Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-28T18:06:21.108Z Has data issue: false hasContentIssue false
  • English
  • Français

A primer on data visualization in infection prevention and antimicrobial stewardship

Published online by Cambridge University Press:  11 May 2020

Jorge L. Salinas Open the ORCID record for Jorge L. Salinas [Opens in a new window] ,
Jeffrey Kritzman ,
Takaaki Kobayashi ,
Michael B. Edmond ,
Dilek Ince  and
Daniel J. Diekema
Jorge L. Salinas*
Affiliation:
The University of Iowa Hospitals & Clinics, Iowa City, Iowa
Jeffrey Kritzman
Affiliation:
The University of Iowa Hospitals & Clinics, Iowa City, Iowa
Takaaki Kobayashi
Affiliation:
The University of Iowa Hospitals & Clinics, Iowa City, Iowa
Michael B. Edmond
Affiliation:
The University of Iowa Hospitals & Clinics, Iowa City, Iowa
Dilek Ince
Affiliation:
The University of Iowa Hospitals & Clinics, Iowa City, Iowa
Daniel J. Diekema
Affiliation:
The University of Iowa Hospitals & Clinics, Iowa City, Iowa
*
Author for correspondence: Jorge L. Salinas, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Data visualization refers to the techniques used to communicate information by encoding it as visual objects (eg, points, lines, or bars) contained in graphics. The recent acceleration in informatics technology has made it possible to obtain and process large amounts of data. Although data visualization can provide insights from large datasets, it can also help simplify messaging, making information more accessible for healthcare stakeholders. The field of data visualization is constantly evolving, and new techniques are frequently being created. However, evidence regarding the best way to visualize data in the fields of infection prevention and antimicrobial stewardship is limited. We provide an overview of data visualization theory and history, as well as recommendations for creating graphs for infection prevention and antimicrobial stewardship.

Type
Review
Information
Infection Control & Hospital Epidemiology , Volume 41 , Issue 8 , August 2020 , pp. 948 - 957
Check for updates
Copyright
© 2020 by The Society for Healthcare Epidemiology of America. All rights reserved.

