Approaches to promoting the appropriate use of antibiotics ...
Approaches to promoting the appropriate use of antibiotics through hospital electronic prescribing systems: a scoping reviewKeywords: antimicrobial resistance, health information technology, reviewWord count: 3106AbstractObjective: To identify approaches of using stand-alone and more integrated hospital ePrescribing systems to promote and support the appropriate use of antibiotics and identify gaps in order to inform future efforts in this area.Methods: A systematic scoping review of the empirical literature from 1997 until 2015, searching the following databases: MEDLINE, EMBASE, Cochrane Database of Systematic Reviews, Google Scholar, Clinical Trials, International Standard Randomised Controlled Trial Number Registry, Economic Evaluation database, and International Prospective Register of Systematic Reviews. Search terms related to different components of systems, hospital settings and antimicrobial stewardship. Two reviewers independently screened papers and mutually agreed papers for inclusion. We undertook an interpretive synthesis.Key findings: We identified 143 papers. The majority of these were single-centre observational studies from North American settings with a wide range of system functionalities. Most evidence related to computerised decision support (CDS) and computerised physician order entry (CPOE) functionalities, of which many were extensively customised. We also found some limited work surrounding integration with laboratory results, pharmacy systems, and organisational surveillance. Outcomes examined included healthcare professional performance, patient outcomes and health economic evaluations. We found at times conflicting conclusions surrounding effectiveness, which may be due to heterogeneity of populations, technologies and outcomes studied. We failed to identify any reports of unintended consequences.Conclusions: Interventions are centred on CPOE and CDS, but also include additional functionality aiming to support various facets of the medicines management process. Wider organisational dimensions appear important to supporting adoption. Evaluations should consider processes, clinical, economic and safety outcomes in order to generate generalisable insights into safety, effectiveness and cost-effectiveness. IntroductionAntimicrobial resistance has emerged as one of the most pressing problems of the 21st Century.1 The threat of antimicrobial resistance is greatly exacerbated by inappropriate antibiotic use, which increases the emergence of resistant bacterial strains.2 Maximising the effectiveness of existing antibiotic treatments through resources that facilitate optimal use in humans is one key approach to mitigating the risks of antimicrobial resistance.3 The focus on promoting the appropriate use of antibiotics in hospital settings is based around organisational approaches to promoting and monitoring the judicious use of antimicrobials to preserve their future effectiveness.4 There is increasing evidence to suggest that antimicrobials are commonly prescribed inappropriately in hospitals. For example with treatment regimens continued for longer than is necessary and prescribing for non-bacterial infections,5-7 both of which can promote antimicrobial resistance.In response to this, many governments are now supporting the development of guidance, intended to support clinicians to more appropriately initiate and maintain antibiotic use.8 ePrescribing systems can potentially offer considerable support for many aspects of the appropriate use of antibiotics in hospitals,9 and the existing evidence from other areas shows that computerised physician order entry (CPOE) and computerised decision support (CDS) systems in particular may offer significant potential.10-12With ePrescribing systems increasingly being embedded within hospital information technology infrastructures and clinical workflows, an opportunity therefore exists to develop interventions that support all key decisions within the antibiotic prescribing process, but there is currently uncertainty as to how these interventions are optimally conceptualised and deployed.13 Existing evidence surrounding CPOE/CDS interventions targeted at individual prescribers from other areas may have limited applicability to the area of antibiotic stewardship (AMS),10-12 and does also not provide insights into wider social and organisational transformations that need to accompany a program of this sort (e.g. the role of clinical pharmacists).14In order to address this gap, we conducted a systematic scoping review of the literature to identify and describe existing and emerging approaches to promoting the appropriate use of antibiotics through hospital ePrescribing systems. In doing so, we were particularly interested in identifying potential approaches to improve the appropriate use of antibiotics beyond interventions targeting individual prescribers.MethodsSearch strategyWe searched the published empirical literature from 1997 until September 2015 for work investigating the use of ePrescribing and associated health information technology systems to improve the appropriate use of antibiotics. The start date was chosen due to published guidelines from the Society for Healthcare Epidemiology of America and Infectious Diseases Society of America that year, emphasising the importance of appropriate use of antibiotics to prevent antimicrobial resistance in hospitals.3Databases in this search included: MEDLINE, EMBASE, and The Cochrane Database of Systematic Reviews. We also electronically searched Clinical Trials (), the International Standard Randomised Controlled Trial Number (ISRCT) Registry (), the Economic Evaluation database (), and the International Prospective Register of Systematic Reviews (crd.york.ac.uk/PROSPERO/) for work in progress. We used the search engine Google Scholar to identify additional potentially eligible studies.Search terms related to different components of ePrescribing systems, hospital settings and terms surrounding the appropriate use of antibiotics. We searched titles and abstracts as well as subject headings (see Appendix 1). More specifically, terms in the first group were based on a search strategy we employed previously in a systematic review of the eHealth literature, focusing on functionality commonly associated with ePrescribing systems.15 The search terms in the second group related to the appropriate use of antibiotics, drawing on a review of published studies underlying the guidelines developed by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America for developing an institutional programme to enhance AMS and existing Cochrane reviews.13 The search terms in the third group related to the hospital setting, as interventions from primary care may not easily be transferable to hospital contexts.Terms within groups were combined using the Boolean operator “OR” and sets were combined with the Boolean operator “AND”. We applied English language restrictions and used no methodological filters. Inclusion and exclusion criteriaWe included experimental studies of various designs that examined the effect of ePrescribing systems on the appropriate use of antibiotics in hospital settings.Studies were excluded if they fell outside our scope of interest, for example if they: Did not relate to both ePrescribing functionality and the appropriate use of antibioticsEvaluated technology that is not commonly associated with core ePrescribing functionality Focused on the implementation of technology in primary care settings due to different contextual circumstancesDid not include experimental designsFocused on the implementation of systems in low-income countries (defined as having a gross national income per capita