***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: OBJECTIVE Many hospitals utilize antimicrobial management teams ( AMTs ) to improve patient care . However , most function with minimal computer support . We evaluated the effectiveness and cost-effectiveness of a computerized clinical decision support system for the management of antimicrobial utilization . DESIGN A r and omized controlled trial in adult in patients between May 10 and August 3 , 2004 . Antimicrobial utilization was managed by an existing AMT using the system in the intervention arm and without the system in the control arm . The system was developed to alert the AMT of potentially inadequate antimicrobial therapy . MEASUREMENTS Outcomes assessed were hospital antimicrobial expenditures , mortality , length of hospitalization , and time spent managing antimicrobial utilization . RESULTS The AMT intervened on 359 ( 16 % ) of 2,237 patients in the intervention arm and 180 ( 8 % ) of 2,270 in the control arm , while spending approximately one hour less each day on the intervention arm . Hospital antimicrobial expenditures were $ 285,812 in the intervention arm and $ 370,006 in the control arm , for a savings of $ 84,194 ( 23 % ) , or $ 37.64 per patient . No significant difference was observed in mortality ( 3.26 % vs. 2.95 % , p = 0.55 ) or length of hospitalization ( 3.84 vs. 3.99 days , p = 0.38 ) . CONCLUSION Use of the system facilitated the management of antimicrobial utilization by allowing the AMT to intervene on more patients receiving inadequate antimicrobial therapy and to achieve substantial time and cost savings for the hospital . This is the first study that demonstrates in a patient-r and omized controlled trial that computerized clinical decision support systems can improve existing antimicrobial management programs OBJECTIVES Antibiotic stewardship is important , but the ideal strategy for providing stewardship in a hospital setting is unknown . A practical , sustainable and transferable strategy is needed . This study evaluates the impact of a novel computerized antimicrobial approval system on antibiotic-prescribing behaviour in a hospital . Effects on drug consumption , antibiotic resistance patterns of local bacteria and patient outcomes were monitored . METHODS The study was conducted at a tertiary referral teaching hospital in Melbourne , Australia . The system was deployed in January 2005 and guided the use of 28 restricted antimicrobials .  Data were collected over 7 years : 5 years before and 2 years after deployment . Uptake of the system was evaluated using an in-built audit trail . Drug utilization was prospect ively monitored using pharmacy data ( as defined daily doses per 1000 bed-days ) and analysed via time-series analysis with segmental linear regression . Antibiograms of local bacteria were prospect ively evaluated . In-hospital mortality and length of stay for patients with Gram-negative bacteraemia were also reported . RESULTS Between 250 and 300 approvals were registered per month during 2006 . The gradients in the use of third- and fourth-generation cephalosporins ( + 0.52 , -0.05 , -0.39 ; P < 0.01 ) , glycopeptides ( + 0.27 , -0.53 ; P = 0.09 ) , carbapenems ( + 0.12 , -0.24 ; P = 0.21 ) , aminoglycosides ( + 0.15 , -0.27 ; P < 0.01 ) and quinolones ( + 0.76 , + 0.11 ; P = 0.08 ) all fell after deployment , while extended-spectrum penicillin use increased . Trends in increased susceptibility of Staphylococcus aureus to methicillin and improved susceptibility of Pseudomonas spp . to many antibiotics were observed . No increase in adverse outcomes for patients with Gram-negative bacteraemia was observed . CONCLUSIONS The system was successfully adopted and significant changes in antimicrobial usage were demonstrated OBJECTIVE To examine whether the Acute Respiratory Infection ( ARI )  Quality Dashboard , an electronic health record (EHR)-based feedback system , changed antibiotic prescribing . STUDY DESIGN Cluster r and omized , controlled trial . METHODS We r and omly assigned 27 primary care practice s to receive the ARI Quality Dashboard or usual care .  