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

Information System Evaluation and Best Practices (40)

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