lower than US$ 4,036) due to different contextual circumstances 16Two reviewers independently screened articles for inclusion, discussed these and resolved discrepancies. Data abstraction and synthesisTwo researchers independently undertook data abstraction from each study. Data were abstracted onto a customised data extraction sheet. Variables included: author and year; title of the study; country of origin; design; hospital setting/specialty; system functionality/intervention; evidence for impact on practitioner performance; evidence for impact on patient outcomes; evidence on cost-effectiveness; conclusions; and potential applicability to UK NHS hospitals. Key findings from each study were summarised and presented in tables. Reviewers coded the variables and resolved any disputes through mutual discussion.We followed three main steps in conducting an interpretive synthesis of our findings:17 (1) noting the range of functions and uses of ePrescribing and related systems to promote/improve antimicrobial stewardship; (2) developing a synthesis of the findings of included studies; (3) and exploring relationships in the findings. ResultsWe screened titles and abstracts of 3471 papers, and full texts of 421 references. 143 papers were selected for inclusion (see Figure 1 and Appendix 2).18 Of these, 121 were observational studies, nine systematic reviews, five surveys, and three randomised controlled trials (RCT). Only a small number of studies (n= 5) included a qualitative component. The majority of papers originated from North American settings (n=87), followed by Australia (n=11), the UK (n=10), France (n=5), Canada (n=5), Israel (n=4), Germany (n=4), South Korea (n=3), Denmark (n=3), the Netherlands (n=3), and Singapore (n=2). One study each originated from Belgium, the Czech Republic, Portugal, Spain, Switzerland, and Taiwan. System functionalities – a focus on improving individual performance in specific clinical settingsMost evidence came from specialty settings (such as intensive care, emergency medicine, and paediatrics);19-72 and from high risk patient groups (e.g. those at risk for or with infections);58, 73-80 and from tertiary care.26, 50, 66, 67, 69, 81-96 We found that most work has been conducted in relation to CPOE and CDS systems, focusing on interventions that targeted individual prescribers through prompts and reminders. 21-26, 28, 31-34, 36, 37, 39-50, 53-62, 64, 65, 67, 68, 71, 73, 76-80, 82-86, 88, 90, 91, 94, 96-138 The majority of systems were “home-grown” standalone systems, implemented in single centres, and extensively customised; but we also identified some work on packaged applications.21, 52, 53, 74, 96, 110, 138-141 Research activity surrounding more complex and distributed functionalities that integrated ePrescribing with medical notes, pharmacy systems and laboratory results was less common.29, 30, 39, 40, 48, 52, 53, 58, 61, 63, 76, 77, 81, 87, 89, 116, 120, 124, 139, 141-144 Overall, system functionalities varied significantly across studies, making comparisons difficult. Outcomes investigated - from practitioner performance to patient outcomes and costWe found that the majority of ePrescribing interventions to promote the appropriate use of antibiotics examined healthcare professional performance, followed by patient outcomes and cost assessments (see Box 1). These were often measured in isolation with little effort to assess relationships between them.In relation to practitioner performance, the studies reported improved turnaround time for first dose of antibiotic,19, 23, 25, 30, 34, 43, 48, 52, 55, 57, 64, 67-69, 73, 82, 86, 90, 94, 106, 124, 129, 131, 133, 134, 145, 146 improved selection of antibiotics (e.g. in line with local protocols),22, 25, 27, 28, 35, 37, 38, 54, 55, 58, 59, 62, 64-66, 68, 70, 71, 76, 77, 90, 92, 95, 101, 108, 112, 115, 117, 122, 129, 131, 134, 135, 147, 148 optimal dosing,31, 39, 41, 43, 44, 55, 69, 79, 80, 87, 95, 107, 125, 126, 149 improved adherence to guidelines/protocols,20, 21, 24-26, 36, 37, 45, 56, 60, 64, 65, 71, 78, 83, 85, 92, 93, 95, 101-103, 105, 127, 133, 142, 150 and a decrease in the total number of antibiotics prescribed.26, 49, 61, 66, 92, 95, 96, 119, 129, 132, 150-153 Some studies also reported a reduction in prescribing errors and increased de-escalation,23, 33, 42, 49, 66, 91, 94, 100, 113, 114, 126, 128, 138, 139, 146, 154, 155 as well as improved antibiotic sensitivity and increase in sensitivity of surgical site infection detection.26, 39, 51, 75, 143, 156, 157 Others reported a timelier discontinuation of antibiotics and a decrease in the meantime to antibiotic administration.45, 49, 53, 87, 88, 116, 119, 120, 140 One study suggested that systems could help to identify prescribers who performed best and those with suboptimal performance.109Examining patient outcomes was less common, but the most frequently reported measures included adverse drug events, 39, 46, 49, 76, 80, 111, 129, 135, 136, 158, 159 mortality,56, 76, 99, 118, 135, 141, 144 and length of hospital stay associated with antibiotics.39, 61, 76, 97, 99, 129, 135 We also found some work relating to examination of rates of post-operative wound infection,78, 129, 134, 154 the emergence of drug-resistant pathogens,115, 135 susceptibility relating to certain classes of antibiotics,72 and reduced rates of drug hypersensitivity reactions.158Cost-effectiveness was least frequently examined, although some studies indicated cost savings relating to total antibiotic expenditure,19, 29, 39, 49, 87, 120, 122, 129, 132, 135, 141, 144, 151 and cost of hospitalisation.39, 44, 97, 129, 139, 151 Others measured costs relating to future resistance 76 and overall pharmacy interventions.102 Embedding systems within their social context of use – from individual performance to social transformationsSome studies indicated a lack of impact of systems in relation to practitioner performance,32, 40, 47, 81, 104, 152 and patient outcomes.19, 26, 27, 46, 50, 61, 67, 70, 98, 101, 121, 122, 129, 136, 137, 160, 161 One study reported worse patient outcomes after implementation of an electronic allergy label.162 Similar issues emerged when considering costs. Two studies concluded that systems did not result in significant cost savings,25, 115 whilst some studies showed an increase in costs, even though the quality of prescribing was better.98, 99, 101 This was mainly due to the fact that although the use of some antibiotics (e.g. restricted antibiotics) rose, the overall cost was reduced (due to a decrease in the use of other types). These conflicting findings appeared to be due to social contexts in which systems were used and deployed. Here, interventions may in some instances lead to unintended consequences that are difficult to anticipate. For example, some system recommendations were rejected as clinicians deemed them inappropriate,84, 91, 104, 144, 160 some expressed difficulty in complying with local guidelines so that the system made no difference to their antibiotic prescribing practices,163 and others reported changes in workflows as potential contributory factors.138 This was particularly true for complex cases.83 Studies also indicated that systems may adversely impact on user time. Whilst some authors reported time-savings, others reported time-increases for clinical users.57, 63, 64, 67-69, 94, 124, 129, 131, 133, 134, 145 These issues may, to some degree, be addressed by increasing system usability;130 and ensuring that interventions are deployed as part of wider organisational transformations, paying attention to the variety of contexts in which they are used.20, 27, 50, 52, 85, 120, 141Secondary uses and medication management processesThere was limited evidence surrounding interventions that had a broader organisational