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Information technology interventions to improve antibiotic prescribing for patients with acute respiratory infection: a systematic review

  • Ehsan Nabovati
    Affiliations
    Health Information Management Research Centre, Department of Health Information Management & Technology, Kashan University of Medical Sciences, Kashan, Iran
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  • Fatemeh Rangraz Jeddi
    Affiliations
    Health Information Management Research Centre, Department of Health Information Management & Technology, Kashan University of Medical Sciences, Kashan, Iran
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  • Razieh Farrahi
    Correspondence
    Corresponding author: Razieh Farrahi, Health Information Management Research Center, Pezeshk Blvd, 5th of Qotbe Ravandi Blvd—Pardis Daneshgah, School of Allied Health Professions, Kashan University of Medical Sciences, Kashan 8715973449, Iran.
    Affiliations
    Student Research Committee, Department of Health Information Management & Technology, Kashan University of Medical Sciences, Kashan, Iran
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  • Shima Anvari
    Affiliations
    Student Research Committee, Department of Health Information Management & Technology, Kashan University of Medical Sciences, Kashan, Iran
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Published:April 01, 2021DOI:https://doi.org/10.1016/j.cmi.2021.03.030

      Abstract

      Objectives

      Information technology (IT) interventions provide physicians with easy and quick access to information at the point of care and can play a major role in clinical decision-making for antibiotic prescribing. This study aimed to examine the effects and characteristics of IT interventions on improving antibiotic prescribing for patients with acute respiratory infection (ARI).

      Methods

      A comprehensive search was performed in Medline (through PubMed), ISI web of science, Embase, and Cochrane databases from inception to 31 August 2020. Randomized controlled trial (RCT) and cluster RCT (CRCT) studies examining the effectiveness of IT interventions in improving antibiotic prescribing for patients with ARI were included. Participants were patients with ARI. IT interventions were used for improving antibiotic prescribing. Two researchers independently extracted data from studies on methods, characteristics of interventions, and results. The characteristics of interventions were extracted based on three dimensions of IT design, data entry source, and implementation characteristics.

      Results

      Eighteen studies (15 CRCTs and three RCTs) were included. Most of included studies (n = 11) were conducted in the United States. In 12 studies (66.7%), IT interventions improved the level of antibiotic prescribing, and in eight of the 12 studies the effect was statistically significant. In two studies the intervention had a statistically significant negative effect, and in two studies the level of antibiotic prescribing was not changed. Seventeen studies (94.4%) used clinical decision support systems (CDSSs) for the intervention. In 12 studies (66.7%) CDSSs were integrated with electronic health records (EHRs).

      Conclusions

      Information technology interventions have the potential to improve prescription of antibiotics for patients with acute respiratory infection and to change physicians' behaviours in this regard. Factors affecting the acceptance of IT-based interventions to improve prescription of antibiotics should be investigated in future studies.

      Keywords

      Introduction

      One of the important reasons for prescribing antibiotics is treatment of acute respiratory infection (ARI). Although mostly viral in origin, unnecessary antibiotics are prescribed for more than 70% of ARIs [
      • Gonzales R.
      • Malone D.C.
      • Maselli J.H.
      • Sande M.A.
      Excessive antibiotic use for acute respiratory infections in the United States.
      ,
      • Harnden A.
      • Perera R.
      • Brueggemann A.B.
      • Mayon-White R.
      • Crook D.
      • Thomson A.
      • et al.
      Respiratory infections for which general practitioners consider prescribing an antibiotic: a prospective study.
      ,
      • Hersh A.L.
      • Shapiro D.J.
      • Pavia A.T.
      • Shah S.S.
      Antibiotic prescribing in ambulatory pediatrics in the United States.
      ,
      • Yu D.T.
      • Volk L.A.
      • Melnikas A.J.
      • Palchuk M.B.
      • Olsha-Yehiav M.
      • Middleton B.
      Electronic health record feedback to improve antibiotic prescribing for acute respiratory infections.
      ]. This unnecessary antibiotic prescribing has become a challenge for healthcare systems, especially in primary healthcare settings. Results of a study in Australia showed that for 5.97 million patients with ARI at least one antibiotic was prescribed [
      • McCullough A.R.
      • Pollack A.J.
      • Plejdrup Hansen M.
      • Glasziou P.P.
      • Looke D.F.
      • Britt H.C.
      • et al.
      Antibiotics for acute respiratory infections in general practice: comparison of prescribing rates with guideline recommendations.
      ]. Results of a study in the US demonstrated that antibiotics are prescribed in more than 100 million visits annually, of which 41% are prescribed for respiratory diseases [
      • Shapiro D.J.
      • Hicks L.A.
      • Pavia A.T.
      • Hersh A.L.
      Antibiotic prescribing for adults in ambulatory care in the USA, 2007–09.
      ]. Another study reported that ARI is one of the reasons for the prescription of 63% of antibiotics in the US [
      • Linder J.A.
      • Schnipper J.L.
      • Tsurikova R.
      • Volk L.A.
      • Middleton B.
      Self-reported familiarity with acute respiratory infection guidelines and antibiotic prescribing in primary care.
      ].
      In response to the global threat of antibiotic resistance, and to alter the pattern of antibiotic prescribing and promote the rational use of antibiotics, different strategies can be adopted. Some of these strategies are training, improving physician–patient relationship, delay in antibiotic prescription, using diagnostic tests at the point-of-care delivery, providing reminders to physicians, and providing audit and feedback. Information technology (IT) interventions can play a major role in implementing these strategies [
      • McDonagh M.
      • Peterson K.
      • Winthrop K.
      • Cantor A.
      • Holzhammer B.
      • Buckley D.I.
      Improving antibiotic prescribing for uncomplicated acute respiratory tract infections.
      ,
      • Tonkin-Crine S.K.
      • San Tan P.
      • van Hecke O.
      • Wang K.
      • Roberts N.W.
      • McCullough A.
      • et al.
      Clinician-targeted interventions to influence antibiotic prescribing behaviour for acute respiratory infections in primary care: an overview of systematic reviews.
      ]. IT interventions include computerized provider order entry (CPOE), clinical decision support systems (CDSS), computerized antimicrobial approval systems (CAAS), surveillance systems (SSs), and electronic health record (EHR) [
      • Baysari M.T.
      • Lehnbom E.C.
      • Li L.
      • Hargreaves A.
      • Day R.O.
      • Westbrook J.I.
      The effectiveness of information technology to improve antimicrobial prescribing in hospitals: a systematic review and meta-analysis.
      ]. These interventions provide physicians with easy and quick access to information at the point of care; thus, they can play a major role in clinical decision-making for antibiotic prescribing [
      • Berner E.S.
      Clinical decision support systems: state of the art.
      ,
      • Shebl N.A.
      • Franklin B.D.
      • Barber N.
      Clinical decision support systems and antibiotic use.
      ].
      Current systematic reviews have investigated the effects of IT-based interventions on the prescription of antimicrobial or antibiotic medications for all diseases [
      • Rawson T.
      • Moore L.
      • Hernandez B.
      • Charani E.
      • Castro-Sanchez E.
      • Herrero P.
      • et al.
      A systematic review of clinical decision support systems for antimicrobial management: are we failing to investigate these interventions appropriately?.
      ], or were only performed in hospitals [
      • Baysari M.T.
      • Lehnbom E.C.
      • Li L.
      • Hargreaves A.
      • Day R.O.
      • Westbrook J.I.
      The effectiveness of information technology to improve antimicrobial prescribing in hospitals: a systematic review and meta-analysis.
      ,
      • Curtis C.E.
      • Al Bahar F.
      • Marriott J.F.
      The effectiveness of computerised decision support on antibiotic use in hospitals: a systematic review.
      ] or just in primary-care settings [
      • Holstiege J.
      • Mathes T.
      • Pieper D.
      Effects of computer-aided clinical decision support systems in improving antibiotic prescribing by primary care providers: a systematic review.
      ], and they included the published studies before 2016. Regarding prescription of antibiotics to patients with ARI, two systematic review studies investigated the effect of all interventions only in children [
      • Boonacker C.W.
      • Hoes A.W.
      • Dikhoff M.-J.
      • Schilder A.G.
      • Rovers M.M.
      Interventions in health care professionals to improve treatment in children with upper respiratory tract infections.
      ] or just in primary-care settings [
      • McDonagh M.
      • Peterson K.
      • Winthrop K.
      • Cantor A.
      • Holzhammer B.
      • Buckley D.I.
      Improving antibiotic prescribing for uncomplicated acute respiratory tract infections.
      ]. The present study was conducted with the aim of investigating the effect of IT-based interventions on prescription of antibiotics to patients with ARI in all age groups and in all medical centres (including primary and secondary) until 2020.

