scholarly journals The impact of drug order complexity on prospective medication order review and verification time

2019 ◽  
Vol 27 (2) ◽  
pp. 284-293
Author(s):  
David S Dakwa ◽  
Vincent D Marshall ◽  
Bruce W Chaffee
Keyword(s):  
Medical Center ◽  
Statistical Significance ◽  
Academic Medical Center ◽  
Low Complexity ◽  
Primary Study ◽  
Medication Order ◽  
High Complexity ◽  
Verification Time ◽  
The Impact ◽  
Drug Order

Abstract Objective To assess if the amount of time a pharmacist spends verifying medication orders increases as medication orders become more complex. Materials and Methods The study was conducted by observing pharmacist verification of adult medication orders in an academic medical center. Drug order complexity was prospectively defined and validated using a classification system derived from 3 factors: the degree of order variability, ISMP high-alert classification, and a pharmacist perception survey. Screen capture software was used to measure pharmacist order review time for each classification. The annualized volume of low complexity drug orders was used to calculate the potential time savings if these were verified using an alternate system that did not require pharmacist review. Results The primary study hypothesis was not achieved. Regression results did not show statistical significance for moderate (n = 30, 23.7 seconds, sd = 23.3) or high complexity (n = 30, 18.6 seconds, sd = 23.1) drugs relative to the low complexity drugs (n = 30, 8.0 seconds, sd = 14.4) nor for moderate vs high complexity; (βmoderate vs low = 15.6, P = .113), (βhigh vs low = 10.3, P = .235), (βmoderate vs high = 5.3, P = .737). The sensitivity analysis showed statistical significance in the high vs low comparison (βhigh vs low = 13.8, P = .017). Discussion This study showed that verifying pharmacists spent less time than projected to verify medication orders of different complexities, but the time did not correlate with the classifications used in our complexity scale. Several mitigating factors, including operational aspects associated with timing antimicrobial orders, likely influenced order verification time. These factors should be evaluated in future studies which seek to define drug order complexity and optimize pharmacist time spent in medication order verification. Conclusion The findings suggest that there may be other factors involved in pharmacist decision-making that should be considered when categorizing drugs by perceived complexity.

scholarly journals Alcohol Hand Rub Significantly Reduces Overall Bacterial Bioburden on Stethoscopes in a Real-World Clinical Setting

2020 ◽  
Vol 41 (S1) ◽  
pp. s114-s115
Author(s):  
Alexandra Johnson ◽  
Bobby Warren ◽  
Deverick John Anderson ◽  
Melissa Johnson ◽  
Isabella Gamez ◽  
...  
Keyword(s):  
Real World ◽  
Medical Center ◽  
Academic Medical Center ◽  
Randomized Study ◽  
Healthcare Providers ◽  
Clinical Workflow ◽  
Provider Type ◽  
Academic Medical ◽  
Rn Students ◽  
The Impact

Background: Stethoscopes are a known vector for microbial transmission; however, common strategies used to clean stethoscopes pose certain barriers that prevent routine cleaning after every use. We aimed to determine whether using readily available alcohol-based hand rub (ABHR) would effectively reduce bacterial bioburden on stethoscopes in a real-world setting. Methods: We performed a randomized study on inpatient wards of an academic medical center to assess the impact of using ABHR (AlcareExtra; ethyl alcohol, 80%) on the bacterial bioburden of stethoscopes. Stethoscopes were obtained from healthcare providers after routine use during an inpatient examination and were randomized to control (no intervention) or ABHR disinfection (2 pumps applied to tubing and bell or diaphragm by study personnel, then allowed to dry). Cultures of the tubing and bell or diaphragm were obtained with premoistened cellulose sponges. Sponges were combined with 1% Tween20-PBS and mixed in the Seward Stomacher. The homogenate was centrifuged and all but ~5 mL of the supernatant was discarded. Samples were plated on sheep’s blood agar and selective media for clinically important pathogens (CIPs) including S. aureus, Enterococcus spp, and gram-negative bacteria (GNB). CFU count was determined by counting the number of colonies on each plate and using dilution calculations to calculate the CFU of the original ~5 mL homogenate. Results: In total, 80 stethoscopes (40 disinfection, 40 control) were sampled from 46 physicians (MDs) and MD students (57.5%), 13 advanced practice providers (16.3%), and 21 nurses (RNs) and RN students (26.3%). The median CFU count was ~30-fold lower in the disinfection arm compared to control (106 [IQR, 50–381] vs 3,320 [986–4,834]; P < .0001). The effect was consistent across provider type, frequency of recent usual stethoscope cleaning, age, and status of pet ownership (Fig. 1). Overall, 26 of 80 (33%) of stethoscopes harbored CIP. The presence of CIP was lower but not significantly different for stethoscopes that underwent disinfection versus controls: S. aureus (25% vs 32.5%), Enterococcus (2.5% vs 10%), and GNB (2.5% vs 5%). Conclusions: Stethoscopes may serve as vectors for clean hands to become recontaminated immediately prior to performing patient care activities. Using ABHR to clean stethoscopes after every use is a practical and effective strategy to reduce overall bacterial contamination that can be easily incorporated into clinical workflow. Larger studies are needed to determine the efficacy of ABHR at removing CIP from stethoscopes as stethoscopes in both arms were frequently contaminated with CIP. Prior cleaning of stethoscopes on the study day did not seem to impact contamination rates, suggesting the impact of alcohol foam disinfection is short-lived and may need to be repeated frequently (ie, after each use).Funding: NoneDisclosures: NoneDisclosures: NoneFunding: None