focus and those that investigated approaches to re-using data held within systems.29, 30, 35, 51, 58, 61, 68, 75-77, 89, 95, 140, 149, 157, 158 We found a limited number of studies that reported on systems that linked patient-specific data with data from different specialty systems including laboratory results.29, 58, 61, 76, 77, 120, 151 These were used mainly to help detect high-risk patients and provide targeted support to individual prescribers.58, 61, 76, 77 Work on multi-faceted interventions that coordinated actions across multi-disciplinary teams, including active pharmacy interventions and medicines administration, was rare.29, 30, 68 A notable exception was work by Waitman and colleagues, who designed an electronic surveillance tool to identify high-risk medication orders and triggered pharmacy intervention to complement CPOE.68 Another study reported on a system that linked CPOE/CDS with pharmacy and medication administration.30 In terms of data re-use, we identified a number of studies that drew on ePrescribing system data to develop screening/surveillance tools for patients at risk for developing infections,75, 157 to identify adverse drug events,89, 158 or to identify patients with antibiotic interventions.140 Others reported how data held within systems could help to predict appropriate therapies and dosages for high-risk patient groups,35, 58, 95, 149 and one study drew on machine learning approaches to improve the appropriate use of antibiotics in children with sepsis.51 Unfortunately, these studies did not describe how these data were used to trigger action across members of the multidisciplinary team other than prescribers (e.g. pharmacists). DiscussionTo date, ePrescribing-based interventions to promote the appropriate use of antibiotics have been investigated mainly through small scale studies with designs prone to bias (mostly non-randomised retrospective observational single centre studies) (see Table 1). The majority of these were conducted in North American settings with “home-grown” and extensively customised systems. CPOE and CDSS were the most common system functionalities examined, but these varied widely across studies thereby complicating meaningful cross-study comparisons. Work surrounding more advanced functionalities that span wider organisational processes such as integration with laboratory results, pharmacy systems, and re-use of data was relatively scarce. Outcomes of existing work related mainly to individual performance of clinicians, followed by patient outcomes and cost assessments, with no attempts to investigate relationships and mechanisms across these dimensions. We also found a lack of insights into unintended sociotechnical consequences of interventions, which may help to in part explain the range of conclusions surrounding effectiveness across studies. A range of other factors such as different settings, populations, functionality, and outcomes assessed are also likely to play a role.In undertaking this scoping review, we have provided an overall picture of the current evidence surrounding existing approaches to promoting the appropriate use of antibiotics through hospital ePrescribing systems and their outcomes (Figure 2). This illustrates the lack of attention paid to social contexts of use and the potential for developing broader organisational interventions associated with integration of ePrescribing systems with other functionalities and re-use of data. Our broad inclusion criteria meant that we included a wide range of studies with different designs, but we did not conduct formal quality assessments of included studies.164 This work now needs to be taken forward as a basis for designing and formally evaluating interventions employing rigorous methods to evaluate the impact of hospital ePrescribing systems to promote the appropriate use of antibiotics on patient outcomes. Central components of such interventions need to build on the existing literature, as identified in our work, and on existing guidance surrounding the development and evaluation of complex interventions.165 Ideally, future work should consist of multi-centre cluster randomised controlled trials with embedded qualitative and economic evaluation. Given the complex effects of ePrescribing systems and variety of settings in which they are implemented, randomised controlled trials may not be feasible, in which case quasi-experimental studies may be considered. In this context paying attention to both social and technical dimensions of change,166 as well as drawing on longitudinal quantitative and qualitative designs is central for going forward as this will allow a degree of comparison across settings and systems.167 Sociotechnically informed work can also help to ensure that improvements in practitioner performance effectively translate to improvements in patient outcomes and cost-effectiveness. Here, examination of technical features and in particular user interfaces, as well as exploring social and organisational factors such as user training will be important. In due course, evidence obtained from evaluations of interventions with rigorous designs can then be aggregated through more formal systematic reviews.168, 169The likely difficulty for any future work in this area is the complexity of interventions. These are likely to consist of multiple components accompanied with organisational change initiatives in specific settings. As a result, evaluators will be faced with challenges surrounding which observed effects are due to which interventional aspects.165, 170 Embedded qualitative work can help to mitigate this risk and facilitate insights into which components are potentially transferable between settings and systems. Such work is also urgently needed to gain deeper insights into “alert fatigue” and impact on time amongst users as well as other reasons as to why systems may not be effectively used.171, 172In parallel to such efforts, it is imperative to more thoroughly explore interventions that draw on electronic data held within systems for re-use to improve the appropriate use of antibiotics and other more innovative approaches as these are likely to present the biggest returns on investment.173 Such work is currently extremely scarce in the empirical literature surrounding the appropriate use of antibiotics, which focuses mainly on approaches to improve practitioner performance, as opposed to interventions that inform more general organisational functioning through iterative cycles of feedback. Such work may involve identifying local and health system patterns of antibiotic resistance or feeding back performance data relating to antimicrobial prescribing to individual clinicians and/or units. This will help to ensure to build learning health systems thereby helping to improve safety, quality and efficiency of care more generally.174ConclusionsWe have provided a starting point for developing interventions in an area of increasing interest internationally, by scoping the literature to identify existing and emerging approaches to ePrescribing-based interventions to promote the appropriate use of antibiotics. Next there is the need for a more focused systematic review on effectiveness and cost-effectiveness of ePrescribing functionalities, which we now plan to carry out. Systems clearly have much potential in improving practitioner performance and patient outcomes, and reducing cost. However, more rigorous multi-faceted interventions now need to be developed and carefully evaluated to take this work forward and help to extract potentially transferable lessons across settings and systems. In relation to systems design, there are various options that can help to promote the appropriate use of antibiotics. These include changing existing systems (this requires suppliers to be engaged in changing their systems), customising systems (local change based on guidelines), or building new standard systems as an add-on to existing ePrescribing functionality. Whatever the approach, appropriate organisational and social components will need to play an essential part in approaches going forward and there is a clear need to pay closer attention to developing interventions with a broader organisational peting interests: All authors declare that they have no competing interests.References1.Phimister EG, Arias CA, Murray BE. A New Antibiotic and the Evolution of Resistance. New England Journal of Medicine. 