      Materials and methods

       Data sources and search strategy

      The electronic databases of Medline (through PubMed), ISI Web of Sciences, Embase, and Cochrane were searched using a combination of subject headings and key words in their titles and abstracts (Supplementary Material Appendix 1). The search strategy covered key words and MeSH terms related to three broad domains: ARI, antibiotic prescribing, and IT. The time limit for the search was set as from inception to January 2018. The search for articles was updated on 31st August 2020. The reference lists of the included studies and similar systematic reviews were also examined to ensure the inclusion of all relevant articles. The search for articles had no language restriction.

       Inclusion and exclusion criteria

      Randomized controlled trial (RCT) and cluster RCT (CRCT) studies were included in this study if they met the following criteria: (a) antibiotic was prescribed for patients with ARI, (b) IT interventions were used for improving antibiotic prescribing, (c) the examined outcomes were the rate of antibiotic prescription or other outcomes related to antibiotic prescribing improvement, and (d) outcomes related to antibiotic prescribing were compared in control and intervention groups.
      The exclusion criteria were: (a) studies conducted on diseases other than ARI, (b) studies using interventions other than IT for improving antibiotic prescribing, (c) studies which focused on the prescription of medications other than antibiotics, (d) studies which did not examine the outcomes related to antibiotic prescribing in intervention and control groups, and (e) review articles, letters to editors, protocols, conference papers, and theses/dissertations.

       Type of outcome measure

      The primary and secondary outcomes (e.g. the rate of antibiotics prescription, adherence to guidelines, and patient outcomes), extracted from the included studies, were classified into two groups of prescriber-related and patient-related outcomes, based on the study by Rawson et al. [
      • Rawson T.
      • Moore L.
      • Hernandez B.
      • Charani E.
      • Castro-Sanchez E.
      • Herrero P.
      • et al.
      A systematic review of clinical decision support systems for antimicrobial management: are we failing to investigate these interventions appropriately?.
      ].

       Data extraction

      The titles, abstracts, and full texts of articles were independently examined by two researchers. The data from the included studies were extracted using a structured form. Disagreements were resolved through discussion with the third researcher if necessary. The data extracted from each article were the general specifications of the article, i.e., authors' names, publication year, country, type of research, number of participants, duration, location, type of intervention, characteristics of intervention, description of intervention and control groups, outcomes (both primary and secondary), findings, and conclusions.

       Data analysis

      Due to difficulty in personalization of data in CRCT studies, the diversity of CDSS interventions in terms of their different capabilities and different populations in the included studies (children and adults), as well as diversity in the study settings (hospital, primary-care centres), a meta-analysis was not planned, and the results were reported in the form of a narrative synthesis of evidence. The included studies were categorized based on different characteristics, including the characteristics of interventions, classification of outcomes, and effects of interventions on outcomes. The characteristics of interventions were examined based on three dimensions: IT design, data entry source, and implementation characteristics [
      • Shekelle P.G.
      • Goldzweig C.L.
      • Orshansky G.
      • Paige N.M.
      • Ewing B.A.
      • Miake-Lye I.M.
      • et al.
      Electronic health record-based interventions for reducing inappropriate imaging in the clinical setting: a systematic review of the evidence.
      ]. The effects of interventions on the outcomes were classified into the following groups, similarly to other systematic reviews [
      • Nabovati E.
      • Vakili-Arki H.
      • Taherzadeh Z.
      • Saberi M.R.
      • Medlock S.
      • Abu-Hanna A.
      • et al.
      Information technology-based interventions to improve drug–drug interaction outcomes: a systematic review on features and effects.
      ,
      • Farzandipour M.
      • Nabovati E.
      • Sharif R.
      • Arani M.H.
      • Anvari S.
      Patient self-management of asthma using mobile health applications: a systematic review of the functionalities and effects.
      ]: significantly positive, positive without reporting significance, no effect (no significant effect), negative without expressing significance, or significantly negative.

       Study quality assessment

      The quality of the included studies was assessed by using the Effective Public Health Practice Project (EPHPP) tool [
      • Thomas B.
      • Ciliska D.
      • Dobbins M.
      • Micucci S.
      Quality assessment tool for quantitative studies dictionary: the effective public health practice project (EPHPP).
      ,
      • Armijo-Olivo S.
      • Stiles C.R.
      • Hagen N.A.
      • Biondo P.D.
      • Cummings G.G.
      Assessment of study quality for systematic reviews: a comparison of the Cochrane collaboration risk of bias tool and the effective public health practice project quality assessment tool: methodological research.
      ]. This tool includes six sections: selection bias, study design, confounders, blinding, data collection methods, and sample attrition (withdrawals and dropouts) for scoring. The quality of each study can be strong, moderate, or weak. When extracting the data, each study was also independently scored by two researchers, and disagreements were resolved upon discussions in the research team.

