Computerized Clinical Decision Support Systems and Antibiotic Prescribing: A Systematic Review and Meta-analysis

Effect of a Feedback Visit and a Clinical Decision Support System Based on Antibiotic Prescription Audit in Primary Care: Multiarm Cluster-Randomized Controlled Trial

BACKGROUND

While numerous antimicrobial stewardship programs aim to decrease inappropriate antibiotic prescriptions, evidence of their positive impact is needed to optimize future interventions.

OBJECTIVE

This study aimed to evaluate 2 multifaceted antibiotic stewardship interventions for inappropriate systemic antibiotic prescription in primary care.

METHODS

An open-label, cluster-randomized controlled trial of 2501 general practitioners (GPs) working in western France was conducted from July 2019 to January 2021. Two interventions were studied: the standard intervention, consisting of a visit by a health insurance representative who gave prescription feedback and provided a leaflet for treating cystitis and tonsillitis; and a clinical decision support system (CDSS)-based intervention, consisting of a visit with prescription feedback and a CDSS demonstration on antibiotic prescribing. The control group received no intervention. Data on systemic antibiotic dispensing was obtained from the National Health Insurance System (Système National d'Information Inter-Régimes de l'Assurance Maladie) database. The overall antibiotic volume dispensed per GP at 12 months was compared between arms using a 2-level hierarchical analysis of covariance adjusted for annual antibiotic prescription volume at baseline.

RESULTS

Overall, 2501 GPs were randomized (n=1099, 43.9% women). At 12 months, the mean volume of systemic antibiotics per GP decreased by 219.2 (SD 61.4; 95% CI -339.5 to -98.8; P<.001) defined daily doses in the CDSS-based visit group compared with the control group. The decrease in the mean volume of systemic antibiotics dispensed per GP was not significantly different between the standard visit group and the control group (-109.7, SD 62.4; 95% CI -232.0 to 12.5 defined daily doses; P=.08).

CONCLUSIONS

A visit by a health insurance representative combining feedback and a CDSS demonstration resulted in a 4.4% (-219.2/4930) reduction in the total volume of systemic antibiotic prescriptions in 12 months.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04028830; https://clinicaltrials.gov/study/NCT04028830.

Hospital antimicrobial stewardship team perceptions and usability of a computerized clinical decision support system

BACKGROUND

Antimicrobial stewardship (AMS) programs aim to optimize antibiotic use through a panel of interventions. The implementation of computerized clinical decision support systems (CDSSs) offers new opportunities for semiautomated antimicrobial review by AMS teams. This study aimed to evaluate the perceived facilitators, barriers and benefits of end-users related to a commercial CDSS recently implemented in a hospital and to assess its usability.

METHODS

A mixed-method approach was used among AMS team members nine months after the implementation of the CDSS in a university hospital in northeastern France. A qualitative analysis based on individual semistructured interviews was conducted to collect end-users' perceptions. A quantitative analysis was performed using the System Usability Scale (SUS).

RESULTS

Eleven AMS team members agreed to participate. The qualitative analysis revealed technical, organizational and human barriers and facilitators of CDSS implementation. Effective collaboration with information technology teams was crucial for ensuring the installation and configuration of the software. CDSS adoption by the AMS team required time, human resources, training, adaptation and a clinical leader. Moreover, the CDSS had to be well designed, user-friendly and provide benefits to AMS activities. The quantitative analysis indicated that the CDSS was a "good" system in terms of perceived ease of use (median SUS score: 77.5/100).

CONCLUSIONS

This study shows the value of the studied CDSS to support AMS activities. It reveals barriers, facilitators and benefits to the implementation and adoption of the CDSS. These barriers and facilitators could be considered to facilitate the implementation of the software in other hospitals.

An Institutional Febrile Neutropenia Protocol Improved the Antibacterial Treatment and Encouraged the Development of a Computerized Clinical Decision Support System

We investigated the influence of a local guideline on the quality of febrile neutropenia (FN) management and the applicability of a computerized decision support system (CDSS) using real-life data. The study included 227 FN patients between April 2016 and January 2019. The primary outcome measure was the achievement of a 20% increase in the rate of appropriate empirical treatment of FN in bacteremic patients. The compatibility of the CDSS (the development of which was completed in November 2021) with local protocols was tested using standard patient scenarios and empirical antibiotic recommendations for bacteremic FN patients. In total, 91 patients were evaluated before (P1: between April 2016 and May 2017) and 136 after (P2: between May 2017 and January 2019) the guideline's release (May 2017). The demographic characteristics were similar. Appropriate empirical antibacterial treatment was achieved in 58.3% of P1 and 88.1% of P2 patients (p = 0.006). The need for escalation of antibacterial treatment was significantly lower in P2 (49.5% vs. 35.3%; p = 0.03). In P2, the performance of the CDSS and consulting physicians was similar (CDSS 88.8% vs. physician 88.83%; p = 1) regarding appropriate empirical antibacterial treatment. The introduction of the local guideline improved the appropriateness of initial empirical treatment and reduced escalation rates in FN patients. The high rate of compliance of the CDSS with the local guideline-based decisions in P2 highlights the usefulness of the CDSS for these patients.

Expanding access to veterinary clinical decision support in resource-limited settings: a scoping review of clinical decision support tools in medicine and antimicrobial stewardship

Introduction

Digital clinical decision support (CDS) tools are of growing importance in supporting healthcare professionals in understanding complex clinical problems and arriving at decisions that improve patient outcomes. CDS tools are also increasingly used to improve antimicrobial stewardship (AMS) practices in healthcare settings. However, far fewer CDS tools are available in lowerand middle-income countries (LMICs) and in animal health settings, where their use in improving diagnostic and treatment decision-making is likely to have the greatest impact. The aim of this study was to evaluate digital CDS tools designed as a direct aid to support diagnosis and/or treatment decisionmaking, by reviewing their scope, functions, methodologies, and quality. Recommendations for the development of veterinary CDS tools in LMICs are then provided.

Methods

The review considered studies and reports published between January 2017 and October 2023 in the English language in peer-reviewed and gray literature.

