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
  4. Saukkonen P, Elovainio M, Virtanen L, Kaihlanen A, Nadav J, Lääveri T, Vänskä J, Viitanen J, Reponen J, Heponiemi T. The Interplay of Work, Digital Health Usage, and the Perceived Effects of Digitalization on Physicians’ Work: Network Analysis Approach. Journal of Medical Internet Research 2022;24(8):e38714 View
  5. Graber M. Reaching 95%: decision support tools are the surest way to improve diagnosis now. BMJ Quality & Safety 2022;31(6):415 View
  6. Wang M, Sun Z, Jia M, Wang Y, Wang H, Zhu X, Chen L, Ji H. Intelligent virtual case learning system based on real medical records and natural language processing. BMC Medical Informatics and Decision Making 2022;22(1) View
  7. Lin C, Chien T, Chen Y, Lee Y, Su S. An app to classify a 5-year survival in patients with breast cancer using the convolutional neural networks (CNN) in Microsoft Excel. Medicine 2022;101(4):e28697 View
  8. Wang X, Xi H, Geng X, Li Y, Zhao M, Li F, Li Z, Ji H, Tian H. Artificial Intelligence-Based Prediction of Lower Extremity Deep Vein Thrombosis Risk After Knee/Hip Arthroplasty. Clinical and Applied Thrombosis/Hemostasis 2023;29:107602962211392 View
  9. Zhang D, Xiao L, Duan J, Chang X, Walsh K, Sandars J, Brown J, Dang X, Shen W, Du J, Cao Y. Understanding online self-directed learning using point of care information systems (POCIS): A plot study using a capability approach perspective. Medical Teacher 2022;44(12):1413 View
  10. Wang M, Lee C, Wei Z, Ji H, Yang Y, Yang C. Clinical assistant decision-making model of tuberculosis based on electronic health records. BioData Mining 2023;16(1) View
  11. Wang M, Yang B, Liu Y, Yang Y, Ji H, Yang C. Emerging infectious disease surveillance using a hierarchical diagnosis model and the Knox algorithm. Scientific Reports 2023;13(1) View
  12. Wang M, Jia M, Wei Z, Wang W, Shang Y, Ji H. Construction and effectiveness evaluation of a knowledge-based infectious disease monitoring and decision support system. Scientific Reports 2023;13(1) View
  13. van Velzen M, de Graaf-Waar H, Ubert T, van der Willigen R, Muilwijk L, Schmitt M, Scheper M, van Meeteren N. 21st century (clinical) decision support in nursing and allied healthcare. Developing a learning health system: a reasoned design of a theoretical framework. BMC Medical Informatics and Decision Making 2023;23(1) View
  14. Armando L, Miglio G, de Cosmo P, Cena C. Clinical decision support systems to improve drug prescription and therapy optimisation in clinical practice: a scoping review. BMJ Health & Care Informatics 2023;30(1):e100683 View
  15. K N, M․ B M. Fuzzy rule based classifier model for evidence based clinical decision support systems. Intelligent Systems with Applications 2024;22:200393 View