Effectiveness of clinical dashboards as audit and feedback or clinical decision support tools on medication use and test ordering: a systematic review of randomized controlled trials

Clinical decision support and electronic interventions to improve care quality in chronic liver diseases and cirrhosis

Significant quality gaps exist in the management of chronic liver diseases and cirrhosis. Clinical decision support systems-information-driven tools based in and launched from the electronic health record-are attractive and potentially scalable prospective interventions that could help standardize clinical care in hepatology. Yet, clinical decision support systems have had a mixed record in clinical medicine due to issues with interoperability and compatibility with clinical workflows. In this review, we discuss the conceptual origins of clinical decision support systems, existing applications in liver diseases, issues and challenges with implementation, and emerging strategies to improve their integration in hepatology care.

Audit and feedback: effects on professional practice

BACKGROUND

Audit and feedback (A&F) is a widely used strategy to improve professional practice. This is supported by prior Cochrane reviews and behavioural theories describing how healthcare professionals are prompted to modify their practice when given data showing that their clinical practice is inconsistent with a desirable target. Yet there remains uncertainty regarding the effects of A&F on improving healthcare practice and the characteristics of A&F that lead to a greater impact.

OBJECTIVES

To assess the effects of A&F on the practice of healthcare professionals and to examine factors that may explain variation in the effectiveness of A&F.

SEARCH METHODS

With the Cochrane Effective Practice and Organisation of Care (EPOC) group information scientist, we updated our search strategy to include studies published from 2010 to June 2020. Search updates were performed on 28 February 2019 and 11 June 2020. We searched MEDLINE (Ovid), Embase (Ovid), CINAHL (EBSCO), the Cochrane Library, clinicaltrials.gov (all dates to June 2020), WHO ICTRP (all dates to February Week 3 2019, no information available in 2020 due to COVID-19 pandemic). An updated search and duplicate screen was completed on February 14, 2022; studies that met inclusion criteria are included in the 'Studies awaiting classification' section.

SELECTION CRITERIA

Randomised trials, including cluster-trials and cross-over and factorial designs, featuring A&F (defined as measurement of clinical performance over a specified period of time (audit) and provision of the resulting data to clinicians or clinical teams (feedback)) in any trial arm that reported objectively measured health professional practice outcomes.

DATA COLLECTION AND ANALYSIS

For this updated review, we re-extracted data for each study arm, including theory-informed variables regarding how the A&F was conducted and behaviour change techniques for each intervention, as well as study-level characteristics including risk of bias. For each study, we extracted outcome data for every healthcare professional practice targeted by A&F. All data were extracted by a minimum of two independent review authors. For studies with dichotomous outcomes that included arms with and without A&F, we calculated risk differences (RDs) (absolute difference between arms in proportion of desired practice completed) and also odds ratios (ORs). We synthesised the median RDs and interquartile ranges (IQRs) across all trials. We then conducted meta-analyses, accounting for multiple outcomes from a given study and weighted by effective sample size, using reported (or imputed, when necessary) intra-cluster correlation coefficients. Next, we explored the role of baseline performance, co-interventions, targeted behaviour, and study design factors on the estimated effects of A&F. Finally, we conducted exploratory meta-regressions to test preselected variables that might be associated with A&F effect size: characteristics of the audit (number of indicators, aggregation of data); delivery of the feedback (multi-modal format, local champion, nature of comparator, repeated delivery); and components supporting action (facilitation, provision of specific plans for improvement, co-development of action plans).