Results

A total of 41 studies and reports detailing CDS tools were included in the final review, with 35 CDS tools designed for human healthcare settings and six tools for animal healthcare settings. Of the tools reviewed, the majority were deployed in high-income countries (80.5%). Support for AMS programs was a feature in 12 (29.3%) of the tools, with 10 tools in human healthcare settings. The capabilities of the CDS tools varied when reviewed against the GUIDES checklist.

Discussion

We recommend a methodological approach for the development of veterinary CDS tools in LMICs predicated on securing sufficient and sustainable funding. Employing a multidisciplinary development team is an important first step. Developing standalone CDS tools using Bayesian algorithms based on local expert knowledge will provide users with rapid and reliable access to quality guidance on diagnoses and treatments. Such tools are likely to contribute to improved disease management on farms and reduce inappropriate antimicrobial use, thus supporting AMS practices in areas of high need.

Decision support systems for antibiotic prescription in hospitals: a survey with hospital managers on factors for implementation

BACKGROUND

Inappropriate antimicrobial use, such as antibiotic intake in viral infections, incorrect dosing and incorrect dosing cycles, has been shown to be an important determinant of the emergence of antimicrobial resistance. Artificial intelligence-based decision support systems represent a potential solution for improving antimicrobial prescribing and containing antimicrobial resistance by supporting clinical decision-making thus optimizing antibiotic use and improving patient outcomes.

OBJECTIVE

The aim of this research was to examine implementation factors of artificial intelligence-based decision support systems for antibiotic prescription in hospitals from the perspective of the hospital managers, who have decision-making authority for the organization.

METHODS

An online survey was conducted between December 2022 and May 2023 with managers of German hospitals on factors for decision support system implementation. Survey responses were analyzed from 118 respondents through descriptive statistics.

RESULTS

Survey participants reported openness towards the use of artificial intelligence-based decision support systems for antibiotic prescription in hospitals but little self-perceived knowledge in this field. Artificial intelligence-based decision support systems appear to be a promising opportunity to improve quality of care and increase treatment safety. Along with the Human-Organization-Technology-fit model attitudes were presented. In particular, user-friendliness of the system and compatibility with existing technical structures are considered to be important for implementation. The uptake of decision support systems also depends on the ability of an organization to create a facilitating environment that helps to address the lack of user knowledge as well as trust in and skepticism towards these systems. This includes the training of user groups and support of the management level. Besides, it has been assessed to be important that potential users are open towards change and perceive an added value of the use of artificial intelligence-based decision support systems.

CONCLUSION

The survey has revealed the perspective of hospital managers on different factors that may help to address implementation challenges for artificial intelligence-based decision support systems in antibiotic prescribing. By combining factors of user perceptions about the systems´ perceived benefits with external factors of system design requirements and contextual conditions, the findings highlight the need for a holistic implementation framework of artificial intelligence-based decision support systems.

Can Decision Support Tools Improve Empiric Antibiotic Prescribing?

Decision Support Tools for Antibiotic PrescribingChoosing the right antibiotic is challenging. Unnecessarily broad-spectrum antibiotic treatment promotes antimicrobial resistance; inappropriately narrow-spectrum antibiotic use can lead to treatment failure. A cluster-randomized trial of a model-informed clinical decision support tool is proposed for guiding empiric antibiotic therapy for hospitalized patients with suspected infection.

A sepsis treatment algorithm to improve early antibiotic de-escalation while maintaining adequacy of coverage (Early-IDEAS): A prospective observational study

BACKGROUND

Empiric antibiotic treatment selection should provide adequate coverage for potential pathogens while minimizing unnecessary broad-spectrum antibiotic use. We sought to pilot a sepsis treatment algorithm to individualize antibiotic recommendations, and thereby improve early antibiotic de-escalation while maintaining adequacy of coverage (Early-IDEAS).

METHODS

In this observational study, the Early-IDEAS decision support algorithm was derived from previous Gram- negative and Gram-positive prediction rules and models along with local guidelines, and then applied to prospectively identified consecutive adults within 24 hours of suspected sepsis. The primary outcome was the proportion of patients for whom de-escalation of the primary antibiotic regimen was recommended by the algorithm. Secondary outcomes included: (1) proportion of patients for whom escalation was recommended; (2) number of recommended de-escalation steps along a pre-specified antibiotic cascade; and (3) adequacy of therapy in patients with culture-confirmed infection.

RESULTS

We screened 578 patients, of whom 107 eligible patients were included. The Early-IDEAS treatment recommendation was informed by Gram-negative models in 76 (71%) patients, Gram-positive rules in 64 (59.8%), and local guidelines in 27 (25.2%). Antibiotic de-escalation was recommended in almost half of all patients (n = 52, 48.6%), with a median of 2 steps down the a priori antibiotic treatment cascade. No treatment change was recommended in 45 patients (42.1%), and escalation was recommended in 10 (9.3%). Among the 17 patients with positive blood cultures, both the clinician prescribed regimen and the algorithm recommendation provided adequate coverage for the isolated pathogen in 12 patients (70.6%), (p = 1). Among the 25 patients with positive relevant, non-blood cultures, both the clinician prescribed regimen and the algorithm recommendation provided adequate coverage in 20 (80%), (p = 1).

CONCLUSION

An individualized decision support algorithm in early sepsis could lead to substantial antibiotic de-escalation without compromising adequate antibiotic coverage.

[Factors influencing the implementation of AI-based decision support systems for antibiotic prescription in hospitals: a qualitative analysis from the perspective of health professionals]

BACKGROUND

Decision support systems based on artificial intelligence might optimize antibiotic prescribing in hospitals and prevent the development of antimicrobial resistance. The aim of this study was to identify impeding and facilitating factors for successful implementation from the perspective of health professionals.

METHODS

Problem-centered individual interviews were conducted with health professionals working in hospitals. Data evaluation was based on the structured qualitative content analysis according Kuckartz.

RESULTS

Attitudes of health professionals were presented along the Human-Organization -Technology-fit model. Technological and organizational themes were the most important factors for system implementation. Especially, compatibility with existing systems and user-friendliness were seen to play a major role in successful implementation. Additionally, the training of potential users and the technical equipment of the organization were considered essential. Finally, the importance of promoting technical skills of potential users in the long term and creating trust in the benefits of the system were highlighted.

