A primer on data visualization in infection prevention and antimicrobial stewardship

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.

[1]  J. Benneyan,et al.  Large-scale empirical optimisation of statistical control charts to detect clinically relevant increases in surgical site infection rates , 2019, BMJ Quality & Safety.

[2]  Julia E. Vogt,et al.  Introduction to Machine Learning in Digital Healthcare Epidemiology , 2018, Infection Control & Hospital Epidemiology.

[3]  G. Wortmann,et al.  Use of whole-genome sequencing to guide a Clostridioides difficile diagnostic stewardship program , 2019, Infection Control & Hospital Epidemiology.

[4]  Samuel J. Weisenthal,et al.  Predicting hospital-onset Clostridium difficile using patient mobility data: A network approach , 2019, Infection Control & Hospital Epidemiology.

[5]  Jorie M. Butler,et al.  Decreases in antimicrobial use associated with multihospital implementation of electronic antimicrobial stewardship tools. , 2019, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[6]  L. Mermel,et al.  Comparison of infection control practices in a Dutch and US hospital using the infection risk scan (IRIS) method. , 2019, American journal of infection control.

[7]  Cole Nussbaumer Knaflic,et al.  Storytelling with Data: A Data Visualization Guide for Business Professionals , 2015 .

[8]  G. Bearman,et al.  You get back what you give: Decreased hospital infections with improvement in CHG bathing, a mathematical modeling and cost analysis. , 2019, American journal of infection control.

[9]  P. Fayers,et al.  The Visual Display of Quantitative Information , 1990 .

[10]  J C Benneyan,et al.  Statistical Quality Control Methods in Infection Control and Hospital Epidemiology, Part II: Chart Use, Statistical Properties, and Research Issues , 1998, Infection Control & Hospital Epidemiology.

[11]  Harold Lehmann,et al.  Visualizing Central Line –Associated Blood Stream Infection (CLABSI) Outcome Data for Decision Making by Health Care Consumers and Practitioners—An Evaluation Study , 2013, Online journal of public health informatics.

[12]  Claude Guérin,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 , 2017, American journal of infection control.

[13]  M. Grayson,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” , 2019, Infection Control & Hospital Epidemiology.

[14]  Stefan Gumhold,et al.  Maximum entropy light source placement , 2002, IEEE Visualization, 2002. VIS 2002..

[15]  Aaron C. Miller,et al.  A Smartphone-Driven Thermometer Application for Real-time Population- and Individual-Level Influenza Surveillance , 2018, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[16]  S. Jensen,et al.  Whole Genome Sequencing in Real-Time Investigation and Management of a Pseudomonas aeruginosa Outbreak on a Neonatal Intensive Care Unit , 2015, Infection Control & Hospital Epidemiology.

[17]  Alberto Maria Segre,et al.  Estimating Time Physicians and Other Health Care Workers Spend with Patients in an Intensive Care Unit Using a Sensor Network. , 2018, The American journal of medicine.

[18]  Michael Friendly,et al.  A Brief History of Data Visualization , 2008 .

[19]  Chaoli Wang,et al.  Information Theory in Scientific Visualization , 2011, Entropy.

[20]  S. Cosgrove,et al.  Rethinking How Antibiotics Are Prescribed: Incorporating the 4 Moments of Antibiotic Decision Making Into Clinical Practice , 2019, JAMA.

[21]  T. L. Gustafson,et al.  Practical risk-adjusted quality control charts for infection control. , 2000, American journal of infection control.

[22]  Landon Fridman Detwiler,et al.  Visualization and analytics tools for infectious disease epidemiology: A systematic review , 2014, J. Biomed. Informatics.

[23]  Steve Wexler,et al.  The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios , 2017 .