Novel Automated Self-adjusting Subcutaneous Insulin Algorithm Improves Glycemic Control and Physician Efficiency in Hospitalized Patients.

BACKGROUND: Hyperglycemia occurs in 22% to 46% of hospitalized patients, negatively affecting patient outcomes, including mortality, inpatient complications, length of stay, and hospital costs. Achieving inpatient glycemic control is challenging due to inconsistent caloric intake, changes from home medications, a catabolic state in the setting of acute illness, consequences of acute inflammation, intercurrent infection, and limitations in labor-intensive glucose monitoring and insulin administration. METHOD: We conducted a retrospective cross-sectional analysis at the University of California San Francisco hospitals between September 3, 2020 and September 2, 2021, comparing point-of-care glucose measurements in patients on nil per os (NPO), continuous total parenteral nutrition, or continuous tube feeding assigned to our novel automated self-adjusting subcutaneous insulin algorithm (SQIA) or conventional, physician-driven insulin dosing. We also evaluated physician efficiency by tracking the number of insulin orders placed or modified. RESULTS: The proportion of glucose in range (70-180 mg/dL) was higher in the SQIA group than in the conventional group (71.0% vs 69.0%, P = .153). The SQIA led to a lower proportion of severe hyperglycemia (>250 mg/dL; 5.8% vs 7.2%, P = .017), hypoglycemia (54-69 mg/dL; 0.8% vs 1.2%, P = .029), and severe hypoglycemia (<54 mg/dL; 0.3% vs 0.5%, P = .076) events. The number of orders a physician had to place while a patient was on the SQIA was reduced by a factor of more than 12, when compared with while a patient was on conventional insulin dosing. CONCLUSIONS: The SQIA reduced severe hyperglycemia, hypoglycemia, and severe hypoglycemia compared with conventional insulin dosing. It also improved physician efficiency by reducing the number of order modifications a physician had to place.

Automated Self-Adjusting Subcutaneous Insulin Algorithm for Patients NPO or on TPN or Enteral Feedings.

BACKGROUND: Perioperative diabetes patients are often treated with sliding-scale insulin, despite a lack of evidence to support therapeutic effectiveness. We introduced an automated subcutaneous insulin algorithm (SQIA) to improve glycemic control in these patients while maintaining the simplicity of a q4 hour adjustable sliding-scale insulin order set. METHODS: In this pilot study, we implemented a fully programmed, self-adjusting SQIA as part of a structured order set in the electronic medical record for adult patients who are nil per os, or on continuous enteral tube feedings or total parenteral nutrition. The nurse only enters the current glucose in the Medication Administration Record, and then the calculated dose is shown. The new dose is based on previous dose, and current and previous glucoses. The SQIA titrates the glucose to 120-180 mg/dL. For this pilot, this order set was utilized for complex perioperative oncologic patients. RESULTS: The median duration on the SQIA was 58 hours. Glucoses at titration initiation were highest at 206 ± 63 mg/dL, and came down to 156 ± 29 mg/dL by 72 hours. The majority of measured glucoses (66.8%, n = 647) were maintained between 80 and 180 mg/dL. There were no glucoses lower than 60 mg/dL, and only 0.3% (n = 3) were below 70 mg/dL. There was a low rate of errors (1%). CONCLUSIONS: A simple automated SQIA can be used to titrate insulin to meet the changing metabolic requirements of individuals perioperatively and maintain glucose within the target range for these hospitalized patients.

Development and Implementation of a Subcutaneous Insulin Pen Label Bar Code Scanning Protocol to Prevent Wrong-Patient Insulin Pen Errors.

PROBLEM DEFINITION: Insulin, a high-alert medication, is regularly prescribed in the inpatient setting for hyperglycemia and diabetes mellitus. Although convenient, insulin pens carry a risk of blood-borne pathogens if the same pen is used on multiple patients. At the University of California, San Francisco (UCSF), a new nursing protocol for insulin pen administration was developed to ensure that insulin was quickly available and to identify and move to eliminate wrong-patient insulin pen errors. This protocol involved unit-based automated dispensing machines and an electronic health record (EHR)-integrated patient-specific bar code label work flow. APPROACH: After piloting on three hospital units, this new patient-specific bar code label process was expanded hospitalwide. "Print Label For Insulin Pen" and "Scan Insulin Pen" buttons were programmed into the EHR to enable nurses to print patient-specific bar code labels. In addition, a "wrong-patient pen alert" was activated to prevent wrong-pen insulin pen administration. OUTCOMES: For the 162,075 inpatient insulin pen administrations during the study period (April 2017-March 2018), monthly errors (rates) ranged from 13 (0.12%) to 36 (0.23%). In total, 296 near-miss events (0.18% of all insulin pen administrations) were observed and prevented. CONCLUSION: Insulin pen work flow and EHR changes implemented at UCSF enable subcutaneous insulin to remain a time-critical medication and ensure patient safety. The wide adoption of EHRs offers an opportunity to integrate patient safety improvements directly into the electronic medication administration record systems to maximize patient safety.