Examining Workflow in a Pediatric Emergency Department to Develop a Clinical Decision Support for an Antimicrobial Stewardship Program

Effects of a Pharmacist-Designed Clinical Decision Support System on Antimicrobial Stewardship

BACKGROUND

 Clinical decision support systems (CDSSs) are computer applications, which can be applied to give guidance to practitioners in antimicrobial stewardship (AS) activities; however, further information is needed for their optimal use.

OBJECTIVES

 Our objective was to analyze the implementation of a CDSS program in a second-level hospital, describing alerts, recommendations, and the effects on consumption and clinical outcomes.

METHODS

 In October 2020, a pharmacist-driven CDSS designed for AS was implemented in a second-level hospital. The program provides a list of alerts related to antimicrobial treatment and microbiology, which were automatized for revision by the AS professionals. To analyze the implementation of the CDSS, a pre-post-intervention, retrospective study was designed. AS-triggered alerts and recommendations (total number and rate of acceptance) were compiled. The effect of the CDSS was measured using antimicrobial consumption, duration of antimicrobial treatments, in-hospital mortality, and length of stay (LOS) for patients admitted for infectious causes.

RESULTS

 The AS team revised a total of 7,543 alerts and 772 patients had at least one recommendation, with an acceptance rate of 79.3%. Antimicrobial consumption decreased from 691.1 to 656.8 defined daily doses (DDD)/1,000 beds-month (p = 0.04) and the duration of antimicrobial treatment from 3.6 to 3.3 days (p < 0.01). In-hospital mortality decreased from 6.6 to 6.2% (p = 0.46) and mean LOS from 7.2 to 6.2 days (p < 0.01).

CONCLUSION

 The implementation of a CDSS resulted in a significant reduction of antimicrobial DDD, duration of antimicrobial treatments, and hospital LOS. There was no significant difference in mortality.

Effects of a QI intervention on pediatric asthma treatment using patient outcomes and workflow in an emergency department

OBJECTIVE

Evaluate a nurse-initiated quality improvement (QI) intervention aimed at enhancing asthma treatment in a pediatric emergency department (ED), utilizing outcomes and workflow.

METHODS

We evaluated the impact of QI interventions for pediatric patients presenting to the ED with asthma with pre-post analysis. A pediatric asthma score (PAS) of >8 indicated moderate to severe asthma. This secondary analysis of the electronic health record (EHR), evaluated on 1) patient outcomes (time to clinical treatment, ED length of stay [EDLOS], admissions and discharges home), 2) clinical workflow.

RESULTS

We compared 886 visits occurring between 01/01/2015 and 09/27/2015 (pre-implementation period) with 752 visits between 01/01/2016 and 09/27/2016 (post-implementation). Time to first documentation of PAS was decreased post-intervention (p<.001) by >30 min (75 ± 57 to 39 ± 54 min). There were significant decreases in time to treatment with both steroid and bronchodilator administration (both p<.001). EDLOS did not significantly change. Based on acuity level, those discharged home from the ED with high acuity (PAS score ≥8), had a significant decrease in time to initial PAS, steroid and bronchodilator use and EDLOS. Of those with high acuity who were admitted to the hospital, there was a difference pre- to post-implementation, in time to first PAS (p<.05), but not to treatment. Workflow visualization provided additional insights and detailed (task level) comparisons of the timing of ED activities.

CONCLUSIONS

Nurse-initiated ED interventions, can significantly improve the timeliness of pediatric asthma evaluation and treatment. Examining workflow along with the outcomes, can better inform QI evaluations and clinical management.

