scholarly journals Transcription Errors of Blood Glucose Values and Insulin Errors in an Intensive Care Unit: Secondary Data Analysis Toward Electronic Medical Record-Glucometer Interoperability

10.2196/11873 ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. e11873 ◽  
Author(s):  
Azizeh Khaled Sowan ◽  
Ana Vera ◽  
Ashwin Malshe ◽  
Charles Reed
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Data Analysis ◽  
Blood Glucose ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Secondary Data ◽  
Secondary Data Analysis

scholarly journals Transcription Errors of Blood Glucose Values and Insulin Errors in an Intensive Care Unit: Secondary Data Analysis Toward Electronic Medical Record-Glucometer Interoperability (Preprint)

2018 ◽  
Author(s):  
Azizeh Khaled Sowan ◽  
Ana Vera ◽  
Ashwin Malshe ◽  
Charles Reed
Keyword(s):  
Intensive Care ◽  
Blood Glucose ◽  
Medical Record ◽  
Intensive Care Units ◽  
Electronic Medical Record ◽  
Point Of Care ◽  
Vital Signs ◽  
Secondary Data ◽  
Flow Sheet ◽  
Glucose Testing

BACKGROUND Critically ill patients require constant point-of-care blood glucose testing to guide insulin-related decisions. Transcribing these values from glucometers into a paper log and the electronic medical record is very common yet error-prone in intensive care units, given the lack of connectivity between glucometers and the electronic medical record in many US hospitals. OBJECTIVE We examined (1) transcription errors of glucometer blood glucose values documented in the paper log and in the electronic medical record vital signs flow sheet in a surgical trauma intensive care unit, (2) insulin errors resulting from transcription errors, (3) lack of documenting these values in the paper log and the electronic medical record vital signs flow sheet, and (4) average time for docking the glucometer. METHODS This secondary data analysis examined 5049 point-of-care blood glucose tests. We obtained values of blood glucose tests from bidirectional interface software that transfers the meters’ data to the electronic medical record, the paper log, and the vital signs flow sheet. We obtained patient demographic and clinical-related information from the electronic medical record. RESULTS Of the 5049 blood glucose tests, which were pertinent to 234 patients, the total numbers of undocumented or untranscribed tests were 608 (12.04%) in the paper log, 2064 (40.88%) in the flow sheet, and 239 (4.73%) in both. The numbers of transcription errors for the documented tests were 98 (2.21% of 4441 documented tests) in the paper log, 242 (8.11% of 2985 tests) in the flow sheet, and 43 (1.64% of 2616 tests) in both. The numbers of transcription errors per patient were 0.4 (98 errors/234 patients) in the paper log, 1 (242 errors/234 patients) in the flow sheet, and 0.2 in both (43 errors/234 patients). Transcription errors in the paper log, the flow sheet, and in both resulted in 8, 24, and 2 insulin errors, respectively. As a consequence, patients were given a lower or higher insulin dose than the dose they should have received had there been no errors. Discrepancies in insulin doses were 2 to 8 U lower doses in paper log transcription errors, 10 U lower to 3 U higher doses in flow sheet transcription errors, and 2 U lower in transcription errors in both. Overall, 30 unique insulin errors affected 25 of 234 patients (10.7%). The average time from point-of-care testing to meter docking was 8 hours (median 5.5 hours), with some taking 56 hours (2.3 days) to be uploaded. CONCLUSIONS Given the high dependence on glucometers for point-of-care blood glucose testing in intensive care units, full electronic medical record-glucometer interoperability is required for complete, accurate, and timely documentation of blood glucose values and elimination of transcription errors and the subsequent insulin-related errors in intensive care units.

Sex-Related Differences in Blood Glucose Responses to Resistance Exercise in Adults With Type 1 Diabetes: A Secondary Data Analysis

2020 ◽  
Vol 44 (3) ◽  
pp. 267-273.e1 ◽  
Author(s):  
Nicole K. Brockman ◽  
Ronald J. Sigal ◽  
Glen P. Kenny ◽  
Michael C. Riddell ◽  
Bruce A. Perkins ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Data Analysis ◽  
Blood Glucose ◽  
Resistance Exercise ◽  
Secondary Data ◽  
Secondary Data Analysis
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