Published on in Vol 8, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21621, first published .
Clinical Decision Support Systems for Pressure Ulcer Management: Systematic Review

Clinical Decision Support Systems for Pressure Ulcer Management: Systematic Review

Clinical Decision Support Systems for Pressure Ulcer Management: Systematic Review

Journals

  1. Ribeiro F, Fidalgo F, Silva A, Metrôlho J, Santos O, Dionisio R. Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities. Informatics 2021;8(4):76 View
  2. Rodriguez‐Arrastia M, Martinez‐Ortigosa A, Ruiz‐Gonzalez C, Ropero‐Padilla C, Roman P, Sanchez‐Labraca N. Experiences and perceptions of final‐year nursing students of using a chatbot in a simulated emergency situation: A qualitative study. Journal of Nursing Management 2022;30(8):3874 View
  3. Rizvi R, Emani S, Rocha H, de Aquino C, Garabedian P, Rui A, Arruda C, Sands-Lincoln M, Rozenblum R, Felix W, Jackson G, Juacaba S, Bates D. Physicians' Perceptions and Expectations of an Artificial Intelligence-Based Clinical Decision Support System in Cancer Care in an Underserved Setting. ACI Open 2022;06(02):e66 View
  4. Lapp L, Egan K, McCann L, Mackenzie M, Wales A, Maguire R. Decision Support Tools in Adult Long-term Care Facilities: Scoping Review. Journal of Medical Internet Research 2022;24(9):e39681 View
  5. Sotoodeh M, Zhang W, Simpson R, Hertzberg V, Ho J. A Comprehensive and Improved Definition for Hospital-Acquired Pressure Injury Classification Based on Electronic Health Records: Comparative Study. JMIR Medical Informatics 2023;11:e40672 View
  6. Akbar S, Lyell D, Magrabi F. Automation in nursing decision support systems: A systematic review of effects on decision making, care delivery, and patient outcomes. Journal of the American Medical Informatics Association 2021;28(11):2502 View
  7. Silva A, Metrôlho J, Ribeiro F, Fidalgo F, Santos O, Dionisio R. A Review of Intelligent Sensor-Based Systems for Pressure Ulcer Prevention. Computers 2021;11(1):6 View
  8. Toffaha K, Simsekler M, Omar M. Leveraging artificial intelligence and decision support systems in hospital-acquired pressure injuries prediction: A comprehensive review. Artificial Intelligence in Medicine 2023;141:102560 View
  9. Hamari L, Parisod H, Siltanen H, Heikkilä K, Kortteisto T, Kunnamo I, Pukkila H, Holopainen A. Clinical decision support in promoting evidence-based nursing in primary healthcare: a cross-sectional study in Finland. JBI Evidence Implementation 2023;21(3):294 View
  10. Kim M, Kim T, Kim D, Lee D, Kim D, Heo J, Kang S, Ha T, Kim J, Moon D, Heo Y, Kim W, Lee S, Kim Y, Park S, Han S, Choi H. In-Advance Prediction of Pressure Ulcers via Deep-Learning-Based Robust Missing Value Imputation on Real-Time Intensive Care Variables. Journal of Clinical Medicine 2023;13(1):36 View
  11. Leão A, Azzolin K, Lucena A, Souza E. Ações de cuidado e diagnósticos de enfermagem ao paciente com oxigenação por membrana extracorpórea: translação do conhecimento. Escola Anna Nery 2024;28 View
  12. Leão A, Azzolin K, Lucena A, Souza E. Care actions and nursing diagnoses for patients with extracorporeal membrane oxygenation: knowledge translation. Escola Anna Nery 2024;28 View
  13. Karpathakis K, Morley J, Floridi L. A Justifiable Investment in AI for Healthcare: Aligning Ambition with Reality. SSRN Electronic Journal 2024 View
  14. Marques R, Mira H, Lopes M, Neves-Amado J, Ramos P, Alves P, Atashzadeh-Shoorideh F. Applying mHealth for Assessment of Complex Wounds: Insights from a Focus Group. Nursing Forum 2024;2024(1) View
  15. Karpathakis K, Morley J, Floridi L. A Justifiable Investment in AI for Healthcare: Aligning Ambition with Reality. Minds and Machines 2024;34(4) View