MAIN RESULTS

We included 292 studies with 678 arms; 133 (46%) had a low risk of bias, 41 (14%) unclear, and 113 (39%) had a high risk of bias. There were 26 (9%) studies conducted in low- or middle-income countries. In most studies (237, 81%), the recipients of A&F were physicians. Professional practices most commonly targeted in the studies were prescribing (138 studies, 47%) and test-ordering (103 studies, 35%). Most studies featured multifaceted interventions: the most common co-interventions were clinician education (377 study arms, 56%) and reminders (100 study arms, 15%). Forty-eight unique behaviour change techniques were identified within the study arms (mean 5.2, standard deviation 2.8, range 1 to 29). Synthesis of 558 dichotomous outcomes measuring professional practices from 177 studies testing A&F versus control revealed a median absolute improvement in desired practice of 2.7%, with an IQR of 0.0 to 8.6. Meta-analyses of these studies, accounting for multiple outcomes from the same study and weighting by effective sample size accounting for clustering, found a mean absolute increase in desired practice of 6.2% (95% confidence interval (CI) 4.1 to 8.2; moderate-certainty evidence) and an OR of 1.47 (95% CI 1.31 to 1.64; moderate-certainty evidence). Effects were similar for pre-planned subgroup analyses focused on prescribing and test-ordering outcomes. Lower baseline performance and increased number of co-interventions were both associated with larger intervention effects. Meta-regressions comparing the presence versus absence of specific A&F components to explore heterogeneity, accounting for baseline performance and number of co-interventions, suggested that A&F effects were greater with individual-recipient-level data rather than team-level data, comparing performance to top-peers or a benchmark, involving a local champion with whom the recipient had a relationship, using interactive modalities rather than just didactic or just written format, and with facilitation to support engagement, and action plans to improve performance. The meta-regressions did not find significant effects with the number of indicators in the audit, comparison to average performance of all peers, or co-development of action plans. Contrary to expectations, repeated delivery was associated with lower effect size. Direct comparisons from head-to-head trials support the use of peer-comparisons versus no comparison at all and the use of design elements in feedback that facilitate the identification and action of high-priority clinical items.

AUTHORS' CONCLUSIONS

A&F can be effective in improving professional practice, but effects vary in size. A&F is most often delivered along with co-interventions which can contribute additive effects. A&F may be most effective when designed to help recipients prioritise and take action on high-priority clinical issues and with the following characteristics: 1. targets important performance metrics where health professionals have substantial room for improvement (audit); 2. measures the individual recipient's practice, rather than their team or organisation (audit); 3. involves a local champion with an existing relationship with the recipient (feedback); 4. includes multiple, interactive modalities such as verbal and written (feedback); 5. compares performance to top peers or a benchmark (feedback); 6. facilitates engagement with the feedback (action); 7. features an actionable plan with specific advice for improvement (action). These conclusions require further confirmatory research; future research should focus on discerning ways to optimise the effectiveness of A&F interventions.

Effectiveness of interactive dashboards to optimise prescribing in primary care: a protocol for a systematic review

Introduction

Advances in therapeutics and healthcare have led to a growing population of individuals living with multimorbidity and polypharmacy making prescribing more challenging. Most prescribing occurs in primary care and General Practitioners (GPs) have expressed interest in comparative feedback on their prescribing performance. Clinical decision support systems (CDSS) and audit and feedback interventions have shown some impact, but changes are often short-lived. Interactive dashboards, a novel approach integrating CDSS and audit and feedback elements, offer longitudinal updated data outside clinical encounters. This systematic review aims to explore the effectiveness of interactive dashboards on prescribing-related outcomes in primary care and examine the characteristics of these dashboards.

Methods

This protocol was prospectively registered on PROSPERO (CRD42023481475) and reported in line with PRISMA-P guidelines. Searches of PubMed, EMBASE, Medline, PsychINFO, CINAHL, Scopus, the Cochrane Library, and grey literature, including trial registries were performed to identify interventional studies (randomised and non-randomised) that assess the effectiveness of interactive dashboards on prescribing related outcomes. The search will be supplemented by searching references of retrieved articles with the use of an automated citation chaser. Identified records will be screened independently by two reviewers and data from eligible studies extracted using a purposely developed data extraction tool. We will narratively summarise the intervention types and those associated with improvements in prescribing outcomes. A quantitative synthesis will be carried out if a sufficient number of homogenous studies are identified. Methodological quality will be assessed by two reviewers using the Cochrane Effective Practice and Organisation of Care risk assessment tool.

Discussion

This systematic review will explore the effect of interactive dashboards on prescribing related outcome measures in primary care and describe the characteristics of interactive dashboards. This research may inform future intervention development and shape policymaking particularly in the context of ongoing and planned developments in e-prescribing infrastructure.

Effectiveness of Electronic Quality Improvement Activities to Reduce Cardiovascular Disease Risk in People With Chronic Kidney Disease in General Practice: Cluster Randomized Trial With Active Control

BACKGROUND

Future Health Today (FHT) is a program integrated with electronic medical record (EMR) systems in general practice and comprises (1) a practice dashboard to identify people at risk of, or with, chronic disease who may benefit from intervention; (2) active clinical decision support (CDS) at the point of care; and (3) quality improvement activities. One module within FHT aims to facilitate cardiovascular disease (CVD) risk reduction in people with chronic kidney disease (CKD) through the recommendation of angiotensin-converting enzyme inhibitor inhibitors (ACEI), angiotensin receptor blockers (ARB), or statins according to Australian guidelines (defined as appropriate pharmacological therapy).

