Publication

Rapid Analysis of Diagnostic and Antimicrobial Patterns in R (RadaR): Interactive Open-Source Software App for Infection Management and Antimicrobial Stewardship

Luz, C., Berends, M., Dik, J-W., Lokate, A., Pulcini, C., Glasner, C. & Sinha, B., 11-Jun-2019, In : Journal of medical internet research. 21, 6, p. e12843 12 p., 12843.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Luz, C., Berends, M., Dik, J-W., Lokate, A., Pulcini, C., Glasner, C., & Sinha, B. (2019). Rapid Analysis of Diagnostic and Antimicrobial Patterns in R (RadaR): Interactive Open-Source Software App for Infection Management and Antimicrobial Stewardship. Journal of medical internet research, 21(6), e12843. [12843]. https://doi.org/10.2196/12843

Author

Luz, Christian ; Berends, Matthias ; Dik, Jan-Willem ; Lokate, Antonia ; Pulcini, Céline ; Glasner, Corinna ; Sinha, Bhanu. / Rapid Analysis of Diagnostic and Antimicrobial Patterns in R (RadaR) : Interactive Open-Source Software App for Infection Management and Antimicrobial Stewardship. In: Journal of medical internet research. 2019 ; Vol. 21, No. 6. pp. e12843.

Harvard

Luz, C, Berends, M, Dik, J-W, Lokate, A, Pulcini, C, Glasner, C & Sinha, B 2019, 'Rapid Analysis of Diagnostic and Antimicrobial Patterns in R (RadaR): Interactive Open-Source Software App for Infection Management and Antimicrobial Stewardship', Journal of medical internet research, vol. 21, no. 6, 12843, pp. e12843. https://doi.org/10.2196/12843

Standard

Rapid Analysis of Diagnostic and Antimicrobial Patterns in R (RadaR) : Interactive Open-Source Software App for Infection Management and Antimicrobial Stewardship. / Luz, Christian; Berends, Matthias; Dik, Jan-Willem; Lokate, Antonia; Pulcini, Céline; Glasner, Corinna; Sinha, Bhanu.

In: Journal of medical internet research, Vol. 21, No. 6, 12843, 11.06.2019, p. e12843.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Luz C, Berends M, Dik J-W, Lokate A, Pulcini C, Glasner C et al. Rapid Analysis of Diagnostic and Antimicrobial Patterns in R (RadaR): Interactive Open-Source Software App for Infection Management and Antimicrobial Stewardship. Journal of medical internet research. 2019 Jun 11;21(6):e12843. 12843. https://doi.org/10.2196/12843


BibTeX

@article{67bffad6436b4f8fbc46616ddf93e824,
title = "Rapid Analysis of Diagnostic and Antimicrobial Patterns in R (RadaR): Interactive Open-Source Software App for Infection Management and Antimicrobial Stewardship",
abstract = "Background: Analyzing process and outcome measures for all patients diagnosed with an infection in a hospital, including those suspected of having an infection, requires not only processing of large datasets but also accounting for numerous patient parameters and guidelines. Substantial technical expertise is required to conduct such rapid, reproducible, and adaptable analyses; however, such analyses can yield valuable insights for infection management and antimicrobial stewardship (AMS) teams.Objective: The aim of this study was to present the design, development, and testing of RadaR (Rapid analysis of diagnostic and antimicrobial patterns in R), a software app for infection management, and to ascertain whether RadaR can facilitate user-friendly, intuitive, and interactive analyses of large datasets in the absence of prior in-depth software or programming knowledge.Methods: RadaR was built in the open-source programming language R, using Shiny, an additional package to implement Web-app frameworks in R. It was developed in the context of a 1339-bed academic tertiary referral hospital to handle data of more than 180,000 admissions.Results: RadaR enabled visualization of analytical graphs and statistical summaries in a rapid and interactive manner. It allowed users to filter patient groups by 17 different criteria and investigate antimicrobial use, microbiological diagnostic use and results including antimicrobial resistance, and outcome in length of stay. Furthermore, with RadaR, results can be stratified and grouped to compare defined patient groups on the basis of individual patient features.Conclusions: AMS teams can use RadaR to identify areas within their institutions that might benefit from increased support and targeted interventions. It can be used for the assessment of diagnostic and therapeutic procedures and for visualizing and communicating analyses. RadaR demonstrated the feasibility of developing software tools for use in infection management and for AMS teams in an open-source approach, thus making it free to use and adaptable to different settings.",
keywords = "antimicrobial stewardship, software, hospital records, data visualization, infection, medical informatics applications, PROGRAM, IMPROVE, CARE",
author = "Christian Luz and Matthias Berends and Jan-Willem Dik and Antonia Lokate and C{\'e}line Pulcini and Corinna Glasner and Bhanu Sinha",
note = "{\textcopyright}Christian Friedemann Luz, Matthijs S Berends, Jan-Willem H Dik, Mari{\"e}tte Lokate, C{\'e}line Pulcini, Corinna Glasner, Bhanu Sinha. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.05.2019.",
year = "2019",
month = jun,
day = "11",
doi = "10.2196/12843",
language = "English",
volume = "21",
pages = "e12843",
journal = "Journal of medical internet research",
issn = "1438-8871",
publisher = "JMIR PUBLICATIONS, INC",
number = "6",

