TY - JOUR
T1 - Validation of data from electronic data warehouse in diabetic ketoacidosis
T2 - Caution is needed
AU - VanderWeele, Jennifer
AU - Pollack, Teresa
AU - Oakes, Diana Johnson
AU - Smyrniotis, Colleen
AU - Illuri, Vidhya
AU - Vellanki, Priyathama
AU - O'Leary, Kevin
AU - Holl, Jane
AU - Aleppo, Grazia
AU - Molitch, Mark E.
AU - Wallia, Amisha
N1 - Funding Information:
This work was supported by the National Institutes of Health's National Center for Advancing Translational Sciences , Grant Number UL1TR001422 .
Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2018/7
Y1 - 2018/7
N2 - Aims: This study validated enterprise data warehouse (EDW) data for a cohort of hospitalized patients with a primary diagnosis of diabetic ketoacidosis (DKA). Methods: 247 patients with 319 admissions for DKA (ICD-9 code 250.12, 250.13, or 250.xx with biochemical criteria for DKA) were admitted to Northwestern Memorial Hospital from 1/1/2010 to 9/1/2013. Validation was performed by electronic medical record (EMR) review of 10% of admissions (N = 32). Classification of diabetes type (Type 1 vs. Type 2) and DKA clinical status were compared between the EMR review and EDW data. Results: Key findings included incorrect classification of diabetes type in 5 of 32 (16%) admissions and indeterminable classification in 5 admissions. DKA was not present, based on the review, in 11 of 32 (34%) admissions. DKA was not present, based on biochemical criteria, in 15 of 32 (47%) admissions. Conclusions: This study found that EDW data have substantial errors. Some discrepancies can be addressed by refining the EDW query code, while others, related to diabetes classification and DKA diagnosis, cannot be corrected without improving clinical coding accuracy, consistency of medical record documentation, or EMR design. These results support the need for comprehensive validation of data for complex clinical populations obtained through data repositories such as the EDW.
AB - Aims: This study validated enterprise data warehouse (EDW) data for a cohort of hospitalized patients with a primary diagnosis of diabetic ketoacidosis (DKA). Methods: 247 patients with 319 admissions for DKA (ICD-9 code 250.12, 250.13, or 250.xx with biochemical criteria for DKA) were admitted to Northwestern Memorial Hospital from 1/1/2010 to 9/1/2013. Validation was performed by electronic medical record (EMR) review of 10% of admissions (N = 32). Classification of diabetes type (Type 1 vs. Type 2) and DKA clinical status were compared between the EMR review and EDW data. Results: Key findings included incorrect classification of diabetes type in 5 of 32 (16%) admissions and indeterminable classification in 5 admissions. DKA was not present, based on the review, in 11 of 32 (34%) admissions. DKA was not present, based on biochemical criteria, in 15 of 32 (47%) admissions. Conclusions: This study found that EDW data have substantial errors. Some discrepancies can be addressed by refining the EDW query code, while others, related to diabetes classification and DKA diagnosis, cannot be corrected without improving clinical coding accuracy, consistency of medical record documentation, or EMR design. These results support the need for comprehensive validation of data for complex clinical populations obtained through data repositories such as the EDW.
KW - Diabetes mellitus
KW - Diabetic ketoacidosis
KW - Electronic medical record
KW - Enterprise data warehouse
KW - Medical informatics
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U2 - 10.1016/j.jdiacomp.2018.05.004
DO - 10.1016/j.jdiacomp.2018.05.004
M3 - Article
C2 - 29903409
AN - SCOPUS:85048264427
VL - 32
SP - 650
EP - 654
JO - Journal of Diabetes and its Complications
JF - Journal of Diabetes and its Complications
SN - 1056-8727
IS - 7
ER -