Regional Perspective of Antimicrobial Stewardship Programs in Latin American Pediatric Emergency Departments

Antibiotic stewardship (AS) programs have become a priority for health authorities to reduce the number of infections by super-resistant microorganisms. The need for these initiatives to minimize the inadequate use of antimicrobials is essential, and the election of the antibiotic in the emergency department usually impacts the choice of treatment if the patients need hospital admission, becoming an opportunity for antibiotic stewardship. In the pediatric population, broad-spectrum antibiotics are more likely to be overprescribed without any evidence-based management, and most of the publications have focused on the prescription of antibiotics in ambulatory settings. Antibiotic stewardship efforts in pediatric emergency departments in Latin American settings are limited. The lack of literature on AS programs in the pediatric emergency departments in Latin America (LA) limits the information available. The aim of this review was to give a regional perspective on how pediatric emergency departments in LA are working towards antimicrobial stewardship.

Neonatal Intensive Care Workflow Analysis Informing NEC-Zero Clinical Decision Support Design

Decision support in neonatal ICUs is needed, especially for prevention and risk awareness of the devastating complication of necrotizing enterocolitis, a major cause of emergency surgery among fragile infants. The purpose of this study was to describe the current clinical workflow and sociotechnical processes among clinicians for necrotizing enterocolitis risk awareness, timely recognition of symptoms, and treatment to inform decision support design. A qualitative descriptive study was conducted. Focus groups were held in two neonatal ICUs (five groups in Unit A and six in Unit B). Transcripts were analyzed using content analysis and compared with field notes. Clinicians (N = 27) included nurses (37%), physicians (30%), neonatal nurse practitioners (19%), and other staff (16%). Workflow processes differed for nurses (who see necrotizing enterocolitis signs and notify providers to order diagnostic tests and treatments) and providers (who receive notification of necrotizing enterocolitis concern and then decide how to act). Clinicians desired (1) a necrotizing enterocolitis-relevant dashboard to support nutrition tracking and necrotizing enterocolitis recognition; (2) features to support decision-making (eg, necrotizing enterocolitis risk and adherence scoring); (3) breast milk tracking and feeding clinical decision support; (4) tools for necrotizing enterocolitis surveillance and quality reporting; and (5) general EHR optimizations to improve user experience.

Barriers and facilitators to implementing a nursing clinical decision support system in a tertiary hospital setting: A qualitative study using the FITT framework

OBJECTIVE

Clinical decision support systems (CDSSs) have been increasingly introduced to health care settings; however, their adoption is far from ideal. Guided by the FITT framework, this study aims to explore barriers and facilitators to the implementation of a CDSS from the perspective of nurses.

METHODS

A qualitative study with 200 h of participatory observation and 21 semi structured interviews was conducted from February to August 2021 in four medical-surgical wards in a 2000-bed tertiary hospital in Shanghai, China. The field notes were typed and the audio-recorded interviews were transcribed to texts verbatim and were coded with a four-step approach. We used the FITT framework to interpret our findings based on the technology, individual and task attributes and the fit between them.

RESULTS

A total of twelve categories were identified, which were integrated into two themes: barriers and facilitators to system implementation. All categories but one can be mapped to the three attributes of the FITT framework: technology, individual and task. We assumed that management has a vital role to play in the following areas: addressing user resistance, improving system usability, setting standards on practice and, finally, building connectivity between nurses and the technical staff to improve the fit between the technology, individual and task attribute and thus promote system implementation.

CONCLUSION

Barriers and facilitators to CDSS implementation include system-related, user-related and organizational factors which can largely be fit io the FITT framework. There is potential to extend the FITT framework to represent management intervention on inter-disciplinary collaboration. Future empirical studies on facilitating strategies from the management to improve user experience and willingness of CDSS adoption are needed.

Principles for Designing and Developing a Workflow Monitoring Tool to Enable and Enhance Clinical Workflow Automation

BACKGROUND

Automation of health care workflows has recently become a priority. This can be enabled and enhanced by a workflow monitoring tool (WMOT).

OBJECTIVES

We shared our experience in clinical workflow analysis via three cases studies in health care and summarized principles to design and develop such a WMOT.

