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. 2021 Mar 4;3(1):dlab018.
doi: 10.1093/jacamr/dlab018. eCollection 2021 Mar.

Feasibility study of hospital antimicrobial stewardship analytics using electronic health records

P F Dutey-Magni  1 , M J Gill  2 , D McNulty  2 , G Sohal  2 , A Hayward  3 , L Shallcross  1 , Niall Anderson  1   2   3 , Elise Crayton  1   2   3 , Gillian Forbes  1   2   3 , Arnoupe Jhass  1   2   3 , Emma Richardson  1   2   3 , Michelle Richardson  1   2   3 , Patrick Rockenschaub  1   2   3 , Catherine Smith  1   2   3 , Elizabeth Sutton  1   2   3 , Rosanna Traina  1   2   3 , Lou Atkins  1   2   3 , Anne Conolly  1   2   3 , Spiros Denaxas  1   2   3 , Ellen Fragaszy  1   2   3 , Rob Horne  1   2   3 , Patty Kostkova  1   2   3 , Fabiana Lorencatto  1   2   3 , Susan Michie  1   2   3 , Jennifer Mindell  1   2   3 , John Robson  1   2   3 , Claire Royston  1   2   3 , Carolyn Tarrant  1   2   3 , James Thomas  1   2   3 , Jonathan West  1   2   3 , Haydn Williams  1   2   3 , Nadia Elsay  1   2   3 , Chris Fuller  1   2   3
Affiliations

Feasibility study of hospital antimicrobial stewardship analytics using electronic health records

P F Dutey-Magni et al. JAC Antimicrob Resist. .

Abstract

Background: Hospital antimicrobial stewardship (AMS) programmes are multidisciplinary initiatives to optimize antimicrobial use. Most hospitals depend on time-consuming manual audits to monitor clinicians' prescribing. But much of the information needed could be sourced from electronic health records (EHRs).

Objectives: To develop an informatics methodology to analyse characteristics of hospital AMS practice using routine electronic prescribing and laboratory records.

Methods: Feasibility study using electronic prescribing, laboratory and clinical coding records from adult patients admitted to six specialities at Queen Elizabeth Hospital, Birmingham, UK (September 2017-August 2018). The study involved: (i) a review of AMS standards of care; (ii) their translation into concepts measurable from commonly available EHRs; and (iii) a pilot application in an EHR cohort study (n = 61679 admissions).

Results: We developed data modelling methods to characterize antimicrobial use (antimicrobial therapy episode linkage methods, therapy table, therapy changes). Prescriptions were linked into antimicrobial therapy episodes (mean 2.4 prescriptions/episode; mean length of therapy 5.8 days), enabling several actionable findings. For example, 22% of therapy episodes for low-severity community-acquired pneumonia were congruent with prescribing guidelines, with a tendency to use broader-spectrum antibiotics. Analysis of therapy changes revealed IV to oral therapy switching was delayed by an average 3.6 days (95% CI: 3.4-3.7). Microbial cultures were performed prior to treatment initiation in just 22% of antibacterial prescriptions. The proposed methods enabled fine-grained monitoring of AMS practice down to specialities, wards and individual clinical teams by case mix, enabling more meaningful peer comparison.

Conclusions: It is feasible to use hospital EHRs to construct rapid, meaningful measures of prescribing quality with potential to support quality improvement interventions (audit/feedback to prescribers), engagement with front-line clinicians on optimizing prescribing, and AMS impact evaluation studies.

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Figures

Figure 1.
Figure 1.
Mean and 95% CI of the total DOT per admission in patients receiving antimicrobials at any point during an admission (September 2017–August 2018).
Figure 2.
Figure 2.
Point and 95% CI estimates of the proportion of IV therapy converted into oral therapy ranked by consultant team by speciality (September 2017–August 2018). The horizontal line indicates the point estimate for the entire speciality.
Figure 3.
Figure 3.
Point and 95% CI estimates of the mean time (days) elapsed between ABCD criteria being met and conversion to oral therapy, ranked by consultant team by speciality (September 2017–August 2018). The horizontal line indicates the point estimate for the entire speciality.
Figure 4.
Figure 4.
Point and 95% CI estimates of the proportion of prescriptions initiated with a blood culture sampled in the 3 days leading up to initiation of prescription and/or therapy by consultant team by speciality by drug type in six selected specialities (September 2017–August 2018). Consultant teams are ranked by percentage with a sample for ‘other’ antibiotic class. The horizontal line indicates the point estimate for the entire speciality.
See this image and copyright information in PMC

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