      Results

       Results of article search

      The process of identifying and selecting the studies based on the PRISMA diagram is shown in Fig. 1. In total, 4651 articles were identified, of which 1560 were duplicates. The screening of the titles and abstracts of articles led to the elimination of 3007 articles. The full text of 49 articles deemed relevant were examined in the first screening, and of these five were identified through manual search, resulting in 18 articles relating to 18 independent studies.
      Fig. 1
      Fig. 1Flow diagram of the literature search and study selection.

       General characteristics of the included studies

      The general characteristics of the included studies are presented in the online Supplementary -Material Appendix 2. The included studies were 15 CRCTs and three RCTs. Of these, 17 studies were conducted in primary healthcare settings. Most of the studies (11, 61.1%) were conducted in the US, three in the UK, two in China, and two in the Netherlands. The included studies were published in English in the period 2001–2020, and their duration varied from 2 to 50 months. The randomization unit was the health centre, hospital, clinic, and general practice in 12 studies, provider in five studies, and community in one study. The number of providers varied from 38 to 168 in RCTs, and clusters varied from eight to 100 in CRCTS.

       Quality assessment of the included studies

      The results of quality assessment are given in Fig. 2 (details in the online Supplementary Material Appendix 3). Based on the sum of scores, most studies were strong in terms of type of study (100%), dropout (100%), and confounders (94.4%), moderate in terms of selection bias (55.6%) and data collection (77.8%), and weak in terms of blinding (88.9%).
      Fig. 2
      Fig. 2Quality assessment of the included studies.
      Based on the global rating score, 11.1% of the studies were strong [
      • Gonzales R.
      • Anderer T.
      • McCulloch C.E.
      • Maselli J.H.
      • Bloom F.J.
      • Graf T.R.
      • et al.
      A cluster randomized trial of decision support strategies for reducing antibiotic use in acute bronchitis.
      ,
      • Christakis D.A.
      • Zimmerman F.J.
      • Wright J.A.
      • Garrison M.M.
      • Rivara F.P.
      • Davis R.L.
      A randomized controlled trial of point-of-care evidence to improve the antibiotic prescribing practices for otitis media in children.
      ], 61.1% were moderate [
      • Gulliford M.C.
      • Prevost A.T.
      • Charlton J.
      • Juszczyk D.
      • Soames J.
      • McDermott L.
      • et al.
      Effectiveness and safety of electronically delivered prescribing feedback and decision support on antibiotic use for respiratory illness in primary care: REDUCE cluster randomised trial.
      ,
      • Blair P.S.
      • Turnbull S.
      • Ingram J.
      • Redmond N.
      • Lucas P.J.
      • Cabral C.
      • et al.
      Feasibility cluster randomised controlled trial of a within-consultation intervention to reduce antibiotic prescribing for children presenting to primary care with acute respiratory tract infection and cough.
      ,
      • Meeker D.
      • Linder J.A.
      • Fox C.R.
      • Friedberg M.W.
      • Persell S.D.
      • Goldstein N.J.
      • et al.
      Effect of behavioral interventions on inappropriate antibiotic prescribing among primary care practices: a randomized clinical trial.
      ,
      • Vervloet M.
      • Meulepas M.A.
      • Cals J.W.
      • Eimers M.
      • van der Hoek L.S.
      • van Dijk L.
      Reducing antibiotic prescriptions for respiratory tract infections in family practice: results of a cluster randomized controlled trial evaluating a multifaceted peer-group-based intervention.
      ,
      • Persell S.D.
      • Doctor J.N.
      • Friedberg M.W.
      • Meeker D.
      • Friesema E.
      • Cooper A.
      • et al.
      Behavioral interventions to reduce inappropriate antibiotic prescribing: a randomized pilot trial.
      ,
      • McGinn T.G.
      • McCullagh L.
      • Kannry J.
      • Knaus M.
      • Sofianou A.
      • Wisnivesky J.P.
      • et al.
      Efficacy of an evidence-based clinical decision support in primary care practices: a randomized clinical trial.
      ,
      • Linder J.A.
      • Schnipper J.L.
      • Tsurikova R.
      • Yu D.T.
      • Volk L.A.
      • Melnikas A.J.
      • et al.
      Electronic health record feedback to improve antibiotic prescribing for acute respiratory infections.
      ,
      • Bourgeois F.C.
      • Linder J.
      • Johnson S.A.
      • Co J.P.T.
      • Fiskio J.
      • Ferris T.G.
      Impact of a computerized template on antibiotic prescribing for acute respiratory infections in children and adolescents.
      ,
      • Linder J.
      • Schnipper J.
      • Tsurikova R.
      • Yu T.
      • Volk L.
      • Melnikas A.
      • et al.
      Documentation-based clinical decision support to improve antibiotic prescribing for acute respiratory infections in primary care: a cluster randomised controlled trial.
      ,
      • Davis R.L.
      • Wright J.
      • Chalmers F.
      • Levenson L.
      • Brown J.C.
      • Lozano P.
      • et al.
      A cluster randomized clinical trial to improve prescribing patterns in ambulatory pediatrics.
      ,
      • Samore M.H.
      • Bateman K.
      • Alder S.C.
      • Hannah E.
      • Donnelly S.
      • Stoddard G.J.
      • et al.
      Clinical decision support and appropriateness of antimicrobial prescribing: a randomized trial.
      ], and 27.8% were weak [
      • van de Maat J.S.
      • Peeters D.
      • Nieboer D.
      • van Wermeskerken A.M.
      • Smit F.J.
      • Noordzij J.G.
      • et al.
      Evaluation of a clinical decision rule to guide antibiotic prescription in children with suspected lower respiratory tract infection in The Netherlands: a stepped-wedge cluster randomised trial.
      ,
      • Shen X.
      • Lu M.
      • Feng R.
      • Cheng J.
      • Chai J.
      • Xie M.
      • et al.
      Web-based just-in-time information and feedback on antibiotic use for village doctors in rural Anhui, China: randomized controlled trial.
      ,
      • Chen Y.
      • Yang K.
      • Jing T.
      • Tian J.
      • Shen X.
      • Xie C.
      • et al.
      Use of text messages to communicate clinical recommendations to health workers in rural China: a cluster-randomized trial.
      ,
      • Gulliford M.C.
      • van Staa T.
      • Dregan A.
      • McDermott L.
      • McCann G.
      • Ashworth M.
      • et al.
      Electronic health records for intervention research: a cluster randomized trial to reduce antibiotic prescribing in primary care (eCRT study).
      ,
      • Forrest C.B.
      • Fiks A.G.
      • Bailey L.C.
      • Localio R.
      • Grundmeier R.W.
      • Richards T.
      • et al.
      Improving adherence to otitis media guidelines with clinical decision support and physician feedback.
      ] in terms of quality (Fig. 2).

