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Inter- and intra-physician variability in insulin injection adjustments compared with Bayesian algorithm

Alessandra Kobayati1,2, Michael A Tsoukas2,3, Natasha Garfield2,3

  • 1Division of Experimental Medicine, Department of Medicine, McGill University, Montreal, QC, Canada.

Diabetologia
|December 15, 2025
PubMed
Summary

Automated insulin adjustments for type 1 diabetes using the McGill decision support system (DSS) showed comparable results to endocrinologists. This highlights the potential for AI in diabetes management and the subjective nature of physician decisions.

Keywords:
Bayesian algorithmDecision support systemMultiple daily injectionsPhysician comparisonType 1 diabetes

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Area of Science:

  • Endocrinology
  • Artificial Intelligence in Medicine
  • Diabetes Management

Background:

  • Type 1 diabetes requires precise insulin management, often involving multiple daily injections.
  • Automated insulin adjustment systems are needed for unsupervised use to improve patient outcomes.
  • Current insulin adjustment protocols can be subjective and vary among clinicians.

Purpose of the Study:

  • To evaluate the McGill decision support system (DSS), a Bayesian algorithm, for automated insulin adjustments.
  • To compare the algorithm's recommendations with those made by endocrinologists for type 1 diabetes management.
  • To assess intra-physician variability in insulin adjustment decisions.

Main Methods:

  • A survey of 13 Canadian endocrinologists was conducted using retrospective participant data.
  • Physicians made mock insulin adjustments based on weekly (Part A) and biweekly (Part C) data, comparing them to the McGill DSS algorithm.
  • Intra-physician variability was assessed by comparing a physician's recommendations on identical datasets (Part B vs. Part A).

Main Results:

  • The agreement rate for the direction of weekly insulin adjustments between the algorithm and physicians was non-inferior to inter-physician agreement for both prandial bolus and basal insulin.
  • Full disagreement rates were comparable between the algorithm and physicians.
  • Physicians showed significant intra-variability, agreeing with their own previous decisions only 66-67% of the time.

Conclusions:

  • The McGill DSS algorithm demonstrated comparable insulin adjustment directions to endocrinologists.
  • The study underscores the potential of automated decision support systems in diabetes care.
  • Significant intra-physician variability highlights the subjective nature of insulin management in type 1 diabetes.