OBJECTIVE

This study aimed to determine if the FHT program increases the proportion of general practice patients with CKD receiving appropriate pharmacological therapy (statins alone, ACEI or ARB alone, or both) to reduce CVD risk at 12 months postrandomization compared with active control (primary outcome).

METHODS

General practices recruited through practice-based research networks in Victoria and Tasmania were randomly allocated 1:1 to the FHT CKD module or active control. The intervention was delivered to practices between October 4, 2021, and September 30, 2022. Data extracted from EMRs for eligible patients identified at baseline were used to evaluate the trial outcomes at the completion of the intervention period. The primary analysis used an intention-to-treat approach. The intervention effect for the primary outcome was estimated with a marginal logistic model using generalized estimating equations with robust SE.

RESULTS

Overall, of the 734 eligible patients from 19 intervention practices and 715 from 21 control practices, 82 (11.2%) and 70 (9.8%), respectively, had received appropriate pharmacological therapy (statins alone, ACEI or ARB alone, or both) at 12 months postintervention to reduce CVD risk, with an estimated between-trial group difference (Diff) of 2.0% (95% CI -1.6% to 5.7%) and odds ratio of 1.24 (95% CI 0.85 to 1.81; P=.26). Of the 470 intervention patients and 425 control patients that received a recommendation for statins, 61 (13%) and 38 (9%) were prescribed statins at follow-up (Diff 4.3%, 95% CI 0 to 8.6%; odds ratio 1.55, 95% CI 1.02 to 2.35; P=.04). There was no statistical evidence to support between-group differences in other secondary outcomes and general practice health care use.

CONCLUSIONS

FHT harnesses the data stored within EMRs to translate guidelines into practice through quality improvement activities and active clinical decision support. In this instance, it did not result in a difference in prescribing or clinical outcomes except for small changes in statin prescribing. This may relate to COVID-19-related disruptions, technical implementation challenges, and recruiting higher performing practices to the trial. A separate process evaluation will further explore factors impacting implementation and engagement with FHT.

TRIAL REGISTRATION

ACTRN12620000993998; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380119.

Development, Implementation, and Evaluation Methods for Dashboards in Health Care: Scoping Review

Background

Dashboards have become ubiquitous in health care settings, but to achieve their goals, they must be developed, implemented, and evaluated using methods that help ensure they meet the needs of end users and are suited to the barriers and facilitators of the local context.

Objective

This scoping review aimed to explore published literature on health care dashboards to characterize the methods used to identify factors affecting uptake, strategies used to increase dashboard uptake, and evaluation methods, as well as dashboard characteristics and context.

Methods

MEDLINE, Embase, Web of Science, and the Cochrane Library were searched from inception through July 2020. Studies were included if they described the development or evaluation of a health care dashboard with publication from 2018-2020. Clinical setting, purpose (categorized as clinical, administrative, or both), end user, design characteristics, methods used to identify factors affecting uptake, strategies to increase uptake, and evaluation methods were extracted.

Results

From 116 publications, we extracted data for 118 dashboards. Inpatient (45/118, 38.1%) and outpatient (42/118, 35.6%) settings were most common. Most dashboards had ≥2 stated purposes (84/118, 71.2%); of these, 54 of 118 (45.8%) were administrative, 43 of 118 (36.4%) were clinical, and 20 of 118 (16.9%) had both purposes. Most dashboards included frontline clinical staff as end users (97/118, 82.2%). To identify factors affecting dashboard uptake, half involved end users in the design process (59/118, 50%); fewer described formative usability testing (26/118, 22%) or use of any theory or framework to guide development, implementation, or evaluation (24/118, 20.3%). The most common strategies used to increase uptake included education (60/118, 50.8%); audit and feedback (59/118, 50%); and advisory boards (54/118, 45.8%). Evaluations of dashboards (84/118, 71.2%) were mostly quantitative (60/118, 50.8%), with fewer using only qualitative methods (6/118, 5.1%) or a combination of quantitative and qualitative methods (18/118, 15.2%).