       Description of interventions

      Based on Table 1 (and Supplementary Material Appendix 4), the intervention was CDSS in 17 out of 18 studies, and one study used text messaging as the intervention. Of the 17 studies using CDSS, the CDSS was integrated with EHR in 12 studies (70.6%). The system provided feedback at the point-of-care provision in 16 studies (94.1%), and in 14 studies (82.4%) CDSS offered the recommended course of action. The type ‘B’ intervention, which presented information on appropriateness or guidelines specifically tailored to the individual patient, often as a pop-up or alert, was used in 13 studies (76.4%). Some of these interventions recommended alternative methods for treatment without preventing the clinician from ordering tests. In 13 studies (76.4%), incentives were used for users when implementing the CDSS. Authors of the 11 included studies believed that not considering the following implementation aspects contributed to the limited use and effectiveness of CDSSs: adequate training, offering incentives to users, integration with EHR, and performing usability evaluation in system implementation (Table 2).
      Table 1Features of clinical decision support systems (CDSSs) in the included studies
      FeaturesIT designData entry sourceImplementation characteristics
      Is it integrated with EHRDoes it give real time feedback at point of care?Does the CDS suggest a recommended course of action?Intervention classificationIs it automated through EHR?Does clinical staff enter data specifically for intervention?Was its pilot tested or used an iterative process of development/implementation?Was there any user training/clinician education?Are the authors also the developers and part of the user group for the CDS?Was there use of audit-and-feedback (or other internal incentive)?
      ResultsYes:

      12 (70.6%)

      No:

      5 (29.4%)
      Yes:

      16 (94.1%)

      No:

      1 (5.9%)
      Yes:

      14 (82.4%)

      No:

      3 (17.6%)
      A:

      1 (5.9%)

      B:

      13 (76.4%)

      C:

      3 (17.7%)
      Yes:

      12 (70.6%)

      No:

      5 (29.4%)
      Yes:

      14 (82.3%)

      No:

      3 (17.7%)
      Pilot:

      7 (41.2%)

      Iterative:

      1 (5.9%)

      Not stated:

      9 (52.9%)
      Yes:

      15 (88.2%)

      No:

      1 (5.9%)

      Not stated:

      1 (5.9%)
      No:

      4 (23.5%)

      Not stated:

      13 (76.5%)
      Yes:

      13 (76.4%)

      No:

      2 (11.8%)

      Not stated:

      2 (11.8%)
      EHR, electronic health records.
      Intervention classification: ‘A’ interventions provided information only. ‘B’ interventions presented information on appropriateness or guidelines specifically tailored to the individual patient, often as a pop-up or alert. Some of these interventions also recommended alternative interventions, but did not include any barrier for the clinician to order the test. ‘C’ interventions in general were similar to ‘B’ interventions, but required the ordering clinician to justify with free text why they were overriding the decision support recommendation that a study was inappropriate (i.e. a ‘soft stop’). ‘D’ interventions included a ‘hard stop,’ meaning the intervention prevented the clinician from ordering a test contrary to the CDS determination of inappropriateness, until additional discussion with or permission obtained from another clinician or radiologist.
      Table 2Implementation aspects attributed to the limited use and effectiveness of clinical decision support systems (CDSSs) (authors' perspectives)
      CDSS implementation aspectStudies
      1Inadequate training25, 30, 34, 36, 37
      2Not offering incentives for using the system25, 30, 34, 36
      3Not integration with EHR25, 36, 37, 38
      4Not performing usability evaluation32, 36, 37
      5Limited time available for implementation of the intervention23, 31, 34, 37
      6Inadequate technical and financial support during implementation34, 36, 37
      7Not offering continuous and automatic notifications (pop-up) to users as part of clinical activities30, 36, 37
      8Conditions of the implementation setting (large educational setting with a number of learners, urban or rural healthcare centres)31, 33
      9Failure to coordinate with the workflow and clinical activities of users30, 35
      10Poor introduction of the system34, 36
      11Not designing the system based on users' opinion36, 37
      12Increased system regulations and complexities38
      13Simple system design for quick access and easy use37
      14System's inflexibility37
      EHR, electronic health records.

       Effects of IT interventions on outcomes

      Table 3 presents a summary of the statistical results regarding the rate of antibiotic prescribing with IT interventions.
      Table 3Summary of findings regarding the rate of antibiotic administration following the use of information technology interventions
      InterventionStudyRelative effect/result
      Antibiotic prescription inEffect size (95%CI)p
      Control groupIntervention group
      Positive effect CDSSGulliford 2019 [
      • Gulliford M.C.
      • Prevost A.T.
      • Charlton J.
      • Juszczyk D.
      • Soames J.
      • McDermott L.
      • et al.
      Effectiveness and safety of electronically delivered prescribing feedback and decision support on antibiotic use for respiratory illness in primary care: REDUCE cluster randomised trial.
      ]
      107.6 per 1000 patients98.7 per 1000 patientsAdjusted rate ratios: 0.88 (0.78–0.99)0.04
      Statistically significant effect.
      Shen 2018 [
      • Shen X.
      • Lu M.
      • Feng R.
      • Cheng J.
      • Chai J.
      • Xie M.
      • et al.
      Web-based just-in-time information and feedback on antibiotic use for village doctors in rural Anhui, China: randomized controlled trial.
      ]
      Baseline: 90.3%

      Endpoint: 89.7%
      Baseline: 87.1%

      Endpoint: 64.3%
      <0.001
      Statistically significant effect.
      Meeker 2016 [
      • Meeker D.
      • Linder J.A.
      • Fox C.R.
      • Friedberg M.W.
      • Persell S.D.
      • Goldstein N.J.
      • et al.
      Effect of behavioral interventions on inappropriate antibiotic prescribing among primary care practices: a randomized clinical trial.
      ]
      Baseline: 24.1%

      Endpoint: 13.1%
      Suggested alternative:

      Baseline: 22.1%

      Endpoint: 6.1%

      Accountable justification:

      Baseline: 23.2%

      Endpoint: 5.2 %
      Difference in differences

      Suggested alternative:

      –5.0% (–7.8% to 0.1%)

      Accountable justification:

      –7.0% (9.1% to –2.9%)
      0.66

      0.001
      Statistically significant effect.
      Vervloet 2016 [
      • Vervloet M.
      • Meulepas M.A.
      • Cals J.W.
      • Eimers M.
      • van der Hoek L.S.
      • van Dijk L.
      Reducing antibiotic prescriptions for respiratory tract infections in family practice: results of a cluster randomized controlled trial evaluating a multifaceted peer-group-based intervention.
      ]
      Baseline: 176.7

      Follow-up: 154.1

      Change: –22.7 (p < 0.05)
      Baseline: 207.9

      Follow-up: 161.2

      Change: –46.7 (p < 0.001)
      0.09
      Persell 2016 [
      • Persell S.D.
      • Doctor J.N.
      • Friedberg M.W.
      • Meeker D.
      • Friesema E.
      • Cooper A.
      • et al.
      Behavioral interventions to reduce inappropriate antibiotic prescribing: a randomized pilot trial.
      ]
      Accountable justification:

      Baseline: 29.4%

      Endpoint: 5.9%

      Suggested alternative:

      Baseline: 24.6%

      Endpoint: 5.6%
      Accountable justification:

      Baseline: 22.2%

      Endpoint: 4.7%

      Suggested alternative:

      Baseline: 24.7%

      Endpoint: 4.6%
      Odd ratios

      Accountable justification:

      1.05 (0.80–1.39)

      Suggested alternative:

      0.72 (0.54–0.96)


      <0.001
      Statistically significant effect.
      Gulliford 2014 [
      • Gulliford M.C.
      • van Staa T.
      • Dregan A.
      • McDermott L.
      • McCann G.
      • Ashworth M.
      • et al.
      Electronic health records for intervention research: a cluster randomized trial to reduce antibiotic prescribing in primary care (eCRT study).
      ]
      Before (mean): 111

      After (mean): 114
      Before (mean): 116

      After (mean): 108
      Adjusted mean difference:

      –9.69 (–18.63 to –0.75)
      0 .034
      Statistically significant effect.
      Gonzales 2013 [
      • Gonzales R.
      • Anderer T.
      • McCulloch C.E.
      • Maselli J.H.
      • Bloom F.J.
      • Graf T.R.
      • et al.
      A cluster randomized trial of decision support strategies for reducing antibiotic use in acute bronchitis.
      ]
      Baseline: 72.5%

      Intervention: 74.3%
      Baseline: 74.0%

      Intervention: 60.7%
      Adjusted odds ratio

      Control: 1.10 (0.85–1.43)

      CDS: 0.64 (0.45–0.91)
      0.014
      Statistically significant effect.
      McGinn 2013 [
      • McGinn T.G.
      • McCullagh L.
      • Kannry J.
      • Knaus M.
      • Sofianou A.
      • Wisnivesky J.P.
      • et al.
      Efficacy of an evidence-based clinical decision support in primary care practices: a randomized clinical trial.
      ]
      29.2%38.4%Age-adjusted relative risk:

      0.74 (0.60–0.92)
      0.008
      Statistically significant effect.
      Bourgeois 2010 [
      • Bourgeois F.C.
      • Linder J.
      • Johnson S.A.
      • Co J.P.T.
      • Fiskio J.
      • Ferris T.G.
      Impact of a computerized template on antibiotic prescribing for acute respiratory infections in children and adolescents.
      ]
      46%39.7%0.84
      Linder 2009 [
      • Linder J.
      • Schnipper J.
      • Tsurikova R.
      • Yu T.
      • Volk L.
      • Melnikas A.
      • et al.
      Documentation-based clinical decision support to improve antibiotic prescribing for acute respiratory infections in primary care: a cluster randomised controlled trial.
      ]
      43%39%Odds ratio (OR):

      0.8 (0.6–1.2)
      0.30
      Samore 2005 [
      • Samore M.H.
      • Bateman K.
      • Alder S.C.
      • Hannah E.
      • Donnelly S.
      • Stoddard G.J.
      • et al.
      Clinical decision support and appropriateness of antimicrobial prescribing: a randomized trial.
      ]
      Baseline: 74.9

      Endpoint: 72.3
      Baseline: 75.3

      Endpoint: 84.1
      Mean difference

      CDSS: –8.8 (–13.2 to –4.2)

      Control:2.6 (–3.7 to 9.4)
      0.03
      Statistically significant effect.
      Christakis 2001 [
      • Christakis D.A.
      • Zimmerman F.J.
      • Wright J.A.
      • Garrison M.M.
      • Rivara F.P.
      • Davis R.L.
      A randomized controlled trial of point-of-care evidence to improve the antibiotic prescribing practices for otitis media in children.
      ]
      Mean change

      Before vs after: 10.48%
      Mean change

      Before vs after: 44.43%
      0.000
      Not effective CDSSVan de Maat 2020 [
      • van de Maat J.S.
      • Peeters D.
      • Nieboer D.
      • van Wermeskerken A.M.
      • Smit F.J.
      • Noordzij J.G.
      • et al.
      Evaluation of a clinical decision rule to guide antibiotic prescription in children with suspected lower respiratory tract infection in The Netherlands: a stepped-wedge cluster randomised trial.
      ]
      30%25%Adjusted rate ratio:

      1.07 (0.57–2.01)
      0.75
      Linder 2010 [
      • Linder J.A.
      • Schnipper J.L.
      • Tsurikova R.
      • Yu D.T.
      • Volk L.A.
      • Melnikas A.J.
      • et al.
      Electronic health record feedback to improve antibiotic prescribing for acute respiratory infections.
      ]
      47%47%Odds ratio (OR):

      0.97 (0.7–1.4)
      0 .87
      Negative effect CDSSBlair 2017 [
      • Blair P.S.
      • Turnbull S.
      • Ingram J.
      • Redmond N.
      • Lucas P.J.
      • Cabral C.
      • et al.
      Feasibility cluster randomised controlled trial of a within-consultation intervention to reduce antibiotic prescribing for children presenting to primary care with acute respiratory tract infection and cough.
      ]
      15.8%25%0.018
      Davis 2007 [
      • Davis R.L.
      • Wright J.
      • Chalmers F.
      • Levenson L.
      • Brown J.C.
      • Lozano P.
      • et al.
      A cluster randomized clinical trial to improve prescribing patterns in ambulatory pediatrics.
      ]
      Site1: Baseline: 32%

      endpoint: 9%

      Change = –23%

      Site2: Baseline: 55%

      endpoint: 28%

      Change = –27%
      Site1: Baseline: 28%

      Endpoint: 8%

      Change = –20%

      Site2: Baseline: 55%

      Endpoint: 50%

      Change = –5%
      Adjusted difference:

      Site1: 15% (2–30%)

      Site2: 24% (8–40%)
      Text messagesChen 2014 [
      • Chen Y.
      • Yang K.
      • Jing T.
      • Tian J.
      • Shen X.
      • Xie C.
      • et al.
      Use of text messages to communicate clinical recommendations to health workers in rural China: a cluster-randomized trial.
      ]
      Before: 50%

      After: 67%
      Before: 68%

      After: 68%
      Difference

      Intervention: 0 (0)

      Control: 17 (10–24)
      CDSS, clinical decision support systems (CDSSs).
      a Statistically significant effect.
      One study used text messaging as the intervention for reducing antibiotic prescribing, and reported that the level of prescription was not changed after the intervention in the intervention group, but it increased in the control group [
      • Chen Y.
      • Yang K.
      • Jing T.
      • Tian J.
      • Shen X.
      • Xie C.
      • et al.
      Use of text messages to communicate clinical recommendations to health workers in rural China: a cluster-randomized trial.
      ].
      Other studies have used CDSS as an intervention; the effects of CDSSs on outcomes in the intervention group compared with control are shown in Table 4. The outcomes examined in these studies are divided into two groups: prescriber-related and patient-related. The results showed that, out of 16 studies examining the antibiotic prescription rate, IT interventions improved this rate in 12 studies, and the improvements were statistically significant in eight of the 12 studies. In two studies [
      • Blair P.S.
      • Turnbull S.
      • Ingram J.
      • Redmond N.
      • Lucas P.J.
      • Cabral C.
      • et al.
      Feasibility cluster randomised controlled trial of a within-consultation intervention to reduce antibiotic prescribing for children presenting to primary care with acute respiratory tract infection and cough.
      ,
      • Davis R.L.
      • Wright J.
      • Chalmers F.
      • Levenson L.
      • Brown J.C.
      • Lozano P.
      • et al.
      A cluster randomized clinical trial to improve prescribing patterns in ambulatory pediatrics.
      ] the antibiotic prescription rate was significantly increased (negative effect of the intervention), and this rate did not change in two other studies [
      • van de Maat J.S.
      • Peeters D.
      • Nieboer D.
      • van Wermeskerken A.M.
      • Smit F.J.
      • Noordzij J.G.
      • et al.
      Evaluation of a clinical decision rule to guide antibiotic prescription in children with suspected lower respiratory tract infection in The Netherlands: a stepped-wedge cluster randomised trial.
      ,
      • Linder J.A.
      • Schnipper J.L.
      • Tsurikova R.
      • Yu D.T.
      • Volk L.A.
      • Melnikas A.J.
      • et al.
      Electronic health record feedback to improve antibiotic prescribing for acute respiratory infections.
      ].
      Table 4The effects of clinical decision support systems (CDSSs) on outcomes in intervention group compared with control
      Outcome categoryOutcomesEffect
      PositiveNo effect (with or without statistical arguments)Negative
      SSDSSD
      Prescriber-relatedThe rate of antibiotics prescription23, 24, 26, 28, 30, 31, 33, 3827, 35, 36, 3922, 3425, 37
      The proportion of consultations for respiratory tract infections with antibiotics prescribed3023
      Adherence to guidelines metrics3732
      Patient-relatedReduced duration of therapy below the 10-day39
      Revisits2633, 3631
      Serious bacterial complications22, 23
      Comorbidity status of patients consulting with RTI23
      Strategy failure
      Strategy failure is a composite outcome based on secondary hospitalization (i.e. hospitalization during follow-up, after the initial discharge), secondary or switched antibiotic prescription (during follow-up), oxygen dependency or fever up to day 7, or the development of complications.
      22
      D, demonstrated effect but without statistical significance; RTI, respiratory tract infection; SS, statistically significant.
      a Strategy failure is a composite outcome based on secondary hospitalization (i.e. hospitalization during follow-up, after the initial discharge), secondary or switched antibiotic prescription (during follow-up), oxygen dependency or fever up to day 7, or the development of complications.
      In two studies [
      • Forrest C.B.
      • Fiks A.G.
      • Bailey L.C.
      • Localio R.
      • Grundmeier R.W.
      • Richards T.
      • et al.
      Improving adherence to otitis media guidelines with clinical decision support and physician feedback.
      ,
      • Davis R.L.
      • Wright J.
      • Chalmers F.
      • Levenson L.
      • Brown J.C.
      • Lozano P.
      • et al.
      A cluster randomized clinical trial to improve prescribing patterns in ambulatory pediatrics.
      ] the outcome was adherence to the guideline, and in one of them [
      • Davis R.L.
      • Wright J.
      • Chalmers F.
      • Levenson L.
      • Brown J.C.
      • Lozano P.
      • et al.
      A cluster randomized clinical trial to improve prescribing patterns in ambulatory pediatrics.
      ] adherence was significantly promoted. Two other studies [
      • Gulliford M.C.
      • Prevost A.T.
      • Charlton J.
      • Juszczyk D.
      • Soames J.
      • McDermott L.
      • et al.
      Effectiveness and safety of electronically delivered prescribing feedback and decision support on antibiotic use for respiratory illness in primary care: REDUCE cluster randomised trial.
      ,
      • Gulliford M.C.
      • van Staa T.
      • Dregan A.
      • McDermott L.
      • McCann G.
      • Ashworth M.
      • et al.
      Electronic health records for intervention research: a cluster randomized trial to reduce antibiotic prescribing in primary care (eCRT study).
      ] considered the proportion of consultation for ARI to the prescribed antibiotic as the outcome, which was significantly reduced (positive effect of the intervention) in one of them [
      • Gulliford M.C.
      • van Staa T.
      • Dregan A.
      • McDermott L.
      • McCann G.
      • Ashworth M.
      • et al.
      Electronic health records for intervention research: a cluster randomized trial to reduce antibiotic prescribing in primary care (eCRT study).
      ]. Among patient-related outcomes, revisit was examined in four studies, and was reduced in one study [
      • Meeker D.
      • Linder J.A.
      • Fox C.R.
      • Friedberg M.W.
      • Persell S.D.
      • Goldstein N.J.
      • et al.
      Effect of behavioral interventions on inappropriate antibiotic prescribing among primary care practices: a randomized clinical trial.
      ], remained unchanged in two studies [
      • McGinn T.G.
      • McCullagh L.
      • Kannry J.
      • Knaus M.
      • Sofianou A.
      • Wisnivesky J.P.
      • et al.
      Efficacy of an evidence-based clinical decision support in primary care practices: a randomized clinical trial.
      ,
      • Linder J.
      • Schnipper J.
      • Tsurikova R.
      • Yu T.
      • Volk L.
      • Melnikas A.
      • et al.
      Documentation-based clinical decision support to improve antibiotic prescribing for acute respiratory infections in primary care: a cluster randomised controlled trial.
      ], and increased in another [
      • Gonzales R.
      • Anderer T.
      • McCulloch C.E.
      • Maselli J.H.
      • Bloom F.J.
      • Graf T.R.
      • et al.
      A cluster randomized trial of decision support strategies for reducing antibiotic use in acute bronchitis.
      ]. Another study examined the emergence of serious bacterial complications and the rate of comorbidity during treatment of ARI, and reported that these outcomes were left unchanged and improved in the intervention group, respectively [
      • van de Maat J.S.
      • Peeters D.
      • Nieboer D.
      • van Wermeskerken A.M.
      • Smit F.J.
      • Noordzij J.G.
      • et al.
      Evaluation of a clinical decision rule to guide antibiotic prescription in children with suspected lower respiratory tract infection in The Netherlands: a stepped-wedge cluster randomised trial.
      ,
      • Gulliford M.C.
      • Prevost A.T.
      • Charlton J.
      • Juszczyk D.
      • Soames J.
      • McDermott L.
      • et al.
      Effectiveness and safety of electronically delivered prescribing feedback and decision support on antibiotic use for respiratory illness in primary care: REDUCE cluster randomised trial.
      ].

