Using Electronically Derived AutoMated RePOrts of Appropriate Antibiotic Use to Inform SteWardship IntERventions
Phase: NA
Conditions: Community-acquired Pneumonia, Acute Otitis Media, Pharyngitis
Sponsor: Children's Hospital of Philadelphia

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Using Electronically Derived AutoMated RePOrts of Appropriate Antibiotic use to Inform 
SteWardship IntERventions (EMPOWER) 
Adult Inpatient Community Acquired Pneumonia 
(EMPOWER-IN) 
 
NCT06027593 
 
 
 
Background: 
Antibiotic resistance (AR) is increasing at an alarming rate, leading to substantial morbidity and 
mortality for patients with infections due to resistant bacteria. Antibiotic use, the main driver of 
antibiotic resistance, is common in clinical practice in both inpatient and outpatient settings. 
More than half of all adult and pediatric patients receive antibiotics during an inpatient stay 1, 2. 
In the outpatient setting, the equivalent of 849 courses of antibiotics are prescribed for every 
1,000 persons 3, 4. However, up to 50% of these prescriptions are inappropriate 5-8.  
Antibiotic stewardship (AS) has been shown to improve patient outcomes, decrease adverse 
events, and decrease antibiotic resistance 9. Tracking antibiotic use is a fundamental 
component of AS. Although strategies to track antibiotic use in meaningful ways have been 
attempted, these metrics typically track aggregate antibiotic use and are at best surrogate 
markers of inappropriate antibiotic use. Further, minimal research has evaluated the relationship 
between aggregate antibiotic use measures and inappropriate antibiotic prescribing. Because 
aggregate antibiotic use metrics don’t necessarily measure inappropriate antibiotic use, 
antibiotic stewardship programs (ASPs) must utilize time-intensive strategies reliant on manual 
electronic health record (EHR) review, such as medication use evaluations, in order to 
qualitatively assess the appropriateness of antibiotic use. Ultimately, these limitations make 
current metrics less meaningful to clinicians and actionable by ASPs.  
The increasing availability of EHRs across healthcare settings provides a unique opportunity 
to extract more data elements than ever before from the level of individual patients to the level 
of healthcare facilities. Utilizing these data elements has the potential to create more actionable 
metrics using individual indicators or groups of indicators to better identify infectious disease 
diagnoses and measure inappropriate antibiotic use for these conditions. In order to utilize EHR 
data in this way, performance characteristics of candidate indicators need to be systematically 
evaluated and then integrated into software that allows ASPs to perform these analyses on an 
ongoing basis.  
In a prior research project conducted at Children’s Hospital of Philadelphia (CHOP) and 
Penn (SHEPheRD Contract #200-2016-91796, titled “Assessing Appropriate Antibiotic Use in 
Hospitals and Outpatient Settings Through Electronic Health Records”), our research group 
developed several electronic indicators measuring appropriate antibiotic use for community 
acquired pneumonia (CAP) that were extractable using electronic data sources10-15. These 
indicators measure two dimensions of antibiotic prescribing: 1) the choice of antibiotic agent and 
2) the duration of antibiotic use10-15.  
The focus of the current protocol is to: 1) refine this electronic definition of CAP and the 
electronic indicators for appropriate antibiotic prescribing through a detailed chart review and 
validation process; 2) assess the impact of generating reports reflecting these indicators on the 
appropriateness of antibiotic use; and 3) to evaluate the acceptability and feasibility of delivering 
these reports to prescribers. If effective, these EHR-based approaches hold the promise to 
greatly enhance effectiveness and efficiency of AS initiatives.  
As a secondary aim, we also seek to identify racial disparities in the appropriateness of 
antibiotic prescribing in patients hospitalized with CAP.  
 