2015;372(12):1168-1170.2.Goossens H, Ferech M, Vander Stichele R, Elseviers M, Group EP. Outpatient antibiotic use in Europe and association with resistance: a cross-national database study. The Lancet. 2005;365(9459):579-587.3.Shlaes DM, Gerding DN, John JF, et al. Society for Healthcare Epidemiology of America and Infectious Diseases Society of America Joint Committee on the Prevention of Antimicrobial Resistance: guidelines for the prevention of antimicrobial resistance in hospitals. Clinical Infectious Diseases. 1997;25(3):584-599.4.Fishman N. Antimicrobial stewardship. American journal of infection control. 2006;34(5):S55-S63.5.Hecker MT, Aron DC, Patel NP, Lehmann MK, Donskey CJ. Unnecessary use of antimicrobials in hospitalized patients: current patterns of misuse with an emphasis on the antianaerobic spectrum of activity. Archives of Internal Medicine. 2003;163(8):972-978.6.Braykov NP, Morgan DJ, Schweizer ML, et al. Assessment of empirical antibiotic therapy optimisation in six hospitals: an observational cohort study. The Lancet Infectious Diseases. 2014;14(12):1220-1227.7.Clancy CJ, Kalil AC, Fowler VG, Ghedin E, Kolls JK, Nguyen MH. Emerging and resistant infections. Annals of the American Thoracic Society. 2014;11(Supplement 4):S193-S200.8.Leung E, Weil DE, Raviglione M, Nakatani H. The WHO policy package to combat antimicrobial resistance. Bulletin of the World Health Organization. 2011;89(5):390-392.9.Burke JP. Antibiotic resistance—squeezing the balloon? JAMA. 1998;280(14):1270-1271.10.Garg AX, Adhikari NK, McDonald H, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005;293(10):1223-1238.11.Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005;330(7494):765.12.Shojania KG, Jennings A, Mayhew A, Ramsay C, Eccles M, Grimshaw J. Effect of point-of-care computer reminders on physician behaviour: a systematic review. Canadian Medical Association Journal. 2010;182(5):E216-E225.13.Dellit TH, Owens RC, McGowan Jr JE, et al. IDSA GUIDELINES-Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America Guidelines for Developing an Institutional Program to Enhance Antimicrobial Stewardship. Clinical Infectious Diseases. 2007;44(2):159-177.14.MacDougall C, Polk RE. Antimicrobial stewardship programs in health care systems. Clinical Microbiology Reviews. 2005;18(4):638-656.15.Black AD, Car J, Pagliari C, et al. The impact of eHealth on the quality and safety of health care: a systematic overview. PLoS Medicine. 2011;8(1):188.16.World Bank. Updated Income Classifications. Available from: ; 2014.17.Cresswell K, Majeed A, Bates DW, Sheikh A. Computerised decision support systems for healthcare professionals: an interpretative review. Informatics in Primary Care. 2012;20(2):115-128.18.Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Reprint—preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Physical Therapy. 2009;89(9):873-880.19.Agwu AL, Lee CKK, Jain SK, et al. A world wide web-based antimicrobial stewardship program improves efficiency, communication, and user satisfaction and reduces cost in a tertiary care pediatric medical center. Clinical Infectious Diseases. 15 Sep 2008;47(6):747-753.20.Albano E, Nakano T, Rannie M, West K, Dolan S, Neiman J. The utilization of electronic medical records (EMR) to improve compliance with blood culture collection in febrile immunocompromised children. Pediatric Blood and Cancer. June 2011;56 (6):944.21.Allan P, Newman M, Collinson J, Bond L, English W. Use of an electronic medical record system to improve antimicrobial stewardship. Critical Care. 16 Mar 2015;19:S37-S38.22.Bernstein SL, Whitaker D, Winograd J, Brennan JA. An electronic chart prompt to decrease proprietary antibiotic prescription to self-pay patients. Academic Emergency Medicine. March 2005;12(3):225-231.23.Bissinger R.L. P, APRN, NNP-BC, FAAN; Martina Mueller, PhD;, Toby Hoffman Cox P, BCPS; John Cahill, MD;, Sandra S. Garner P, BCPS; Michael Irving; David J. Annibale, MD. Antibiotic Timing in Neonates With Suspected Hospital-Acquired Infections. Advances in Neonatal Care. 2013;13(1).24.Bochicchio GV, Smit PA, Moore R, et al. Pilot study of a web-based antibiotic decision management guide. Journal of the American College of Surgeons; 2006:459-467.25.Buising KL, Thursky KA, Black JF, et al. Improving antibiotic prescribing for adults with community acquired pneumonia: Does a computerised decision support system achieve more than academic detailing alone? - A time series analysis. BMC Medical Informatics and Decision Making. 2008;8(35).26.Buising KL, Thursky KA, Robertson MB, et al. Electronic antibiotic stewardship--reduced consumption of broad-spectrum antibiotics using a computerized antimicrobial approval system in a hospital setting. Journal of Antimicrobial Chemotherapy.2008;62(3):608-616.27.Burkitt KH, Mor MK, Jain R, et al. Toyota production system quality improvement initiative improves perioperative antibiotic therapy. American Journal of Managed Care.15(9):633-642.28.Carman MJ, Phipps J, Raley J, Li S, Thornlow D. Use of a clinical decision support tool to improve guideline adherence for the treatment of methicillin-resistant staphylococcus aureus: Skin and soft tissue infections. Advanced Emergency Nursing Journal. July-September 2011;33(3):252-266.29.Carr JR, Fitzpatrick P, Izzo JL, et al. Changing the infection control paradigm from off-line to real time: the experience at Millard Fillmore Health System. Infection Control and Hospital Epidemiology. Apr 1997;18(4):255-259.30.Cartmill RS, Walker JM, Blosky MA, et al. Impact of electronic order management on the timeliness of antibiotic administration in critical care patients. International Journal of Medical Informatics. November 2012;81(11):782-791.31.Chan ALF, Wang HY, Leung HWC. Incorporation of a gentamicin dosage calculator into a computerized prescriber-order-entry system. American Journal of Health-System Pharmacy. 15 Jul 2006;63(14):1344-1345.32.Chapman AK, Lehmann CU, Donohue PK, Aucott SW. Implementation of computerized provider order entry in a neonatal intensive care unit: Impact on admission workflow. International Journal of Medical Informatics. May 2012;81(5):291-295.33.Colpaert K, Claus B, Somers A, Vandewoude K, Robays H, Decruyenaere J. Impact of computerized physician order entry on medication prescription errors in the intensive care unit: A controlled cross-sectional trial. Critical Care. 26 Jan 2006;10(1).34.Crum B, Green J, Nettiksimmons J, et al. Effect of a computerized decision support system on time to antibiotic initiation for severe sepsis. Academic Emergency Medicine. May 2013;1):S289-S290.35.de Bruijn N, Lucas P, Schurink K, Hoepelman A. A probabilistic approach to improved antibiotic therapy. Studies in Health Technology & Informatics.68:690-695.36.Dean NC, Suchyta MR, Bateman KA, Aronsky D, Hadlock CJ. Implementation of admission decision support for community-acquired pneumonia. Chest. 117(5):1368-1377.37.Demonchy E, Dufour JC, Gaudart J, et al. Impact of a computerized decision support system on compliance with guidelines on antibiotics prescribed for urinary tract infections in emergency departments: a multicentre prospective before-and-after controlled interventional study. The Journal of antimicrobial chemotherapy. 