scholarly journals Communications and Screening for 2019 Novel Coronavirus at a Tertiary-Care Medical Center

2020 ◽  
Vol 41 (S1) ◽  
pp. s84-s84
Author(s):  
Lorinda Sheeler ◽  
Mary Kukla ◽  
Oluchi Abosi ◽  
Holly Meacham ◽  
Stephanie Holley ◽  
...  
Keyword(s):  
Screening Tool ◽  
Medical Center ◽  
Academic Medical Center ◽  
Tertiary Care ◽  
The United States ◽  
World Health ◽  
Communication Campaign ◽  
Electronic Screening ◽  
Novel Coronavirus ◽  
The Impact

Background: In December of 2019, the World Health Organization reported a novel coronavirus (severe acute respiratory coronavirus virus 2 [SARS-CoV-2)]) causing severe respiratory illness originating in Wuhan, China. Since then, an increasing number of cases and the confirmation of human-to-human transmission has led to the need to develop a communication campaign at our institution. We describe the impact of the communication campaign on the number of calls received and describe patterns of calls during the early stages of our response to this emerging infection. Methods: The University of Iowa Hospitals & Clinics is an 811-bed academic medical center with >200 outpatient clinics. In response to the coronavirus disease 2019 (COVID-19) outbreak, we launched a communications campaign on January 17, 2020. Initial communications included email updates to staff and a dedicated COVID-19 webpage with up-to-date information. Subsequently, we developed an electronic screening tool to guide a risk assessment during patient check in. The screening tool identifies travel to China in the past 14 days and the presence of symptoms defined as fever >37.7°C plus cough or difficulty breathing. The screening tool was activated on January 24, 2020. In addition, university staff contacted each student whose primary residence record included Hubei Province, China. Students were provided with medical contact information, signs and symptoms to monitor for, and a thermometer. Results: During the first 5 days of the campaign, 3 calls were related to COVID-19. The number of calls increased to 18 in the 5 days following the implementation of the electronic screening tool. Of the 21 calls received to date, 8 calls (38%) were generated due to the electronic travel screen, 4 calls (19%) were due to a positive coronavirus result in a multiplex respiratory panel, 4 calls (19%) were related to provider assessment only (without an electronic screening trigger), and 2 calls (10%) sought additional information following the viewing of the web-based communication campaign. Moreover, 3 calls (14%) were for people without travel history but with respiratory symptoms and contact with a person with recent travel to China. Among those reporting symptoms after travel to China, mean time since arrival to the United States was 2.7 days (range, 0–11 days). Conclusion: The COVID-19 outbreak is evolving, and providing up to date information is challenging. Implementing an electronic screening tool helped providers assess patients and direct questions to infection prevention professionals. Analyzing the types of calls received helped tailor messaging to frontline staff.Funding: NoneDisclosures: None

scholarly journals 154. Antibiotic Use During Three Separate Waves of the COVID-19 Pandemic at a Large Academic Medical Center in Detroit, MI

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S188-S189
Author(s):  
Deepika Sivakumar ◽  
Shelbye R Herbin ◽  
Raymond Yost ◽  
Marco R Scipione
Keyword(s):  
Retrospective Review ◽  
Bacterial Pneumonia ◽  
Medical Center ◽  
Academic Medical Center ◽  
Antibiotic Use ◽  
Percent Change ◽  
Academic Medical ◽  
Days Of Therapy ◽  
Time Periods ◽  
The Impact

