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Data Quality SHARPn

Data Quality SHARPn. Nov 18, 2013. Recent summary of goals. Objectives 1. Enumeration of data sources for each of 4 types of data: a) Diagnoses b) Laboratory values c) Vital signs ( Ht , Wt , BMI, SBP, DBP, HR) d) Medications

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Data Quality SHARPn

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  1. Data QualitySHARPn Nov 18, 2013

  2. Recent summary of goals • Objectives • 1. Enumeration of data sources for each of 4 types of data: • a) Diagnoses • b) Laboratory values • c) Vital signs (Ht, Wt, BMI, SBP, DBP, HR) • d) Medications • 2. Characterize sources, availability, quality, other characteristics • 3. Draw samples from each source • 4. Compare sources and data • a) Within institution • b) Across institution

  3. List of projects • “John Henry” study. Compare machine and person in screening patients • Intra-institution heterogeneity • Parallel analyses of data variation within site • Inter-institution heterogeneity • Data mining to enhance space of relevant terms • BMI in the EHR at 2 sites

  4. 1) John Henry Project

  5. Charts requiring manual review to find 15 patients

  6. Cost comparison

  7. 2) Intra-institution heterogeneity • Compare different sources of “Same data” • Diagnoses • Labs • Meds

  8. Heterogeneity of labsMayo

  9. Intra-institution comparison of data sourcesMayo Billing data vs. Problem List

  10. Intra-institution comparison of data sourcesIMtn regional differences in data

  11. Intra-institution comparison of data sourcesIMtn Type of Encounter differences

  12. Intra-institution comparison of data sourcesIMtn data recorded IP v Ambon same random adult cohort (n = 3191)with IP and Amb encounters 2007-2011

  13. Mayo/Intermountain comparisons • Example of template for inter-institutional data source comparison

  14. Mayo/Intermountain comparison of BMI data availability

  15. 4) Machine Learning • Use of Frequent Item Sets & Associative Classification methods • Discover most ‘interesting’ (mathematically) differences in two large data sets • Applied to measure differences in sources of data • Will apply to compare Mayo, Imtn data sets • Susan Rea, InterMountain

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