Conclusions

Most dashboards forego steps during development to ensure they suit the needs of end users and the clinical context; qualitative evaluation-which can provide insight into ways to improve dashboard effectiveness-is uncommon. Education and audit and feedback are frequently used to increase uptake. These findings illustrate the need for promulgation of best practices in dashboard development and will be useful to dashboard planners.

Impact of visualising healthcare quality performance: a systematic review

OBJECTIVE

Performance visualisation tools are increasingly being applied in healthcare to enhance decision-making and improve quality of care. However, there is a lack of comprehensive synthesis of their overall effectiveness and the contextual factors that influence their success in different clinical settings. This study aims to provide a broad synthesis of visualisation interventions not limited to a specific department.

DESIGN

Systematic review.

DATA SOURCES

MEDLINE and Embase were searched until December 2022.

ELIGIBILITY CRITERIA

Randomised controlled trials (RCTs) and observational studies in English involving a visualisation intervention, either alone or as a core intervention, that reported quantitative outcomes including process and outcome indicators.

DATA EXTRACTION AND SYNTHESIS

Data on study characteristics, intervention characteristics, outcome measures and results were extracted. The quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation approach, and risk of bias was evaluated with Risk of Bias 2 for RCTs and Risk of Bias in Non-randomised Studies - of Interventions for non-randomised studies. RESULTS : Of the 12 studies included, 2 were RCTs and 10 were observational studies, including 1 before-after study and 1 interrupted time series study. Five studies (42%) were conducted in teaching hospital settings. Compared with the control group or baseline, 10 studies reported a statistically significant change in at least one of their outcome measures. A majority of the studies reported a positive impact, including prescription adherence (6/10), screening tests (3/10) and monitoring (3/10). Visualisation tool factors like type, clinical setting, workflow integration and clinician engagement, may have some influence on the effectiveness of the intervention, but no reliable evidence was identified.

CONCLUSION

Performance visualisation tools have the potential to improve clinical performance indicators. More studies with standardised outcome measures and integrating qualitative methods are needed to understand the contextual factors that influence the effectiveness of these interventions.

Impact of a Disease-Focused Electronic Health Record Dashboard on Clinical Staff Efficiency in Previsit Patient Review in an Ambulatory Pulmonary Hypertension Care Clinic

OBJECTIVES

 We aimed to improve the operational efficiency of clinical staff, including physicians and allied health professionals, in the previsit review of patients by implementing a disease-focused dashboard within the electronic health record system. The dashboard was tailored to the unique requirements of the clinic and patient population.

METHODS

 A prospective quality improvement study was conducted at an accredited pulmonary hypertension (PH) clinic within a large academic center, staffed by two full time physicians and two allied health professionals. Physicians' review time before and after implementation of the PH dashboard was measured using activity log data derived from an EHR database. The review time for clinic staff was measured through direct observation, with review method-either conventional or newly implemented dashboard-randomly assigned.

RESULTS

 Over the study period, the median number of patients reviewed by physicians per day increased slightly from 5.50 (interquartile range [IQR]: 1.35) before to 5.95 (IQR: 0.85) after the implementation of the PH dashboard (p = 0.535). The median review time for the physicians decreased with the use of the dashboard, from 7.0 minutes (IQR: 1.55) to 4.95 minutes (IQR: 1.35; p < 0.001). Based on the observed timing of 70 patient encounters among allied clinical staff, no significant difference was found for experienced members (4.65 minutes [IQR: 2.02] vs. 4.43 minutes [IQR: 0.69], p = 0.752), while inexperienced staff saw a significant reduction in review time after familiarization with the dashboard (5.06 minutes [IQR: 1.51] vs. 4.12 minutes [IQR: 1.99], p = 0.034). Subjective feedback highlighted the need for further optimization of the dashboard to align with the workflow of allied health staff to achieve similar efficiency benefits.

CONCLUSION

 A disease-focused dashboard significantly reduced physician previsit review time while that for clinic staff remained unchanged. Validation studies are necessary with our patient populations to explore further qualitative impacts on patient care efficiency and long-term benefits on workflow.

