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Data Quality (a.k.a. “ Data Heterogeneity ” )

Data Quality (a.k.a. “ Data Heterogeneity ” ). Kent Bailey, Susan Rea Welch, Lacey Hart, Kevin Bruce, Susan Fenton. Objectives. Assess Data variability within and across institutions Assess impact of this variability on Secondary Use of EMR Generate specifications for Widgets

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Data Quality (a.k.a. “ Data Heterogeneity ” )

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  1. Data Quality(a.k.a. “Data Heterogeneity”) Kent Bailey, Susan Rea Welch, Lacey Hart, Kevin Bruce, Susan Fenton

  2. Objectives • Assess Data variability within and across institutions • Assess impact of this variability on Secondary Use of EMR • Generate specifications for Widgets • “Warning Label” for suspect data categories • Data quality audits with logs • Batch data correction / removal

  3. Current Research: Effects of Variation on Diabetes Phenotyping Algorithm • Purpose: Compare data relevant to Type 2 DM eMERGE phenotyping algorithm between Intermountain and Mayo • Methods: 1. Identify adult subjects with evidence in any semantic category of algorithm: • ICD-9-CM codes for Diabetes Mellitus • Abnormal glucose or HbA1C • Antihyperglycemic medications • Capillary glucose (Glucometer) procedures

  4. Methods • Collect relevant data on these subjects • ICD-9-CM codes • Procedure codes • Demographic data • Smoking status • Body Mass index • Specialty of provider • Geographic info • Frequency of health care encounters • Describe variation between institutions

  5. Analysis • Compare (between institutions) frequencies of data elements • ICD9 codes– overall and specific codes • Compare lab values– number and values • Compare medications– • Control for: • Provider specialty • Geographic variables • Demographic variables

  6. Interpretation • Assess impact of data heterogeneity on phenotyping at different institutions • Recommendations for • High throughput Phenotyping • High throughput screening for clinical trials • Generalization to other phenotypes • Hypothesis generation

  7. Preliminary Mayo Results • Mayo Data: (ICD or abn.labs or capill. Glucose, limited to Olmsted and surrounding counties) • 13,754 subjects • 89% Caucasian, • 2.5% African-American, • 2.0% Asian • 6.5% Native Am, Pac. Isl., other, unknown, refuse • Mean current age 64, range 20 to 104 • Sex: 53% male, 47% female

  8. Preliminary Mayo resultsN=13,754 • Smoking (n=11,626) • Current 66%, past 16%, never 13%, Unk 6% • BMI (limited to < 60) (n=6,338) • Mean 32.6 +/- 7.2 • Median 31.6, quartiles (27.5, 36.6)

  9. Preliminary Results: ICD9 codes • Complications • None 6743 (250.0) • Ketoacidosis 1 (250.1) • Hyperosmolality 2 (250.2) • Renal 398 (250.4) • Opthalmic 1385 (250.5) • Neuro 586 (250.6) • Peripheral Circ. 25 (250.7) • “other specified” 312 (250.8) • Unspecified 336 (250.9)

  10. Preliminary Results: ICD9 codes • 250.X0 Type 2 or unspecified, controlled or not • specified as uncontrolled • 250.X1 Type 1, controlled or not • Specified as uncontrolled • 250.X2 Type 2 or unspecified, uncontrolled • 250.X3 Type 1, uncontrolled

  11. Type 2/U vs. Type 1 DM codesMayo Data: n=13707

  12. Disclaimer– don’t assume data are ready to compare between sites at this point Intermountain peek (sic)

  13. Back to Mayo SummarySample Lab data

  14. Future Directions • Carry out inter-institution comparison • Study effects of geography, race, etc. • Implement chart review (on random sample) for “gold standard” definition of Type 2 DM • Use of lab values /meds for definition of continuous phenotype (DM-ness) • Extrapolation / generalization to other diseases /phenotypes

  15. Data Quality(a.k.a. “Data Heterogeneity”) Susan Rea Welch

  16. Conclusions: PhD ResearchCohort Amplification • Knowledge Discovery from Databases (KDD) • Associative Classification Methods • Classification Rules for Diabetes and Asthma • comparably accurate • Concise • consistent with domain knowledge • Contributed new knowledge • Attributes for cohort identification • Unanticipated comorbidity associations

  17. Consistency and NoveltyDiabetes • Elevated quantitative lab glucose assays • Frequency 19%, Likelihood 87% • LesspredictivethanglucosebyglucometerorUrineMicroalbumin • Abnormal HbA1c test • Equivalent predictive power of HBA1c test order • Antihyperglycemic medications • Variable predictive strength: Metformin, Insulin, Insulin Release Stimulators, Insulin Response Enhancers

  18. Consistency and NoveltyAsthma • Medications were most predictive • High Likelihood: Salmeterol, Leukotriene receptor antagonist • Albuterol / Glucocorticoid combine: • Pulmonary Procedures (CPT hierarchy) • Female gender • Abnormal CBC • Unexpected comorbidity associations • Suggests discovery of shared pathways

  19. Associative Classification – What? • Pattern discovery in transaction database • Independent of domain expertise • Deductive, global associations in data • Induce a general & accurate classifier

  20. Associative Classification – Why? • No domain expertise attribute selection • Not affected by missing data • Proven accuracy • Understandable rules • Independent rules

  21. Core Candidate Attributes One Dimensional • Diagnosis codes • Provider specialty • Lab observations • Procedure codes • ‘Abnormal’ lab obs. • Imaging procedures • Medication list • Age groups • Female gender

  22. SHARPn Y2 Research Aims • Associations reliable across EHRs? • Improve algorithms’ sensitivity / specificity? • AC attribute selection + other classifiers Two Dimen- sional Data Three Dimen- sional Data

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