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Project Goals: 
The goals of this project are to: 
Aim 1: Refine the electronic definition of CAP and the electronic indicators for 
appropriate antibiotic prescribing through a detailed chart review and validation process 
Aim 2: Design and implement a scalable and sustainable AS feedback reported-based 
intervention for these populations informed by a rapid user-centered design process 
Aim 3: Track the impact of the stewardship intervention and report to key stakeholders 
(e.g., prescribers) 
 
Secondary aim: To identify racial disparities in the appropriateness of antibiotic 
prescribing in patients hospitalized with CAP 
 
 
Study Design: 
Aim 1:  
We will first conduct a retrospective descriptive study to refine and validate the previously 
developed electronic definition of CAP and indicators for appropriate antibiotic choice and 
duration.  
 
To do this, we will first refine an electronic definition of CAP, which may include ICD-10 codes, 
antibiotic orders, and orders for radiographic studies suggesting a diagnosis of CAP. We will 
validate this definition through chart review to ensure that the electronic definition of CAP 
correctly identifies patients being for CAP based on documentation in the EHR. We will also 
evaluate the impact of various inclusion and exclusion criteria within the cohort, such as ICU 
admission and presence of chronic comorbid conditions (e.g., lung cancer, cardiopulmonary 
disease) on the sensitivity and specificity of this electronic definition of CAP.  
 
As a secondary component of this aim, we will also compare this electronic definition of CAP 
with provider-selected indication. Upon ordering antibiotics for inpatients, providers must select 
the indication for the antibiotic; one of the possible indications is CAP. We will perform chart 
review on a subset of patients ordered for azithromycin or doxycycline within the first 48 hours 
of admission, given that these are common antibiotics ordered for CAP. Chart review to assess 
for provider intention to treat for CAP will serve as the gold standard. We will then compare the 
performance of the electronic definition and provider-selected indication with the chart review-
based findings, assessing sensitivity and specificity of these two methods.  
 
We will also validate our indicators of antibiotic appropriateness, which include appropriate 
antibiotic choice and duration. To validate these indicators, we will compare sensitivity and 
specificity of the electronic definition of appropriate antibiotic choice and duration compared to a 
“gold standard” of manual chart review. We will iterate these definitions until acceptable 
performance characteristics for the electronic indicators are achieved. These measures of 
appropriateness are based on national guidelines for appropriate antibiotic prescribing. 
 
Finally, we will also collect data on prescribers, including the attending of record and clinicians 
placing orders, to evaluate whether the indicators for appropriate antibiotic choice and duration 
can be attributed to individual prescribers. Study measures for this aim will be purely 
descriptive.   
 
 
 

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Aim 2:  
We will then generate reports of appropriate antibiotic choice and duration using the indicators 
validated in Aim 1. Through a user-centered design process, we will optimize the structure and 
delivery of these feedback reports through conducting interviews with a small number of clinical 
provider stakeholders. We will also perform surveys of all clinicians during the intervention 
focused on feasibility and acceptability of the intervention.  
 
Aim 3:  
We will conduct a quasi-experimental study in the adult inpatient settings measuring the impact 
of implementing the feedback reports on appropriateness of antibiotic choice and duration for 
CAP using the validated indicators. The baseline, pre-intervention period will last approximately 
up to 24 months and the intervention period will last up to 24 months.  
 
Secondary aim: 
We will use the retrospective cohort from aim 1 to assess racial disparities in antibiotic 
prescribing for CAP. Briefly, we will use the two measures of antibiotic prescribing 
appropriateness to assess for differences in appropriate prescribing by race, adjusting for other 
variables.  
 
 
Study sites: 
Adult 
The Hospital of the University of Pennsylvania (HUP) is a 774-bed academic quaternary acute 
care medical center. There are over 36,000 admissions, 1,500,000 outpatient visits, and 65,000 
Emergency Department visits annually.  
 
Penn Presbyterian Medical Center (PPMC) is a 324-bed urban community acute care hospital in 
West Philadelphia. There are 12,000 admissions, 130,000 outpatient visits, and 31,000 ED 
visits annually.  
 
Chester County Hospital (CCH) is a 257-bed facility with 13,521 admissions, 425,107 outpatient 
visits, and 45,317 ED visits annually. The service area is primarily urban and suburban Chester 
County, Pennsylvania. 
 
