***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: A prospect i ve quasi-experimental study was undertaken in 218 patients with suspicion of nosocomial infection hospitalized in a polyvalent ICU where a new electronic device ( GERB ) has been design ed for antibiotic prescriptions . Two GERB-based applications were developed to provide local resistance maps ( LRMs ) and preliminary microbiological reports with therapeutic recommendation ( PMRTRs ) . Both applications used the data in the Laboratory Information System of the Microbiology Department to report on the optimal empiric therapeutic option , based on the most likely susceptibility profile of the microorganisms potentially responsible for infection in patients and taking into account the local epidemiology of the hospital department/unit . LRMs were used for antibiotic prescription in 20.2 % of the patients and PMRTRs in 78.2 % , and active antibiotics against the finally identified bacteria were prescribed in 80.0 % of the former group and 82.4 % of the latter . When neither LMRs nor PMRTRs were considered for empiric treatment prescription , only around 40 % of the antibiotics prescribed were active . Hence , the percentage appropriateness of the empiric antibiotic treatments was significantly higher when LRM or PMRTR guidelines were followed rather than other criteria . LRMs and PMRTRs applications are dynamic , highly accessible , and readily interpreted instruments that contribute to the appropriateness of empiric antibiotic treatments OBJECTIVE The objective was to develop non-invasive predictive models for  late-onset neonatal sepsis  from off-the-shelf medical data and electronic medical records ( EMR ) . DESIGN The data used in this study are from 299 infants admitted to the neonatal intensive care unit in the Monroe Carell Jr. Children 's Hospital at V and erbilt and evaluated for late-onset sepsis . Gold st and ard diagnostic labels ( sepsis negative , culture positive sepsis , culture negative/ clinical sepsis ) were assigned based on all the laboratory , clinical and microbiology data available in EMR . Only data that were available up to 12 h after phlebotomy for blood culture testing were used to build predictive models using machine learning ( ML ) algorithms . MEASUREMENT We compared sensitivity , specificity , positive predictive value and negative predictive value of sepsis treatment of physicians with the predictions of models generated by ML algorithms . RESULTS The treatment sensitivity of all the nine ML algorithms and specificity of eight out of the nine ML algorithms tested exceeded that of the physician when culture-negative sepsis was included . When culture-negative sepsis was excluded both sensitivity and specificity exceeded that of the physician for all the ML algorithms . The top three predictive variables were the hematocrit or packed cell volume , chorioamnionitis and respiratory rate . CONCLUSIONS Predictive models developed from off-the-shelf and EMR data using ML algorithms exceeded the treatment sensitivity and treatment specificity of clinicians . A prospect i ve study is warranted to assess the clinical utility of the ML algorithms in improving the accuracy of antibiotic use in the management of neonatal sepsis Introduction : The promise of Clinical Decision Support ( CDS ) has always been to transform patient care and improve patient outcomes through the delivery of timely and appropriate recommendations that are patient specific and , more often than not , are appropriately actionable . However , the users of CDS — providers — are frequently bombarded with inappropriate and inapplicable CDS that often are not informational , not integrated into the workflow , not patient specific , and that may present out of date and irrelevant recommendations . Methods : The integrated clinical prediction rule ( iCPR ) project was a r and omized clinical trial ( RCT ) conducted to determine if a novel form of CDS , i.e. , clinical prediction rules ( CPRs ) , could be efficiently integrated into workflow and result in changes in outcomes ( e.g. , antibiotic ordering ) when embedded within a commercial electronic health record ( EHR ) . We use the lessons learned from the iCPR project to illustrate a framework for constructing usable , useful , and effective actionable CDS while employing off-the-shelf functionality in a production system . Innovations that make up the framework combine the following : ( 1 ) active and actionable decision support , ( 2 ) multiple rounds of usability testing with iterative development for user acceptance , ( 3 ) numerous context sensitive triggers , ( 4 ) dedicated training and support for users of the CDS tool for user adoption , and ( 5 ) support from clinical and administrative leadership . We define “ context sensitive triggers ” as being workflow