
  • adjust future
  • paid lip

  • generate evidence
  • changes implicitly
  • resolve existing
  • mccall noted
  • future care


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