 
Study subjects: 
There are two study populations for this study: patients and clinicians.  
 
Patients with community acquired pneumonia 
Inclusion:  
1. Diagnosis of community acquired pneumonia based on ICD-10 diagnostic codes (see 
appendix 1)  
2. Prescribed one or more antibiotic within the first 48 hours of hospitalization AND at least 
one dose administered between 24 and 48 hours OR discharged within 48 hours on 
antibiotics 
3. Chest x-ray or chest CT within 48 hours of admission 
 
Exclusion 
1. Transfer from another healthcare facility 
2. Died within the first 48 hours of admission 
 

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For the secondary component of Aim 1 in which we compare the electronic definition of CAP 
and provider selected indication with manual chart review, our eligible patient population will 
include all adult patients who received at least one dose of doxycycline or azithromycin within 
the first 48 hours of admission. Patients who die within the first 48 hours of admission will be 
excluded. A random sample will then be selected for chart review.  
 
Our interaction with patients will be limited to review of existing EHR data. We will have no 
direct contact with patients. Appropriateness of antibiotic use will be reported on an encounter 
level; data will be aggregated prior to giving feedback to clinicians, though, clinicians may be 
provided with information on prescribing only for patients for whom they prescribed (or 
supervised the prescribing of) antibiotics.  
 
Adult clinicians 
The focus of the intervention is on delivering feedback reports to physicians caring for inpatients 
with CAP. Clinicians providing care to patients with CAP on the inpatient medical services, or in 
the ICU will be included. In addition, we will identify up to 30 clinician stakeholders for semi-
structured interviews as part of the user centered design process to develop the feedback 
reports. Because feedback reports are a novel intervention in the inpatient environment, this 
stakeholder engagement and formative evaluation are critical elements of this protocol. In 
addition, all providers will receive a survey to evaluate their perceptions of the intervention post-
intervention.  
 
 
Intervention design and implementation: 
The intervention will consist primarily of an automated prescribing feedback report delivered 
in aggregate to clinicians. The precise content of the report will be developed with stakeholder 
feedback, but we anticipate these reports will include such data as: 1) number of patients seen 
by the prescriber group with the target condition; 2) proportion of patients for which the choice of 
antibiotic was correct (based on electronic indicators); 3) proportion of patients for which 
duration of prescribing was correct; and 4) proportion of patients for which both indicators were 
correct. Clinicians may be provided with patient-level information on prescribing only for patients 
for whom they prescribed (or supervised the prescribing of) antibiotics. 
We will work with our sponsor (the CDC) and key stakeholder champions to engage in a 
rapid user centered design process to create feedback reports that will fit the prescribing 
context17. In previous work we have found this contributes to greater levels of acceptance by 
clinicians18,19. First, we will optimize the form of the report (how information is displayed) and 
how the reports will be delivered to prescribers (e.g. individually via email, embedded in a 
dashboard, provided verbally and division quality improvement meetings). Second, we will 
determine appropriate implementation supports that will be needed to accompany the 
introduction of the feedback reports to each clinical setting. Potential implementation supports 
will include educational materials for prescribers, communication about the feedback reports to 
bolster prescriber understanding and trust, and champion-led meetings to ensure that 
prescribers are aware of the intervention. These activities will be designed with scalability in 
mind, and so they may be used across diverse settings without negatively impacting operations. 
Clinician champions will be engaged to inform this intervention as part of the study team, and 
providers will also be recruited as research subjects to participate in the design process. 
We will implement the intervention at the completion of the rapid user centered design 
process. We will start by launching any educational materials or awareness-building activities as 
previously described before activating the feedback reports. Following the initial awareness-
building activities, we would propose to circulate the antibiotic prescribing feedback reports 
using the format determined in consultation with stakeholders. In the post-intervention period, 

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we will assess feasibility and acceptability of the reports and implementation activities. To do 
this, we will conduct semi-structured interviews with our key stakeholder champions and 
administer a brief survey to prescribers. The survey will contain demographic questions, 
attitudinal questions about AS used in our previous research20, the Feasibility of Intervention 
Measure (FIM) and the Acceptability of Intervention Measures (AIM). FIM and AIM have 
demonstrated strong psychometric properties to determine the extent to which stakeholders 
believe an intervention is feasible and acceptable21. Because inpatient providers may be on 
clinical service very sporadically, rather than administering the survey at one point in time, we 
will circulate the survey at multiple time points post-intervention to prescribers who have been 
on clinical service and have one or more encounters with a CAP patient. 
 
