This paper is in the following e-collection/theme issue:

Information System Evaluation and Best Practices (44)

Published on in Vol 5, No 2 (2013):

Visualizing Central Line-Associated Blood Stream Infection (CLABSI) Outcome Data to Health Care Consumers and Practitioners for Decision Making – Evaluation Study

Visualizing Central Line-Associated Blood Stream Infection (CLABSI) Outcome Data to Health Care Consumers and Practitioners for Decision Making – Evaluation Study

Visualizing Central Line-Associated Blood Stream Infection (CLABSI) Outcome Data to Health Care Consumers and Practitioners for Decision Making – Evaluation Study

Authors of this article:

Yair Rajwan1
The full text of this article is available as a PDF download by clicking here.
Article Authors Cited by (17) Tweetations (5) Metrics

Journals

  1. Sandhu M, Tickoo M, Bardia A. Data Science and Geriatric Anesthesia Research. Anesthesiology Clinics 2023;41(3):631 View
  2. Simpao A, Ahumada L, Rehman M. Big data and visual analytics in anaesthesia and health care. British Journal of Anaesthesia 2015;115(3):350 View
  3. Simpao A, Ahumada L, Gálvez J, Rehman M. A Review of Analytics and Clinical Informatics in Health Care. Journal of Medical Systems 2014;38(4) View
  4. Masnick M, Morgan D, Sorkin J, Kim E, Brown J, Rheingans P, Harris A. Lack of Patient Understanding of Hospital-Acquired Infection Data Published on the Centers for Medicare and Medicaid Services Hospital Compare Website. Infection Control & Hospital Epidemiology 2016;37(2):182 View
  5. Wang Q, Laramee R. EHR STAR: The State‐Of‐the‐Art in Interactive EHR Visualization. Computer Graphics Forum 2022;41(1):69 View
  6. Freeman J, Gadepalli S, Siddiqui S, Jarboe M, Hirschl R. Improving central line infection rates in the neonatal intensive care unit: Effect of hospital location, site of insertion, and implementation of catheter-associated bloodstream infection protocols. Journal of Pediatric Surgery 2015;50(5):860 View
  7. Govindan S, Prenovost K, Chopra V, Iwashyna T, Scherag A. A comprehension scale for central-line associated bloodstream infection: Results of a preliminary survey and factor analysis. PLOS ONE 2018;13(9):e0203431 View
  8. Govindan S, Chopra V, Iwashyna T. Do Clinicians Understand Quality Metric Data? An Evaluation in a Twitter‐Derived Sample. Journal of Hospital Medicine 2017;12(1):18 View
  9. Rostamzadeh N, Abdullah S, Sedig K. Visual Analytics for Electronic Health Records: A Review. Informatics 2021;8(1):12 View
  10. Raj M, Banaszak-Holl J. Consumer Engagement With Information on Performance: A Narrative Review. Quality Management in Health Care 2021;30(3):153 View
  11. Zhao X, Liu H, Hu Y, Huang J, Zhang S, Ja F. A novel gelatin-AgNPs coating preparing method for fabrication of antibacterial and no inflammation inducible coatings on PHBV. Reactive and Functional Polymers 2016;107:54 View
  12. Yang F, Wang M. A review of systematic evaluation and improvement in the big data environment. Frontiers of Engineering Management 2020;7(1):27 View
  13. Salinas J, Kritzman J, Kobayashi T, Edmond M, Ince D, Diekema D. A primer on data visualization in infection prevention and antimicrobial stewardship. Infection Control & Hospital Epidemiology 2020;41(8):948 View
  14. Sandhu M, Tickoo M, Bardia A. Data Science and Geriatric Anesthesia Research. Clinics in Geriatric Medicine 2025;41(1):101 View

Books/Policy Documents

  1. Koşarsoy Ağçeli G. Next-Generation Antimicrobial Nanocoatings for Medical Devices and Implants. View
  2. Hettinger A, Hoffman D, Weldon D, Blumenthal H. Clinical Engineering Handbook. View

Conference Proceedings

  1. Iftikhar A, Bond R, McGilligan V, J Leslie S, Rjoob K, Knoery C, Peace A. Proceedings of the 31st European Conference on Cognitive Ergonomics. Role of dashboards in improving decision making in healthcare: Review of the literature View