BACKGROUND: Hospitals use antibiograms to guide optimal empiric antibiotic therapy, reduce inappropriate antibiotic usage, and identify areas requiring intervention by antimicrobial stewardship programs. Creating a hospital antibiogram is a time-consuming manual process that is typically performed annually. OBJECTIVE: We aimed to apply visual analytics software to electronic health record (EHR) data to build an automated, electronic antibiogram ("e-antibiogram") that adheres to national guidelines and contains filters for patient characteristics, thereby providing access to detailed, clinically relevant, and up-to-date antibiotic susceptibility data. METHODS: We used visual analytics software to develop a secure, EHR-linked, condition- and patient-specific e-antibiogram that supplies susceptibility maps for organisms and antibiotics in a comprehensive report that is updated on a monthly basis. Antimicrobial susceptibility data were grouped into nine clinical scenarios according to the specimen source, hospital unit, and infection type. We implemented the e-antibiogram within the EHR system at Children's Hospital of Philadelphia, a tertiary pediatric hospital and analyzed e-antibiogram access sessions from March 2016 to March 2017. RESULTS: The e-antibiogram was implemented in the EHR with over 6,000 inpatient, 4,500 outpatient, and 3,900 emergency department isolates. The e-antibiogram provides access to rolling 12-month pathogen and susceptibility data that is updated on a monthly basis. E-antibiogram access sessions increased from an average of 261 sessions per month during the first 3 months of the study to 345 sessions per month during the final 3 months. CONCLUSION: An e-antibiogram that was built and is updated using EHR data and adheres to national guidelines is a feasible replacement for an annual, static, manually compiled antibiogram. Future research will examine the impact of the e-antibiogram on antibiotic prescribing patterns. Schattauer GmbH Stuttgart.
BACKGROUND: Hospitals use antibiograms to guide optimal empiric antibiotic therapy, reduce inappropriate antibiotic usage, and identify areas requiring intervention by antimicrobial stewardship programs. Creating a hospital antibiogram is a time-consuming manual process that is typically performed annually. OBJECTIVE: We aimed to apply visual analytics software to electronic health record (EHR) data to build an automated, electronic antibiogram ("e-antibiogram") that adheres to national guidelines and contains filters for patient characteristics, thereby providing access to detailed, clinically relevant, and up-to-date antibiotic susceptibility data. METHODS: We used visual analytics software to develop a secure, EHR-linked, condition- and patient-specific e-antibiogram that supplies susceptibility maps for organisms and antibiotics in a comprehensive report that is updated on a monthly basis. Antimicrobial susceptibility data were grouped into nine clinical scenarios according to the specimen source, hospital unit, and infection type. We implemented the e-antibiogram within the EHR system at Children's Hospital of Philadelphia, a tertiary pediatric hospital and analyzed e-antibiogram access sessions from March 2016 to March 2017. RESULTS: The e-antibiogram was implemented in the EHR with over 6,000 inpatient, 4,500 outpatient, and 3,900 emergency department isolates. The e-antibiogram provides access to rolling 12-month pathogen and susceptibility data that is updated on a monthly basis. E-antibiogram access sessions increased from an average of 261 sessions per month during the first 3 months of the study to 345 sessions per month during the final 3 months. CONCLUSION: An e-antibiogram that was built and is updated using EHR data and adheres to national guidelines is a feasible replacement for an annual, static, manually compiled antibiogram. Future research will examine the impact of the e-antibiogram on antibiotic prescribing patterns. Schattauer GmbH Stuttgart.
Authors: Allan F Simpao; Luis M Ahumada; Bimal R Desai; Christopher P Bonafide; Jorge A Gálvez; Mohamed A Rehman; Abbas F Jawad; Krisha L Palma; Eric D Shelov Journal: J Am Med Inform Assoc Date: 2014-10-15 Impact factor: 4.497
Authors: Rakesh D Mistry; Jason G Newland; Jeffrey S Gerber; Adam L Hersh; Larissa May; Sarah M Perman; Nathan Kuppermann; Peter S Dayan Journal: Infect Control Hosp Epidemiol Date: 2017-02-08 Impact factor: 3.254
Authors: Jorge A Gálvez; Luis Ahumada; Allan F Simpao; Elaina E Lin; Christopher P Bonafide; Dhruv Choudhry; William R England; Abbas F Jawad; David Friedman; Debora A Sesok-Pizzini; Mohamed A Rehman Journal: J Am Med Inform Assoc Date: 2013-12-20 Impact factor: 4.497
Authors: Pranita D Tamma; Gwen L Robinson; Jeffrey S Gerber; Jason G Newland; Chloe M DeLisle; Theoklis E Zaoutis; Aaron M Milstone Journal: Infect Control Hosp Epidemiol Date: 2013-10-28 Impact factor: 3.254
Authors: Jenny E Dolan; Hannah Lonsdale; Luis M Ahumada; Amish Patel; Jibin Samuel; Ali Jalali; Jacquelin Peck; JoAnn C DeRosa; Mohamed Rehman; Anna M Varughese; Allison M Fernandez Journal: Appl Clin Inform Date: 2019-07-31 Impact factor: 2.342
Authors: Xiaomei Wang; Tracy C Kim; Sudeep Hegde; Daniel J Hoffman; Natalie C Benda; Ella S Franklin; David Lavergne; Shawna J Perry; Rollin J Fairbanks; A Zachary Hettinger; Emilie M Roth; Ann M Bisantz Journal: Appl Clin Inform Date: 2019-09-18 Impact factor: 2.342
Authors: Mustafa Ozkaynak; Noel Metcalf; Daniel M Cohen; Larissa S May; Peter S Dayan; Rakesh D Mistry Journal: Appl Clin Inform Date: 2020-09-09 Impact factor: 2.342
Authors: Danny T Y Wu; Scott Vennemeyer; Kelly Brown; Jason Revalee; Paul Murdock; Sarah Salomone; Ashton France; Katherine Clarke-Myers; Samuel P Hanke Journal: Appl Clin Inform Date: 2019-11-13 Impact factor: 2.342
Authors: Olivia Nelson; Brian Sturgis; Keri Gilbert; Elizabeth Henry; Kelly Clegg; Jonathan M Tan; Jack O Wasey; Allan F Simpao; Jorge A Gálvez Journal: Appl Clin Inform Date: 2019-08-07 Impact factor: 2.342