 
Data collection: 
Data elements obtained from the EHR include: patient demographics, address to calculate area 
deprivation index), preferred language, comorbidities (ICD-10 codes), admitting service, 
provider, antibiotic allergies, all medications for the previous 1 year, all diagnosis codes for the 
previous 1 year, laboratory testing, radiographic testing, discharge disposition, and ED 
visits/hospitalizations within 30 days after discharge. Antibiotic use measures specific to the 
condition (choice of agent and duration of therapy) will also be collected. This data will be 
extracted from the UPHS data warehouse (Clarity) that stores data from the electronic health 
record. These data will be used in feedback report generation as well as in outcome 
assessment.  
 
Data will also be collected during the rapid user centered design process as well as during the 
intervention period in the form of surveys related to feasibility and acceptability of the 
intervention. Data collected will include interview and survey responses from individual 
providers. No direct identifiers will be collected. 
 
Finally, we will measure provider engagement as is feasible, including attendance at 
educational sessions, interaction with data displays of feedback measures.   
 
 
Outcomes: 
For aim 1, we will assess sensitivity and specificity of the electronic definition of CAP and 
the antibiotic prescribing indicators.  
 
In order to demonstrate the effect of the intervention (aims 2 and 3), we will track 
appropriateness of antibiotic use based on the key indicators in the automated reports for the 
selected targeted conditions prior to and following implementation of the stewardship 
interventions. The following indicators will be assessed:  
1) Choice of antibiotic therapy: administration of a first-line agent within 48 hours of 
admission  
2) Duration of therapy: all antibiotic durations ≤ 5 days are classified as appropriate 
 
The primary outcome in this study is appropriate antibiotic use for CAP at the encounter level. If 
an encounter is associated with appropriate antibiotic use for both metrics, the encounter will be 
classified in the numerator as “appropriate” antibiotic use. The denominator will consist of all 
CAP encounters. As a secondary analysis, we will separately analyze appropriate antibiotic 
choice for CAP and appropriate antibiotic duration for CAP. Additional secondary outcomes 
include: hospital readmission within 30 days from the index visit, ED or primary care revisit 
within 30 days of the index visit with a new antibiotic prescription for a different antibiotic.   

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Finally, we will assess implementation metrics, including provider engagement with the 
feedback reports, as well as provider responses to interviews and surveys conducted as part of 
the assessment of feasibility and acceptability of the intervention.  
 
Secondary aim: The same two outcomes of appropriateness (choice of antibiotic therapy and 
duration of therapy) will be used for this aim.  
 
 
Analysis: 
At the level of the encounter, patient-specific variables will be compared in the baseline and 
intervention periods using chi-squared or Wilcoxon rank-sum testing as appropriate. For the 
primary analysis, a multivariate logistic regression model will be performed at the encounter 
level, with the intervention (i.e., baseline period vs intervention period) as the primary binary 
exposure of interest. Calendar month will be included separately in the model, allowing for 
adjustment for seasonal variation in antibiotic prescribing. We will additionally analyze these 
data as a pre-post study with segmented regression analysis. This would allow us to assess the 
immediate change in the outcomes of interest (i.e., appropriate antibiotic use, reflecting 
antibiotic choice and antibiotic duration) as well as the slope of change occurring associated 
with the implementation of the intervention. In this analysis we would also control for potential 
confounders such as changes in the patient population, census, admissions, nurse staffing, etc. 
 
