Development of an antimicrobial stewardship module in an electronic health record: Options to enhance daily antimicrobial stewardship activities

Improving the Efficiency of Antimicrobial Stewardship Action in Acute Care Facilities

Inpatient antimicrobial stewardship (AS) programs are quality improvement programs tasked with improving antibiotic practices by augmenting frontline providers' antibiotic prescription. Prospective audit and feedback (PAF) and preauthorization (PRA) are essential activities in the hospital that can be resource intensive for AS teams. Improving efficiency in AS activities is needed when there are limited resources or when programs are looking to expand tasks beyond PAF and PRA, such as broad education or guideline development. Guidance on the creation and maintenance of alerts for the purpose of PAF reviews, modifications of antibiotic restrictions for PRA polices, and overall initiative prioritization strategies are reviewed. In addition, daily prioritization tools, such as the tiered approach, scoring systems, and regression modeling, are available for stewards to prioritize their daily workflow. Using these tools and guidance, AS programs can be productive and impactful in the face of resource limitation or competing priorities in the hospital.

Real-World Antimicrobial Stewardship Experience in a Large Academic Medical Center: Using Statistical and Machine Learning Approaches to Identify Intervention “Hotspots” in an Antibiotic Audit and Feedback Program

Background

Prospective audit with feedback (PAF) is an impactful strategy for antimicrobial stewardship program (ASP) activities. However, because PAF requires reviewing large numbers of antimicrobial orders on a case-by-case basis, PAF programs are highly resource-intensive. The current study aimed to identify predictors of ASP intervention (i.e., feedback), and to build models to identify orders that can be safely bypassed from review, to make PAF programs more efficient.

Methods

We performed a retrospective cross-sectional study of inpatient antimicrobial orders reviewed by the University of Maryland Medical Center’s PAF program between 2017–2019. We evaluated the relationship between antimicrobial and patient characteristics with ASP intervention using multivariable logistic regression models. Separately, we built prediction models for ASP intervention using statistical and machine learning approaches and evaluated performance on held-out data.

Results

Across 17,503 PAF reviews, 4,219 (24%) resulted in intervention. In adjusted analyses, a clinical pharmacist on the ordering unit or receipt of an ID consult were associated with 17% and 56% lower intervention odds, respectively (aORs 0.83 and 0.44, P values ≤ 0.001). Fluoroquinolones had the highest adjusted intervention odds (aOR 3.22, 95% CI: 2.63–3.96). A machine learning classifier (C-statistic 0.76) reduced reviews by 49% while achieving 78% sensitivity. A “workflow simplified” regression model that restricted to antimicrobial class and clinical indication variables, two strong machine-learning-identified predictors, reduced reviews by one-third while achieving 81% sensitivity.

Conclusions

Prediction models substantially reduced PAF review caseloads while maintaining high sensitivities. Our results and approach may offer a blueprint for other ASPs.

Development of an Electronic Algorithm to Identify in Real Time Adults Hospitalized With Suspected Community-Acquired Pneumonia

Background

Community-acquired pneumonia (CAP) is a major driver of hospital antibiotic use. Efficient methods to identify patients treated for CAP in real time using the electronic health record (EHR) are needed. Automated identification of these patients could facilitate systematic tracking, intervention, and feedback on CAP-specific metrics such as appropriate antibiotic choice and duration.

Methods

Using retrospective data, we identified suspected CAP cases by searching for patients who received CAP antibiotics AND had an admitting International Classification of Diseases, Tenth Revision (ICD-10) code for pneumonia OR chest imaging within 24 hours OR bacterial urinary antigen testing within 48 hours of admission (denominator query). We subsequently explored different structured and natural language processing (NLP)–derived data from the EHR to identify CAP cases. We evaluated combinations of these electronic variables through receiver operating characteristic (ROC) curves to assess which best identified CAP cases compared to cases identified by manual chart review. Exclusion criteria were age <18 years, absolute neutrophil count <500 cells/mm³, and admission to an oncology unit.

Results

Compared to the gold standard of chart review, the area under the ROC curve to detect CAP was 0.63 (95% confidence interval [CI], .55–.72; P < .01) using structured data (ie, laboratory and vital signs) and 0.83 (95% CI, .77–.90; P < .01) when NLP-derived data from radiographic reports were included. The sensitivity and specificity of the latter model were 80% and 81%, respectively.

Conclusions

Creating an electronic tool that effectively identifies CAP cases in real time is possible, but its accuracy is dependent on NLP-derived radiographic data.