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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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2 Data Quality Challenges and Opportunities in a Learning Health System
Pages 9-14

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From page 9...
... • Data collection should aim to maximize value by balancing the burden of collection with its usefulness. Heywood • Clinical research is not currently focused on what patients consider valuable.
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From page 10...
... Marc Overhage, Chief Medical Informatics Officer at Siemens Healthcare, focused his presentation on the challenges for data collections and the limitations inherent in aggregating data across sources. Jamie Heywood, Co-Founder and Chairman of PatientsLikeMe, examined the issue of data quality as it relates to patient-reported data, and how patient value must be a central strategy in building a learning health system.
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From page 11...
... Successful data aggregation, according to Overhage, will need to account for the fact that there are going to be repeated observations and conflicting evidence, and combine evidence in a meaningful way. Fortunately, there are computational advances that can improve this process.
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From page 12...
... The true quality of digital health data is an assessment of whether they are fit for their intended purpose. For example, he noted, data quality for population health measurement may be able to tolerate more error since researchers are looking for trends and changes at the population level.
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From page 13...
... Patients create profiles on PLM which detail personal information, medical history, treatment history, and track functional status over time (using accepted patient reported outcome measures)
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From page 14...
... Heywood concluded his remarks with a series of paradigm shifts necessary to move toward a learning health system (Figure 2-2)
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Key Terms

  • data quality
  • structured data
  • patient reported
  • reported data
  • health data

  • data aggregation
  • aggregating data
  • clinical trials
  • clinical care
  • common platform

  • population level
  • marc overhage
  • als patients
  • overhage focused
  • patient value

  • data remains
  • data collections
  • combine data
  • successful data
  • bring data

  • ehr data
  • clinical event
  • individual patient
  • conducting clinical
  • traditional clinical

  • clinical trial
  • converge clinical
  • correct patient
  • converging clinical
  • clinical outcomes


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