Secondary aim: At the level of the encounter, differences in proportion of appropriate 
prescribing will be compared by race, using chi-squared testing. Then multivariate logistic 
regression will be performed at the encounter level, with race as the primary exposure of 
interest, incorporating other potential confounders in the model.  
 
 
Sample size: 
For the chart review/validation components of this study, we estimate needing up to 1500 charts 
for review.  
 
The primary outcome of the interventional component of this study is appropriate antibiotic use 
for adults with CAP. In order to be classified as “appropriate,” both the antibiotic choice indicator 
and the duration indicator must be appropriate.  
 
Based on preliminary data, we anticipate including up to up to 10,000 adults with CAP over the 
four-year study period (24 months pre-intervention and 24 months post-intervention). We 
estimate that approximately 50% of encounters will be associated with appropriate antibiotic 
prescribing. To detect an improvement to 60%, we require a sample size of 776 (total in the pre 
and post periods). Given the projected CAP case numbers, have adequate sample size to 
detect this difference.   
 
 

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Appendix 1. Diagnostic codes used to define Community Acquired Pneumonia (CAP)  
ICD-10 Code 
Code Description 
J09.X1 
INFLUENZA WITH PNEUMONIA 
J10.00 
INFLUENZA DUE TO OTHER IDENTIFIED INFLUENZA VIRUS WITH 
PNEUMONIA 
J10.01 
INFLUENZA DUE TO OTHER IDENTIFIED INFLUENZA VIRUS WITH THE SAME 
OTHER IDENTIFIED INFLUENZA VIRUS PNEUMONIA  
J10.08 
INFLUENZA DUE TO OTHER IDENTIFIED INFLUENZA VIRUS WITH OTHER 
SPECIFIED PNEUMONIA 
J11.00 
INFLUENZA DUE TO UNIDENTIFIED INFLUENZA VIRUS WITH UNSPECIFIED 
TYPE OF PNEUMONIA 
J11.08 
INFLUENZA DUE TO UNIDENTIFIED INFLUENZA VIRUS WITH SPECIFIED 
PNEUMONIA  
J12.0 
PNEUMONIA DUE TO ADENOVIRUS 
J12.1 
PNEUMONIA DUE TO RSV 
J12.2 
PNEUMONIA DUE TO PARAINFLUENZA VIRUS 
J12.3 
METAPNEUMOVIRUS PNEUMONIA 
J12.8 
OTHER VIRAL PNEUMONIA 
J12.81 
PNEUMONIA DUE TO SARS-ASSOCIATED CORONAVIRUS 
J12.82 
PNEUMONIA DUE TO CORONAVIRUS DISEASE 2019 
J12.89 
OTHER VIRAL PNEUMONIA 
J12.9 
VIRAL PNEUMONIA, UNSPECIFIED 
J13 
PNEUMONIA DUE TO STREPTOCOCCUS PNEUMONIAE 
J14 
PNEUMONIA DUE TO HEMOPHILUS INFLUENZAE 
J15 
PNEUMONIA DUE TO KLEBSIELLA PNEUMONIAE 
J15.1 
PNEUMONIA DUE TO PSEUDOMONAS 
J15.2 
PNEUMONIA DUE TO STAPHYLOCOCCUS, UNSPECIFIED 
J15.21 
PNEUMONIA DUE TO STAPHYLOCOCCUS AUREUS  
J15.211 
PNEUMONIA DUE TO METHICILLIN SUSCEPTIBLE STAPHYLOCOCCUS 
AUREUS 
J15.212 
PNEUMONIA DUE TO METHICILLIN RESISTANT STAPHYLOCOCCUS 
AUREUS 
J15.29 
PNEUMONIA DUE TO OTHER STAPHYLOCOCCUS 
J15.3 
PNEUMONIA DUE TO STREPTOCOCCUS, GROUP B 
J15.4 
PNEUMONIA DUE TO OTHER STREPTOCOCCI 
J15.5 
PNEUMONIA DUE TO ESCHERICHIA COLI 
J15.6 
PNEUMONIA DUE TO OTHER GRAM-NEGATIVE BACTERIA 
J15.7 
PNEUMONIA DUE TO MYCOPLASMA PNEUMONIAE 
J15.8 
PNEUMONIA DUE TO OTHER SPECIFIED BACTERIA 
J15.9 
UNSPECIFIED BACTERIAL PNEUMONIA 
J16.0 
CHLAMYDIAL PNEUMONIA 
J16.8 
PNEUMONIA DUE TO OTHER SPECIFIED INFECTIOUS ORGANISMS 
J17 
PNEUMONIA IN DISEASES CLASSIFIED ELSEWHERE 
J18.0 
BRONCHOPNEUMONIA, UNSPECIFIED ORGANISM 
J18.1 
LOBAR PNEUMONIA, UNSPECIFIED ORGANISM 
J18.8 
OTHER PNEUMONIA, UNSPECIFIED ORGANISM 
J18.9 
PNEUMONIA, UNSPECIFIED ORGANISM 
A37.01 
WHOOPING COUGH DUE TO BORDETELLA PERTUSSIS WITH PNEUMONIA 

