Ann Cabri1, Berit Bagley2, Kevin Brown3. 1. UC Davis Medical Center, Sacramento, CA, USA. 2. Inpatient Glycemic Team, UC Davis Medical Center, Sacramento, CA, USA. 3. Digital Hospital, Inc., San Jose, CA, USA.
Abstract
BACKGROUND: No current technology exists to ensure the dose of insulin administered in hospitals matches the physician order. OBJECTIVE: Assess the feasibility of using computer vision to identify insulin syringe preparation errors. METHODS: Twenty-two nurses prepared 50 insulin doses (n=1100) each. A computer vision device (CVD) measured the volume drawn up and identified air present. Syringes identified as inaccurate by the CVD were confirmed by two observers, and a random sample of 100 syringes identified as accurate was validated by two independent observers. RESULTS: Ten syringes (1.0%) had the wrong volume prepared, and 68 syringes (6.5%) contained air sufficient to meet the definition of inaccuracy. All errors were confirmed by two independent observers. CONCLUSION: CVDs could reduce insulin administration errors in hospitalized patients.
BACKGROUND: No current technology exists to ensure the dose of insulin administered in hospitals matches the physician order. OBJECTIVE: Assess the feasibility of using computer vision to identify insulin syringe preparation errors. METHODS: Twenty-two nurses prepared 50 insulin doses (n=1100) each. A computer vision device (CVD) measured the volume drawn up and identified air present. Syringes identified as inaccurate by the CVD were confirmed by two observers, and a random sample of 100 syringes identified as accurate was validated by two independent observers. RESULTS: Ten syringes (1.0%) had the wrong volume prepared, and 68 syringes (6.5%) contained air sufficient to meet the definition of inaccuracy. All errors were confirmed by two independent observers. CONCLUSION: CVDs could reduce insulin administration errors in hospitalized patients.
Authors: Alain K Koyama; Claire-Sophie Sheridan Maddox; Ling Li; Tracey Bucknall; Johanna I Westbrook Journal: BMJ Qual Saf Date: 2019-08-07 Impact factor: 7.035