Abstract Background Inpatient antibiotic use early on in the COVID-19 pandemic may have increased due to the inability to distinguish between bacterial and COVID-19 pneumonia. The purpose of this study was to determine the impact of COVID-19 on antimicrobial usage during three separate waves of the COVID-19 pandemic. Methods We conducted a retrospective review of patients admitted to Detroit Medical Center between 3/10/19 to 4/24/21. Median days of therapy per 1000 adjusted patient days (DOT/1000 pt days) was evaluated for all administered antibiotics included in our pneumonia guidelines during 4 separate time periods: pre-COVID (3/3/19-4/27/19); 1st wave (3/8/20-5/2/20); 2nd wave (12/6/21-1/30/21); and 3rd wave (3/7/21-4/24/21). Antibiotics included in our pneumonia guidelines include: amoxicillin, azithromycin, aztreonam, ceftriaxone, cefepime, ciprofloxacin, doxycycline, linezolid, meropenem, moxifloxacin, piperacillin-tazobactam, tobramycin, and vancomycin. The percent change in antibiotic use between the separate time periods was also evaluated. Results An increase in antibiotics was seen during the 1st wave compared to the pre-COVID period (2639 [IQR 2339-3439] DOT/1000 pt days vs. 2432 [IQR 2291-2499] DOT/1000 pt days, p=0.08). This corresponded to an increase of 8.5% during the 1st wave. This increase did not persist during the 2nd and 3rd waves of the pandemic, and the use decreased by 8% and 16%, respectively, compared to the pre-COVID period. There was an increased use of ceftriaxone (+6.5%, p=0.23), doxycycline (+46%, p=0.13), linezolid (+61%, p=0.014), cefepime (+50%, p=0.001), and meropenem (+29%, p=0.25) during the 1st wave compared to the pre-COVID period. Linezolid (+39%, p=0.013), cefepime (+47%, p=0.08) and tobramycin (+47%, p=0.05) use remained high during the 3rd wave compared to the pre-COVID period, but the use was lower when compared to the 1st and 2nd waves. Figure 1. Antibiotic Use 01/2019 to 04/2019 Conclusion Antibiotics used to treat bacterial pneumonia during the 1st wave of the pandemic increased and there was a shift to broader spectrum agents during that period. The increased use was not sustained during the 2nd and 3rd waves of the pandemic, possibly due to the increased awareness of the differences between patients who present with COVID-19 pneumonia and bacterial pneumonia. Disclosures All Authors: No reported disclosures

The Impact of Delayed Symptomatic Treatment Implementation in the Intensive Care Unit

Healthcare ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 35
Author(s):  
Lesley Meng ◽  
Krzysztof Laudanski ◽  
Mariana Restrepo ◽  
Ann Huffenberger ◽  
Christian Terwiesch
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Medical Center ◽  
Academic Medical Center ◽  
Symptomatic Treatment ◽  
Medication Administration ◽  
Average Delay ◽  
Treatment Implementation ◽  
Medication Delivery ◽  
The Impact

We estimated the harm related to medication delivery delays across 12,474 medication administration instances in an intensive care unit using retrospective data in a large urban academic medical center between 2012 and 2015. We leveraged an instrumental variables (IV) approach that addresses unobserved confounds in this setting. We focused on nurse shift changes as disruptors of timely medication (vasodilators, antipyretics, and bronchodilators) delivery to estimate the impact of delay. The average delay around a nurse shift change was 60.8 min (p < 0.001) for antipyretics, 39.5 min (p < 0.001) for bronchodilators, and 57.1 min (p < 0.001) for vasodilators. This delay can increase the odds of developing a fever by 32.94%, tachypnea by 79.5%, and hypertension by 134%, respectively. Compared to estimates generated by a naïve regression approach, our IV estimates tend to be higher, suggesting the existence of a bias from providers prioritizing more critical patients.

