TY - JOUR
T1 - Comparison of Insulin Dose Adjustments Made by Artificial Intelligence-Based Decision Support Systems and by Physicians in People with Type 1 Diabetes Using Multiple Daily Injections Therapy
AU - Nimri, Revital
AU - Tirosh, Amir
AU - Muller, Ido
AU - Shtrit, Yael
AU - Kraljevic, Ivana
AU - Alonso, Montserrat Martín
AU - Milicic, Tanja
AU - Saboo, Banshi
AU - Deeb, Asma
AU - Christoforidis, Athanasios
AU - Den Brinker, Marieke
AU - Bozzetto, Lutgarda
AU - Bolla, Andrea Mario
AU - Krcma, Michal
AU - Rabini, Rosa Anna
AU - Tabba, Shadi
AU - Gerasimidi-Vazeou, Andriani
AU - Maltoni, Giulio
AU - Giani, Elisa
AU - Dotan, Idit
AU - Liberty, Idit F.
AU - Toledano, Yoel
AU - Kordonouri, Olga
AU - Bratina, Natasa
AU - Dovc, Klemen
AU - Biester, Torben
AU - Atlas, Eran
AU - Phillip, Moshe
N1 - Publisher Copyright:
© Copyright 2022, Mary Ann Liebert, Inc.
PY - 2022/8/1
Y1 - 2022/8/1
N2 - Objective: Artificial intelligence-based decision support systems (DSS) need to provide decisions that are not inferior to those given by experts in the field. Recommended insulin dose adjustments on the same individual data set were compared among multinational physicians, and with recommendations made by automated Endo-Digital DSS (ED-DSS). Research Design and Methods: This was a noninterventional study surveying 20 physicians from multinational academic centers. The survey included 17 data cases of individuals with type 1 diabetes who are treated with multiple daily insulin injections. Participating physicians were asked to recommend insulin dose adjustments based on glucose and insulin data. Insulin dose adjustments recommendations were compared among physicians and with the automated ED-DSS. The primary endpoints were the percentage of comparison points for which there was agreement on the trend of insulin dose adjustments. Results: The proportion of agreement and disagreement in the direction of insulin dose adjustment among physicians was statistically noninferior to the proportion of agreement and disagreement observed between ED-DSS and physicians for basal rate, carbohydrate-to insulin ratio, and correction factor (P < 0.001 and P ≤ 0.004 for all three parameters for agreement and disagreement, respectively). The ED-DSS magnitude of insulin dose change was consistently lower than that proposed by the physicians. Conclusions: Recommendations for insulin dose adjustments made by automatization did not differ significantly from recommendations given by expert physicians regarding the direction of change. These results highlight the potential utilization of ED-DSS as a useful clinical tool to manage insulin titration and dose adjustments.
AB - Objective: Artificial intelligence-based decision support systems (DSS) need to provide decisions that are not inferior to those given by experts in the field. Recommended insulin dose adjustments on the same individual data set were compared among multinational physicians, and with recommendations made by automated Endo-Digital DSS (ED-DSS). Research Design and Methods: This was a noninterventional study surveying 20 physicians from multinational academic centers. The survey included 17 data cases of individuals with type 1 diabetes who are treated with multiple daily insulin injections. Participating physicians were asked to recommend insulin dose adjustments based on glucose and insulin data. Insulin dose adjustments recommendations were compared among physicians and with the automated ED-DSS. The primary endpoints were the percentage of comparison points for which there was agreement on the trend of insulin dose adjustments. Results: The proportion of agreement and disagreement in the direction of insulin dose adjustment among physicians was statistically noninferior to the proportion of agreement and disagreement observed between ED-DSS and physicians for basal rate, carbohydrate-to insulin ratio, and correction factor (P < 0.001 and P ≤ 0.004 for all three parameters for agreement and disagreement, respectively). The ED-DSS magnitude of insulin dose change was consistently lower than that proposed by the physicians. Conclusions: Recommendations for insulin dose adjustments made by automatization did not differ significantly from recommendations given by expert physicians regarding the direction of change. These results highlight the potential utilization of ED-DSS as a useful clinical tool to manage insulin titration and dose adjustments.
KW - Artificial intelligence
KW - Automated decision support
KW - Glycemic control
KW - Insulin therapy
KW - Multiple daily injections
KW - Type 1 diabetes
UR - http://www.scopus.com/inward/record.url?scp=85135410637&partnerID=8YFLogxK
U2 - 10.1089/dia.2021.0566
DO - 10.1089/dia.2021.0566
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C2 - 35325567
AN - SCOPUS:85135410637
SN - 1520-9156
VL - 24
SP - 564
EP - 572
JO - Diabetes Technology and Therapeutics
JF - Diabetes Technology and Therapeutics
IS - 8
ER -
