Intraoperative blood glucose management: impact of a real-time decision support system on adherence to institutional protocol

Abstract

Poor perioperative glycemic management can lead to negative surgical outcome. Improved compliance to glucose control protocol could lead to better glucose management. An Anesthesia Information Management System based decision support system-Smart Anesthesia Manager™ (SAM) was used to generate real-time reminders to the anesthesia providers to closely adhere to our institutional glucose management protocol. Compliance to hourly glucose measurements and correct insulin dose adjustments was compared for the baseline period (12 months) without SAM and the intervention period (12 months) with SAM decision support. Additionally, glucose management parameters were compared for the baseline and intervention periods. A total of 1587 cases during baseline and 1997 cases during intervention met the criteria for glucose management (diabetic patients or non-diabetic patients with glucose level >140 mg/dL). Among the intervention cases anesthesia providers chose to use SAM reminders 48.7 % of the time primarily for patients who had diabetes, higher HbA1C or body mass index, while disabling the system for the remaining cases. Compliance to hourly glucose measurement and correct insulin doses increased significantly during the intervention period when compared with the baseline (from 52.6 to 71.2 % and from 13.5 to 24.4 %, respectively). In spite of improved compliance to institutional protocol, the mean glucose levels and other glycemic management parameters did not show significant improvement with SAM reminders. Real-time electronic reminders improved intraoperative compliance to institutional glucose management protocol though glycemic parameters did not improve even when there was greater compliance to the protocol.

Perioperative Hyperglycemia: A Literature Review

“…The protocol used four separate algorithms: one for type 1 diabetic patients, one for type 2 diabetic patients, and two for patients in which the target glucose level did not decrease by a prescribed amount. Intraoperative glucose was measured by a point‐of‐care test completed by anesthesia providers …”

Feasibility and indicator outcomes using computerized clinical decision support in pediatric traumatic brain injury anesthesia care

“…At the time of the project, clinical decision support provided alert-based guidance on timely antibiotic administration to facilitate infection prevention, suggestions to reduce fresh gas flows to lower waste and cost, prompts to perform blood glucose monitoring in patients with diabetes, and reminders to complete documentation for invasive procedures. [8][9][10]12 There was no pediatric-specific clinical decision support or other TBI anesthesia care initiative. A written best-practice pediatric TBI anesthesia care protocol was available electronically (author MSV).…”

“…Previous studies demonstrate that clinical decision support results in delivery of higher quality anesthesia care. On‐time antibiotic administration, adherence to a blood glucose protocol, and management of intraoperative hypotension and hypertension are shown to be superior with decision support . We previously reported the development of a novel, real‐time algorithmic clinical decision support to guide pediatric TBI anesthesia care .…”

“…On-time antibiotic administration, adherence to a blood glucose protocol, and management of intraoperative hypotension and hypertension are shown to be superior with decision support. [8][9][10][11][12][13][14] We previously reported the development of a novel, real-time algorithmic clinical decision support to guide pediatric TBI anesthesia care. 15 We extend this work and describe the use of this clinical decision system.…”

Continuous intraoperative epidural infusions affect recovery room length of stay and analgesic requirements: a single-center observational study

“…[11] Our department submits data to the Multicenter Perioperative Outcome Group (MPOG - http://www.mpogresearch.org/) on a regular basis from our AIMS database and as part of this submission we validate data quality and accuracy. Additionally for this study, we verified data extracted from the AIMS database against source records (anesthesia and other medical records) in a subset of 25 randomly selected cases.…”