CONCLUSION

The identified factors provide a basis for prioritizing and quantifying needs and attitudes in a next step. It becomes clear that, beside technological factors, attention to context-specific and user-related conditions are of fundamental importance to ensure successful implementation and system trust in the long term.

Effectiveness of decision support tools on reducing antibiotic use for respiratory tract infections: a systematic review and meta-analysis

Background: Clinical decision support tools (CDSs) have been demonstrated to enhance the accuracy of antibiotic prescribing among physicians. However, their effectiveness in reducing inappropriate antibiotic use for respiratory tract infections (RTI) is controversial. Methods: A literature search in 3 international databases (Medline, Web of science and Embase) was conducted before 31 May 2023. Relative risk (RR) and corresponding 95% confidence intervals (CI) were pooled to evaluate the effectiveness of intervention. Summary effect sizes were calculated using a random-effects model due to the expected heterogeneity (I 2 over 50%). Results: A total of 11 cluster randomized clinical trials (RCTs) and 5 before-after studies were included in this meta-analysis, involving 900,804 patients met full inclusion criteria. Among these studies, 11 reported positive effects, 1 reported negative results, and 4 reported non-significant findings. Overall, the pooled effect size revealed that CDSs significantly reduced antibiotic use for RTIs (RR = 0.90, 95% CI = 0.85 to 0.95, I 2 = 96.10%). Subgroup analysis indicated that the intervention duration may serve as a potential source of heterogeneity. Studies with interventions duration more than 2 years were found to have non-significant effects (RR = 1.00, 95% CI = 0.96 to 1.04, I 2 = 0.00%). Egger's test results indicated no evidence of potential publication bias (p = 0.287). Conclusion: This study suggests that CDSs effectively reduce inappropriate antibiotic use for RTIs among physicians. However, subgroup analysis revealed that interventions lasting more than 2 years did not yield significant effects. These findings highlight the importance of considering intervention duration when implementing CDSs. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023432584, Identifier: PROSPERO (CRD42023432584).

An Integrated Process for Co-Developing and Implementing Written and Computable Clinical Practice Guidelines

The goal of this article is to describe an integrated parallel process for the co-development of written and computable clinical practice guidelines (CPGs) to accelerate adoption and increase the impact of guideline recommendations in clinical practice. From February 2018 through December 2021, interdisciplinary work groups were formed after an initial Kaizen event and using expert consensus and available literature, produced a 12-phase integrated process (IP). The IP includes activities, resources, and iterative feedback loops for developing, implementing, disseminating, communicating, and evaluating CPGs. The IP incorporates guideline standards and informatics practices and clarifies how informaticians, implementers, health communicators, evaluators, and clinicians can help guideline developers throughout the development and implementation cycle to effectively co-develop written and computable guidelines. More efficient processes are essential to create actionable CPGs, disseminate and communicate recommendations to clinical end users, and evaluate CPG performance. Pilot testing is underway to determine how this IP expedites the implementation of CPGs into clinical practice and improves guideline uptake and health outcomes.

Clinician acceptability of an antibiotic prescribing knowledge support system for primary care: a mixed-method evaluation of features and context

BACKGROUND

Overprescribing of antibiotics is a major concern as it contributes to antimicrobial resistance. Research has found highly variable antibiotic prescribing in (UK) primary care, and to support more effective stewardship, the BRIT Project (Building Rapid Interventions to optimise prescribing) is implementing an eHealth Knowledge Support System. This will provide unique individualised analytics information to clinicians and patients at the point of care. The objective of the current study was to gauge the acceptability of the system to prescribing healthcare professionals and highlight factors to maximise intervention uptake.

METHODS

Two mixed-method co-design workshops were held online with primary care prescribing healthcare professionals (n = 16). Usefulness ratings of example features were collected using online polls and online whiteboards. Verbal discussion and textual comments were analysed thematically using inductive (participant-centred) and deductive perspectives (using the Theoretical Framework of Acceptability).

RESULTS

Hierarchical thematic coding generated three overarching themes relevant to intervention use and development. Clinician concerns (focal issues) were safe prescribing, accessible information, autonomy, avoiding duplication, technical issues and time. Requirements were ease and efficiency of use, integration of systems, patient-centeredness, personalisation, and training. Important features of the system included extraction of pertinent information from patient records (such as antibiotic prescribing history), recommended actions, personalised treatment, risk indicators and electronic patient communication leaflets. Anticipated acceptability and intention to use the knowledge support system was moderate to high. Time was identified as a focal cost/ burden, but this would be outweighed if the system improved patient outcomes and increased prescribing confidence.

CONCLUSION

Clinicians anticipate that an eHealth knowledge support system will be a useful and acceptable way to optimise antibiotic prescribing at the point of care. The mixed method workshop highlighted issues to assist person-centred eHealth intervention development, such as the value of communicating patient outcomes. Important features were identified including the ability to efficiently extract and summarise pertinent information from the patient records, provide explainable and transparent risk information, and personalised information to support patient communication. The Theoretical Framework of Acceptability enabled structured, theoretically sound feedback and creation of a profile to benchmark future evaluations. This may encourage a consistent user-focused approach to guide future eHealth intervention development.

Factors influencing the implementation of decision support systems for antibiotic prescription in hospitals: a systematic review

BACKGROUND

Antibiotic resistance is a major health threat. Inappropriate antibiotic use has been shown to be an important determinant of the emergence of antibiotic resistance. Decision support systems for antimicrobial management can support clinicians to optimize antibiotic prescription.

OBJECTIVE

The aim of this systematic review is to identify factors influencing the implementation of decision support systems for antibiotic prescription in hospitals.

METHODS

A systematic search of factors impeding or facilitating successful implementation of decision support systems for antibiotic prescription was performed in January 2022 in the databases PubMed, Web of Science and The Cochrane Library. Only studies were included which comprised decision support systems in hospitals for prescribing antibiotic therapy, published in English with a qualitative, quantitative or mixed-methods study design and between 2011 and 2021. Factors influencing the implementation were identified through text analysis by two reviewers.

