Published on in Vol 3, No 4 (2015): Oct-Dec

Disrupting Electronic Health Records Systems: The Next Generation

Disrupting Electronic Health Records Systems: The Next Generation

Disrupting Electronic Health Records Systems: The Next Generation

Journals

  1. Johar D, Bernstein L. A targeted approach toward more accurate assessment of hypertension. Egyptian Journal of Chest Diseases and Tuberculosis 2017;66(3):517 View
  2. Lee J. Patient-Specific Predictive Modeling Using Random Forests: An Observational Study for the Critically Ill. JMIR Medical Informatics 2017;5(1):e3 View
  3. Deliberato R, Celi L, Stone D. Clinical Note Creation, Binning, and Artificial Intelligence. JMIR Medical Informatics 2017;5(3):e24 View
  4. Willis A, Crasto W, Gray L, Dallosso H, Waheed G, Gray G, Davies M, Khunti K. The General Practitioner Prompt Study to Reduce Cardiovascular and Renal Complications in Patients With Type 2 Diabetes and Renal Complications: Protocol and Baseline Characteristics for a Cluster Randomized Controlled Trial. JMIR Research Protocols 2018;7(6):e152 View
  5. Beeksma M, Verberne S, van den Bosch A, Das E, Hendrickx I, Groenewoud S. Predicting life expectancy with a long short-term memory recurrent neural network using electronic medical records. BMC Medical Informatics and Decision Making 2019;19(1) View
  6. Streefkerk H, Verkooijen R, Bramer W, Verbrugh H. Electronically assisted surveillance systems of healthcare-associated infections: a systematic review. Eurosurveillance 2020;25(2) View
  7. Alkureishi M, Lee W, Webb S, Arora V. Integrating Patient-Centered Electronic Health Record Communication Training into Resident Onboarding: Curriculum Development and Post-Implementation Survey Among Housestaff. JMIR Medical Education 2018;4(1):e1 View
  8. Berrouiguet S, Barrigón M, Brandt S, Nitzburg G, Ovejero S, Alvarez-Garcia R, Carballo J, Walter M, Billot R, Lenca P, Delgado-Gomez D, Ropars J, de la Calle Gonzalez I, Courtet P, Baca-García E. Ecological Assessment of Clinicians’ Antipsychotic Prescription Habits in Psychiatric Inpatients: A Novel Web- and Mobile Phone–Based Prototype for a Dynamic Clinical Decision Support System. Journal of Medical Internet Research 2017;19(1):e25 View
  9. Mathieu A, Sauthier M, Jouvet P, Emeriaud G, Brossier D. Validation process of a high‐resolution database in a paediatric intensive care unit—Describing the perpetual patient's validation. Journal of Evaluation in Clinical Practice 2021;27(2):316 View
  10. Kim H, Kim H, Park Y, Lee W, Lim Y, Kim J. Clinical Genome Data Model (cGDM) provides Interactive Clinical Decision Support for Precision Medicine. Scientific Reports 2020;10(1) View
  11. Tyler P, Du H, Feng M, Bai R, Xu Z, Horowitz G, Stone D, Celi L. Assessment of Intensive Care Unit Laboratory Values That Differ From Reference Ranges and Association With Patient Mortality and Length of Stay. JAMA Network Open 2018;1(7):e184521 View
  12. 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
  13. Janssen A, Donnelly C, Kay J, Thiem P, Saavedra A, Pathmanathan N, Elder E, Dinh P, Kabir M, Jackson K, Harnett P, Shaw T. Developing an Intranet-Based Lymphedema Dashboard for Breast Cancer Multidisciplinary Teams: Design Research Study. Journal of Medical Internet Research 2020;22(4):e13188 View
  14. Narducci F, Lops P, Semeraro G. Power to the patients: The HealthNetsocial network. Information Systems 2017;71:111 View
  15. Rousseau J, Ip I, Raja A, Schuur J, Khorasani R. Can emergency department provider notes help to achieve more dynamic clinical decision support?. Journal of the American College of Emergency Physicians Open 2020;1(6):1269 View
  16. Lin S, Shanafelt T, Asch S. Reimagining Clinical Documentation With Artificial Intelligence. Mayo Clinic Proceedings 2018;93(5):563 View
  17. Martinez-Garcia A, Naranjo-Saucedo A, Rivas J, Romero Tabares A, Marín Cassinello A, Andrés-Martín A, Sánchez Laguna F, Villegas R, Pérez León F, Moreno Conde J, Parra Calderón C. A Clinical Decision Support System (KNOWBED) to Integrate Scientific Knowledge at the Bedside: Development and Evaluation Study. JMIR Medical Informatics 2021;9(3):e13182 View
  18. Mori M, Durant T, Huang C, Mortazavi B, Coppi A, Jean R, Geirsson A, Schulz W, Krumholz H. Toward Dynamic Risk Prediction of Outcomes After Coronary Artery Bypass Graft: Improving Risk Prediction With Intraoperative Events Using Gradient Boosting. Circulation: Cardiovascular Quality and Outcomes 2021;14(6) View

Books/Policy Documents

  1. Samerski S, Müller H. Digitale Transformation von Dienstleistungen im Gesundheitswesen VI. View