Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12469, first published .
Interruptive Versus Noninterruptive Clinical Decision Support: Usability Study

Interruptive Versus Noninterruptive Clinical Decision Support: Usability Study

Interruptive Versus Noninterruptive Clinical Decision Support: Usability Study

Journals

  1. Sakib N, Ahamed S, Khan R, Griffin P, Haque M. Unpacking Prevalence and Dichotomy in Quick Sequential Organ Failure Assessment and Systemic Inflammatory Response Syndrome Parameters: Observational Data–Driven Approach Backed by Sepsis Pathophysiology. JMIR Medical Informatics 2020;8(12):e18352 View
  2. Jankovic I, Chen J. Clinical Decision Support and Implications for the Clinician Burnout Crisis. Yearbook of Medical Informatics 2020;29(01):145 View
  3. Ramirez M, Chen K, Follett R, Mangione C, Moreno G, Bell D. Impact of a “Chart Closure” Hard Stop Alert on Prescribing for Elevated Blood Pressures Among Patients With Diabetes: Quasi-Experimental Study. JMIR Medical Informatics 2020;8(4):e16421 View
  4. Schaaf J, Sedlmayr M, Sedlmayr B, Prokosch H, Storf H. Evaluation of a clinical decision support system for rare diseases: a qualitative study. BMC Medical Informatics and Decision Making 2021;21(1) View
  5. 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
  6. Olakotan O, Mohd Yusof M. The appropriateness of clinical decision support systems alerts in supporting clinical workflows: A systematic review. Health Informatics Journal 2021;27(2):146045822110075 View
  7. Trinkley K, Kroehl M, Kahn M, Allen L, Bennett T, Hale G, Haugen H, Heckman S, Kao D, Kim J, Matlock D, Malone D, Page 2nd R, Stine J, Suresh K, Wells L, Lin C. Applying Clinical Decision Support Design Best Practices With the Practical Robust Implementation and Sustainability Model Versus Reliance on Commercially Available Clinical Decision Support Tools: Randomized Controlled Trial. JMIR Medical Informatics 2021;9(3):e24359 View
  8. Tomašev N, Harris N, Baur S, Mottram A, Glorot X, Rae J, Zielinski M, Askham H, Saraiva A, Magliulo V, Meyer C, Ravuri S, Protsyuk I, Connell A, Hughes C, Karthikesalingam A, Cornebise J, Montgomery H, Rees G, Laing C, Baker C, Osborne T, Reeves R, Hassabis D, King D, Suleyman M, Back T, Nielson C, Seneviratne M, Ledsam J, Mohamed S. Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records. Nature Protocols 2021;16(6):2765 View