01 Oct 2014;69(10):2857-2863.38.Douglas IS, Price CS, Overdier KH, et al. Rapid automated microscopy for microbiological surveillance of ventilator-associated pneumonia. American Journal of Respiratory and Critical Care Medicine; 2015:566-573.39.Evans RS, Pestotnik SL, Classen DC, et al. A computer-assisted management program for antibiotics and other antiinfective agents. New England Journal of Medicine. 1998;338(4):232-238.40.Ferng YH, DeLaMora P, Patel S, et al. Improving antimicrobial stewardship in the neonatal ICU with computer decision support. American Journal of Infection Control. June 2012;40 (5):e131.41.Frankel KC, Rosini JM, Levine BJ, Papas MA, Jasani NB. Computerized provider order entry improves compliance of vancomycin dosing guidelines in the emergency department. American Journal of Emergency Medicine.31(12):1715-1716.42.Graham TAD, Bullard MJ, Kushniruk AW, Holroyd BR, Rowe BH. Assessing the sensibility of two clinical decision support systems. Journal of Medical Systems. October 2008;32(5):361-368.43.Hall AB, Montero J, Cobian J, Regan T. The effects of an electronic order set on vancomycin dosing in the ED. American Journal of Emergency Medicine. 2015;33(1):92-94.44.Helmons PJ, Grouls RJ, Roos AN, et al. Using a clinical decision support system to determine the quality of antimicrobial dosing in intensive care patients with renal insufficiency. Quality & Safety in Health Care. Feb 2010;19(1):22-26.45.Hill PM, Rothman R, Saheed M, Deruggiero K, Hsieh YH, Kelen GD. A comprehensive approach to achieving near 100% compliance with the Joint Commission Core Measures for pneumonia antibiotic timing. American Journal of Emergency Medicine. November 2011;29(9):989-998.46.Holdsworth MT, Fichtl RE, Raisch DW, et al. Impact of computerized prescriber order entry on the incidence of adverse drug events in pediatric inpatients. Pediatrics. November 2007;120(5):1058-1066.47.Horng S, Chiu DT, Joh JW, Calder SA, Wolfe RE, Nathanson LA. Prospective evaluation of computerized physician order entry in the emergency department. Academic Emergency Medicine. May 2013;1):S160.48.Hum RS, Cato K, Sheehan B, et al. Developing clinical decision support within a commercial electronic health record system to improve antimicrobial prescribing in the neonatal ICU. Applied Clinical Informatics. 2014;5(2):368-387.49.Kaki R, Elligsen M, Walker S, Simor A, Palmay L, Daneman N. Impact of antimicrobial stewardship in critical care: a systematic review. Journal of Antimicrobial Chemotherapy; 2011:1223-1230.50.King WJ, Le Saux N, Sampson M, Gaboury I, Norris M, Moher D. Effect of point of care information on inpatient management of bronchiolitis. BMC Pediatrics. 2007;7(4).51.Mani S, Ozdas A, Aliferis C, et al. Medical decision support using machine learning for early detection of late-onset neonatal sepsis. Journal of the American Medical Informatics Association. 2014;21(2):326-336.52.March CA, Steiger D, Scholl G, Mohan V, Hersh WR, Gold JA. Use of simulation to assess electronic health record safety in the intensive care unit: A pilot study. BMJ Open. 2013;3(4).53.Mayorga CA, Rockey DC. Clinical utility of a standardized electronic order set for the management of acute upper gastrointestinal hemorrhage in patients with cirrhosis. Clinical Gastroenterology and Hepatology. October 2013;11(10):1342-1348.54.Micek ST, Heard KM, Gowan M, Kollef MH. Identifying critically ill patients at risk for inappropriate antibiotic therapy: A pilot study of a point-of-care decision support alert. Critical Care Medicine. August 2014;42(8):1832-1838.55.Mullett CJ, Evans RS, Christenson JC, Dean JM. Development and impact of a computerized pediatric antiinfective decision support program. Pediatrics. Oct 2001;108(4):E75.56.Nachtigall I, Tafelski S, Deja M, et al. Long-term effect of computer-assisted decision support for antibiotic treatment in critically ill patients: A prospective 'before/after' cohort study. BMJ Open. 2014;4(12).57.Pascale C, Djurkovic S, Cartmill R, et al. Computerized provider order entry implementation decreases the time to the administration of the first dose of antibiotic. Critical Care Medicine. December 2009;37 (12 SUPPL.):A232.58.Rodriguez-Maresca M, Sorlozano A, Grau M, Rodriguez-Castano R, Ruiz-Valverde A, Gutierrez-Fernandez J. Implementation of a computerized decision support system to improve the appropriateness of antibiotic therapy using local microbiologic data. BioMed Research International. 2014;2014:395434.59.Rohrig R, Niczko EJ, Beutefuhr H, et al. Examination of computer assisted prescribing of an initial calculated antibiotic treatment. Studies in Health Technology and Informatics. 2008;136:63-68.60.Sintchenko V, Coiera E, Iredell JR, Gilbert GL. Comparative impact of guidelines, clinical data, and decision support on prescribing decisions: an interactive web experiment with simulated cases. Journal of the American Medical Informatics Association. 2004;11(1):71-77.61.Sintchenko V, Iredell JR, Gilbert GL, Coiera E. Handheld computer-based decision support reduces patient length of stay and antibiotic prescribing in critical care. Journal of the American Medical Informatics Association. July/August 2005;12(4):398-402.62.Stephenson LS, Gorsuch A, Hersh WR, Mohan V, Gold JA. Participation in EHR based simulation improves recognition of patient safety issues. BMC Medical Education. 2014;14:224.63.Steurbaut K, van Hoecke S, Colpaert K, et al. Use of web services for computerized medical decision support, including infection control and antibiotic management, in the intensive care unit. Journal of Telemedicine and Telecare. January 2009;16(1):25-29.64.Tafelski S, Nachtigall I, Deja M, et al. Computer-assisted Decision Support for Changing Practice in Severe Sepsis and Septic Shock. Journal of International Medical Research. 2010;38(5):1605-1616.65.Thomas S, Schwartz I, Topal J, Sinha V, Pace C, Wira CR. Empiric antibiotic prescribing practices after the introduction of a computerized order entry system in an adult emergency department. Academic Emergency Medicine. April 2012;19:S166-S167.66.Thursky KA, Buising KL, Bak N, et al. Reduction of broad-spectrum antibiotic use with computerized decision support in an intensive care unit. International Journal for Quality in Health Care. 15 Jun 2006;18(3):224-231.67.Vilella AL, Seifert CF. Timing and appropriateness of initial antibiotic therapy in newly presenting septic patients. American Journal of Emergency Medicine. January 2014;32(1):7-13.68.Waitman LR, Phillips IE, McCoy AB, et al. Adopting real-time surveillance dashboards as a component of an enterprisewide medication safety strategy. Joint Commission journal on quality and patient safety / Joint Commission Resources. Jul 2011;37(7):326-332.69.Ward MJ, Boyd JS, Harger NJ, Deledda JM, Smith CL, Walker SM, Hice JD, Hart KW, Lindsell C. J, Wright SW. An automated dispensing system for improving medication timing in the emergency department. World Journal Emergency Medicine. 2012;3(2).70.Weiss CH, Dibardino D, Rho J, Sung N, Collander B, Wunderink RG. A clinical trial comparing physician prompting with an unprompted automated electronic checklist to reduce empirical antibiotic utilization. Critical Care Medicine. November 2013;41(11):2563-2569.71.Westphal JF, Jehl F, Javelot H, Nonnenmacher C. Enhanced physician adherence to antibiotic use guidelines through increased availability of guidelines at the time of drug ordering in hospital setting. Pharmacoepidemiology and Drug Safety. February 2011;20(2):162-168.72.Yong MK, Buising KL, Cheng AC, Thursky KA. Improved susceptibility of Gram-negative bacteria in an intensive care unit following implementation of a computerized antibiotic decision support system. Journal of Antimicrobial Chemotherapy. 