Literature DB >> 31476347

Use of a real-time, algorithm-driven, publicly displayed, automated signal to improve insulin prescribing practices.

Thérèse Franco1, Barry Aaronson2, Barbara Williams3, Craig Blackmore4.   

Abstract

AIM: The clinical andon board (CAB) is a novel electronic surveillance and communication system, which alerts providers to and prompts treatment of dysglycemia. This investigation was designed to determine the CAB's effectiveness in supporting adherence to standardized evidence-based protocols, as well as improving glycemic control.
METHODS: This study was a retrospective pre/post analysis of insulin orders and blood glucose values. We used a Student's t-test for continuous variables and Chi2 for all other variables. This study included patients 18 years or older admitted to the hospital medical service as an inpatient with a length of stay greater than 24 h and less than 90 days. We used Pearson's correlation coefficient to evaluate the relationship between CAB and blood glucose.
RESULTS: The rate of compliance in prescribing basal insulin for patient with diabetes increased from 56% to 77% (p < 0.001). Similarly, compliance rates for prescribing correctional insulin in patients without diabetes increased from 15% to 37% (p < 0.001). Performance on the CAB was linearly related to blood glucose (p = 0.004), and there was a small statistically (not clinically) significant improvement in mean blood glucose values.
CONCLUSION: This approach is effective in alerting and engaging providers to prescribe insulin in a standardized manner with potential to improve glycemic control.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Automation; Diabetes; Insulin; Medication alert systems; Quality improvement

Mesh:

Substances:

Year:  2019        PMID: 31476347     DOI: 10.1016/j.diabres.2019.107833

Source DB:  PubMed          Journal:  Diabetes Res Clin Pract        ISSN: 0168-8227            Impact factor:   5.602


  2 in total

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

Authors:  Sophie Patzek; Heidemarie W MacMaster; Esther Rov-Ikpah; Craig San Luis; Craig Johnson; Venkateswarlu Juttukonda; Robert J Rushakoff
Journal:  J Diabetes Sci Technol       Date:  2020-08-12

2.  Quality improvement project for improving inpatient glycaemic control in non-critically ill patients admitted on medical floor with type 2 diabetes mellitus.

Authors:  Adeel Ahmad Khan; Aamir Shahzad; Samman Rose; Dabia Hamad S H Al Mohanadi; Muhammad Zahid
Journal:  BMJ Open Qual       Date:  2020-08
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.