Introduction: The objective of this quality improvement, interventional study regarding patients with diabetes undergoing diabetic ophthalmology outpatient surgery aimed to develop, implement, and evaluate a new diabetic algorithm to improve safety, operating room efficiency, and decrease supply cost.
Methods: A multidisciplinary study team was assembled, including ophthalmologists, endocrinologists, anesthesiologists, management, and nurses to review the current diabetic protocol. From August 2016 to July 2017, 13 patient safety concerns or incident reports were reviewed that identified two serious cases of hypoglycemia. Using the concerns data, frontline perspectives, and reviewing best practice guidelines, a new diabetic algorithm was developed and trialed for 24 months. The new algorithm limited the use of an existing preoperative insulin protocol and reduced the number of nurses required. The number of adverse events, nursing setup process steps, setup time, and preoperative insulin infusion protocols used were collected. An evaluation of the supply costs was performed.
Results: After implementing the new diabetic algorithm, zero safety incidents were reported, and a 97.5% reduction in the use of preoperative insulin protocol resulted. Nursing staff perceived that the new diabetic algorithm was easier to configure, 23 minutes faster to set up, and required one nursing staff member. Supply cost was reduced by $30.63 (Canadian Dollars, CAD) per patient.
Conclusion: Perioperative glucose irregularities may threaten patient safety and surgical outcomes. Healthcare professionals must improve patient safety, decrease healthcare expenditure, and prevent unnecessary delays. Multidisciplinary frontline staff experiential knowledge aided in the recognition of potential problems and comprehensive solutions to optimize patient care.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10229036 | PMC |
http://dx.doi.org/10.36401/JQSH-21-18 | DOI Listing |
BMC Public Health
December 2024
Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada.
Background: Machine learning (ML) is increasingly used in population and public health to support epidemiological studies, surveillance, and evaluation. Our objective was to conduct a scoping review to identify studies that use ML in population health, with a focus on its use in non-communicable diseases (NCDs). We also examine potential algorithmic biases in model design, training, and implementation, as well as efforts to mitigate these biases.
View Article and Find Full Text PDFBMC Infect Dis
December 2024
Infectious Disease Hospital of Heilongjiang Province, No. 1 Jian She Street, Hulan District, Harbin, Heilongjiang, 150500, China.
Background: Tuberculosis (TB) remains a significant global health issue. Drug-resistant TB and comorbidities exacerbate its burden, influencing treatment outcomes and healthcare utilization. Despite the growing prevalence of TB comorbidities, research often focuses on single comorbidities rather than comorbidity patterns.
View Article and Find Full Text PDFSubst Use Misuse
December 2024
Dirección de Investigación y Enseñanza, Centros de Integración Juvenil AC, Ciudad de México, México.
Objectives: Tobacco smoking remains a major public health risk, responsible for millions of deaths worldwide. While smoking patterns in Mexico differ from those in countries with higher rates, comorbidities such as diabetes pose a health risk. Although many smokers want to quit, access to cessation services is limited.
View Article and Find Full Text PDFStress
December 2025
Technology Transfer and Innovation-Support Office, North-West University, Potchefstroom, South Africa.
Background: Self-reported mental stress is not consistently recognized as a risk factor for stroke. This prompted development of a novel algorithm for stress-phenotype indices to quantify chronic stress prevalence in relation to a modified stroke risk score in a South African cohort. The algorithm is based on biomarkers adrenocorticotrophic hormone, high-density lipoprotein cholesterol, high-sensitive cardiac-troponin-T, and diastolic blood pressure which exemplifies the stress-ischemic-phenotype index.
View Article and Find Full Text PDFCancer Genet
December 2024
School of Life Sciences, Shanghai University, Shanghai 200444, China. Electronic address:
CD4 T cells play a pivotal role in the immune system, particularly in adaptive immunity, by orchestrating and enhancing immune responses. CD4 T cell-related immune responses exhibit diverse characteristics in different diseases. This study utilizes gene expression analysis of CD4 T cells to classify and understand complex diseases.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!