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. 2024 Jul 3;7:44. [Version 1] doi: 10.12688/hrbopenres.13909.1

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

Patrick Moynagh 1,a, Áine Mannion 1, Ashley Wei 1, Barbara Clyne 2, Frank Moriarty 3, Caroline McCarthy 1
PMCID: PMC11808840  PMID: 39931386

Abstract

Introduction

Advances in therapeutics and healthcare have led to a growing population of older people 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.

Keywords: Interactive dashboards, potentially inappropriate prescribing, audit and feedback, preventable drug related morbidity, polypharmacy

Introduction

Advances in therapeutics and chronic disease management mean there is a growing population of older people living with multimorbidity and polypharmacy 1 . While polypharmacy is often necessary and appropriate, it is associated with adverse events, and it is estimated that almost 9% of emergency department admissions in older people are due to preventable drug related morbidity (PDRM) 2 . Adverse drug reactions (ADRs) are the third most common type of reported adverse event in the Irish health care system 3 . A recently published prospective cohort study estimated that one in four older people experienced an ADR 4 . Most prescribing occurs in primary care 5 and qualitative data from general practitioners (GPs) indicates prescribing has become more challenging, particularly for patients with multimorbidity and polypharmacy 6 . Irish GPs in a nationwide cluster randomised controlled trial (RCT) evaluating a deprescribing intervention, viewed participation as an opportunity to review their prescribing practices and were interested in getting performance feedback 7 .

Primary care prescribers receive feedback on their prescribing through various means, such as clinical decision support systems (CDSS) and audit and feedback. CDSS are real-time, electronic tools that provide prescribers with knowledge and person-specific information at the point of care, which supplement decision-making processes 8 . CDSS are embedded in clinical software and typically appear as “alerts” for the prescriber. However problems such as interrupting work flow and too many alerts can cause “alert fatigue” resulting in the user ignoring recommendations 9 . Evidence suggests CDSS probably have a small effect on practitioner performance but the effect on patient reported and clinical outcomes is less clear 10, 11 . Audit and feedback involves retrospectively reviewing clinical performance or practices, enabling peer comparison and social norm feedback and it has been identified as an effective strategy for improving prescribing 12, 13 . However, audit and feedback data typically provide a snapshot at one time point and therefore improvements may be temporary 14 . Interactive dashboards combine elements of both CDSS and audit and feedback; the data is longitudinal and updated on an ongoing basis but is outside the clinical encounter. The prescriber can visualise their data graphically and the data can be manipulated and interacted with through various interactive elements, identifying both time trends and comparisons with peers 15 .

Medicines optimisation interventions that target a heterogonous population often use prescribing-related outcome measures, as clinical outcome’s such as ADRs or unplanned hospital admissions may take time to manifest or reach measurable levels 16 . Various tools have been developed to assess the quality of prescribing and broadly speaking these can be categorised into two groups: explicit tools and implicit tools. A systematic review published in 2014 identified 46 different explicit and implicit tools that have been developed to assess medication appropriateness 17 . Examples of explicit measures of medication appropriateness include the United States (US) Beers criteria 18 and the European Screening Tool for Older People’s potentially inappropriate Prescriptions (STOPP) criteria 19 . Multiple observational studies have demonstrated an association between potentially inappropriate prescribing, measured using these indicators, and clinical outcomes such as increased emergency admissions, ADRs and reduced health related quality of life 20– 22 . In addition, specific research groups have identified high-risk and low-value prescribing criteria and evaluated the effectiveness of interventions utilising these criteria 23– 25 . For example the Data-driven Quality Improvement in Primary Care (DQIP) intervention included an informatics tool that provided weekly updates of selected high risk prescribing indicators to clinicians, and facilitated medication review by graphically displaying relevant drug history data 26 . The pharmacist-led information technology intervention (PINCER) was effective at reducing hazardous prescribing, however the effect may have been temporary as the original intervention provided a snapshot of data from the electronic health record 23 . More recently an interactive dashboard utilizing the PINCER criteria has been developed whereby the user can track their performance across different criteria compared to other practices and over time 27 , and this intervention resulted in a reduction of potentially hazardous prescribing by 27.9% (95% CI 20.3% to 36.8%, p < 0.001) 28 .

This systematic review aims to explore the effectiveness of interactive dashboards on prescribing related outcomes in primary care and to describe the characteristics of these interventions with the ultimate aim of informing future intervention development and e-prescribing infrastructure.