Abstract 16125: Impact of COVID-19 on Echocardiography Volume at a Large Academic Medical Center

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Merilyn S Varghese ◽  
Jordan B Strom ◽  
Sarah Fostello ◽  
Warren J Manning
Keyword(s):  
Patient Outcomes ◽  
Hospital Admissions ◽  
Medical Center ◽  
Academic Medical Center ◽  
Academic Medical ◽  
Hospital Systems ◽  
The Impact ◽  
At Home

Introduction: COVID-19 has significantly impacted hospital systems worldwide. The impact of statewide stay-at-home mandates on echocardiography volumes is unclear. Methods: We queried our institutional echocardiography database from 6/1/2018 to 6/13/2020 to examine rates of transthoracic (TTE), stress (SE), and transesophageal echocardiograms (TEE) prior to and following the COVID-19 Massachusetts stay-at-home order on March 15, 2020. Results: Among 36,377 total studies performed during the study period, mean weekly study volume dropped from 332 + 3 TTEs/week, 30 + 1 SEs/week, and 21 + 1 TEEs/week prior to the stay-at-home order (6/1/2018-3/15/2020) to 158 + 13 TTEs/week, 8 + 2 SEs/week, and 8 + 1 TEEs/week after (% change, -52%, -73%, and -62% respectively, all p < 0.001 when comparing volume prior to March 15 versus after). Weekly TTEs correlated strongly with hospital admissions throughout the study period (r = 0.93, 95% CI 0.89-0.95, p < 0.001) ( Figure ). Outpatient TTEs declined more than inpatient TTEs (% change, -74% vs. -39%, p <0.001). As of 3 weeks following the cessation of the stay-at-home order, TTE, SE, and TEE weekly volumes have increased to 73%, 66%, and 81% of pre-pandemic levels, respectively. Conclusions: Echocardiography volumes fell precipitously following the Massachusetts stay-at-home order, strongly paralleling declines in overall hospitalizations. Outpatient TTEs declined more than inpatient TTEs. Despite lifting of the order, echocardiography volumes remain substantially below pre-pandemic levels. The impact of the decreased use of echocardiographic services on patient outcomes remains to be determined.

Hardwiring diagnostic stewardship using electronic ordering restrictions for gastrointestinal pathogen testing

2019 ◽  
Vol 40 (6) ◽  
pp. 668-673 ◽  
Author(s):  
Jasmine R. Marcelin ◽  
Charlotte Brewer ◽  
Micah Beachy ◽  
Elizabeth Lyden ◽  
Tammy Winterboer ◽  
...  
Keyword(s):  
Best Practice ◽  
Medical Center ◽  
Academic Medical Center ◽  
Cost Savings ◽  
Poisson Model ◽  
Panel Testing ◽  
Period 2 ◽  
Gastrointestinal Pathogen ◽  
Pathogen Testing ◽  
The Impact

AbstractObjective:To evaluate the impact of a hard stop in the electronic health record (EHR) on inappropriate gastrointestinal pathogen panel testing (GIPP).Design:We used a quasi-experimental study to evaluate testing before and after the implementation of an EHR alert to stop inappropriate GIPP ordering.Setting:Midwest academic medical center.Participants:Hospitalized patients with diarrhea for which GIPP testing was ordered, between January 2016 through March 2017 (period 1) and April 2017 through June 2018 (period 2).Intervention:A hard stop in the EHR prevented clinicians from ordering a GIPP more than once per admission or in patients hospitalized for >72 hours.Results:During period 1, 1,587 GIPP tests were ordered over 212,212 patient days, at a rate of 7.48 per 1,000 patient days. In period 2, 1,165 GIPP tests were ordered over 222,343 patient days, at a rate of 5.24 per 1,000 patient days. The Poisson model estimated a 30% reduction in total GIPP ordering rates between the 2 periods (relative risk, 0.70; 95% confidence interval [CI], 0.63–0.78; P < .001). The rate of inappropriate tests ordered decreased from 21.5% to 4.9% between the 2 periods (P < .001). The total savings calculated factoring only GIPP orders that triggered the hard stop was ∼$67,000, with potential savings of $168,000 when factoring silent best-practice alert data.Conclusions:A simple hard stop alert in the EHR resulted in significant reduction of inappropriate GIPP testing, which was associated with significant cost savings. Clinicians can practice diagnostic stewardship by avoiding ordering this test more than once per admission or in patients hospitalized >72 hours.

scholarly journals Lack of impact of nil-per-os (NPO) time on goal-directed fluid delivery in first case versus afternoon case starts: a retrospective cohort study

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
R. Ryan Field ◽  
Tuan Mai ◽  
Samouel Hanna ◽  
Brian Harrington ◽  
Michael-David Calderon ◽  
...  
Keyword(s):  
Operating Room ◽  
Medical Center ◽  
Fluid Administration ◽  
Fluid Replacement ◽  
Primary Study ◽  
Start Time ◽  
Fluid Delivery ◽  
First Case ◽  
Patient Reported ◽  
The Impact