METHODS

The case studies were conducted in different clinical settings with distinct goals. Each study used at least two types of workflow data to create a more comprehensive picture of work processes and identify bottlenecks, as well as quantify them. The case studies were synthesized using a data science process model with focuses on data input, analysis methods, and findings.

RESULTS

Three case studies were presented and synthesized to generate a system structure of a WMOT. When developing a WMOT, one needs to consider the following four aspects: (1) goal orientation, (2) comprehensive and resilient data collection, (3) integrated and extensible analysis, and (4) domain experts.

DISCUSSION

We encourage researchers to investigate the design and implementation of WMOTs and use the tools to create best practices to enable workflow automation and improve workflow efficiency and care quality.

Predicting hospitalization of pediatric asthma patients in emergency departments using machine learning

MOTIVATION

The timely identification of patients for hospitalization in emergency departments (EDs) can facilitate efficient use of hospital resources. Machine learning can help the early prediction of ED disposition; however, application of machine learning models requires both computer science skills and domain knowledge. This presents a barrier for those who want to apply machine learning to real-world settings.

OBJECTIVES

The objective of this study was to construct a competitive predictive model with a minimal amount of human effort to facilitate decisions regarding hospitalization of patients.

METHODS

This study used the electronic health record data from five EDs in a single healthcare system, including an academic urban children's hospital ED, from January 2009 to December 2013. We constructed two machine learning models by using automated machine learning algorithm (autoML) which allows non-experts to use machine learning model: one with data only available at ED triage, the other adding information available one hour into the ED visit. Random forest and logistic regression were employed as bench-marking models. The ratio of the training dataset to the test dataset was 8:2, and the area under the receiver operating characteristic curve (AUC), accuracy, and F1 were calculated to assess the quality of the models.

RESULTS

Of the 9,069 ED visits analyzed, the study population was made up of males (62.7 %), median (IQR) age was 6 (4, 10) years, and public insurance coverage (66.0 %). The majority had an Emergency Severity Index score of 3 (52.9 %). The prevalence of hospitalization was 22.5 %. The AUCs were 0.914 and 0.942. AUCs were 0.831 and 0.886 for random forests, and 0.795 and 0.823 for logistic regression. Among the predictors, an outcome at a prior visit, ESI level, time to first medication, and time to triage were the most important features for the prediction of the need for hospitalization.

CONCLUSIONS

In comparison with the conventional approaches, the use of autoML improved the predictive ability for the need for hospitalization. The findings can optimize ED management, hospital-level resource utilization and improve quality. Furthermore, this approach can support the design of a more effective patient ED flow for pediatric asthma care.

How does innovative technology impact nursing in infectious diseases and infection control? A scoping review

Aim

Considering the increasing number of emerging infectious diseases, innovative approaches are strongly in demand. Additionally, research in this field has expanded exponentially. Thus, faced with this diverse information, we aim to clarify key concepts and knowledge gaps of technology in nursing and the field of infectious diseases.

Design

This scoping review followed the methodology of scoping review guidance from Arksey and O’Malley.

Methods

Six databases were searched systematically (PubMed, Web of Science, IEEE Explore, EBSCOhost, Cochrane Library and Summon). After the removal of duplicates, 532 citations were retrieved and 77 were included in the analysis.

Results

We identified five major trends in technology for nursing and infectious diseases: artificial intelligence, the Internet of things, information and communications technology, simulation technology and e‐learning. Our findings indicate that the most promising trend is the IoT because of the many positive effects validated in most of the reviewed studies.

Applying Computational Ethnography to Examine Nurses' Workflow Within Electronic Health Records

OBJECTIVE

The aim of this study was to describe computational ethnography as a contemporary and supplemental methodology in EHR workflow analysis and the relevance of this method to nursing research.