}

RIS

TY - JOUR

T1 - Rapid Analysis of Diagnostic and Antimicrobial Patterns in R (RadaR)

T2 - Interactive Open-Source Software App for Infection Management and Antimicrobial Stewardship

AU - Luz, Christian

AU - Berends, Matthias

AU - Dik, Jan-Willem

AU - Lokate, Antonia

AU - Pulcini, Céline

AU - Glasner, Corinna

AU - Sinha, Bhanu

N1 - ©Christian Friedemann Luz, Matthijs S Berends, Jan-Willem H Dik, Mariëtte Lokate, Céline Pulcini, Corinna Glasner, Bhanu Sinha. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.05.2019.

PY - 2019/6/11

Y1 - 2019/6/11

N2 - Background: Analyzing process and outcome measures for all patients diagnosed with an infection in a hospital, including those suspected of having an infection, requires not only processing of large datasets but also accounting for numerous patient parameters and guidelines. Substantial technical expertise is required to conduct such rapid, reproducible, and adaptable analyses; however, such analyses can yield valuable insights for infection management and antimicrobial stewardship (AMS) teams.Objective: The aim of this study was to present the design, development, and testing of RadaR (Rapid analysis of diagnostic and antimicrobial patterns in R), a software app for infection management, and to ascertain whether RadaR can facilitate user-friendly, intuitive, and interactive analyses of large datasets in the absence of prior in-depth software or programming knowledge.Methods: RadaR was built in the open-source programming language R, using Shiny, an additional package to implement Web-app frameworks in R. It was developed in the context of a 1339-bed academic tertiary referral hospital to handle data of more than 180,000 admissions.Results: RadaR enabled visualization of analytical graphs and statistical summaries in a rapid and interactive manner. It allowed users to filter patient groups by 17 different criteria and investigate antimicrobial use, microbiological diagnostic use and results including antimicrobial resistance, and outcome in length of stay. Furthermore, with RadaR, results can be stratified and grouped to compare defined patient groups on the basis of individual patient features.Conclusions: AMS teams can use RadaR to identify areas within their institutions that might benefit from increased support and targeted interventions. It can be used for the assessment of diagnostic and therapeutic procedures and for visualizing and communicating analyses. RadaR demonstrated the feasibility of developing software tools for use in infection management and for AMS teams in an open-source approach, thus making it free to use and adaptable to different settings.

AB - Background: Analyzing process and outcome measures for all patients diagnosed with an infection in a hospital, including those suspected of having an infection, requires not only processing of large datasets but also accounting for numerous patient parameters and guidelines. Substantial technical expertise is required to conduct such rapid, reproducible, and adaptable analyses; however, such analyses can yield valuable insights for infection management and antimicrobial stewardship (AMS) teams.Objective: The aim of this study was to present the design, development, and testing of RadaR (Rapid analysis of diagnostic and antimicrobial patterns in R), a software app for infection management, and to ascertain whether RadaR can facilitate user-friendly, intuitive, and interactive analyses of large datasets in the absence of prior in-depth software or programming knowledge.Methods: RadaR was built in the open-source programming language R, using Shiny, an additional package to implement Web-app frameworks in R. It was developed in the context of a 1339-bed academic tertiary referral hospital to handle data of more than 180,000 admissions.Results: RadaR enabled visualization of analytical graphs and statistical summaries in a rapid and interactive manner. It allowed users to filter patient groups by 17 different criteria and investigate antimicrobial use, microbiological diagnostic use and results including antimicrobial resistance, and outcome in length of stay. Furthermore, with RadaR, results can be stratified and grouped to compare defined patient groups on the basis of individual patient features.Conclusions: AMS teams can use RadaR to identify areas within their institutions that might benefit from increased support and targeted interventions. It can be used for the assessment of diagnostic and therapeutic procedures and for visualizing and communicating analyses. RadaR demonstrated the feasibility of developing software tools for use in infection management and for AMS teams in an open-source approach, thus making it free to use and adaptable to different settings.

KW - antimicrobial stewardship

KW - software

KW - hospital records

KW - data visualization

KW - infection

KW - medical informatics applications

KW - PROGRAM

KW - IMPROVE

KW - CARE

U2 - 10.2196/12843

DO - 10.2196/12843

M3 - Article

C2 - 31199325

VL - 21

SP - e12843

JO - Journal of medical internet research

JF - Journal of medical internet research

SN - 1438-8871

IS - 6

M1 - 12843

ER -

ID: 78467714