The primary outcome was the intent-to-intervene antibiotic prescribing rate for ARI visits . We also compared antibiotic prescribing between ARI Quality Dashboard users and nonusers . RESULTS During the 9-month intervention , there was no difference between intervention and control practice s in antibiotic prescribing for all ARI visits ( 47 % vs 47 % ; P = .87 ) , antibiotic-appropriate ARI visits ( 65 % vs 64 % ; P = .68 ) , or non – antibiotic-appropriate ARI visits ( 38 % vs 40 % ; P = .70 ) . Among the 258 intervention clinicians , 72 ( 28 % ) used the ARI Quality Dashboard at least once . These clinicians had a lower overall ARI antibiotic prescribing rate ( 42 % vs 50 % for nonusers ; P = .02 ) . This difference was due to less antibiotic prescribing for non-antibiotic-appropriate ARIs ( 32 % vs 43 % ; P = .004 ) , including nonstreptococcal pharyngitis ( 31 % vs 41 % ; P = .01 ) and nonspecific upper respiratory infections ( 19 % vs 34 % ; P = .01 ) . CONCLUSIONS The ARI Quality Dashboard was not associated with an overall change in antibiotic prescribing for ARIs , although when used , it was associated with improved antibiotic prescribing . EHR-based quality reporting , as part of " meaningful use , " may not improve care in the absence of other changes to primary care practice BACKGROUND Physicians frequently need to start antibiotic therapy before the results of bacterial cultures and antibiotic susceptibility tests are available . We developed and evaluated a computerized antibiotic consultant to assist physicians in the selection of appropriate empiric antibiotics . METHODS We used a two-stage r and om- selection study to compare antibiotics suggested by the antibiotic consultant with 482 associated antibiotic susceptibility results and the concurrent antibiotics ordered by physicians . The antibiotics ordered by r and omized physicians were then compared between crossover periods of antibiotic consultant use . RESULTS The antibiotic consultant suggested an antibiotic regimen to which all isolated pathogens were shown to be susceptible for 453 ( 94 % ) of 482 culture results , while physicians ordered an antibiotic regimen to which all isolated pathogens were susceptible for 369 culture results ( 77 % ) ( P < .001 ) . The physicians who prescribed antibiotics to which all pathogens were susceptible did so a mean of 21 hours after the culture specimens were collected . Physicians ordered appropriate antibiotics within 12 hours of the culture collection significantly more often when they had use of the antibiotic consultant than during the period before use ( P < .035 ) . Moreover , 88 % of the physicians stated they would recommend the program to other physicians , 85 % said the program improved their antibiotic selection , and 81 % said they felt use of the program improved patient care . CONCLUSIONS Information from computer-based medical records can be used to help improve physicians ' selection of empiric antibiotics for infections Background There is growing interest in conducting clinical and cluster r and omized trials through electronic health records . This paper reports on the method ological issues identified during the implementation of two cluster r and omized trials using the electronic health records of the Clinical Practice Research Data link ( CPRD ) . Methods Two trials were completed in primary care : one aim ed to reduce inappropriate antibiotic prescribing for acute respiratory infection ; the other aim ed to increase physician adherence with secondary prevention interventions after first stroke . The paper draws on documentary records and trial data sets to report on the method ological experience with respect to research ethics and research governance approval , general practice recruitment and allocation , sample size calculation and power , intervention implementation , and trial analysis . Results We obtained research governance approvals from more than 150 primary care organizations in Engl and , Wales , and Scotl and . There were 104 CPRD general practice s recruited to the antibiotic trial and 106 to the stroke trial , with the target number of practice s being recruited within six months . Interventions were installed into practice information systems remotely over the internet . The mean number of participants per practice was 5,588 in the antibiotic trial and 110 in the stroke trial , with the coefficient of variation of practice sizes being 0.53 and 0.56 respectively . Outcome measures showed substantial correlations between the 12 months