RESULTS

A total of 14 publications were identified matching the inclusion criteria. The majority of factors relate to technological and organizational aspects of decision support system implementation. Some factors include the integration of the decision support systems into existing systems, system design, consideration of potential end-users as well as training and support for end-users. In addition, user-related factors, like user attitude towards the system, computer literacy and prior experience with the system seem to be important for successful implementation of decision support systems for antibiotic prescription in hospitals.

CONCLUSION

The results indicate a broad spectrum of factors of decision support system implementation for antibiotic prescription and contributes to the literature by identifying important organizational as well as user-related factors. Wider organizational dimensions as well as the interaction between user and technology appear important for supporting implementation.

Computerized clinical decision support systems for prescribing in primary care: main characteristics and implementation impact-protocol of an evidence and gap map

BACKGROUND

Computerized clinical decision support systems are used by clinicians at the point of care to improve quality of healthcare processes (prescribing error prevention, adherence to clinical guidelines, etc.) and clinical outcomes (preventive, therapeutic, and diagnostics). Attempts to summarize results of computerized clinical decision support systems to support prescription in primary care have been challenging, and most systematic reviews and meta-analyses failed due to an extremely high degree of heterogeneity present among the included primary studies. The aim of our study will be to synthesize the evidence, considering all methodological factors that could explain these differences, and build an evidence and gap map to identify important remaining research questions.

METHODS

A literature search will be conducted from January 2010 onwards in MEDLINE, Embase, the Cochrane Library, and Web of Science databases. Two reviewers will independently screen all citations, full text, and abstract data. The study methodological quality and risk of bias will be appraised using appropriate tools if applicable. A flow diagram with the screened studies will be presented, and all included studies will be displayed using interactive evidence and gap maps. Results will be reported in accordance with recommendations from the Campbell Collaboration on the development of evidence and gap maps.

DISCUSSION

Evidence behind computerized clinical decision support systems to support prescription use in primary care has so far been difficult to be synthesized. Evidence and gap maps represent an innovative approach that has emerged and is increasingly being used to address a broader research question, where multiple types of intervention and outcomes reported may be evaluated. Broad inclusion criteria have been chosen with regard to study designs, in order to collect all available information. Regarding the limitations, we will only include English and Spanish language studies from the last 10 years, we will not perform a grey literature search, and we will not carry out a meta-analysis due to the predictable heterogeneity of available studies.

SYSTEMATIC REVIEW REGISTRATION

This study is registered in Open Science Framework https://bit.ly/2RqKrWp.

Digital interventions for antimicrobial prescribing and monitoring: a qualitative meta-synthesis of factors influencing user acceptance

OBJECTIVE

To understand and synthesize factors influencing user acceptance of digital interventions used for antimicrobial prescribing and monitoring in hospitals.

MATERIALS AND METHODS

A meta-synthesis was conducted to identify qualitative studies that explored user acceptance of digital interventions for antimicrobial prescribing and/or monitoring in hospitals. Databases were searched and qualitative data were extracted and systematically classified using the unified theory of acceptance and use of technology (UTAUT) model.

RESULTS

Fifteen qualitative studies met the inclusion criteria. Eleven papers used interviews and four used focus groups. Most digital interventions evaluated in studies were decision support for prescribing (n = 13). Majority of perceptions were classified in the UTAUT performance expectancy domain in perceived usefulness and relative advantage constructs. Key facilitators in this domain included systems being trusted and credible sources of information, improving performance of tasks and increasing efficiency. Reported barriers were that interventions were not considered useful for all settings or patient conditions. Facilitating conditions was the second largest domain, which highlights the importance of users having infrastructure to support system use. Digital interventions were viewed positively if they were compatible with values, needs, and experiences of users.

CONCLUSIONS

User perceptions that drive users to accept and utilize digital interventions for antimicrobial prescribing and monitoring were predominantly related to performance expectations and facilitating conditions. To ensure digital interventions for antimicrobial prescribing are accepted and used, we recommend organizations ensure systems are evaluated and benefits are conveyed to users, that utility meets expectations, and that appropriate infrastructure is in place to support use.

Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis

Objectives

Electronic health record-based clinical decision support (CDS) has the potential to improve health outcomes. This systematic review investigates the design, effectiveness, and economic outcomes of CDS targeting several common chronic diseases.

Material and Methods

We conducted a search in PubMed (Medline), EBSCOHOST (CINAHL, APA PsychInfo, EconLit), and Web of Science. We limited the search to studies from 2011 to 2021. Studies were included if the CDS was electronic health record-based and targeted one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolemia. Studies with effectiveness or economic outcomes were considered for inclusion, and a meta-analysis was conducted.

Results

The review included 76 studies with effectiveness outcomes and 9 with economic outcomes. Of the effectiveness studies, 63% described a positive outcome that favored the CDS intervention group. However, meta-analysis demonstrated that effect sizes were heterogenous and small, with limited clinical and statistical significance. Of the economic studies, most full economic evaluations (n = 5) used a modeled analysis approach. Cost-effectiveness of CDS varied widely between studies, with an estimated incremental cost-effectiveness ratio ranging between USD$2192 to USD$151 955 per QALY.

Conclusion

We summarize contemporary chronic disease CDS designs and evaluation results. The effectiveness and cost-effectiveness results for CDS interventions are highly heterogeneous, likely due to differences in implementation context and evaluation methodology. Improved quality of reporting, particularly from modeled economic evaluations, would assist decision makers to better interpret and utilize results from these primary research studies.

Registration

PROSPERO (CRD42020203716)

The development and implementation of a guideline-based clinical decision support system to improve empirical antibiotic prescribing

BACKGROUND

To describe and evaluate a clinical decision support system (CDSS) for empirical antibiotic therapy using a systematic framework.

METHODS

A reporting framework for behavior change intervention implementation was used, which includes several domains: development, evaluation and implementation. Within the development domain a description is given of the engagement of stakeholders, a rationale for how the CDSS may influence antibiotic prescribing and a detailed outline of how the system was developed. Within the evaluation domain a technical validation is performed and the interaction between potential users and the CDSS is analyzed. Within the domain of implementation a description is given on how the CDSS was tested in the real world and the strategies that were used for implementation and adoption of the CDSS.

RESULTS

Development: a CDSS was developed, with the involvement of stakeholders, to assist empirical antibiotic prescribing by physicians.

