A unified Hyperglycemia and Diabetic ketoacidosis (DKA) insulin infusion protocol based on an Excel algorithm and implemented via Electronic Medical Record (EMR) in Intensive Care Units

2017 ◽  
Vol 11 (4) ◽  
pp. 265-271 ◽  
Author(s):  
Deepashree Gupta ◽  
Meredith Kirn ◽  
Zafar A. Jamkhana ◽  
Richard Lee ◽  
Stewart G. Albert ◽  
...  
Keyword(s):  
Intensive Care ◽  
Diabetic Ketoacidosis ◽  
Medical Record ◽  
Intensive Care Units ◽  
Electronic Medical Record ◽  
Insulin Infusion ◽  
Insulin Infusion Protocol ◽  
Infusion Protocol

Variability in Vancomycin Use in Newborn Intensive Care Units Determined From Data in an Electronic Medical Record

2008 ◽  
Vol 29 (7) ◽  
pp. 667-670 ◽  
Author(s):  
Cody Arnold ◽  
Reese Clark ◽  
Jaclyn Bosco ◽  
Craig Shoemaker ◽  
Alan R. Spitzer
Keyword(s):  
Intensive Care ◽  
Medical Record ◽  
Intensive Care Units ◽  
Electronic Medical Record ◽  
Antibiotic Use ◽  
Medical Record Data ◽  
Electronic Medical Record Data ◽  
Record Data ◽  
Newborn Intensive Care

Data from an electronic medical record were used to demonstrate a large variation in the proportion of patients treated with vancomycin in 56 newborn intensive care units, which ranged from 18% to 70% . Use of oxacillin or nafcillin instead of vancomycin was rare during the first few years of the study period but was routine in 13% of the newborn intensive care units during the last fewyears of the study period. The use of electronic medical record data for studies of antibiotic use is discussed here.

scholarly journals The Effects of Insulin Infusion Protocol on the Glycemic Level of the Intensive Care Patients

2020 ◽  
Vol 53 (2) ◽  
pp. 15964-15969
Author(s):  
Jihan Zukhi ◽  
Fatanah M. Suhaimi ◽  
Mohd Zulfakar Mazlan ◽  
Ummu K. Jamaludin ◽  
Normy Razak ◽  
...  
Keyword(s):  
Intensive Care ◽  
Insulin Infusion ◽  
Glycemic Level ◽  
Intensive Care Patients ◽  
Insulin Infusion Protocol ◽  
Infusion Protocol

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.

Implementation of a nurse-driven intensive insulin infusion protocol in a surgical intensive care unit

2007 ◽  
Vol 64 (14) ◽  
pp. 1529-1540 ◽  
Author(s):  
Angela B. Smith ◽  
Pascal O. Udekwu ◽  
Sukanto Biswas ◽  
Eric Harvey ◽  
Angela Bullock ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Insulin Infusion ◽  
Surgical Intensive Care Unit ◽  
Surgical Intensive Care ◽  
Insulin Infusion Protocol ◽  
Infusion Protocol
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