before , and after intervention , with coefficients ranging from 0.42 for diastolic blood pressure to 0.91 for proportion of consultations with antibiotics prescribed , defining practice and participant eligibility for analysis requires careful consideration . Conclusions Cluster r and omized trials may be performed efficiently in large sample s from UK general practice s using the electronic health records of a primary care data base . The geographical dispersal of trial sites presents a difficulty for research governance approval and intervention implementation . Pretrial data analyses should inform trial design and analysis plans . Trial registration Current Controlled Trials IS RCT N47558792 and IS RCT N35701810 ( both registered on 17 March 2010 ) BACKGROUND Prediction of bloodstream infection at the time of sepsis onset allows one to make appropriate and economical management decisions . METHODS The TREAT computerized decision-support system uses a causal probabilistic network , which is locally calibrated , to predict cases of bacteremia . We assessed the system 's performance in 2 independent cohorts that included patients with suspected sepsis . Both studies were conducted in Israel , Italy , and Germany . Data were collected prospect ively and were entered into the TREAT system at the time that blood sample s were obtained for culture . Discriminative power was assessed using a receiver-operating characteristics curve . RESULTS In the first cohort , 790 patients were included . The area under the receiver-operating characteristics curve for prediction of bacteremia using the TREAT system was 0.68 ( 95 % confidence interval [ CI ] , 0.63 - 0.73 ) . We used TREAT 's prediction values to draw thresholds defining a low- , intermediate- , and high-risk groups for bacteremia , in which 3 ( 2.4 % ) of 123 , 62 ( 12.8 % ) of 483 , and 55 ( 29.9 % ) of 184 patients were bacteremic , respectively . In the second cohort , 1724 patients were included . The area under the receiver-operating characteristics curve was 0.70 ( 95 % CI , 0.67 - 0.73 ) . The prevalence of bacteremia observed in the low- , intermediate- , and high-risk groups defined by the first cohort were 1.3 % ( 4 of 300 patients ) , 13.2 % ( 150 of 1139 patients ) , and 28.1 % ( 80 of 285 patients ) , respectively . The low-risk groups in the 2 cohorts comprised 15%-17 % of all patients . Performance was stable in the 3 sites . CONCLUSIONS Using variables available at the time that blood cultures were performed , the TREAT system successfully stratified patients on the basis of the risk for bacteremia . The system 's predictions were stable in 3 locations . The TREAT system can define a low-risk group of in patients with suspected sepsis for whom blood cultures may not be needed OBJECTIVE To implement and evaluate the effect of a computerized decision support tool on  antibiotic use in an intensive care unit ( ICU ) . DESIGN Prospect i ve before- and -after cohort study . SETTING Twenty-four bed tertiary hospital adult medical/surgical ICU . PARTICIPANTS All consecutive patients from May 2001 to November 2001 ( N = 524 ) and March 2002 to September 2002 ( N = 536 ) . INTERVENTION A real-time microbiology browser and computerized decision support system for isolate directed antibiotic prescription . MAIN OUTCOME MEASURES Number of courses of antibiotic prescribed , antibiotic utilization ( defined daily doses (DDDs)/100 ICU bed-days ) , antibiotic susceptibility mismatches , and system uptake . RESULTS There was a significant reduction in the proportion of patients prescribed carbapenems [ odds ratio ( OR ) = 0.61 , 95 % confidence interval ( CI ) = 0.39 - 0.97 , P = 0.04 ] , third-generation cephalosporins ( OR = 0.58 , 95 % CI = 0.42 - 0.79 , P = 0.001 ) , and vancomycin ( OR = 0.67 , 95 % CI = 0.45 - 1.00 , P = 0.05 ) after adjustment for risk factors including Apache II score , suspected infection , positive microbiology , intubation , and length of stay . The decision support tool was associated with a 10.5 % reduction in both total antibiotic utilization ( 1 
    Output:
    
On evaluation against the intervention reporting framework , CDSS studies fail to report consideration of the non-expert , end-user workflow .
They have narrow focus , such as antimicrobial selection , and use proxy outcome measures .