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Data visualization. Wikipedia website. https://en.wikipedia.org/wiki/Data_visualization. Published 2019. Accessed November 26, 2019.Google Scholar
Ohannessian, R, Benet, T, Argaud, L, et al.Heat map for data visualization in infection control epidemiology: an application describing the relationship between hospital-acquired infections, Simplified Acute Physiological Score II, and length of stay in adult intensive care units. Am J Infect Control 2017;45:746–749.CrossRefGoogle ScholarPubMed
Rajwan, YG, Barclay, PW, Lee, T, Sun, IF, Passaretti, C, Lehmann, H. Visualizing central line -associated blood stream infection (CLABSI) outcome data for decision making by health care consumers and practitioners—an evaluation study. Online J Public Health Inform 2013;5:218.CrossRefGoogle ScholarPubMed
Carroll, LN, Au, AP, Detwiler, LT, Fu, TC, Painter, IS, Abernethy, NF. Visualization and analytics tools for infectious disease epidemiology: a systematic review. J Biomed Inform 2014;51:287–298.CrossRefGoogle ScholarPubMed
Willemsen, I, Jefferson, J, Mermel, L, Kluytmans, J. Comparison of infection control practices in a Dutch and US hospital using the infection risk scan (IRIS) method. Am J Infect Control 2019.Google Scholar
Wang, C, Shen, H-W. Information theory in scientific visualization. Entropy 2011;13:254–273.CrossRefGoogle Scholar
Gumhold, S. Maximum entropy light source placement. Proceedings of the Conference on Visualization ‘02. November 1, 2002; Boston, Massachusetts.CrossRefGoogle Scholar
Friendly, M. A brief history of data visualization. In: Handbook of Data Visualization. Berlin, Heidelberg: Springer; 2008:15–56.CrossRefGoogle Scholar
Tufte, ER. The Visual Display of Quantitative Information. Cheshire, UK: Graphics Press; 2001.Google Scholar
Data scientist leads 50 best jobs in America for 2019 according to glassdoor. Forbes website. https://www.forbes.com/sites/louiscolumbus/2019/01/23/data-scientist-leads-50-best-jobs-in-america-for-2019-according-to-glassdoor/#3a0fdb47474f. Published January 23, 2019. Accessed October 30, 2019.Google Scholar
Benneyan, JC. Statistical quality control methods in infection control and hospital epidemiology, part II: chart use, statistical properties, and research issues. Infect Control Hosp Epidemiol 1998;19:265–283.CrossRefGoogle ScholarPubMed
Ilieş, I, Anderson, DJ, Salem, J, et al.Large-scale empirical optimisation of statistical control charts to detect clinically relevant increases in surgical site infection rates. BMJ Qual Saf 2019 Nov 8. doi: 10.1136/bmjqs-2018-008976. [Epub ahead of print].Google ScholarPubMed
Gustafson, TL. Practical risk-adjusted quality control charts for infection control. Am J Infect Control 2000;28:406–414.CrossRefGoogle ScholarPubMed
Graber, CJ, Jones, MM, Goetz, MB, et al.Decreases in antimicrobial use associated with multihospital implementation of electronic antimicrobial stewardship tools. Clin Infect Dis 2019.CrossRefGoogle ScholarPubMed
Facts about color blindness. National Eye Instute website. https://nei.nih.gov/health/color_blindness/facts_about. Published 2019. Accessed April 21, 2020.Google Scholar
Rost, LC. Your Friendly guide to colors in data visualisation. https://blog.datawrapper.de/. Published 2019. Accessed November 26, 2019. doi: 10.1093/cid/ciz941.CrossRefGoogle Scholar
Devchand, M, Stewardson, AJ, Urbancic, KF, et al.Outcomes of an electronic medical record (EMR)-driven intensive care unit (ICU)-antimicrobial stewardship (AMS) ward round: assessing the “Five Moments of Antimicrobial Prescribing.” Infect Control Hosp Epidemiol 2019;40:1170–1175.CrossRefGoogle Scholar
Tamma, PD, Miller, MA, Cosgrove, SE. Rethinking how antibiotics are prescribed: incorporating the 4 moments of antibiotic decision making into clinical practice. JAMA 2019;321:139–140.CrossRefGoogle ScholarPubMed
Bush, K, Barbosa, H, Farooq, S, et al.Predicting hospital-onset Clostridium difficile using patient mobility data: A network approach. Infect Control Hosp Epidemiol 2019:1–7.Google ScholarPubMed
Reagan, KA, Chan, DM, Vanhoozer, G, et al.You get back what you give: decreased hospital infections with improvement in CHG bathing, a mathematical modeling and cost analysis. Am J Infect Control 2019;47:1471–1473.CrossRefGoogle ScholarPubMed
Davis, RJ, Jensen, SO, Van Hal, S, et al.Whole-genome sequencing in real-time investigation and management of a Pseudomonas aeruginosa outbreak on a neonatal intensive care unit. Infect Control Hosp Epidemiol 2015;36:1058–1064.CrossRefGoogle ScholarPubMed
Jakharia, KK, Ilaiwy, G, Moose, SS, et al.Use of whole-genome sequencing to guide a Clostridioides difficile diagnostic stewardship program. Infect Control Hosp Epidemiol 2019;40:804–806.CrossRefGoogle ScholarPubMed
Roth, JA, Battegay, M, Juchler, F, Vogt, JE, Widmer, AF. Introduction to machine learning in digital healthcare epidemiology. Infect Control Hosp Epidemiol 2018;39:1457–1462.CrossRefGoogle ScholarPubMed
Butler, R, Monsalve, M, Thomas, GW, et al.Estimating time physicians and other health care workers spend with patients in an intensive care unit using a sensor network. Am J Med 2018;131:972.e979–972.e915.CrossRefGoogle Scholar
Miller, AC, Singh, I, Koehler, E, Polgreen, PM. A smartphone-driven thermometer application for real-time population- and individual-level influenza surveillance. Clin Infect Dis 2018;67:388–397.CrossRefGoogle ScholarPubMed
Knaflic, CN. Storytelling with Data: A Data Visualization Guide for Business Professionals. Hoboken, NJ: John Wiley & Sons; 2015.CrossRefGoogle Scholar
Wexler, S, Shaffer, J, Cotgreave, A. The Big Book of Dashboards : Visualizing Your Data Using Real-World Business Scenarios. Hoboken, NJ: John Wiley & Sons; 2017.CrossRefGoogle Scholar
Benevento, D, Rowell, KS, Steeger, J, Cutrell, A, Morales, M. The Best Boring Book Ever of Tableau for Heathcare, third edition. Boston: HealthDatViz; 2017.Google Scholar
NCHS data visualization gallery. Centers for Disease Control and Prevention website. https://www.cdc.gov/nchs/data-visualization/index.htm. Published 2019. Accessed November 26, 2019.Google Scholar
Data visualizations. Institute for Health Metrics and Evaluation website. http://www.healthdata.org/results/data-visualizations. Published 2019. Accessed November 26, 2019.Google Scholar
Visualizing health. The University of Michigan and the Robert Wood JOhnson Foundation website. http://www.vizhealth.org/. Published 2014. Accessed November 26, 2019.Google Scholar
Knaflic, CN. Storytelling with data website. http://www.storytellingwithdata.com/. Published 2019. Accessed November 26, 2019.Google Scholar
Cairo, A. The Functional Art website. http://www.thefunctionalart.com/. Published 2019. Accessed November 26, 2019.Google Scholar
Evergreen, S. Stephanie Evergreen Data Visualization blog. https://stephanieevergreen.com/blog/. Published 2019. Accessed November 26, 2019.Google Scholar
Chartable. Datawrapper blog. https://blog.datawrapper.de/. Published 2019. Accessed November 26, 2019.Google Scholar
Kritzman, J, Salinas, JL. Infectious Data Viz website. https://infectiousdataviz.com/. Published 2019. Accessed November 26, 2019.Google Scholar