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A37.11 
WHOOPING COUGH DUE TO BORDETELLA PARAPERTUSSIS WITH 
PNEUMONIA 
A37.81 
WHOOPING COUGH DUE TO OTHER BORDETELLA SPECIES WITH 
PNEUMONIA 
A37.91 
WHOOPING COUGH, UNSPECIFIED SPECIES WITH PNEUMONIA 
A48.1 
LEGIONNAIRES' DISEASE 
J22 
UNSPECIFIED ACUTE LOWER RESPIRATORY INFECTION 
 
 
 

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References: 
 
1. 
Gerber JS, Newland JG, Coffin SE, et al. Variability in antibiotic use at children's hospitals. Pediatrics. 
Dec 2010;126(6):1067-73. doi:10.1542/peds.2010-1275 
2. 
Polk RE, Hohmann SF, Medvedev S, Ibrahim O. Benchmarking risk-adjusted adult antibacterial drug use 
in 70 US academic medical center hospitals. Clin Infect Dis. Dec 2011;53(11):1100-10. doi:10.1093/cid/cir672 
3. 
Roberts RM, Hicks LA, Bartoces M. Variation in US outpatient antibiotic prescribing quality measures 
according to health plan and geography. Am J Manag Care. 2016;22(8):519-523.  
4. 
Hicks LA, Bartoces MG, Roberts RM, et al. US outpatient antibiotic prescribing variation according to 
geography, patient population, and provider specialty in 2011. Clin Infect Dis. May 1 2015;60(9):1308-16. 
doi:10.1093/cid/civ076 
5. 
Barnett ML, Linder JA. Antibiotic Prescribing for Adults With Acute Bronchitis in the United States, 1996-
2010. JAMA. 2014;311(19):2020-2022. doi:10.1001/jama.2013.286141 
6. 
Barnett ML, Linder JA. Antibiotic prescribing to adults with sore throat in the United States, 1997-2010. 
JAMA Intern Med. Jan 2014;174(1):138-40. doi:10.1001/jamainternmed.2013.11673 
7. 
Fairlie T, Shapiro DJ, Hersh AL, Hicks LA. National Trends in Visit Rates and Antibiotic Prescribing for 
Adults With Acute Sinusitis. Archives of Internal Medicine. 2012;172(19):1513-1514. 
doi:10.1001/archinternmed.2012.4089 
8. 
Fleming-Dutra KE, Hersh AL, Shapiro DJ, et al. Prevalence of Inappropriate Antibiotic Prescriptions 
Among US Ambulatory Care Visits, 2010-2011. Jama. May 3 2016;315(17):1864-73. doi:10.1001/jama.2016.4151 
9. 
Dellit TH, Owens RC, McGowan JE, Jr., et al. Infectious Diseases Society of America and the Society for 
Healthcare Epidemiology of America guidelines for developing an institutional program to enhance antimicrobial 
stewardship. Clin Infect Dis. Jan 15 2007;44(2):159-77. doi:10.1086/510393 
10. 
Gerber J, Grundmeier R, Hamilton K, et al. Development of an Electronic Algorithm to Identify 
Inappropriate Antibiotic Prescribing for Pediatric Pharyngitis. Infection Control & Hospital Epidemiology. 
2020;41(S1):s188-s189. doi:10.1017/ice.2020.728 
11. 
Gerber J, Grundmeier R, Hamilton K, et al. 151. An Electronic Algorithm to Better Target Antimicrobial 