METHODS

We explore the use of audit logs as a computational ethnographic data source and the utility of data mining techniques, including sequential pattern mining (SPM) and Markov chain analysis (MCA), to analyze nurses' workflow within the EHRs. SPM extracts frequent patterns in a given transactional database (e.g., audit logs from the record). MCA is a stochastic process that models a sequence of states and allows for calculating the probability of moving from one state to the next. These methods can help uncover nurses' global navigational patterns (i.e., how nurses navigate within the record) and enable robust workflow analyses.

RESULTS

We demonstrate hypothetical examples from SPM and MCA, such as (a) the most frequent sequential pattern of nurses' workflow when navigating the EHR using SPM and (b) transition probability from one record screen to the next using MCA. These examples demonstrate new methods to address the inflexibility of current approaches used to examine nursing EHR workflow.

DISCUSSION

Within a clinical context, the use of computational ethnographic data and data mining techniques can inform the optimization of the EHR. Results from these analyses can be used to supplement the data needed in redesigning the EHR, such as organizing and combining features within a screen or predicting future navigation to improve the record that nurses use.

Factors That Impact the Adoption of Clinical Decision Support Systems (CDSS) for Antibiotic Management

The study evaluated individual and setting-specific factors that moderate clinicians’ perception regarding use of clinical decision support systems (CDSS) for antibiotic management. A cross-sectional online survey examined clinicians’ perceptions about CDSS implementation for antibiotic management in Australia. Multivariable logistic regression determined the association between drivers of CDSS adoption and different moderators. Clinical experience, CDSS use and care setting were important predictors of clinicians’ perception concerning CDSS adoption. Compared to nonusers, CDSS users were less likely to lack confidence in CDSS (OR = 0.63, 95%, CI = 0.32, 0.94) and consider it a threat to professional autonomy (OR = 0.47, 95%, CI = 0.08, 0.83). Conversely, there was higher likelihood in experienced clinicians (>20 years) to distrust CDSS (OR = 1.58, 95%, CI = 1.08, 2.23) due to fear of comprising their clinical judgement (OR = 1.68, 95%, CI = 1.27, 2.85). In primary care, clinicians were more likely to perceive time constraints (OR = 1.96, 95%, CI = 1.04, 3.70) and patient preference (OR = 1.84, 95%, CI = 1.19, 2.78) as barriers to CDSS adoption for antibiotic prescribing. Our findings provide differentiated understanding of the CDSS implementation landscape by identifying different individual, organisational and system-level factors that influence system adoption. The individual and setting characteristics can help understand the variability in CDSS adoption for antibiotic management in different clinicians.

Analysis of medication patterns for pediatric asthma patients in emergency department: Does the sequence placement of glucocorticoids administration matter?

Objectives: Timely glucocorticoid administration is associated with decreased admission rate and is thus a common quality metric for ED asthma care; less is known about the impact of the timing of glucocorticoids in the context of the sequence of asthma medications administered. Therefore, we investigated the distribution of asthma medication sequences in one ED and analyzed the effect of the sequence placement of glucocorticoids administration on treatment outcomes.Methods: A retrospective study using five-year electronic health record data obtained from an academic urban children's hospital ED was conducted. We clustered the sequences of medication administration using an exact string-matching algorithm to identify the most frequently used asthma medication sequences. Then, we used the identified patterns to perform statistical tests to examine the effect of the sequence placement of glucocorticoids administration on the outcomes length-of-stay and ED disposition.Results: A total of 4,844 encounters were included in our study. The ten most common treatment sequences accounted for 43% of all encounters. Stratified analyses confirmed that treatment sequences pattern was correlated with patient severity, but ED crowding does not impact treatment sequences. In multivariable models, glucocorticoids administration earlier in the treatment sequence was associated with shorter length of stay and lower hospital admission rates.Conclusions: By analyzing medication sequence patterns for the ED encounter of pediatric asthma, we found that the earlier sequence placement of glucocorticoids administration is associated with improved outcomes. Our findings can help inform quality improvement and clinical guideline development related to ED asthma care for children.