2010;65(5):1062-1069.73.Brooks HL, Hodson J, Richardson SJ, Stezhka L, Gill MJ, Coleman JJ. Improving the timeliness of meticillin-resistant Staphylococcus aureus antimicrobial decolonization therapy administration: A descriptive account. Journal of Hospital Infection. March 2014;86(3):209-215.74.Diero L, Stiffler T, Einterz RM, Tierney WM. Can data from an electronic medical record identify which patients with pneumonia have Pneumocystis carinii infection? International Journal of Medical Informatics. November 2004;73(11-12):743-750.75.Leth RA, Norgaard M, Uldbjerg N, Thomsen RW, Moller JK. Surveillance of selected post-caesarean infections based on electronic registries: Validation study including post-discharge infections. Journal of Hospital Infection. July 2010;75(3):200-204.76.Paul M, Andreassen S, Tacconelli E, et al. Improving empirical antibiotic treatment using TREAT, a computerized decision support system: cluster randomized trial. Journal of Antimicrobial Chemotherapy; 2006:1238-1245.77.Paul M, Nielsen AD, Goldberg E, et al. Prediction of specific pathogens in patients with sepsis: Evaluation of TREAT, a computerized decision support system. Journal of Antimicrobial Chemotherapy. June 2007;59(6):1204-1207.78.Schwann NM, Bretz KA, Eid S, et al. Point-of-care electronic prompts: an effective means of increasing compliance, demonstrating quality, and improving outcome. Anesthesia & Analgesia. 2011;113(4):869-876.79.St. Jacques P, Sanders N, Patel N, Talbot TR, Deshpande JK, Higgins M. Improving timely surgical antibiotic prophylaxis redosing administration using computerized record prompts. Surgical Infections. Summer 2005;6(2):215-221.80.Wang HY, Lu CL, Wu MP, Huang MH, Huang YB. Effectiveness of an integrated CPOE decision-supporting system with clinical pharmacist monitoring practice in preventing antibiotic dosing errors. International Journal of Clinical Pharmacology and Therapeutics. June 2012;50(6):375-382.81.Bruins MJ, Ruijs GJ, Wolfhagen MJ, Bloembergen P, Aarts JE. Does electronic clinical microbiology results reporting influence medical decision making: a pre- and post-interview study of medical specialists. BMC Medical Informatics and Decision Making. 2011;11:19.82.Chan A, Toy G, Wong PP, Horner D, Gross AK. Impact of computerized provider order entry (CPOE) implementation on the timing of antibiotic administration in inpatients with severe sepsis and septic shock. Critical Care Medicine. December 2012;1):118.83.Chow A, Lye DCB. Predictors of patients' receipt of antibiotic recommendations by a computerised decision support system: A cohort study. Annals of the Academy of Medicine Singapore. September 2014;1):S118.84.Chow A, Lye DCB, Arah OA. Psychosocial determinants of physicians' acceptance of recommendations by antibiotic computerised decision support systems: A mixed methods study. International Journal of Antimicrobial Agents. March 2015;45(3):295-304.85.Cosgrove SE, Patel A, Song X, et al. Impact of different methods of feedback to clinicians after postprescription antimicrobial review based on the Centers For Disease Control and Prevention's 12 Steps to Prevent Antimicrobial Resistance Among Hospitalized Adults. Infection Control & Hospital Epidemiology.28(6):641-646.86.Cunningham TR, Geller ES, Clarke SW. Impact of electronic prescribing in a hospital setting: A process-focused evaluation. International Journal of Medical Informatics. August 2008;77(8):546-554.87.Evans RS, Pestotnik SL, Classen DC, Burke JP. Evaluation of a computer-assisted antibiotic-dose monitor. Annals of Pharmacotherapy. 1999;33(10):1026-1031.88.Haynes K, Linkin DR, Fishman NO, et al. Effectiveness of an information technology intervention to improve prophylactic antibacterial use in the postoperative period. Journal of the American Medical Informatics Association. 2007;18(2):164-168.89.Kilbridge PM, Campbell UC, Cozart HB, Mojarrad MG. Automated surveillance for adverse drug events at a community hospital and an academic medical center. Journal of the American Medical Informatics Association. 2005;13(4):372-377.90.Kim J, Joo EJ, Ha YE, et al. Impact of a computerized alert system for bacteremia notification on the appropriate antibiotic treatment of Staphylococcus aureus bloodstream infections. European Journal of Clinical Microbiology & Infectious Diseases. 2013;32(7):937-945.91.Koppel R, Metlay JP, Cohen A, et al. Role of computerized physician order entry systems in facilitating medication errors. Journal of the American Medical Association. 09 Mar 2005;293(10):1197-1203.92.Richards MJ, Robertson MB, Dartnell JGA, et al. Impact of a web-based antimicrobial approval system on broad-spectrum cephalosporin use at a teaching hospital. Medical Journal of Australia. 2003;178(8):386-390.93.Sikka R, Sweis R, Kaucky C, Kulstad E. Automated dispensing cabinet alert improves compliance with obtaining blood cultures before antibiotic administration for patients admitted with pneumonia. Joint Commission journal on quality and patient safety / Joint Commission Resources. May 2012;38(5):224-228.94.Vincent WR, Martin CA, Winstead PS, Smith KM, Gatz J, Lewis DA. Effects of a Pharmacist-to-Dose Computerized Request on Promptness of Antimicrobial Therapy. Journal of the American Medical Informatics Association. January 2009/February 2009;16(1):47-53.95.Yoo S, Kim S, Lee KH, et al. Electronically implemented clinical indicators based on a data warehouse in a tertiary hospital: Its clinical benefit and effectiveness. International Journal of Medical Informatics. July 2014;83(7):507-516.96.Schulz L, Osterby K, Fox B. The use of best practice alerts with the development of an antimicrobial stewardship navigator to promote antibiotic De-escalation in the electronic medical record. Infection Control and Hospital Epidemiology. December 2013;34(12):1259-1265.97.Gillaizeau F, Chan E, Trinquart L, et al. Computerized advice on drug dosage to improve prescribing practice. Cochrane Database of Systematic Reviews. 2013;11:CD002894.98.Arboe B, Laub RR, Kronborg G, Knudsen JD. Evaluation of the decision support system for antimicrobial treatment, TREAT, in an acute medical ward of a university hospital. International Journal of Infectious Diseases. December 01 2014;29:156-161.99.Arnold R, Dellinger RP, Hollenberg SM. Defining the impact of delayed antibiotic administration using a comprehensive electronic health record screen to identify severe sepsis. Academic Emergency Medicine. May 2013;1):S278-S279.100.Bonnabry P, Despont-Gros C, Grauser D, et al. A Risk Analysis Method to Evaluate the Impact of a Computerized Provider Order Entry System on Patient Safety. Journal of the American Medical Informatics Association. July 2008/August 2008;15(4):453-460.101.Burke JP, Pestotnik SL. Antibiotic use and microbial resistance in intensive care units: Impact of computer-assisted decision support. Journal of Chemotherapy. 1999;11(6):530-535.102.Calloway S, Akilo H, Bierman K. Impact of a clinical decision support system on pharmacy clinical interventions, documentation efforts, and costs. Hospital Pharmacy. 2013;48(9):744-752.103.Charani E, Kyratsis Y, Lawson W, et al. An analysis of the development and implementation of a smartphone application for the delivery of antimicrobial prescribing policy: Lessons learnt. Journal of Antimicrobial Chemotherapy. April 2013;68(4):960-967.104.Diasinos N, Baysari M, Kumar S, Day RO. Does the availability of therapeutic drug monitoring, computerised dose recommendation and prescribing decision support services promote compliance with national gentamicin prescribing guidelines? Internal Medicine Journal. 