      Discussion

      The present study examined the effects and characteristics of IT interventions on the outcomes related to antibiotic prescribing for ARI and the authors' perspective on factors influencing the failure of these interventions. IT interventions improved the rate of antibiotic prescription for patients with ARI in 12 studies, and led to its significant improvement in eight of the 12 studies. CDSS was used in 17 studies, and was integrated with the EHR in 12 of them (70.6%). In terms of intervention implementation, the authors of five studies (29%) and four studies (24%) attributed failure of these systems to inadequate training and the absence of incentives and lack of integration with EHR, respectively.
      The results of a meta-analysis by Baysari et al. [
      • Baysari M.T.
      • Lehnbom E.C.
      • Li L.
      • Hargreaves A.
      • Day R.O.
      • Westbrook J.I.
      The effectiveness of information technology to improve antimicrobial prescribing in hospitals: a systematic review and meta-analysis.
      ] similarly showed that, in eight out of nine studies, IT interventions were related to appropriate and necessary prescription of antimicrobials at the hospital (reduction of about 50%) and patient mortality (reduction up to 10%). Results of a systematic review [
      • Curtis C.E.
      • Al Bahar F.
      • Marriott J.F.
      The effectiveness of computerised decision support on antibiotic use in hospitals: a systematic review.
      ] indicated that CDSS decreased antibiotic prescription at hospital. Findings reported by Bonacker et al. [
      • Boonacker C.W.
      • Hoes A.W.
      • Dikhoff M.-J.
      • Schilder A.G.
      • Rovers M.M.
      Interventions in health care professionals to improve treatment in children with upper respiratory tract infections.
      ] demonstrated that computerized interventions reduced antibiotic prescription up to 34%, and increased adherence to guidelines up to 41%. Results of another study [
      • McDonagh M.
      • Peterson K.
      • Winthrop K.
      • Cantor A.
      • Holzhammer B.
      • Buckley D.I.
      Improving antibiotic prescribing for uncomplicated acute respiratory tract infections.
      ] showed that CDSS reduced antibiotic prescription for patients with ARI up to <10%, and this reduction was related to an increase in CDSS use. Results of this study and similar studies show that IT interventions, especially CDSS, can effectively improve antibiotic prescribing if they are adapted and used by the clinicians, especially physicians.
      The results of the present and similar studies show that as long as they are used by physicians, IT-based interventions, especially CDSSs, can affect their behavioural pattern in prescribing antibiotics. The results obtained by Chauhan et al. showed that interactive and multifaceted programmes—such as education through audit and feedback—and using CDSSs are the most effective interventions in changing the physicians' behaviour, which improve knowledge, optimize medication administration, improve patient outcomes, and reduce side effects [
      • Chauhan B.F.
      • Jeyaraman M.
      • Mann A.S.
      • Lys J.
      • Skidmore B.
      • Sibley K.M.
      • et al.
      Behavior change interventions and policies influencing primary healthcare professionals’ practice—an overview of reviews.
      ]. Thus, when IT-based interventions are designed according to behaviour change theories, and factors affecting their acceptance are considered, they can improve the physicians' clinical knowledge and adherence to guidelines.
      In the majority of the included studies, CDSSs were integrated with the EHR. These systems also offered feedback and therapeutic recommendations for prescription during care provision. Rawson et al. [
      • Rawson T.
      • Moore L.
      • Hernandez B.
      • Charani E.
      • Castro-Sanchez E.
      • Herrero P.
      • et al.
      A systematic review of clinical decision support systems for antimicrobial management: are we failing to investigate these interventions appropriately?.
      ] examined the characteristics of CDSS in their systematic review; they noted that CDSS in most studies provided support and recommendations on antibiotic prescribing, and in most studies CDSS was integrated with EMR. Hostiego et al. [
      • Holstiege J.
      • Mathes T.
      • Pieper D.
      Effects of computer-aided clinical decision support systems in improving antibiotic prescribing by primary care providers: a systematic review.
      ] reported automatic presentation of medical recommendations to be contributing to acceptability of IT interventions, including CDSS, in optimizing antibiotic prescription. Results of other systematic reviews [
      • Boonacker C.W.
      • Hoes A.W.
      • Dikhoff M.-J.
      • Schilder A.G.
      • Rovers M.M.
      Interventions in health care professionals to improve treatment in children with upper respiratory tract infections.
      ,
      • Hemens B.J.
      • Holbrook A.
      • Tonkin M.
      • Mackay J.A.
      • Weise-Kelly L.
      • Navarro T.
      • et al.
      Computerized clinical decision support systems for drug prescribing and management: a decision-maker-researcher partnership systematic review.
      ,
      • Kawamoto K.
      • Houlihan C.A.
      • Balas E.A.
      • Lobach D.F.
      Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success.
      ] showed that ‘automatic presentation of decision support as part of the clinical workflow’, ‘presentation of decision support at the time and place of care provision’, ‘presentation of medical recommendations’, and ‘integration of the system with EHR or CPOE’ contribute to the success of CDSSs for improving clinical performance. Findings of many other studies also show that CDSSs improved antibiotic prescribing behaviour, enhanced the provision of beneficial care to patients, and reduced patients' length of stay by offering medical recommendations at the time and place of decision-making [
      • Thomas B.
      • Ciliska D.
      • Dobbins M.
      • Micucci S.
      Quality assessment tool for quantitative studies dictionary: the effective public health practice project (EPHPP).
      ,
      • Aabenhus R.
      • Jensen J.U.S.
      • Jørgensen K.J.
      • Hróbjartsson A.
      • Bjerrum L.
      Biomarkers as point-of-care tests to guide prescription of antibiotics in patients with acute respiratory infections in primary care.
      ,
      • Huckvale C.
      • Car J.
      • Akiyama M.
      • Jaafar S.
      • Khoja T.
      • Bin Khalid A.
      • et al.
      Information technology for patient safety.
