Novel Visualization of Clostridium difficile Infections in Intensive Care UnitsFunding This project was supported by the Institute for the Design of Environments Aligned for Patient Safety (IDEA4PS) at The Ohio State University which is sponsored by the Agency for Healthcare Research & Quality (AHRQ) (P30HS024379). The opinions expressed in this document are those of the authors and do not reflect the official position of AHRQ.
19 October 2018
04 June 2019
21 August 2019 (online)
Background Accurate and timely surveillance and diagnosis of health care facility onset Clostridium difficile infection (HO-CDI) is vital to controlling infections within the hospital, but there are limited tools to assist with timely outbreak investigations.
Objectives The objective of this study was to integrate spatiotemporal factors with HO-CDI cases and to develop a map-based dashboard to support infection preventionists (IPs) in performing surveillance and outbreak investigations for HO-CDI.
Methods Clinical laboratory results and Admit-Transfer-Discharge data for admitted patients over 2 years were extracted from the information warehouse of a large academic medical center (AMC) and processed according to the Center for Disease Control National Healthcare Safety Network definitions to classify CDI cases by onset date. Results were validated against the internal infection surveillance database maintained by IPs in Clinical Epidemiology of this AMC. Hospital floor plans were combined with HO-CDI case data, to create a dashboard of intensive care units. Usability testing was performed with a think-aloud session and a survey.
Results The simple classification algorithm identified all 265 HO-CDI cases from January 1, 2015 to November 30, 2015 with a positive predictive value (PPV) of 96.3%. When applied to data from 2014, the PPV was 94.6%. All users “strongly agreed” that the dashboard would be a positive addition to Clinical Epidemiology and would enable them to present hospital-acquired infection information to others more efficiently.
Conclusion The CDI dashboard demonstrates the feasibility of mapping clinical data to hospital patient care units for more efficient surveillance and potential outbreak investigations.
Keywordselectronic health records and systems - biosurveillance and case reporting - data visualization
Protection of Human and Animal Subjects
All research activities reported in this publication were reviewed and approved by the AMC's institutional review board.
- 1 Zimlichman E, Henderson D, Tamir O. , et al. Health care-associated infections: a meta-analysis of costs and financial impact on the US health care system. JAMA Intern Med 2013; 173 (22) 2039-2046
- 2 O'Brien JA, Lahue BJ, Caro JJ, Davidson DM. The emerging infectious challenge of Clostridium difficile-associated disease in Massachusetts hospitals: clinical and economic consequences. Infect Control Hosp Epidemiol 2007; 28 (11) 1219-1227
- 3 Lessa FC, Mu Y, Bamberg WM. , et al. Burden of Clostridium difficile infection in the United States. N Engl J Med 2015; 372 (09) 825-834
- 4 Quality Net. Measures. Available at: https://www.qualitynet.org/dcs/ContentServer?c=Page&pagename=QnetPublic%2FPage%2FQnetTier3&cid=1138900298473 . Accessed June 26, 2019
- 5 Faires MC, Pearl DL, Berke O, Reid-Smith RJ, Weese JS. The identification and epidemiology of methicillin-resistant Staphylococcus aureus and Clostridium difficile in patient rooms and the ward environment. BMC Infect Dis 2013; 13: 342
- 6 Barra-Carrasco J, Paredes-Sabja D. Clostridium difficile spores: a major threat to the hospital environment. Future Microbiol 2014; 9 (04) 475-486
- 7 Weber DJ, Anderson DJ, Sexton DJ, Rutala WA. Role of the environment in the transmission of Clostridium difficile in health care facilities. Am J Infect Control 2013; 41 (5, Suppl): S105-S110
- 8 Echaiz JF, Veras L, Zervos M, Dubberke E, Johnson L. Hospital roommates and development of health care-onset Clostridium difficile infection. Am J Infect Control 2014; 42 (10) 1109-1111
- 9 Freedberg DE, Salmasian H, Cohen B, Abrams JA, Larson EL. Receipt of antibiotics in hospitalized patients and risk for clostridium difficile infection in subsequent patients who occupy the same bed. JAMA Intern Med 2016; 176 (12) 1801-1808
- 10 Davis GS, Sevdalis N, Drumright LN. Spatial and temporal analyses to investigate infectious disease transmission within healthcare settings. J Hosp Infect 2014; 86 (04) 227-243
- 11 Kho A, Johnston K, Wilson J, Wilson SJ. Implementing an animated geographic information system to investigate factors associated with nosocomial infections: a novel approach. Am J Infect Control 2006; 34 (09) 578-582
- 12 Murray SG, Yim JWL, Croci R. , et al. Using spatial and temporal mapping to identify nosocomial disease transmission of Clostridium difficile. JAMA Intern Med 2017; 177 (12) 1863-1865
- 13 Cooper PH, Haney NM, Morey JM. Visual Analysis of Infection Surveillance. Poster Presented at the 2014 Workshop on Visual Analytics in Healthcare, American Medical Informatics Association 2014. Washington, DC; 2014
- 14 Middleton B, Bloomrosen M, Dente MA. , et al; American Medical Informatics Association. Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA. J Am Med Inform Assoc 2013; 20 (e1): e2-e8
- 15 Riddle DJ, Dubberke ER. Clostridium difficile infection in the intensive care unit. Infect Dis Clin North Am 2009; 23 (03) 727-743
- 16 CDC NHSN. Multidrug-Resistant Organism & Clostridium difficile Infection (MDRO/CDI) Module; 2017 . Available at: https://www.cdc.gov/nhsn/pdfs/pscmanual/12pscmdro_cdadcurrent.pdf . Accessed June 26, 2019
- 17 CDC NHSN. Identifying Healthcare-associated Infections (HAI) for NHSN Surveillance; 2017 [November 21, 2017]. Available at: https://www.cdc.gov/nhsn/PDFs/pscManual/2PSC_IdentifyingHAIs_NHSNcurrent.pdf . Accessed June 26, 2019
- 18 Lewis CH. Using the “Thinking Aloud” Method in Cognitive Interface Design (Technical Report) 1982. IBM. RC-9265
- 19 Yen PY, Wantland D, Bakken S. Development of a customizable health IT usability evaluation scale. AMIA Annu Symp Proc 2010; 2010: 917-921
- 20 Foraker RE, Shoben AB, Lopetegui MA. , et al. Assessment of Life's Simple 7 in the primary care setting: the Stroke Prevention in Healthcare Delivery EnviRonmEnts (SPHERE) study. Contemp Clin Trials 2014; 38 (02) 182-189