Simulating Teamwork for Better Decision Making in Pediatric Emergency Medical Services

Emergency Medical Services (EMS) are an essential component of health systems and are critical to the provision of pediatric emergency care. Challenges in this setting include fast pace, need for advanced teamwork, situational awareness and limited resources. The purpose of this study was to identify human factors-related obstacles during care delivery by EMS teams that could lead to inefficiencies and patient safety issues. We examined video recordings of 24 simulations of EMS teams (paramedics and EMTs) who were providing care to pediatric patients. Two reviewers documented a total of 262 efficiency and patient safety issues in 4.25 hours of videos. These issues were grouped into 28 categories. Reviewers also documented 19 decision support opportunities. These issues and decision support opportunities can inform the design of clinical decision support systems that can improve EMS related patient outcomes.

User-Centered Design in Pediatric Acute Care Settings Antimicrobial Stewardship

BACKGROUND

Antibiotic prescribing in ambulatory care centers is increasing. Previous research suggests that 20 to 50% of antibiotic prescriptions are either unnecessary or inappropriate. Unnecessary antibiotic consumption can harm patients by increasing antibiotic resistance and drug-associated toxicities, and the reasons for such use are multifactorial. Antimicrobial Stewardship Programs (ASP) were developed to guide better use of antibiotics. A core element of ASP is to provide feedback to clinical providers. To create clinically meaningful feedback, user-center design (UCD) is a robust approach to include end-users in the design process to improve systems.

OBJECTIVE

The study aimed to take a UCD approach to developing antibiotic prescribing feedback through input from clinicians in two ambulatory care settings.

METHODS

We conducted two group prototyping sessions with pediatric clinicians who practice in the emergency department and urgent care settings at a tertiary care children's hospital. Participants received background on the problem of antibiotic prescribing and then were interviewed about their information needs, perceived value, and desired incentives for a prescribing feedback system. Sessions concluded with their response and recommendations to sample sections of an antibiotic feedback report including orienting material, report detail, targeted education, and resources.

RESULTS

A UCD approach was found to be highly valuable in the development of a feedback mechanism that is viewed as desirable by clinicians. Clinicians preferred interpreting the data themselves with aids such as diagrams and charts over the researcher concluded statements about the clinician's behavior. Specific feedback that clinicians considered redundant were removed from the model if preexisting alerts were established.

CONCLUSION

Integrating a UCD approach in developing ASP feedback identified desirable report characteristics that substantially modified preliminary wireframes for feedback. Future research will evaluate the clinical effectiveness of our feedback reports in outpatient settings.

Considerations for Designing EHR-Embedded Clinical Decision Support Systems for Antimicrobial Stewardship in Pediatric Emergency Departments

OBJECTIVE

This study was aimed to explore the intersection between organizational environment, workflow, and technology in pediatric emergency departments (EDs) and how these factors impact antibiotic prescribing decisions.

METHODS

Semistructured interviews with 17 providers (1 fellow and 16 attending faculty), and observations of 21 providers (1 physician assistant, 5 residents, 3 fellows, and 12 attendings) were conducted at three EDs in the United States. We analyzed interview transcripts and observation notes using thematic analysis.

RESULTS

Seven themes relating to antibiotic prescribing decisions emerged as follows: (1) professional judgement, (2) cognition as a critical individual resource, (3) decision support as a critical organizational resource, (4) patient management with imperfect information, (5) information-seeking as a primary task, (6) time management, and (7) broad process boundaries of antibiotic prescribing.

DISCUSSION

The emerging interrelated themes identified in this study can be used as a blueprint to design, implement, and evaluate clinical decision support (CDS) systems that support antibiotic prescribing in EDs. The process boundaries of antibiotic prescribing are broader than the current boundaries covered by existing CDS systems. Incongruities between process boundaries and CDS can under-support clinicians and lead to suboptimal decisions. We identified two incongruities: (1) the lack of acknowledgment that the process boundaries go beyond the physical boundaries of the ED and (2) the lack of integration of information sources (e.g., accessibility to prior cultures on an individual patient outside of the organization).