01 Jan 2015;45(1):55-62.105.Dixon-Woods M, Redwood S, Leslie M, Minion J, Martin GP, Coleman JJ. Improving quality and safety of care using technovigilance: An ethnographic case study of secondary use of data from an electronic prescribing and decision support system. Milbank Quarterly. September 2013;91(3):424-454.106.Dow J, Brummond P, Cesarz J, Ludwig B, Rough S. Evaluation of the impact of computerized prescriber order entry on medication use system performance at an academic medical center. Hospital Pharmacy. 01 Oct 2012;47(10):776-782.107.Fernandez-Morato J, Grau S, Conde-Estevez D, et al. Pharmacist interventions on antibiotic dosage performed trough a computer-assisted management programme with real-time alerts. Clinical Microbiology and Infection. April 2010;16:S433.108.Filice GA, Drekonja DM, Thurn JR, et al. Use of a computer decision support system and antimicrobial therapy appropriateness. Infection Control and Hospital Epidemiology. June 2013;34(6):558-565.109.Grayson ML, Melvani S, Kirsa SW, et al. Impact of an electronic antibiotic advice and approval system on antibiotic prescribing in an Australian teaching hospital. Medical Journal of Australia. 2004;180(9):455-458.110.Hermsen ED, VanSchooneveld TC, Sayles H, Rupp ME. Implementation of a clinical decision support system for antimicrobial stewardship. Infection Control Hospital Epidemiology. 2012;33(4).111.Hug BL, Witkowski DJ, Sox CM, et al. Adverse drug event rates in six community hospitals and the potential impact of computerized physician order entry for prevention. Journal of General Internal Medicine. January 2010;25(1):31-38.112.Hulgan T, Rosenbloom ST, Hargrove F, et al. Oral quinolones in hospitalized patients: An evaluation of a computerized decision support intervention. Journal of Internal Medicine. October 2004;256(4):349-357.113.Jayawardena S, Eisdorfer J, Indulkar S, Pal SA, Sooriabalan D, Cucco R. Prescription errors and the impact of computerized prescription order entry system in a community-based hospital. American Journal of Therapeutics. July/August 2007;14(4):336-340.114.Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Archives of Internal Medicine. 2003;163(12):1409-1416.115.Kofoed K, Zalounina A, Andersen O, et al. Performance of the TREAT decision support system in an environment with a low prevalence of resistant pathogens. Journal of Antimicrobial Chemotherapy. 2009;63(2):400-404.116.Kurczewski L, Sweet M, McKnight R, Halbritter K. Evaluation of time to first action as a result of electronic alerts for early sepsis recognition. Critical Care Medicine. December 2013;1):A259.117.Leibovici L, Gitelman V, Yehezkelli Y, et al. Improving empirical antibiotic treatment: prospective, nonintervention testing of a decision support system. Journal of Internal Medicine. 1997;242(5):395-400.118.Leibovici L, Kariv G, Paul M. Long-term survival in patients included in a randomized controlled trial of treat, a decision support system for antibiotic treatment. Journal of Antimicrobial Chemotherapy. November 2013;68(11):2664-2666.119.Lesprit P, Duong T, Girou E, Hemery F, Brun-Buisson C. Impact of a computer-generated alert system prompting review of antibiotic use in hospitals. Journal of Antimicrobial Chemotherapy. 2005;63(5):1058-1063.120.Liu H, Cushinotto L, Mutneja H, Williams H. Amultidisciplinary approach to antimicrobial stewardship: Taking advantage of information systems technology. International Journal of Infectious Diseases. June 2012;16:e444-e445.121.Makam AN, Nguyen OK, Auerbach A. Automated electronic sepsis alerts: A systematic review. Journal of General Internal Medicine. April 2014;29:S36.122.McGregor JC, Weekes E, Forrest GN, et al. Impact of a Computerized Clinical Decision Support System on Reducing Inappropriate Antimicrobial Use: A Randomized Controlled Trial. Journal of the American Medical Informatics Association. July/August 2006;13(4):378-384.123.Mullett CJ, Thomas JG, Smith CL, Sarwari AR, Khakoo RA. Computerized antimicrobial decision support: An offline evaluation of a database-driven empiric antimicrobial guidance program in hospitalized patients with a bloodstream infection. International Journal of Medical Informatics. 15 Jun 2004;73(5):455-460.124.Panosh N, Drew R, Sharpe M. Effect of closed-loop order processing on the time,toinitial antimicrobial therapy. American Journal of Health-System Pharmacy. 15 Aug 2012;69(16):1423-1426.125.Raschke RA, Gollihare B, Wunderlich TA, et al. A computer alert system to prevent injury from adverse drug events: Development and evaluation in a community teaching hospital. Journal of the American Medical Association. 21 Oct 1998;280(15):1317-1320.126.Rinke ML, Bundy DG, Velasquez CA, et al. Interventions to reduce pediatric medication errors: A systematic review. Pediatrics. 01 Aug 2014;134(2):338-360.127.Rodrigues JF, Casado A, Palos C, Santos C, Duarte A, Fernandez-Llimos F. A computer-assisted prescription system to improve antibacterial surgical prophylaxis. Infection Control and Hospital Epidemiology. April 2012;33(4):435-437.128.Sawyer AM, Deal EN, Labelle AJ, et al. Implementation of a real-time computerized sepsis alert in nonintensive care unit patients. Critical Care Medicine. March 2011;39(3):469-473.129.Shebl NA, Franklin BD, Barber N. Clinical decision support systems and antibiotic use. Pharmacy World and Science. August 2007;29(4):342-349.130.Sheehan B, Kaufman D, Stetson P, Currie LM. Cognitive analysis of decision support for antibiotic prescribing at the point of ordering in a neonatal intensive care unit. AMIA 2009;Annual Symposium proceedings / AMIA Symposium. AMIA Symposium. 2009:584-588.131.Traugott KA, Maxwell PR, Green K, Frei C, Lewis IJS. Effects of therapeutic drug monitoring criteria in a computerized prescriber-order-entry system on the appropriateness of vancomycin level orders. American Journal of Health-System Pharmacy. 15 Feb 2011;68(4):347-352.132.Van Rossum B. Evaluation of alternative alerts as a tool for antimicrobial stewardship. Pharmacotherapy. October 2014;34 (10):e208-e209.133.Wax DB, Beilin Y, Levin M, Chadha N, Krol M, Reich DL. The effect of an interactive visual reminder in an anesthesia information management system on timeliness of prophylactic antibiotic administration. Anesthesia and Analgesia. June 2007;104(6):1462-1466.134.Webb ALB, Flagg RL, Fink AS. Reducing surgical site infections through a multidisciplinary computerized process for preoperative prophylactic antibiotic administration. American Journal of Surgery. 2006;192(5):663-668.135.Wilton P, Smith R, Coast J, Millar M. Strategies to contain the emergence of antimicrobial resistance: a systematic review of effectiveness and cost-effectiveness (Structured abstract). Journal of Health Services Research and Policy; 2002:111-117.136.Wolfstadt JI, Gurwitz JH, Field TS, et al. The effect of computerized physician order entry with clinical decision support on the rates of adverse drug events: A systematic review. Journal of General Internal Medicine. April 2008;23(4):451-458.137.Zillich A.J. PJMS, PhD; Stephen J. Wilson, MD, MPH; Daniel J. Diekema, MD, MS;, Erika J. Ernst PTEV, PhD; Bradley N. Doebbeling, MD, MSc. Infection Control and Hospital Epidemiology. 2006;27(10).138.Webber EC, Warhurst HM, Smith SS, Cox EG, Crumby AS, Nichols KR. Conversion of a single-facility pediatric antimicrobial stewardship program to multi-facility application with computerized provider order entry and clinical decision support. Applied Clinical Informatics. 