Effectiveness of de-implementation of low-value healthcare practices: an overview of systematic reviews

BACKGROUND

Reducing low-value care (LVC) is crucial to improve the quality of patient care while increasing the efficient use of scarce healthcare resources. Recently, strategies to de-implement LVC have been mapped against the Expert Recommendation for Implementing Change (ERIC) compilation of strategies. However, such strategies' effectiveness across different healthcare practices has not been addressed. This overview of systematic reviews aimed to investigate the effectiveness of de-implementation initiatives and specific ERIC strategy clusters.

METHODS

We searched MEDLINE (Ovid), Epistemonikos.org and Scopus (Elsevier) from 1 January 2010 to 17 April 2023 and used additional search strategies to identify relevant systematic reviews (SRs). Two reviewers independently screened abstracts and full texts against a priori-defined criteria, assessed the SR quality and extracted pre-specified data. We created harvest plots to display the results.

RESULTS

Of 46 included SRs, 27 focused on drug treatments, such as antibiotics or opioids, twelve on laboratory tests or diagnostic imaging and seven on other healthcare practices. In categorising de-implementation strategies, SR authors applied different techniques: creating self-developed strategies (n = 12), focussing on specific de-implementation strategies (n = 14) and using published taxonomies (n = 12). Overall, 15 SRs provided evidence for the effectiveness of de-implementation interventions to reduce antibiotic and opioid utilisation. Reduced utilisation, albeit inconsistently significant, was documented in the use of antipsychotics and benzodiazepines, as well as in laboratory tests and diagnostic imaging. Strategies within the adapt and tailor to context, develop stakeholder interrelationships, and change infrastructure and workflow ERIC clusters led to a consistent reduction in LVC practices.

CONCLUSION

De-implementation initiatives were effective in reducing medication usage, and inconsistent significant reductions were observed for LVC laboratory tests and imaging. Notably, de-implementation clusters such as change infrastructure and workflow and develop stakeholder interrelationships emerged as the most encouraging avenues. Additionally, we provided suggestions to enhance SR quality, emphasising adherence to guidelines for synthesising complex interventions, prioritising appropriateness of care outcomes, documenting the development process of de-implementation initiatives and ensuring consistent reporting of applied de-implementation strategies.

REGISTRATION

OSF Open Science Framework 5ruzw.

Clinical dashboard development and implementation to standardize data capture and reporting across health-system specialty pharmacies

PURPOSE

To describe the development and implementation of clinical dashboards to standardize data capturing and reporting across multiple partner health systems.

SUMMARY

Between July and September 2020, clinical dashboards were developed and implemented across multiple partner health-system specialty pharmacies (HSSPs) located throughout the United States. The dashboards were developed via collaboration between personnel involved in clinical subcommittees, clinical outcomes, data analytics, information technology, and clinical and central operations. Utilizing a cloud-scale business intelligence service, patient clinical data documented in a shared patient management system was utilized to create customizable dashboards that displayed patient-reported outcome measures, collected laboratory or test results, and completed pharmacist interventions. Separate dashboards were developed for several disease states and/or medication classes. Based on specialty pharmacy recommendations, medical literature, and clinical guidelines, internally developed disease-specific protocols defined data included in the dashboards and ensured consistent data collection amongst partner health systems. Having access to real-time clinical information allows health systems to closely monitor performance metrics, track patient outcomes, and identify operational gaps.

CONCLUSION

Accurately capturing and reporting clinical metrics using clinical dashboards can assist HSSPs in delivering high-quality care. Having access to clinical outcome measures allows HSSPs to better understand the impact of their services on patients' health and quality of life. Health systems can utilize this data to analyze trends and recognize areas of opportunity so that measures can be taken to improve patient care.

Compulsory Indications in Hospital Prescribing Software Tested with Antibacterial Prescriptions

The aim was to assess how making the indication field compulsory in our electronic prescribing system influenced free text documentation and to visualise prescriber behaviour. The indication field was made compulsory for seven antibacterial medicines. Text recorded in the indication field was manually classified as 'indication present', 'other text', 'rubbish text', or 'blank'. The proportion of prescriptions with an indication was compared for four weeks before and after the intervention. Indication provision increased from 10.6% to 72.4% (p<0.01) post-intervention. 'Other text' increased from 7.6% to 25.1% (p<0.01), and 'rubbish text' from 0.0% to 0.6% (p<0.01). Introducing the compulsory indication field increased indication documentation substantially with only a small increase in 'rubbish text'. An interactive report was developed using a live data extract to illustrate indication provision for all medicines prescribed at our tertiary hospital. The interactive report was validated and locally published to support audit and quality improvement projects.