Stewardship Program (ASP) Efforts for Children Hospitalized with Community-Acquired Pneumonia (CAP). Open 
Forum Infectious Diseases. 12/31 2020;7:S85-S86. doi:10.1093/ofid/ofaa439.196 
12. 
Lautenbach E, Gerber J, Grundmeier R, et al. 91. Development of an Electronic Algorithm to Identify 
Inappropriate Antibiotic Prescribing for Pediatric Otitis Media. Open Forum Infectious Diseases. 2020;7(Suppl 
1):S176-S177. doi:10.1093/ofid/ofaa439.401 
13. 
Lautenbach E, Hamilton K, Grundmeier R, et al. Development of an Electronic Algorithm to Target 
Outpatient Antimicrobial Stewardship Efforts for Acute Bronchitis. Infection Control & Hospital Epidemiology. 
2020;41(S1):s32-s32. doi:10.1017/ice.2020.510 
14. 
Lautenbach E, Hamilton KW, Grundmeier R, et al. 89. Construction of an Electronic Algorithm to 
Efficiently Target Antimicrobial Stewardship Efforts for Adults Hospitalized with Community-acquired Pneumonia. 
Open Forum Infectious Diseases. 2020;7(Supplement_1):S175-S176. doi:10.1093/ofid/ofaa439.399 
15. 
Lautenbach E, Hamilton KW, Grundmeier R, et al. 154. Development of an Electronic Algorithm to Target 
Outpatient Antimicrobial Stewardship Efforts for Adults with Acute Pharyngitis. Open Forum Infectious Diseases. 
2020;7(Supplement_1):S87-S87. doi:10.1093/ofid/ofaa439.199 
16. 
Meeker D, Linder JA, Fox CR, et al. Effect of Behavioral Interventions on Inappropriate Antibiotic 
Prescribing Among Primary Care Practices: A Randomized Clinical Trial. Jama. Feb 9 2016;315(6):562-70. 
doi:10.1001/jama.2016.0275 
17. 
Landis-Lewis Z, Kononowech J, Scott WJ, et al. Designing clinical practice feedback reports: three steps 
illustrated in Veterans Health Affairs long-term care facilities and programs. Implement Sci. Jan 21 2020;15(1):7. 
doi:10.1186/s13012-019-0950-y 
18. 
Redding LE, Muller BM, Szymczak JE. Small and Large Animal Veterinarian Perceptions of 
Antimicrobial Use Metrics for Hospital-Based Stewardship in the United States. Original Research. Frontiers in 
Veterinary Science. 2020-September-08 2020;7(582)doi:10.3389/fvets.2020.00582 
19. 
Szymczak JE, Feemster KA, Zaoutis TE, Gerber JS. Pediatrician perceptions of an outpatient antimicrobial 
stewardship intervention. Infect Control Hosp Epidemiol. Oct 2014;35 Suppl 3:S69-78. doi:10.1086/677826 
20. 
Szymczak JE, Kitt E, Hayes M, et al. Threatened efficiency not autonomy: Prescriber perceptions of an 
established pediatric antimicrobial stewardship program. Infect Control Hosp Epidemiol. May 2019;40(5):522-527. 
doi:10.1017/ice.2019.47 

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21. 
Weiner BJ, Lewis CC, Stanick C, et al. Psychometric assessment of three newly developed implementation 
outcome measures. Implementation Science. 2017/08/29 2017;12(1):108. doi:10.1186/s13012-017-0635-3