Abstract Background Goal Directed Fluid Therapy (GDFT) represents an objective fluid replacement algorithm. The effect of provider variability remains a confounder. Overhydration worsens perioperative morbidity and mortality; therefore, the impact of the calculated NPO deficit prior to the operating room may reach harm. Methods A retrospective single-institution study analyzed patients at UC Irvine Medical Center main operating rooms from September 1, 2013 through September 1, 2015 receiving GDFT. The primary study question asked if GDFT suggested different fluid delivery after different NPO periods, while reducing inter-provider variability. We created two patient groups distinguished by 0715 surgical start time or start time after 1200. We analyzed fluid administration totals with either a 1:1 crystalloid to colloid ratio or a 3:1 ratio. We performed direct group-wise testing on total administered volume expressed as total ml, total ml/hr., and total ml/kg/hr. between the first case start (AM) and afternoon case (PM) groups. A linear regression model included all baseline covariates that differed between groups as well as plausible confounding factors for differing fluid needs. Finally, we combined all patients from both groups, and created NPO time to total administered fluid scatterplots to assess the effect of patient-reported NPO time on fluid administration. Results Whether reported by total administered volume or net fluid volume, and whether we expressed the sum as ml, ml/hr., or ml/kg/hr., the AM group received more fluid on average than the PM group in all cases. In the general linear models, for all significant independent variables evaluated, AM vs PM case start did not reach significance in both cases at p = 0.64 and p = 0.19, respectively. In scatterplots of NPO time to fluid volumes, absolute adjusted and unadjusted R2 values are < 0.01 for each plot, indicating virtually non-existent correlations between uncorrected NPO time and fluid volumes measured. Conclusions This study showed NPO periods do not influence a patient’s volume status just prior to presentation to the operating room for surgical intervention. We hope this data will influence the practice of providers routinely replacing calculated NPO period volume deficit; particularly with those presenting with later surgical case start times.

scholarly journals Variability of Antibiotic Prescribing in a Large Healthcare Network Despite Adjusting for Patient-Mix: Reconsidering Targets for Improved Prescribing

2019 ◽  
Vol 6 (2) ◽  
Author(s):  
Sophia Jung ◽  
Mary Elizabeth Sexton ◽  
Sallie Owens ◽  
Nathan Spell ◽  
Scott Fridkin
Keyword(s):  
Antibiotic Prescription ◽  
Medical Center ◽  
Academic Medical Center ◽  
Primary Care Clinic ◽  
Multivariable Logistic Regression Analysis ◽  
Comorbid Condition ◽  
Antibiotic Prescribing ◽  
Cross Sectional ◽  
Care Clinic ◽  
The Impact

Abstract Background In the outpatient setting, the majority of antibiotic prescriptions are for acute respiratory infections (ARIs), but most of these infections are viral and antibiotics are unnecessary. We analyzed provider-specific antibiotic prescribing in a group of outpatient clinics affiliated with an academic medical center to inform future interventions to minimize unnecessary antibiotic use. Methods We conducted a cross-sectional study of patients who presented with an ARI to any of 15 The Emory Clinic (TEC) primary care clinic sites between October 2015 and September 2017. We performed multivariable logistic regression analysis to examine the impact of patient, provider, and clinic characteristics on antibiotic prescribing. We also compared provider-specific prescribing rates within and between clinic sites. Results A total of 53.4% of the 9600 patient encounters with a diagnosis of ARI resulted in an antibiotic prescription. The odds of an encounter resulting in an antibiotic prescription were independently associated with patient characteristics of white race (adjusted odds ratio [aOR] = 1.59; 95% confidence interval [CI], 1.47–1.73), older age (aOR = 1.32, 95% CI = 1.20–1.46 for patients 51 to 64 years; aOR = 1.32, 95% CI = 1.20–1.46 for patients ≥65 years), and comorbid condition presence (aOR = 1.19; 95% CI, 1.09–1.30). Of the 109 providers, 13 (12%) had a rate significantly higher than predicted by modeling. Conclusions Antibiotic prescribing for ARIs within TEC outpatient settings is higher than expected based on prescribing guidelines, with substantial variation in prescribing rates by site and provider. These data lay the foundation for quality improvement interventions to reduce unnecessary antibiotic prescribing.

scholarly journals Differential resting state connectivity responses to glycemic state in type 1 diabetes

