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 –“Warning Label” for suspect data categories –Data quality audits with logs –Batch data correction / removal

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

Methods 2.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 3.Describe variation between institutions

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

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

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

Preliminary Mayo results N=13,754  Smoking (n=11,626) –Current 66%, past 16%, never 13%, Unk 6%  BMI (limited to < 60) (n=6,338) –Mean /- 7.2 –Median 31.6, quartiles (27.5, 36.6)

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)

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

Type 2/U vs. Type 1 DM codes Mayo Data: n=13707 Type 1 DM codes Type 2/U DM codes (46%) 6631 (48%) (4%) 254 (2%)

Intermountain peek (sic) Type 1 ICD9 codes Type 2/U ICD9 codes , ,0836,629  Disclaimer– don’t assume data are ready to compare between sites at this point

Back to Mayo Summary Sample Lab data Test name NMin1%Med.99%Max Glucose (P) 40, Glucose POCT 211, Hemogl obin A1c, B 35, % 5.1 % 6.9 % 12.1 % 16.7 %

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

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

Conclusions: PhD Research Cohort 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

Consistency and Novelty Diabetes  Elevated quantitative lab glucose assays –Frequency 19%, Likelihood 87% –Less predictive than glucose by glucometer or Urine Microalbumin  Abnormal HbA1c test –Equivalent predictive power of HBA1c test order  Antihyperglycemic medications –Variable predictive strength: Metformin, Insulin, Insulin Release Stimulators, Insulin Response Enhancers

Consistency and Novelty Asthma  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

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

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

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

SHARPn Y2 Research Aims  Associations reliable across EHRs?  Improve algorithms’ sensitivity / specificity? –AC attribute selection + other classifiers