events ( i.e. , context ) that result in a CDS intervention . Discussion : Success of the framework can be measured by CDS adoption ( i.e. , intervention is being used ) , acceptance ( compliance with recommendations ) , and clinical outcomes ( where appropriate ) . This framework may have broader implication s for the deployment of Health Information Technology ( HIT ) . Results and Conclusion : iCPR was well adopted(57.4 % of users ) and accepted ( 42.7 % of users ) . Usability testing identified and fixed many issues before the iCPR RCT . The level of leadership support and clinical guidance for iCPR was key in establishing a culture of acceptance for both the tool and its recommendations contributing to adoption and acceptance . The dedicated training and support lead to the majority of the residents reporting a high level of comfort with both iCPR tools strep pharyngitis ( 64.4 percent ) and pneumonia ( 62.7 percent ) as well as a high likelihood of using the tools in the future . A surprising framework addition result ed from usability testing : context sensitive triggers Background Timely switch from intravenous ( iv ) antibiotics to oral therapy is a key component of antimicrobial stewardship programs in order to improve patient safety , promote early discharge and reduce costs . We have introduced a time-efficient and easily implementable intervention that relies on a computerized trigger tool , which identifies patients who are c and i date s for an iv to oral antibiotic switch . Methods The intervention was introduced on all internal medicine wards in a teaching hospital . Patients were automatically identified by an electronic trigger tool when parenteral antibiotics were used for > 48 h and clinical or pharmacological data did not preclude switch therapy . A weekly educational session was introduced to alert the physicians on the intervention wards . The intervention wards were compared with control wards , which included all other hospital wards . An interrupted time-series analysis was performed to compare the pre-intervention period with the post-intervention period using ‘ % of i.v . prescriptions > 72 h ’ and ‘ median duration of iv therapy per prescription ’ as outcomes . We performed a detailed prospect i ve evaluation on a subset of 244 prescriptions to evaluate the efficacy and appropriateness of the intervention . Results The number of intravenous prescriptions longer than 72 h was reduced by 19 % in the intervention group ( n = 1519 ) ( p < 0.01 ) and the median duration of iv antibiotics was reduced with 0.8 days ( p = < 0.05 ) . Compared to the  control group ( n = 4366 ) the intervention was responsible for an additional decrease of 13 % ( p < 0.05 ) in prolonged prescriptions . The detailed prospect i ve evaluation of a subgroup of patients showed that adherence to the electronic reminder was 72 % . Conclusions An electronic trigger tool combined with a weekly educational session was effective in reducing the duration of intravenous antimicrobial therapy OBJECTIVE Antibiotic computerized decision support systems ( CDSSs ) were developed to guide antibiotic decisions , yet prescriptions of CDSS-recommended antibiotics have remained low . Our aim was to identify predictors of patients ' receipt of empiric antibiotic therapies recommended by a CDSS when the prescribing physician had an initial preference for using broad-spectrum antibiotics . METHODS We conducted a prospect i ve cohort study in a 1 500-bed tertiary-care hospital in Singapore . We included all patients admitted from October 1 , 2011 through September 30 , 2012 , who were prescribed piperacillin-tazobactam or carbapenem for empiric therapy and auto-triggered to receive antibiotic recommendations by the in-house antibiotic CDSS . Relevant data on the patient , prescribing and attending physicians were collected via electronic linkages of medical records and administrative data bases . To account for clustering , we used multilevel logistic regression models to explore factors associated with receipt of CDSS-recommended antibiotic therapy . RESULTS One-quarter of the 1 886 patients received CDSS-recommended antibiotics . More patients treated for pneumonia ( 33.2 % ) than sepsis ( 12.1 % ) and urinary tract infections ( 7.1 % ) received CDSS-recommended antibiotic therapies . The prescribing physician - but not the attending physician or clinical specialty - accounted for some ( 13.3 % ) of the variation . Prior hospitalization ( odds ratio [ OR ] 1.32 , 95 % CI , 1.01 - 1.71 ) , presumed pneumonia ( OR 6.77 , 95 % CI , 3.28 - 13.99 ) , intensive care unit ( ICU ) admission ( OR 0.38 , 95 % CI , 0.21 - 0.66 ) , and renal impairment ( OR 0.70 , 95 % CI , 0.52 - 0.93 ) were factors associated with patients ' receipt of CDSS-recommended antibiotic therapies . CONCLUSIONS We observed that ICU