2019 ◽  
Author(s):  
Lisa Parikh ◽  
Dongju Seo ◽  
Cheryl Lacadie ◽  
Renata Belfort-DeAguiar ◽  
Derek Groskreutz ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Functional Connectivity ◽  
Resting State ◽  
Medical Center ◽  
Academic Medical Center ◽  
Angular Gyrus ◽  
Resting State Functional Connectivity ◽  
The Impact ◽  
Resting State Connectivity

Abstract Context Individuals with type 1 diabetes (T1DM) have alterations in brain activity which have been postulated to contribute to the adverse neurocognitive consequences of T1DM; however, the impact of T1DM and hypoglycemic unawareness on the brain’s resting state activity remains unclear. Objective To determine whether individuals with T1DM and hypoglycemia unawareness (T1DM-Unaware) had changes in the brain resting state functional connectivity compared to healthy controls (HC) and those with T1DM and hypoglycemia awareness (T1DM-Aware). Design Observational study Setting Academic medical center Participants 27 individuals with T1DM and 12 healthy control volunteers participated in the study. Intervention All participants underwent BOLD resting state fMRI brain imaging during a 2-step hyperinsulinemic euglycemic (90 mg/dl)-hypoglycemic (60mg/dl) clamp. Outcome Changes in resting state functional connectivity Results Using two separate methods of functional connectivity analysis, we identified distinct differences in the resting state brain responses to mild hypoglycemia amongst HC, T1DM-Aware and T1DM-Unaware participants, particularly in the angular gyrus, an integral component of the default mode network (DMN). Furthermore, changes in angular gyrus connectivity also correlated with greater symptoms of hypoglycemia (r = 0.461, P = 0.003) as well as higher scores of perceived stress (r = 0.531, P = 0.016). Conclusion These findings provide evidence that individuals with T1DM have changes in the brain’s resting state connectivity patterns, which may be further associated with differences in awareness to hypoglycemia. These changes in connectivity may be associated with alterations in functional outcomes amongst individuals with T1DM.

scholarly journals 1154. Impact of Educational Feedback on Reducing Hospital-Onset Catheter-Associated Urinary tract Infections at an Academic Medical Center

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S412-S412
Author(s):  
Bhagyashri D Navalkele ◽  
Nora Truhett ◽  
Miranda Ward ◽  
Sheila Fletcher
Keyword(s):  
Infection Prevention ◽  
Medical Center ◽  
Academic Medical Center ◽  
Prevention Strategies ◽  
Intervention Period ◽  
Post Intervention ◽  
Frontline Staff ◽  
Post Intervention Period ◽  
Tract Infections ◽  
The Impact

Abstract Background High regulatory burden on hospital-onset (HO) infections has increased performance pressure on infection prevention programs. Despite the availability of comprehensive prevention guidelines, a major challenge has been communication with frontline staff to integrate appropriate prevention measures into practice. The objective of our study was to evaluate the impact of educational intervention on HO CAUTI rates and urinary catheter days. Methods At the University of Mississippi Medical Center, Infection prevention (IP) reports unit-based monthly HO infections via email to respective unit managers and ordering physician providers. Starting May 2018, IP assessed compliance to CAUTI prevention strategies per SHEA/IDSA practice recommendations (2014). HO CAUTI cases with noncompliance were labeled as “preventable” infections and educational justification was provided in the email report. No other interventions were introduced during the study period. CAUTI data were collected using ongoing surveillance per NHSN and used to calculate rates per 1,000 catheter days. One-way analysis of variance (ANOVA) was used to compare pre- and post-intervention data. Results Prior to intervention (July 2017–March 2018), HO CAUTI rate was 1.43 per 1,000 catheter days. In the post-intervention period (July 2018–March 2019), HO CAUTI rate decreased to 0.62 per 1,000 catheter days. Comparison of pre- and post-intervention rates showed a statistically significant reduction in HO CAUTIs (P = 0.04). The total number of catheter days reduced, but the difference was not statistically significant (8,604 vs. 7,583; P = 0.06). Of the 14 HO CAUTIs in post-intervention period, 64% (8/14) were reported preventable. The preventable causes included inappropriate urine culturing practice in asymptomatic patients (5) or as part of pan-culture without urinalysis (2), and lack of daily catheter assessment for necessity (1). Conclusion At our institute, regular educational feedback by IP to frontline staff resulted in a reduction of HO CAUTIs. Feedback measure improved accountability, awareness and engagement of frontline staff in practicing appropriate CAUTI prevention strategies. Disclosures All authors: No reported disclosures.

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