Methods

This systematic review was prospectively registered on PROSPERO (CRD42023481475), it will be conducted in line with guidance set out in the Cochrane Handbook for Systematic Reviews of Interventions 29 , and reported in adherence to PRISMA-P reporting guidelines 30 . At the time of writing, the search strategy has been finalised, title and abstract screening has been completed, and full text review is currently in process.

Search strategy

An information specialist in the host institution’s library with extensive experience in supporting systematic reviews was involved in developing search strategies. A systematic literature search was conducted and included the following databases; PubMed, EMBASE, MEDLINE (OVID), PsycINFO (EBSCOhost), CINAHL (EBSCOhost), Scopus and the Cochrane Library (OVID).

A search of grey literature was conducted by running keyword searches in OpenGrey, CADTH Grey Matters and web-based clinical trial registries. The search was supplemented by searching references of retrieved articles with the use of an automated citation chaser 31 . No restrictions were placed on language or year of publication. Search terms included “interactive dashboard” and the medical subject heading (MeSH) “clinical audit”, “medical audit”, “benchmarking” and “feedback” and keywords to capture concepts related to providing prescribers with feedback, such as “electronic health record” and “alerts”. See supplementary file 1 for electronic search reports, including the full search terms.

Study selection

Identified records were uploaded to Covidence systematic review software and de-duplicated. Reviewers were blinded to minimise potential bias and ensure impartial evaluation of the included studies. Two reviewers independently read the titles/abstracts of identified records and eliminated studies not meeting inclusion criteria. The full text of the remaining studies will be reviewed again by two reviewers who will assess their suitability for inclusion. Disagreement will be resolved through discussion with the wider study group. Eligibility criteria are described in Table 1. All interventional designs will be included including randomised controlled trials (RCTs) (e.g. cluster RCTs, step wedged RCTs and individually randomised RCTs) and non-randomised interventional studies (e.g. interrupted time series design and controlled before and after studies) 32 .

Table 1. Study eligibility criteria.

Criteria Inclusion Exclusion
Population Primary care prescribers (e.g. General Practitioners, non-medical
prescribers based in primary care such as pharmacists and
advanced nurse practitioners)		 Primary care prescribers working
in a secondary care setting.
Dentists
Intervention An interactive dashboard designed to provide feedback on
prescribing data to prescribers and including the following
characteristics:
Visual display of data: Data is presented in the form of graphs or
tables.
Interactivity: Allows direct manipulation with visual analytical
tools or provides multiple parameters from the dataset,
accessible online or via email.
Real-time data: Offers real-time or relatively contemporaneous
data, no older than one year.
Frequent data feedback: Provides data feedback more than
once.
Comparative analysis: Compares data to peers or set
standards.		 Simple CDSS interventions
Audit and feedback interventions
that do not give longitudinal and
ongoing feedback
Comparator Usual care
Outcomes Primary Outcome: Prescribing related outcome measures such
as implicit/explicit criteria, high-risk or low-value criteria or where
relevant prescribing rates (e.g. where a higher rate may reflect
lower quality such as benzodiazepine or opioid use).		 No prescribing outcomes
measured
Setting Primary care (Family practice, general practice) Studies focused on specialist
clinics, nursing homes, hospital
based, dental surgeries.
Study design Interventional studies both randomised and non-randomised
designs (e.g. Randomised controlled trials, non-randomised
controlled trials, controlled before and after studies and
interrupted time series)		 Systematic reviews, descriptive
study designs (e.g., case reports,
uncontrolled before and after
studies).
Letters, commentaries, editorials.
Publication
language 		No language restriction
Dates of
publication 		No year limitation
Open in a new tab

Data extraction and management

Two review authors will independently extract data using a purposely developed data extraction tool in Covidence, developed with use of the Template for Intervention Description and Replication (TIDieR) checklist 33 . Extracted data will include study details (e.g. setting, design), population (e.g. GPs), intervention details, comparison group, outcome measures and results. Table 2 outlines example data that may be extracted to describe the intervention using the TIDieR checklist. We will attempt to contact the lead authors of primary studies to locate missing data. Discrepancies will be resolved through discussion and consensus between two reviewers with consultation with a third reviewer if necessary.

Table 2. Intervention data extraction using adapted TIDieR checklist.