EVALUATION

Technical problems were determined during the validation process and corrected in a new CDSS version. A usability study was performed to assess problems in the system-user interaction.

IMPLEMENTATION

In 114 patients the antibiotic advice that was generated by the CDSS was followed. For 54 patients the recommendations were not adhered to.

CONCLUSIONS

This study describes the development and validation of a CDSS for empirical antibiotic therapy and shows the usefulness of the systematic framework for reporting CDSS interventions. In addition it shows that CDSS recommendations are not always adhered to which is associated with incorrect use of the system.

Evaluating antibiotic stewardship and healthcare-associated infections surveillance assisted by computer: protocol for an interrupted time series study

INTRODUCTION

Antibiotic resistance is one of the most pressing health threats that mankind faces now and in the coming decades. Antibiotic resistance leads to longer hospital stays, higher medical costs and increased mortality. In order to tackle antibiotic resistance, we will implement in our tertiary care university hospital a computerised-decision support system (CDSS) facilitating antibiotic stewardship and an electronic surveillance software (ESS) facilitating infection prevention and control activities. We describe the protocol to evaluate the impact of the CDSS/ESS combination in adult inpatients.

METHODS AND ANALYSIS

We conduct a pragmatic, prospective, single-centre, before-after uncontrolled study with an interrupted time-series analysis 12 months before and 12 months after the introduction of the CDSS for antibiotic stewardship (APSS) and ESS for infection surveillance (ZINC). APSS and ZINC will assist, respectively, the antibiotic stewardship and the infection prevention and control teams of Nancy University Hospital (France). We will evaluate the impact of the CDSS/ESS on the antibiotic use in adult (≥18 years) inpatients (hospitalised ≥48 hours). The primary outcome is the prescription rate by all healthcare professionals from the hospital of all systemic antibiotics expressed in defined daily doses/1000 patients/month. Concurrently, we will assess the safety of the intervention, its impact on the appropriateness of antibiotic prescriptions and on additional precautions (isolation precautions) as recommended in guidelines, and on bacterial epidemiology (multidrug-resistant bacteria and Clostridioides difficile infections) in the hospital. Finally, we will evaluate the users' satisfaction and the cost of this intervention from the hospital perspective.

ETHICS AND DISSEMINATION

The protocol has been approved by the Ethics Committee of Nancy University Hospital and registered on the ClinicalTrials platform. Results will be disseminated through conferences' presentations and publications in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT04976829.

How to use new antibiotics in the therapy of ventilator-associated pneumonia

PURPOSE OF REVIEW

Ventilator-associated pneumonia (VAP) is a common nosocomial infection in critically ill patients requiring endotracheal intubation and mechanical ventilation. Recently, the emergence of multidrug-resistant Gram-negative bacteria, including carbapenem-resistant Enterobacterales, multidrug-resistant Pseudomonas aeruginosa and Acinetobacter species, has complicated the selection of appropriate antimicrobials and contributed to treatment failure. Although novel antimicrobials are crucial to treating VAP caused by these multidrug-resistant organisms, knowledge of how to optimize their efficacy while minimizing the development of resistance should be a requirement for their use.

RECENT FINDINGS

Several studies have assessed the efficacy of novel antimicrobials against multidrug-resistant organisms, but high-quality studies focusing on optimal dosing, infusion time and duration of therapy in patients with VAP are still lacking. Antimicrobial and diagnostic stewardship should be combined to optimize the use of these novel agents.

SUMMARY

Improvements in diagnostic tests, stewardship practices and a better understanding of dosing, infusion time, duration of treatment and the effects of combining various antimicrobials should help optimize the use of novel antimicrobials for VAP and maximize clinical outcomes while minimizing the development of resistance.

External validation of a mobile clinical decision support system for diarrhea etiology prediction in children: a multicenter study in Bangladesh and Mali

Background: Diarrheal illness is a leading cause of antibiotic use for children in low- and middle-income countries. Determination of diarrhea etiology at the point-of-care without reliance on laboratory testing has the potential to reduce inappropriate antibiotic use.

Methods: This prospective observational study aimed to develop and externally validate the accuracy of a mobile software application ('App') for the prediction of viral-only etiology of acute diarrhea in children 0-59 months in Bangladesh and Mali. The App used a previously derived and internally validated model consisting of patient-specific ('present patient') clinical variables (age, blood in stool, vomiting, breastfeeding status, and mid-upper arm circumference) as well as location-specific viral diarrhea seasonality curves. The performance of additional models using the 'present patient' data combined with other external data sources including location-specific climate, data, recent patient data, and historical population-based prevalence were also evaluated in secondary analysis. Diarrhea etiology was determined with TaqMan Array Card using episode-specific attributable fraction (AFe) >0.5.

Results: Of 302 children with acute diarrhea enrolled, 199 had etiologies above the AFe threshold. Viral-only pathogens were detected in 22% of patients in Mali and 63% in Bangladesh. Rotavirus was the most common pathogen detected (16% Mali; 60% Bangladesh). The present patient + viral seasonality model had an AUC of 0.754 (0.665-0.843) for the sites combined, with calibration-in-the-large α=-0.393 (-0.455 - -0.331) and calibration slope β=1.287 (1.207 - 1.367). By site, the present patient + recent patient model performed best in Mali with an AUC of 0.783 (0.705 - 0.86); the present patient + viral seasonality model performed best in Bangladesh with AUC 0.710 (0.595 - 0.825).

Conclusion: The App accurately identified children with high likelihood of viral-only diarrhea etiology. Further studies to evaluate the App's potential use in diagnostic and antimicrobial stewardship are underway.

Funding: Funding for this study was provided through grants from the Bill and Melinda Gates Foundation (OPP1198876) and the National Institute of Allergy and Infectious Diseases (R01AI135114). Several investigators were also partially supported by a grant from the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK116163). This investigation was also supported by the University of Utah Population Health Research (PHR) Foundation, with funding in part from the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002538. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in the study design, data collection, data analysis, interpretation of data, or in the writing or decision to submit the manuscript for publication.

