Published on in Vol 8, No 1 (2020): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16912, first published .
Accuracy and Effects of Clinical Decision Support Systems Integrated With BMJ Best Practice–Aided Diagnosis: Interrupted Time Series Study

Accuracy and Effects of Clinical Decision Support Systems Integrated With BMJ Best Practice–Aided Diagnosis: Interrupted Time Series Study

Accuracy and Effects of Clinical Decision Support Systems Integrated With BMJ Best Practice–Aided Diagnosis: Interrupted Time Series Study

Journals

  1. Siaki L, LIN V, Marshall R, Highley R. Feasibility of a Clinical Decision Support Tool to Manage Resistant Hypertension: Team-HTN, a Single-arm Pilot Study. Military Medicine 2021;186(1-2):e225 View
  2. Poly T, Islam M, Muhtar M, Yang H, Nguyen P, Li Y. Machine Learning Approach to Reduce Alert Fatigue Using a Disease Medication–Related Clinical Decision Support System: Model Development and Validation. JMIR Medical Informatics 2020;8(11):e19489 View
  3. Beldhuis I, Marapin R, Jiang Y, Simões de Souza N, Georgiou A, Kaufmann T, Castela Forte J, van der Horst I. Cognitive biases, environmental, patient and personal factors associated with critical care decision making: A scoping review. Journal of Critical Care 2021;64:144 View