2013;4(4):556-568.139.Hudali T, Almomani A, Low L, Masel M. Controlling antibiotic dosing errors in patients with impaired renal function using an EMR alert. American Journal of Kidney Diseases. May 2014;63 (5):A56.140.Liu L, Hamby C, Wychowski M, et al. Prospective evaluation of Epic Antibiotic Report and Safety Surveillor to improve efficiency and identification of clinical pharmacy interventions. Pharmacotherapy. October 2013;33 (10):e255.141.Barenfanger J, Short MA, Groesch AA. Improved antimicrobial interventions have benefits. Journal of Clinical Microbiology.39(8):2823-2828.142.El-Kareh R, Roy C, Poon EG. A cluster randomized controlled trial of automated notification of post-discharge microbiology results. Journal of General Internal Medicine; 2011:S210-s211.143.Gerbier-Colomban S, Bourjault M, Cetre JC, Baulieux J, Metzger MH. Evaluation study of different strategies for detecting surgical site infections using the hospital information system at Lyon University Hospital, France. Annals of Surgery. May 2012;255(5):896-900.144.Potasman I, Naftali G, Grupper M. Impact of a computerized integrated antibiotic authorization system. Israel Medical Association Journal. July 2012;14(7):415-419.145.Ray SM, Clark S, Jeter JW, Treadway SA. Assessing the impact of mobile technology on order verification during pharmacist participation in patient rounds. American Journal of Health-System Pharmacy. 2013;70(7):633-636.146.Hamad A, Cavell G, Hinton J, Wade P, Whittlesea C. A pre-postintervention study to evaluate the impact of dose calculators on the accuracy of gentamicin and vancomycin initial doses. BMJ Open. 2015;5(6).147.Van Sise MA, Chappelle J, Figueroa R. Improving the selection of recommended prophylactic antibiotics using an electronic medical record. Obstetrics and Gynecology. December 2012;120(6):1382-1385.148.Mullett CJ, Thomas JG, Smith CL, Sarwari AR, Khakoo RA. Computerized antimicrobial decision support for hospitalized patients with a bloodstream infection. AMIA 2003; Annual Symposium proceedings / AMIA Symposium. AMIA Symposium:946.149.Mohan M, Batty KT, Cooper JA, Wojnar-Horton RE, Ilett KF. Comparison of gentamicin dose estimates derived from manual calculations, the Australian 'Therapeutic Guidelines: Antibiotic' nomogram and the SeBA-GEN and DoseCalc software programs. British Journal of Clinical Pharmacology. 2000;58(5):521-527.150.Cook PP, Rizzo S, Gooch M, Jordan M, Fang X, Hudson S. Sustained reduction in antimicrobial use and decrease in methicillin-resistant Staphylococcus aureus and Clostridium difficile infections following implementation of an electronic medical record at a tertiary-care teaching hospital. Journal of Antimicrobial Chemotherapy. January 2011;66(1):205-209.151.Mach R, Vlcek J, Prusova M, Batka P, Rysavy V, Kubena A. Impact of a multidisciplinary approach on antibiotic consumption, cost and microbial resistance in a Czech hospital. Pharmacy World and Science. October 2007;29(5):565-572.152.Davey P, Brown E, Charani E, et al. Interventions to improve antibiotic prescribing practices for hospital inpatients. Cochrane Database of Systematic Reviews: John Wiley & Sons, Ltd; 2013.153.Shojania KG, Yokoe D, Platt R, Fiskio J, Ma'Luf N, Bates DW. Reducing vancomycin use utilizing a computer guideline. Journal of the American Medical Informatics Association. 1998;5(6):554-562.154.Abramson EL, Kern LM, Brenner S, Hufstader M, Patel V, Kaushal R. Expert panel evaluation of health information technology effects on adverse events. Journal of Evaluation in Clinical Practice. August 2014;20(4):375-382.155.Coleman JJ, Hodson J, Brooks HL, Rosser D. Missed medication doses in hospitalised patients: A descriptive account of quality improvement measures and time series analysis. International Journal for Quality in Health Care. October 2013;25(5):564-572.156.Mondain V, Lieutier F, Dumas S, et al. An antibiotic stewardship program in a French teaching hospital. Medecine et Maladies Infectieuses. January 2013;43(1):17-21.157.Cooper PB, Heuer AJ, Warren CA. Electronic screening of patients for predisposition to Clostridium difficile infection in a community hospital. American Journal of Infection Control. March 2013;41(3):232-235.158.Park CS, Kim TB, Kim SL, et al. The use of an electronic medical record system for mandatory reporting of drug hypersensitivity reactions has been shown to improve the management of patients in the university hospital in Korea. Pharmacoepidemiology and Drug Safety. September 2008;17(9):919-925.159.Kilbridge PM, Noirot LA, Reichley RM, et al. Computerized surveillance for adverse drug events in a pediatric hospital. Journal of the American Medical Informatics Association.16(5):607-612.160.Stevenson KB, Barbera J, Moore JW, Samore MH, Houck P. Understanding keys to successful implementation of electronic decision support in rural hospitals: Analysis of a pilot study for antimicrobial prescribing. American Journal of Medical Quality. November/December 2005;20(6):313-318.161.Hooper MH, Weavind L, Wheeler AP, et al. Randomized trial of automated, electronic monitoring to facilitate early detection of sepsis in the intensive care unit. Critical Care Medicine; 2012:2096-2101.162.Charneski L, Deshpande G, Weiss Smith S. Impact of an antimicrobial allergy label in the medical record on clinical outcomes in hospitalized patients. Pharmacotherapy. August 2011;31(8):742-747.163.Baysari MT, Oliver K, Egan B, et al. Audit and feedback of antibiotic use: utilising electronic prescription data. Applied Clinical Informatics.4(4):583-595.164.Arksey H, O'Malley L. Scoping studies: towards a methodological framework. International Journal of Social Research Methodology. 2005;8(1):19-32.165.Campbell M, Fitzpatrick R, Haines A, et al. Framework for design and evaluation of complex interventions to improve health. BMJ. 2000;321(7262):694-696.166.Berg M. Patient care information systems and health care work: a sociotechnical approach. International Journal of Medical Informatics. 1999;55(2):87-101.167.Cresswell KM, Sadler S, Rodgers S, et al. An embedded longitudinal multi-faceted qualitative evaluation of a complex cluster randomized controlled trial aiming to reduce clinically important errors in medicines management in general practice. Trials. 2012;13(1):78-78.168.Higgins JP, Green S. Cochrane handbook for systematic reviews of interventions. Vol 5: Wiley Online Library; 2008.169.Shepperd S, Lewin S, Straus S, et al. Can we systematically review studies that evaluate complex interventions. PLoS Med. 2009;6(8):e1000086.170.Oakley A, Strange V, Bonell C, Allen E, Stephenson J, Team RS. Health services research: process evaluation in randomised controlled trials of complex interventions. BMJ: British Medical Journal. 2006;332(7538):413.171.Phansalkar S, Van der Sijs H, Tucker AD, et al. Drug—drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records. Journal of the American Medical Informatics Association. 2013;20(3):489-493.172.Kesselheim AS, Cresswell K, Phansalkar S, Bates DW, Sheikh A. Clinical decision support systems could be modified to reduce ‘alert fatigue’while still minimizing the risk of litigation. Health Affairs. 2011;30(12):2310-2317.173.Cresswell K, Sheikh A. Electronic health record technology. JAMA. 2012;307(21):2255-2255.174.Kohn LT, Corrigan JM, Donaldson MS. To err is human: building a safer health system. Vol 6: National Academies Press; 2000.Tables and FiguresFigure 1: PRISMA diagram of included studiesBox 1: Summary of outcomes measured across studiesPractitioner performanceChoice of antibioticTurnaround time for first dose of antibioticAntibiotic dosingAdherence to protocols/guidelinesTotal antibiotic usePatient outcomesAdverse drug eventsMortalityLength of hospital stay associated with antibioticsCost effectivenessTotal antibiotic expenditureFuture resistanceCost of hospitalisationFigure 2: Emerging dimensions surrounding ePrescribing-based approaches to promote the appropriate use of antibiotics identified in the scoping review and areas for future work ................
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