Overdiagnosis of urinary tract infections by nursing home clinicians versus a clinical guideline

PURPOSE

To inform overprescribing and antibiotic stewardship in nursing homes (NHs), we examined the concordance between clinicians' (NH primary care providers and registered nurses) diagnosis of suspected UTI with a clinical guideline treated as the gold standard, and whether clinician characteristics were associated with diagnostic classification.

METHODS

We conducted a cross-sectional web-based survey of a U.S. national convenience sample of NH clinicians. The survey included a discrete choice experiment with 19 randomly selected clinical scenarios of NH residents with possible UTIs. For each scenario, participants were asked if they thought a UTI was likely. Responses were compared to the guideline to determine the sensitivity and specificity of clinician judgment and performance indicators. Multivariable logistic mixed effects regression analysis of demographic, work, personality, and UTI knowledge/attitudes characteristics was conducted.

RESULTS

One thousand seven hundred forty-eight NH clinicians responded to 33,212 discrete choice scenarios; 867 (50%) were NH primary care providers and 881 (50%) were NH registered nurses, 39% were male, and the mean age was 45 years. Participants were uncertain about diagnosis in 30% of scenarios. Correct classification occurred for 66% of all scenarios (providers: 70%; nurses: 62%). Respondent judgment had a sensitivity of 78% (providers: 81%; nurses: 74%) and specificity of 54% (providers: 59%; nurses: 49%) compared to the clinical guideline. Adjusting for covariates in multivariable models, being a nurse and having higher closemindedness were associated higher odds of false positive UTI (odds ratio [OR] 1.61, p < 0.001; and OR 1.09, p = 0.039, respectively), although higher UTI knowledge and conscientiousness were associated with lower odds of false positive UTI ratings (OR 0.80, p < 0.001; OR 0.90, p = 0.005, respectively).

CONCLUSIONS

Clinicians tend to over-diagnose urinary tract infections, necessitating systems-based interventions to augment clinical decision-making. Clinician type, UTI knowledge, and personality traits may also influence behavior and deserve further study.

A roadmap for the development and evaluation of the eHealthResp online course

Background

Inappropriate antibiotic use constitutes one of the most concerning public health issues, being one of the main causes of antibiotic resistance. Hence, to tackle this issue, it is important to encourage the development of educational interventions for health practitioners, namely by using digital health tools. This study focuses on the description of the development and validation process of the eHealthResp online course, a web platform directed to physicians and pharmacists, with the overall goal of improving antibiotic use for respiratory tract infections, along with the assessment of its usability.

Methods

The eHealthResp platform and the courses, developed with a user-centered design and based on Wordpress and MySQL, were based on a previously developed online course. A questionnaire to assess the usability was distributed among physicians (n = 6) and pharmacists (n = 6). Based on the obtained results, statistical analyses were conducted to calculate the usability score and appraise the design of the online course, as well as to compare the overall scores attributed by both groups. Further qualitative comments provided by the participants have also been analyzed.

Results

The eHealthResp contains two online courses directed to physicians and pharmacists aiming to aid in the management of respiratory tract infections. The average usability score of the eHealthResp online courses for physicians and pharmacists was of 78.33 (±11.57, 95%CI), and 83.75 (±15.90, 95%CI), respectively. Qualitative feedback emphasized the usefulness of the course, including overall positive reviews regarding user-friendliness and consistency.

Conclusions

This study led us to conclude that the eHealthResp online course is not recognized as a complex web platform, as both qualitative and quantitative feedback obtained were globally positive.

OUP accepted manuscript

Background

Antimicrobial stewardship (AMS) programmes in hospitals support optimal antimicrobial use by utilizing strategies such as restriction policies and education. Several systematic reviews on digital interventions supporting AMS have been conducted but they have focused on specific interventions and outcomes.

Objectives

To provide a systematic overview and synthesis of evidence on the effectiveness of digital interventions to improve antimicrobial prescribing and monitoring in hospitals.

Methods

Multiple databases were searched from 2010 onwards. Review papers were eligible if they included studies that examined the effectiveness of AMS digital interventions in an inpatient hospital setting. Papers were excluded if they were not systematic reviews, were limited to a paediatric setting, or were not in English.

Results

Eight systematic reviews were included for data extraction. A large number of digital interventions were evaluated, with a strong focus on clinical decision support. Due to the heterogeneity of the interventions and outcome measures, a meta-analysis could not be performed. The majority of reviews reported that digital interventions reduced antimicrobial use and improved antimicrobial appropriateness. The impact of digital interventions on clinical outcomes was inconsistent.

Conclusions

Digital interventions reduce antimicrobial use and improve antimicrobial appropriateness in hospitals, but no firm conclusions can be drawn about the degree to which different types of digital interventions achieve these outcomes. Evaluation of sociotechnical aspects of digital intervention implementation is limited, despite the critical role that user acceptance, uptake and feasibility play in ensuring improvements in AMS are achieved with digital health.

Reducing Delays in Diagnosing Primary Immunodeficiency Through the Development and Implementation of a Clinical Decision Support Tool: A Study Protocol

Background

Primary immunodeficiencies (PIs) are a set of heterogeneous chronic disorders characterized by immune dysfunction. They are diagnostically challenging because of their clinical heterogeneity, knowledge gaps among primary care physicians, and continuing shortages of clinically trained immunologists. As a result, patients with undiagnosed PIs are at increased risk for recurrent infections, cancers, and autoimmune diseases.

Objective

The aim of this research is to develop and implement a clinical decision support (CDS) tool for the identification of underlying PIs.

Methods

We will develop and implement a CDS tool for the identification of underlying PIs among patients who receive primary care through a health care provider at the University of Iowa Hospitals and Clinics. The CDS tool will function through an algorithm that is based on the Immune Deficiency Foundation’s 10 Warning Signs for Primary Immunodeficiency. Over the course of a year, we will use Lean Six Sigma principles and the Define, Measure, Analyze, Improve, and Control (DMAIC) framework to guide the project. The primary measure is the number of newly diagnosed PI patients per month. Secondary measures include the following: (1) the number of new patients identified by the CDS as being at high risk for PI, (2) the number of new PI cases in which immunoglobulin replacement or rotating antibiotics are started, (3) the cost of evaluation of each patient identified by the CDS tool as being at high risk for PIs, (4) the number of new consults not diagnosed with a PI, and (5) patient satisfaction with the process of referral to the Immunology Clinic.