CONCLUSION

Significant opportunities exist to improve appropriateness of antibiotic prescribing by considering process boundaries in the design, implementation, and evaluation of CDS systems.

Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study

Background

Patient-generated health data (PGHD) have been largely collected through mobile health (mHealth) apps and wearable devices. PGHD can be especially helpful in mental health, as patients’ illness history and symptom narratives are vital to developing diagnoses and treatment plans. However, the extent to which clinicians use mental health–related PGHD is unknown.

Objective

A mixed methods study was conducted to understand clinicians’ perspectives on PGHD and current mental health apps. This approach uses information gathered from semistructured interviews, workflow analysis, and user-written mental health app reviews to answer the following research questions: (1) What is the current workflow of mental health practice and how are PGHD integrated into this workflow, (2) what are clinicians’ perspectives on PGHD and how do they choose mobile apps for their patients, (3) and what are the features of current mobile apps in terms of interpreting and sharing PGHD?

Methods

The study consists of semistructured interviews with 12 psychiatrists and clinical psychologists from a large academic hospital. These interviews were thematically and qualitatively analyzed for common themes and workflow elements. User-posted reviews of 56 sleep and mood tracking apps were analyzed to understand app features in comparison with the information gathered from interviews.

Results

The results showed that PGHD have been part of the workflow, but its integration and use are not optimized. Mental health clinicians supported the use of PGHD but had concerns regarding data reliability and accuracy. They also identified challenges in selecting suitable apps for their patients. From the app review, it was discovered that mHealth apps had limited features to support personalization and collaborative care as well as data interpretation and sharing.

Conclusions

This study investigates clinicians’ perspectives on PGHD use and explored existing app features using the app review data in the mental health setting. A total of 3 design guidelines were generated: (1) improve data interpretation and sharing mechanisms, (2) consider clinical workflow and electronic health record integration, and (3) support personalized and collaborative care. More research is needed to demonstrate the best practices of PGHD use and to evaluate their effectiveness in improving patient outcomes.

The potential role of dashboard use and navigation in reducing medical errors of an electronic health record system: a mixed-method simulation handoff study

The dashboards of electronic health record (EHR) systems could potentially support the chart biopsy that occurs before or after physician handoffs. In this study, we conducted a simulation handoff study and recorded the participants' navigation patterns in an EHR system mock-up. We analyzed the navigation patterns of dashboard use in terms of duration, frequency, and sequence, and we examined the relationship between dashboard use in chart biopsy and the errors identified after handoffs. The results show that the participants frequently used the dashboard as an information hub and as an information resource to help them navigate the EHR system and answer the questions in a nursing call. Moreover, using the dashboard as an information hub can help reduce imprecision and factual errors in handoffs. Our findings suggest the need for a "context-aware" dashboard to accommodate dynamic navigation patterns and to support clinical work as well as to reduce medical errors.

Improving the Effectiveness of Health Information Technology: The Case for Situational Analytics

Health information technology has contributed to improvements in quality and safety in clinical settings. However, the implementation of new technologies in health care has also been associated with the introduction of new sociotechnical hazards, produced through a range of complex interactions that vary with social, physical, temporal, and technological context. Other industries have been confronted with this problem and have developed advanced analytics to examine context-specific activities of workers and related outcomes. The skills and data exist in health care to develop similar insights through situational analytics, defined as the application of analytic methods to characterize human activity in situations and identify patterns in activity and outcomes that are influenced by contextual factors. This article describes the approach of situational analytics and potentially useful data sources, including trace data from electronic health record activity, reports from users, qualitative field data, and locational data. Key implementation requirements are discussed, including the need for collaboration among qualitative researchers and data scientists, organizational and federal level infrastructure requirements, and the need to implement a parallel research program in ethics to understand how the data are being used by organizations and policy makers.