      ]. In the present study, a significant reduction was reported in antibiotic prescription in eight studies which used CDSS with the following characteristics: integration of CDSS with the EHR, automatically receiving information from the system, provision of feedback in real time, provision of medical recommendations, training the users, and offering incentives during implementation. Thus, it is recommended that characteristics such as integration with EHR, provision of feedback at the point-of-care delivery, provision of medical recommendations appropriate for patients' condition, training the users, and offering incentives be considered for enhancing the effectiveness of CDSSs in order to improve antibiotic prescribing.
      According to the authors of the included studies, inadequate training, lack of integration with the EHR, failing to offer incentives to users, not performing usability evaluation, and inadequate technical support during system implementation are the major causes of failure and not accepting IT interventions in optimizing antibiotic prescribing for ARI. Results of other studies [
      • Callen J.L.
      • Braithwaite J.
      • Westbrook J.I.
      Contextual implementation model: a framework for assisting clinical information system implementations.
      ,
      • Byrne C.
      • Sherry D.
      • Mercincavage L.
      • Johnston D.
      • Pan E.
      • Schiff G.
      Key lessons in clinical decision support implementation.
      ,
      • Osheroff J.A.
      • Pifer E.A.
      • Sittig D.F.
      • Jenders R.A.
      • Teich J.M.
      Clinical decision support implementers’ workbook.
      ,
      • van Rooij T.
      • Rix S.
      • Moore J.B.
      • Marsh S.
      A bridging opportunities work-frame to develop mobile applications for clinical decision making.
      ,
      • Eichner J.S.
      • Das M.
      Challenges and barriers to clinical decision support (CDS) design and implementation experienced in the Agency for Healthcare Research and Quality CDS demonstrations.
      ,
      • Litvin C.B.
      • Ornstein S.M.
      • Wessell A.M.
      • Nemeth L.S.
      • Nietert P.J.
      Adoption of a clinical decision support system to promote judicious use of antibiotics for acute respiratory infections in primary care.
      ,
      • McDermott L.
      • Yardley L.
      • Little P.
      • van Staa T.
      • Dregan A.
      • McCann G.
      • et al.
      Process evaluation of a point-of-care cluster randomised trial using a computer-delivered intervention to reduce antibiotic prescribing in primary care.
      ] also showed that poor acceptance and failure of IT interventions are often the results of disregard for numerous factors, including personal factors (e.g. failure to attend to the workflow and users' needs), clinical factors (e.g. defining clinical objectives in system design), and organizational factors (e.g. the fit between technology and workflow, involvement of stakeholders before system deployment, using their clinical experience in system design and implementation, and system usability evaluation). Therefore, considering all these factors in IT intervention design and implementation can enhance the use of these interventions by users.
      This study has a number of strengths and some limitations. This was the first systematic review to examine the effectiveness of all IT interventions for improving antibiotic prescribing for ARI. Almost half of the included studies were conducted after 2013, and they were of the most important types of study (RCTs and CRCTs). Other strengths that can be attributed to reduce publication bias in this study include: comprehensive searches in several reliable databases with no time limits (namely Medline, ISI, Cochrane, Embase), no language limitation in the search for articles, review of the included articles' reference lists, exclusion of repeated articles extracted from a study, as well as approval of all included studies by the institutional review boards or ethics review boards.
      Nevertheless, there were items that could have led to limitation and publication bias, such as articles published in journals which are not indexed by well-known international databases such as Medline and Scopus, and failure to publish the results of effects of commercially designed and implemented IT interventions.
      Results of the studies showed that IT interventions, especially automatic CDSSs, improve antibiotic prescribing for patients with ARI. In these studies, inadequate acceptance of the system by users has been attributed to insufficient training, failure to provide incentives, and lack of integration with EHR; therefore, project managers must pay attention to these factors during system implementation to overcome these challenges and promote the success of these interventions.
      In studies examining the effects of IT interventions, it is better to investigate these effects over a longer period, and also to examine the persistence of the intervention effects. The cost-effectiveness of these interventions for antibiotic prescribing for ARI could also be examined. Furthermore, future studies could evaluate the improvements of patient-specific outcome measures: e.g. mortality rate, length of stay, drug side effects, and nosocomial infections. It is suggested that the effectiveness of these interventions be examined in secondary healthcare settings as well.

      Conclusion

      Information technology interventions, especially CDSS, have the potential to improve prescription of antibiotics for patients with acute respiratory infection and change physicians' behaviours in this regard. For CDSSs to be more effective, it is necessary to integrate these systems with EHR, and feedback as well as therapeutic recommendations should be made available to healthcare providers when providing care. It is recommended that factors affecting the acceptance of IT-based interventions to improve prescription of antibiotics for patients with acute respiratory infection be investigated in future review studies.

      Author contributions

      EN searched the databases. EN, FR, RF and ShA were involved in study selection. EN, RF and ShA critically appraised the included studies and extracted data. RF drafted the first version of the manuscript. All authors contributed to the design of the review, critically revised the manuscript, and gave final approval for publication.

      Transparency declaration

      The authors declare that they have no competing interests. This study was part of a PhD thesis in the field of health information management which was approved by the Ethics Committee of Kashan University of Medical Sciences (IR.KAUMS.MEDNT.REC.1398.141) and funded by deputy of research in Kashan University of Medical Sciences with the grant number Reg. Code: 98225 .

      Appendix A. Supplementary data

      The following are the Supplementary data to this article:

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