admission and renal impairment were negative predictors of patients ' receipt of CDSS-recommended antibiotic therapies . Patients admitted to ICU and those with renal impairment might have more complex clinical conditions that require a physician 's assessment in addition to antibiotic CDSS STUDY OBJECTIVE Despite evidence that guideline adherence improves clinical outcomes , management of pneumonia patients varies in emergency departments ( EDs ) . We study the effect of a real-time , ED , electronic clinical decision support tool that provides clinicians with guideline -recommended decision support for diagnosis , severity assessment , disposition , and antibiotic selection . METHODS This was a prospect i ve , controlled , quasi-experimental trial in 7 Intermountain Healthcare hospital EDs in Utah 's urban corridor . We studied adults with International Classification of Diseases , Ninth Revision codes and radiographic evidence for pneumonia during 2 periods : baseline ( December 2009 through November 2010 ) and post-tool deployment ( December 2011 through November 2012 ) . The tool was deployed at 4 intervention EDs in May 2011 , leaving 3 as usual care controls . We compared 30-day , all-cause mortality adjusted for illness severity , using a mixed-effect , logistic regression model . RESULTS The study population comprised 4,758 ED pneumonia patients ; 14 % had health care-associated pneumonia . Median age was 58 years , 53 % were female patients , and 59 % were admitted to the hospital . Physicians applied the tool for 62.6 % of intervention ED study patients . There was no difference overall in severity-adjusted mortality between intervention and usual care EDs post-tool deployment ( odds ratio [OR]=0.69 ; 95 % confidence interval [ CI ] 0.41 to 1.16 ) . Post hoc analysis showed that patients with community-acquired pneumonia experienced significantly lower mortality ( OR=0.53 ; 95 % CI 0.28 to 0.99 ) , whereas mortality was unchanged among patients with health care-associated pneumonia ( OR=1.12 ; 95 % CI 0.45 to 2.8 ) . Patient disposition from the ED postdeployment adhered more to tool recommendations . CONCLUSION This study demonstrates the feasibility and potential benefit of real-time electronic clinical decision support for ED pneumonia patients Objectives : Prospect i ve audit and feedback interventions are the core components of an antimicrobial stewardship programme . Herein , we describe the sustained impact of an antimicrobial stewardship programme , based on a novel clinical decision-support system ( Antimicrobial Prescription Surveillance System ; APSS ) , on antimicrobial use and costs , hospital length of stay ( LOS ) in days and the proportion of inappropriate antimicrobial prescriptions . Methods : A quasi-experimental , retrospective study was conducted using interrupted time series between 2008 and 2013 . Data on all hospitalized adults receiving antimicrobials were extracted from the data warehouse of a 677 bed academic centre . The intervention started in August 2010 . Prospect i ve audit and feedback interventions , led by a pharmacist , were triggered by APSS based on deviations from published and local guidelines . Changes in outcomes before and after the intervention were compared using segmented regression analysis . Results : APSS review ed 40 605 hospitalizations for 35 778 patients who received antimicrobials . The intervention was associated with a decrease in the average LOS ( level change −0.92 , P < 0.01 ; trend −0.08 , P < 0.01 ; intercept 11.4 days ) , antimicrobial consumption in DDDs/1000 inpatient days ( level change −32.4 , P < 0.01 ; trend −1.12 , P < 0.02 ; intercept 243 DDDs per 1000 days of hospitalization ) , antimicrobial spending in Canadian dollars ( level change −19 649 , P = 0.01 ; trend −1881 , P < 0.01 ; intercept $ 74 683 ) and proportion of non-concordance with local guidelines for prescribing antimicrobials ( level change −2.3 , P = 0.04 ; intercept 41 % ) 
    Output:
    
CDDS interventions found a significant impact on multiple outcomes relevant to antibiotic stewardship .
Passive interventions were associated with more significant outcomes ; however , both interventions appeared effective .
In the review ed literature , CDSSs were consistently associated with decreasing antibiotic consumption and narrowing the spectrum of antibiotic usage .
Generally , guideline adherence was improved with CDSS , although this was not universal .
The effect on other outcomes , such as mortality , Clostridiodes difficile infections , length of stay , and cost , inconsistently showed a significant difference .
Overall , CDDS implementation has effectively decreased antibiotic consumption and improved guideline adherence across the various types of CDSS .
Other positive outcomes were noted in certain setting s , but were not universal .