Results

This study was determined to not be Human Subjects Research by the Institutional Review Board at the University of Iowa. Data collection will begin in August 2021.

Conclusions

The development and implementation of a CDS tool is a promising approach to identifying patients with underlying PI. This protocol assesses whether such an approach will be able to achieve its objective of reducing diagnostic delays. The disciplined approach, using Lean Six Sigma and the DMAIC framework, will guide implementation to maximize opportunities for a successful intervention that meets the study’s goals and objectives as well as to allow for replication and adaptation of these methods at other sites.

International Registered Report Identifier (IRRID)

PRR1-10.2196/32635

Health professionals perception and beliefs about drug- related problems on polymedicated older adults- a focus group study

BACKGROUND

Polymedicated older patients are at greater risk of suffering from adverse events. For this reason, the detection of both inappropriate polypharmacy and polypharmacy-associated Drug-Related Problems (DRPs) are essential to improve the health and wellbeing of older adults and to reduce healthcare costs. This work aims to explore health professionals' perceptions and opinions about polypharmacy and the handling of medicines by polymedicated older adults.

METHODS

Thirteen focus groups with 94 health professionals (20 community pharmacists, 40 general practitioners and, 34 nurses) were conducted in primary healthcare centers of the center region of Portugal. Participants were asked to discuss their perceptions and beliefs concerning DRPs in polymedicated older adults. The sessions were audiotaped. After the transcription and coding of focus group sessions, a thematic analysis was done.

RESULTS

The following four main themes emerged from the 13 focus group sessions: poor compliance and polypharmacy- A perpetuated vicious cycle, organization of the healthcare system, interaction and communication between the health professionals, and strategies to prevent inappropriate polypharmacy.

CONCLUSIONS

The lack of both an efficient network of information and Interaction and communication between Health professionals makes the detection and/ or prevention of polypharmacy in older adults difficult. The implementation of new models to manage and/or prevent polypharmacy based on health professional perception and beliefs is essential to prevent DRPs and improve compliance among older adults.

Practice-enhancing publications about the medication-use process in 2019

PURPOSE

This article identifies, prioritizes, and summarizes published literature on the medication-use process (MUP) from calendar year 2019 that can impact health-system pharmacy daily practice. The MUP is the foundational system that provides the framework for safe medication utilization within the healthcare environment. The MUP is defined in this article as having the following components: prescribing/transcribing, dispensing, administration, and monitoring. Articles that evaluated one of the steps were gauged for their usefulness in promoting daily practice change.

SUMMARY

A PubMed search was conducted in January 2020 for calendar year 2019 using targeted Medical Subject Headings keywords; in addition, searches of the table of contents of selected pharmacy journals were conducted. A total of 4,317 articles were identified. A thorough review identified 66 potentially practice-enhancing articles: 17 for prescribing/transcribing, 17 for dispensing, 7 for administration, and 25 for monitoring. Ranking of the articles for importance by peers led to the selection of key articles from each category. The highest-ranked articles are briefly summarized, with a mention of why each article is important within health-system pharmacy. The other articles are listed for further review and evaluation.

CONCLUSION

It is important to routinely review the published literature and to incorporate significant findings into daily practice; this article assists in identifying and summarizing the most impactful recently published literature in this area. Health-system pharmacists have an active role in improving the MUP in their institution, and awareness of the significant published studies can assist in changing practice at the institutional level.

The Winter Respiratory Viral Season During the COVID-19 Pandemic

The winter respiratory virus season always poses challenges for long-term care settings; this winter, severe acute respiratory syndrome coronavirus 2 will compound the usual viral infection challenges. This special article discusses unique considerations that Coronavirus Disease 2019 (COVID-19) brings to the health and well-being of residents and staff in nursing homes and other long-term care settings this winter. Specific topics include preventing the spread of respiratory viruses, promoting immunization, and the diagnosis and treatment of suspected respiratory infection. Policy-relevant issues are discussed, including whether to mandate influenza immunization for staff, the availability and use of personal protective equipment, supporting staff if they become ill, and the distribution of a COVID-19 vaccine when it becomes available. Research is applicable in all of these areas, including regarding the use of emerging electronic decision support tools. If there is a positive side to this year's winter respiratory virus season, it is that staff, residents, family members, and clinicians will be especially vigilant about potential infection.

Effectiveness of Electronic Guidelines (GERH®) to Improve the Clinical Use of Antibiotics in An Intensive Care Unit

The objective of the study was to evaluate the capacity of GERH^®-derived local resistance maps (LRMs) to predict antibiotic susceptibility profiles and recommend the appropriate empirical treatment for ICU patients with nosocomial infection. Data gathered between 2007 and 2016 were retrospectively studied to compare susceptibility information from antibiograms of microorganisms isolated in blood cultures, lower respiratory tract samples, and urine samples from all ICU patients meeting clinical criteria for infection with the susceptibility mapped by LRMs for these bacterial species. Susceptibility described by LRMs was concordant with in vitro study results in 73.9% of cases. The LRM-predicted outcome agreed with the antibiogram result in >90% of cases infected with the bacteria for which GERH^® offers data on susceptibility to daptomycin, vancomycin, teicoplanin, linezolid, and rifampicin. Full adherence to LRM recommendations would have improved the percentage adequacy of empirical prescriptions by 2.2% for lower respiratory tract infections (p = 0.018), 3.1% for bacteremia (p = 0.07), and 5.3% for urinary tract infections (p = 0.142). LRMs may moderately improve the adequacy of empirical antibiotic therapy, especially for lower respiratory tract infections. LRMs recommend appropriate prescriptions in approximately 50% of cases but are less useful in patients with bacteremia or urinary tract infection.