Clinical Decision Support Systems and Their Role in Antibiotic Stewardship: a Systematic Review

PURPOSE OF REVIEW

The purpose of this article is to perform a systematic review over the past 5 years on the role and effectiveness of clinical decision support systems (CDSSs) on antibiotic stewardship.

RECENT FINDINGS

CDDS interventions found a significant impact on multiple outcomes relevant to antibiotic stewardship. There are various types of CDSS implementations, both active and passive (provider initiated). Passive interventions were associated with more significant outcomes; however, both interventions appeared effective. In the reviewed literature, CDSSs were consistently associated with decreasing antibiotic consumption and narrowing the spectrum of antibiotic usage. Generally, guideline adherence was improved with CDSS, although this was not universal. The effect on other outcomes, such as mortality, Clostridiodes difficile infections, length of stay, and cost, inconsistently showed a significant difference. Overall, CDDS implementation has effectively decreased antibiotic consumption and improved guideline adherence across the various types of CDSS. Other positive outcomes were noted in certain settings, but were not universal. When creating a new intervention, it is important to identify the optimal structure and deployment of a CDSS for a specific setting.

Usability Testing of a Mobile Clinical Decision Support App for Urinary Tract Infection Diagnosis in Nursing Homes

The aim of the current study was to conduct usability testing of a mobile clinical decision support (CDS) prototype designed for urinary tract infection (UTI) assessment by nurses in nursing homes (NHs). Usability of the UTIDecide smartphone application (app) was evaluated using cognitive walk-through and think-aloud protocol sessions with nurses (n = 6) at two NH sites. This evaluation was followed by unsupervised field tests lasting ≥1 week with nurses at one site (n = 4) and posttest interviews and administration of the System Usability Scale (SUS). Cognitive walk-through/think-aloud sessions yielded interface design recommendations that were implemented prior to field tests. All test sessions resulted in highly positive perceived usability and usefulness from participants. Average SUS score was 92.5 (n = 3), which equates to an "A" grade for usability. Design recommendations identified for future app versions are: (a) integration of the mobile CDS app with organizational information systems; and (b) expanded features to support assessment of other conditions. [Journal of Gerontological Nursing, 45(7), 11-17.].

Comparison of Antibiotic Dosing Before and After Implementation of an Electronic Order Set

BACKGROUND

To maximize resources, the antimicrobial stewardship program at a pediatric tertiary care hospital made pediatric dosing specific guidance within the electronic health record available to all hospitals within the health system.

OBJECTIVE

The objective of this study was to compare the appropriateness of antibiotic dosing before and after the implementation of an electronic intravenous (IV) antibiotic order set.

METHODS

This was a retrospective cohort study evaluating orders from patients younger than 18 years who received cefepime, piperacillin-tazobactam, tobramycin, or gentamicin at 12 health-system hospitals. Antibiotic dosing regimens and order set use were evaluated in patients who received the specified antibiotics during the 6-month time frame prior to and following electronic order set availability at each hospital.

RESULTS

In the before and after implementation periods, 360 and 387 total antibiotic orders were included, respectively. Most orders were gentamicin (55.8% in the before implementation period and 54.5% in the after implementation period) followed by piperacillin-tazobactam (22.5% in the before period and 22.2% in the after period). Overall, 663 orders were classified as appropriate (88.8%). Appropriateness was similar in the before or after implementation periods (87.8 vs. 89.7%, p = 0.415). There was a significant difference in appropriateness if a blank order versus the electronic IV antibiotic order set was used (82.8 vs. 90.5%; p = 0.024).

CONCLUSION

No difference in antibiotic appropriateness overall was found in the before and after implementation periods. However, when specifically compared with the appropriateness of dosing when blank order forms were used, dosing was more appropriate when electronic antibiotic order sets were used.