What is the quantity, quality and type of systematic review evidence available to inform the optimal prescribing of statins and antihypertensives? A systematic umbrella review and evidence and gap map

OBJECTIVES

We aimed to map the systematic review evidence available to inform the optimal prescribing of statins and antihypertensive medication.

DESIGN

Systematic umbrella review and evidence and gap map (EGM).

DATA SOURCES

Eight bibliographic databases (Cochrane Database of Systematic Reviews, CINAHL, EMBASE, Health Management Information Consortium, MEDLINE ALL, PsycINFO, Conference Proceedings Citation Index-Science and Science Citation Index) were searched from 2010 to 11 August 2020. Update searches conducted in MEDLINE ALL 2 August 2022. We searched relevant websites and conducted backwards citation chasing.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

We sought systematic reviews of quantitative or qualitative research where adults 16 years+ were currently receiving, or being considered for, a prescription of statin or antihypertensive medication. Eligibility criteria were applied to the title and abstract and full text of each article independently by two reviewers.

DATA EXTRACTION AND SYNTHESIS

Quality appraisal was completed by one reviewer and checked by a second. Review characteristics were tabulated and incorporated into an EGM based on a patient care pathway. Patients with lived experience provided feedback on our research questions and EGM.

RESULTS

Eighty reviews were included within the EGM. The highest quantity of evidence focused on evaluating interventions to promote patient adherence to antihypertensive medication. Key gaps included a lack of reviews synthesising evidence on experiences of specific interventions to promote patient adherence or improve prescribing practice. The evidence was predominantly of low quality, limiting confidence in the findings from individual reviews.

CONCLUSIONS

This EGM provides an interactive, accessible format for policy developers, service commissioners and clinicians to view the systematic review evidence available relevant to optimising the prescribing of statin and antihypertensive medication. To address the paucity of high-quality research, future reviews should be conducted and reported according to existing guidelines and address the evidence gaps identified above.

Acceptance and use of a clinical decision support system in musculoskeletal pain disorders - the SupportPrim project

BACKGROUND

We have developed a clinical decision support system (CDSS) based on methods from artificial intelligence to support physiotherapists and patients in the decision-making process of managing musculoskeletal (MSK) pain disorders in primary care. The CDSS finds the most similar successful patients from the past to give treatment recommendations for a new patient. Using previous similar patients with successful outcomes to advise treatment moves management of MSK pain patients from one-size fits all recommendations to more individually tailored treatment. This study aimed to summarise the development and explore the acceptance and use of the CDSS for MSK pain patients.

METHODS

This qualitative study was carried out in the Norwegian physiotherapy primary healthcare sector between October and November 2020, ahead of a randomised controlled trial. We included four physiotherapists and three of their patients, in total 12 patients, with musculoskeletal pain in the neck, shoulder, back, hip, knee or complex pain. We conducted semi-structured telephone interviews with all participants. The interviews were analysed using the Framework Method.

RESULTS

Overall, both the physiotherapists and patients found the system acceptable and usable. Important findings from the analysis of the interviews were that the CDSS was valued as a preparatory and exploratory tool, facilitating the therapeutic relationship. However, the physiotherapists used the system mainly to support their previous and current practice rather than involving patients to a greater extent in decisions and learning from previous successful patients.

CONCLUSIONS

The CDSS was acceptable and usable to both the patients and physiotherapists. However, the system appeared not to considerably influence the physiotherapists' clinical reasoning and choice of treatment based on information from most similar successful patients. This could be due to a smaller than optimal number of previous patients in the CDSS or insufficient clinical implementation. Extensive training of physiotherapists should not be underestimated to build understanding and trust in CDSSs.

Post-implementation optimization of medication alerts in hospital computerized provider order entry systems: a scoping review

OBJECTIVES

A scoping review identified interventions for optimizing hospital medication alerts post-implementation, and characterized the methods used, the populations studied, and any effects of optimization.

MATERIALS AND METHODS

A structured search was undertaken in the MEDLINE and Embase databases, from inception to August 2023. Articles providing sufficient information to determine whether an intervention was conducted to optimize alerts were included in the analysis. Snowball analysis was conducted to identify additional studies.