E-Health Tools to Improve Antibiotic Use and Resistances: A Systematic Review

(1) Background: e-Health tools, especially in the form of clinical decision support systems (CDSSs), have been emerging more quickly than ever before. The main objective of this systematic review is to assess the influence of these tools on antibiotic use for respiratory tract infections. (2) Methods: The scientific databases, MEDLINE-PubMed and EMBASE, were searched. The search was conducted by two independent researchers. The search strategy was mainly designed to identify relevant studies on the effectiveness of CDSSs in improving antibiotic use, as a primary outcome, and on the acceptability and usability of CDSSs, as a secondary outcome. (3) Results: After the selection, 22 articles were included. The outcomes were grouped either into antibiotics prescription practices or adherence to guidelines concerning antibiotics prescription. Overall, 15 out of the 22 studies had statistically significant outcomes related to the interventions. (4) Conclusions: Overall, the results show a positive impact on the prescription and conscientious use of antibiotics for respiratory tract infections, both with respect to patients and prescribing healthcare professionals. CDSSs have been shown to have great potential as powerful tools for improving both clinical care and patient outcomes.

Effective Antimicrobial StewaRdship StrategIES (ARIES): Cluster Randomized Trial of Computerized Decision Support System and Prospective Review and Feedback

Background

Prospective review and feedback (PRF) of antibiotic prescriptions and compulsory computerized decision support system (CDSS) are 2 strategies of antimicrobial stewardship. There are limited studies investigating their combined effects. We hypothesized that the use of on-demand (voluntary) CDSS would achieve similar patient outcomes compared with automatically triggered (compulsory) CDSS whenever broad-spectrum antibiotics are ordered.

Methods

A parallel-group, 1:1 block cluster randomized crossover study was conducted in 32 medical and surgical wards from March to August 2017. CDSS use for piperacillin-tazobactam or carbapenem in the intervention clusters was at the demand of the doctor, while in the control clusters CDSS use was compulsory. PRF was continued for both arms. The primary outcome was 30-day mortality.

Results

Six hundred forty-one and 616 patients were randomized to voluntary and compulsory CDSS, respectively. There were no differences in 30-day mortality (hazard ratio [HR], 0.87; 95% CI, 0.67–1.12), re-infection and re-admission rates, antibiotic duration, length of stay, or hospitalization cost. The proportion of patients receiving PRF recommendations was not significantly lower in the voluntary CDSS arm (62 [10%] vs 81 [13%]; P = .05). Appropriate indication of antibiotics was high in both arms (351/448 [78%] vs 330/433 [74%]; P = .18). However, in geriatric medicine patients where antibiotic appropriateness was <50%, prescription via compulsory CDSS resulted in a shorter length of stay and lower hospitalization cost.

Conclusions

Voluntary broad-spectrum antibiotics with PRF via CDSS did not result in differing clinical outcomes, antibiotic duration, or length of stay. However, in the setting of low antibiotic appropriateness, compulsory CDSS may be beneficial.

The impact of a computerised decision support system on antibiotic usage in an English hospital

Background Antimicrobial resistance is correlated with the inappropriate use of antibiotics. Computerised decision support systems may help practitioners to make evidence-based decisions when prescribing antibiotics. Objective This study aimed to evaluate the impact of computerized decision support systems on the volume of antibiotics used. Setting A very large 1200-bed teaching hospital in Birmingham, England. Main outcome measure The primary outcome measure was the defined daily doses/1000 occupied bed-days. Method A retrospective longitudinal study was conducted to examine the impact of computerised decision support systems on the volume of antibiotic use. The study compared two periods: one with computerised decision support systems, which lasted for 2 years versus one without which lasted for 2 years after the withdrawal of computerised decision support systems. Antibiotic use data from June 2012 to June 2016 were analysed (comprising 2 years with computerised decision support systems immediately followed by 2 years where computerised decision support systems had been withdrawn). Regression analysis was applied to assess the change in antibiotic consumption through the period of the study. Result From June 2012 to June 2016, total antibiotic usage increased by 13.1% from 1436 to 1625 defined daily doses/1000 bed-days: this trend of increased antibiotic prescribing was more pronounced following the withdrawal of structured prescribing (computerised decision support systems). There was a difference of means of - 110.14 defined daily doses/1000 bed days of the total usage of antibiotics in the period with and without structured prescribing, and this was statistically significant (p = 0.026). From June 2012 to June 2016, the dominant antibiotic class used was penicillins. The trends for the total consumption of all antibiotics demonstrated an increase of use for all antibiotic classes except for tetracyclines, quinolones, and anti-mycobacterial drugs, whereas aminoglycoside usage remained stable. Conclusion The implementation of computerised decision support systems appears to influence the use of antibiotics by reducing their consumption. Further research is required to determine the specific features of computerised decision support systems, which influence increased higher adoption and uptake of this technology.

Drug-related Problems in Home-dwelling Older Adults: A Systematic Review

PURPOSE

The complex combination of medicines associated with age-related physiological alterations leads older adults to experience drug-related problems (DRPs). The goal of this study was to review the frequency and type of DRPs and DRP risk factors in home-dwelling older adults.

METHODS

A MEDLINE PubMed and EMBASE scientific databases search was performed. Articles published from January 2000 through December 2018 reporting DRPs in home-dwelling older adults were included.

FINDINGS

From 668 articles screened, 13 met the inclusion criteria and were included in this study. Overall, the studies included 8935 home-dwelling patients. The mean number of DRPs per patient observed was 4.16 (1.37-10). The main causes of DRPs were "drug selection" (51.41%), "dose selection" (11.62%), and "patient related" (10.70%) problems. The drug classes more frequently associated with DRPs were "cardiovascular system," "alimentary tract and metabolism," and "nervous system," and they represented 32.1%, 29.4%, and 16.5% of all drug selection problems, respectively. Respiratory system medicines accounted for 6.65% of all DRPs, of which "patient related" problems accounted for 97.28%.

IMPLICATIONS

Despite the heterogeneity of methodology of the included studies and the heterogeneity of tools used to identify DRPs, this analysis clearly shows the high prevalence of DRPs in home-dwelling older adults and highlights the need for interventions to improve medicine use in this population. This work also provides useful information for the development of strategies to improve medication use in home-dwelling older adults.

Collaborative Antimicrobial Stewardship: Working with Information Technology

Information technology (IT) is vitally important to making antimicrobial stewardship a scalable endeavor in modern health care systems. Without IT, many antimicrobial interventions in patient care would be missed. Clinical decision support systems and smartphone apps, either stand-alone or integrated into electronic health records, can all be effective tools to help augment the work of antimicrobial stewardship programs and support the management of infectious diseases in any health care setting.