Data Quality of the Arkansas Clinical Data Repository Saly Abouelenein1,Hoda A. Hagrass 1,2, , Dina M. Elsayed 1, Ahmad Baghal 1, 1 The Department of Biomedical Informatics, University of Arkansas for Medical Sciences; 2 Pathology department , University of Arkansas for Medical Sciences Introduction Methods Conclusions We randomly selected the first one hundred diabetic patients, type 1 (ICD 10; E10) or type 2(ICD10; E11) , from UAMS data warehouse who had visits between June 2016 and December 2016. We conducted manual chart review of patients from UAMS EPIC system using the MRN, ( patient ID number), as a primary key. For the purpose of this pilot study we focused on three data quality dimensions: Completeness, accuracy and validity. These dimensions were measured for the following data : demographics (date of birth, gender, race, address, city, state, and zip code); laboratory results (plasma and urinary glucose, glucose POCT and HbA1C), and antidiabetic medications. We used SAS 9.4 to measure the accuracy and validity of data and assess completeness. Completeness was evaluated by calculating the percentage of complete data elements present in AR-CDR. Accuracy was determined by comparing both data sources for mismatching data values . Validity was evaluated by assessing the boundaries of each data element values. This study revealed that there is a complete concordance between data stored in AR-CDR and Epic data. The data in AR-CDR are complete and valid figure(3) Based on the results of this pilot study we confirm that the data warehouse has a high quality for clinical research. EPIC production system Information on individual patients Visits, Admissions, Patients, Advisors, Medications, Labs, Diagnosis Others AR-CDR SQL Server Easy to use Information on population Data Staging Run complex reports ETL Results The patients in the study were consisting of more females than males (70% versus 30%) with more African American (57%) than white (43%). We excluded antidiabetic medications from accuracy measurement due to different coding systems used in both databases. Validity: 100 % of the data was valid; all demographic data values are logic, and laboratory results are within the clinical reportable range. Completeness: Nearly 98.8% of the data was complete with only missing two plasma glucose and two HbA1c values. Accuracy: 100% concordance in all data elements except antidiabetic medications. There are some data values in two different formats (qualitative and quantitative) which will affect the secondary use of these data in the clinical research. Figure(3): Results of Accuracy, Validity and Completeness . Limitation In this pilot study we selected small sample size and one disease. We encountered a specific constrain when we tried to compare drugs list extracted from data warehouse and the corresponding drugs in EPIC because each database has its own coding systems. Accuracy Indicates the concordance of the data elements in AR-CDR with EPIC system Completeness Indicates whether all data about a patient are reported completely or not. Validity Indicates whether the data make sense in the light of the knowledge background for each attribute. Consistency Considers the extent to which data is collected using the same process and procedures. Verifiability Considers the extent to which grantees have ways to verify that data was collected according to plans. Future project We plan to conduct future studies that have larger sample sizes and different diseases. AR-CDR data References   Dedić, N. and Stanier C., 2016., "An Evaluation of the Challenges of Multilingualism in Data Warehouse Development" in 18th International Conference on Enterprise Information Systems - ICEIS 2016, p. 196. Exploring Data Warehouses and Data Quality". Spotlessdata.com. Retrieved 2017-04-30. ^ Patil, Preeti S.; Srikantha Rao; Suryakant B. Patil (2011). 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Secondary Use of EHR: Data Quality Issues and Informatics Opportunities. AMIA Jt Summits Transl Sci Proc. 2010 Mar 1; 2010:1-5 Table(1): Definitions of data quality dimensions Epic data Objective and Aims The objective of this study was to evaluate the completeness, accuracy and validity of clinical data available in the AR-CDR. Assess whether the data warehouse has a good quality for clinical research. Glucose test: The blood glucose level in patent s. reference values (40-600) Glucose POC test: point of care test of blood glucose level. Reference values (20-500) Hemoglobin A1C test: level of hemoglobin A1C in blood. Reference value(1-13) Figure(2) : Accuracy and validity of different laboratory results in both AR-CDR and Epic data