RESULTS

Sixteen studies were identified. Most were based in the United States and used a wide range of clinical software. Many studies used inpatient cohorts and conducted more than one intervention during the trial period. Alert types studied included drug-drug interactions, drug dosage alerts, and drug allergy alerts. Six types of interventions were identified: alert inactivation, alert severity reclassification, information provision, use of contextual information, threshold adjustment, and encounter suppression. The majority of interventions decreased alert quantity and enhanced alert acceptance. Alert quantity decreased with alert inactivation by 1%-25.3%, and with alert severity reclassification by 1%-16.5% in 6 of 7 studies. Alert severity reclassification increased alert acceptance by 4.2%-50.2% and was associated with a 100% acceptance rate for high-severity alerts when implemented. Clinical errors reported in 4 studies were seen to remain stable or decrease.

DISCUSSION

Post-implementation medication optimization interventions have positive effects for clinicians when applied in a variety of settings. Less well reported are the impacts of these interventions on the clinical care of patients, and how endpoints such as alert quantity contribute to changes in clinician and pharmacist perceptions of alert fatigue.

CONCLUSION

Well conducted alert optimization can reduce alert fatigue by reducing overall alert quantity, improving clinical acceptance, and enhancing clinical utility.

Use of a Medication Safety Audit and Feedback Tool in the Emergency Department Is Affected by Prescribing Characteristics

BACKGROUND

The Enhancing Quality of Prescribing Practices for Older Veterans Discharged from the Emergency Department (EQUIPPED) program developed an audit and feedback health information technology (IT) solution with the intent to replace the in-person academic detailing service provided by the program. The EQUIPPED dashboard provides emergency department (ED) providers with a personalized view of their prescribing performance.

OBJECTIVES

Here, we analyze the association between ED provider characteristics and viewership of the EQUIPPED dashboard, adding insight into strategies for addressing barriers to initial use.

METHODS

We performed a retrospective analysis of EQUIPPED dashboard viewership among four Veterans Affairs (VA) EDs. We extracted quantitative data from user interaction logs to determine evidence of dashboard use. Provider characteristics and baseline potentially inappropriate medication (PIM) prescribing rate were extracted from the VA's Corporate Data Warehouse. Logistic regression was used to examine the association between dashboard use and provider characteristics.

RESULTS

A total of 82 providers were invited to receive audit and feedback via the EQUIPPED dashboard. Among invited providers, 40 (48.7%) had evidence of at least 1 dashboard view during the 1-year feedback period. Adjusted analyses suggest that providers with a higher baseline PIM prescribing rate were more likely to use the dashboard (odds ratio [OR]: 1.22; 95% confidence interval [CI]: 1.01-1.47). Furthermore, providers at ED site D were more likely to use the dashboard in comparison to the other sites (OR: 9.99; 95% CI: 1.72-58.04) and reportedly had the highest site-level baseline PIM rate.

CONCLUSION

Providers with lower PIM prescribing rates (i.e., <5%) receive communication from an integrated dashboard reminder system that they are "optimal prescribers" which may have discouraged initial attempts to view the dashboard. Site D had the highest baseline PIM rate, but further qualitative investigation is warranted to better understand why site D had the greatest users of the dashboard.

The Effectiveness of Interactive Dashboards to Optimise Antibiotic Prescribing in Primary Care: A Systematic Review

Governments and healthcare organisations collect data on antibiotic prescribing (AP) for surveillance. This data can support tools for visualisations and feedback to GPs using dashboards that may prompt a change in prescribing behaviour. The objective of this systematic review was to assess the effectiveness of interactive dashboards to optimise AP in primary care. Six electronic databases were searched for relevant studies up to August 2022. A narrative synthesis of findings was conducted to evaluate the intervention processes and results. Two independent reviewers assessed the relevance, risk of bias and quality of the evidence. A total of ten studies were included (eight RCTs and two non-RCTs). Overall, seven studies showed a slight reduction in AP. However, this reduction in AP when offering a dashboard may not in itself result in reductions but only when combined with educational components, public commitment or behavioural strategies. Only one study recorded dashboard engagement and showed a difference of 10% (95% CI 5% to 15%) between intervention and control. None of the studies reported on the development, pilot or implementation of dashboards or the involvement of stakeholders in design and testing. Interactive dashboards may reduce AP in primary care but most likely only when combined with other educational or behavioural intervention strategies.