Description Detail Example Intervention
1. Brief name Provide a concise name
for the intervention.		 "Prescribing Dashboard"
2. Why Rationale or goal of the
intervention.		 To improve prescribing quality by providing GPs with real-time
data on high-risk prescribing for comparative benchmarking
against peers.
3. What (materials
and procedures) 		Materials used and
procedures for the
intervention.		 Web-based or practice software-embedded dashboard displaying
prescribing quality metrics. GPs access their prescribing metrics
on the dashboard at their convenience for self-assessment and
benchmarking.
4. Who provided Description of the
intervention providers.		 Developed by healthcare IT specialists in collaboration with
clinicians.
5. How and where Modes and location of
delivery.		 Delivered via a secure web-based platform or embedded within
practice management software, accessible in primary care clinics
and offices on various devices.
6. When and how
much 		Frequency and duration. Dashboards updated monthly with new prescribing data to
reflect recent prescribing practices.
7. Tailoring Personalization of the
intervention.		 Dashboard data tailored to individual prescriber level or practice
level, depending on the setting and objectives of the feedback.
8. Modifications Changes made during the
study.		 N/A or describe any modifications based on user feedback or
technological updates.
9. How well (planned
and actual) 		Assessment of adherence
or fidelity and actual
engagement.		 Usage analytics to monitor frequency of dashboard interactions,
time spent on the dashboard, and engagement with specific
metrics.
Open in a new tab

Methodological quality assessment. Methodological Quality Assessment will be performed by two reviewers using the Cochrane Effective Practice and Organisation of Care (EPOC) risk of bias tool 32 . Discrepancies will be resolved through discussion and consensus between two reviewers with consultation with a third reviewer if necessary.

Analysis. We will narratively summarise the intervention types and those associated with improvements in prescribing outcomes. Additionally, we will narratively describe the prescribing related outcomes used by included studies. A quantitative synthesis (i.e. meta-analysis) will be considered if a sufficient number of homogenous studies are identified which examine the same outcome.

If a meta-analysis is conducted, a random-effects model will likely be appropriate given the review question. We will not combine results from different study designs and interventions in an overall meta-analysis. Results will be presented in separate subgroups in the same forest plot (with no summary effect estimate) Heterogeneity will be assessed through a visual assessment and a logic-based assessment of study differences. We will conduct a standard Q-test statistic for heterogeneity and evaluate the heterogeneity via the I² statistic, which can be interpreted as the proportion of variability in the meta-analysis due to between study heterogeneity. Funnel plots will explore publication bias if more than ten studies are identified. These plots will help assess the relationship between effect size and study precision.

All missing outcome data for included studies will be recorded on the data extraction form and reported in the risk of bias table. If there is insufficient information on the primary outcomes (due to inability to contact authors, unavailable data), these studies will be reported separately. Reasons for exclusion will be described and included in a supplementary table. As this systematic review is assessing an intervention targeted at primary care prescribers, included studies may have aggregate level patient data, thus it may not be possible to conduct population level subgroup analysis.

Discussion

With a growing population of older people living with multimorbidity and polypharmacy, prescribing has become more challenging with a greater propensity for adverse outcomes 6, 16 . PDRM has significant economic and social consequences at both the individual patient-level and for the wider healthcare system 34 , it is thus vital to develop interventions to support safe and effective prescribing.

Interactive dashboards have become increasingly prevalent in healthcare settings, offering a versatile tool for visualising clinical data across various levels ranging from organisational, physician to patient-focused applications. They have the potential to enhance patient care and safety by providing contemporaneous feedback on potentially suboptimal treatment or care when integrated into clinical record systems 35 .

Interactive dashboards have demonstrated varied effects on prescribing-related outcomes, such as antibiotic prescribing rates and appropriate statin use 36, 37 . Current evidence suggests they are most effective when combined with additional strategies which include education and/or behavioural components 37 . Given the limited number of eligible studies identified in previous reviews, the present systematic review will not restrict its focus to specific medication classes.

Strengths and limitations

This research will be conducted in line with Cochrane guidance. To increase transparency and reduce the risk of selective reporting this systematic review has been prospectively registered on PROSPERO, and will involve a search of the grey literature and trial registries to reduce the risk of publication bias. Title and abstract screening, full-text review, and methodological quality assessment will be performed by two reviewers working independently and blinded to each other's assessments, thereby minimising the potential for bias and errors. Excluding studies in progress but not yet published may lead to publication bias.

Potential implications for future research, policy and clinical practice

This research may identify gaps in the current literature and inform future intervention development with respect to how prescribing data may be fed back to prescribers. The findings from this review may inform policies aimed at enhancing or expanding the infrastructure necessary for effective e-prescribing, particularly those focused on optimising prescribing behaviours. In addition, this review will provide prescribers with a synthesised understanding of how interactive dashboards have been used, highlighting their potential benefits and limitations. This may lead to more informed decisions in regards adopting or optimising use of such tools in clinical practice, with the ultimate aim of improving patient safety and reducing medication related harm.

Acknowledgments

Killian Walsh, Information Specialist, RCSI library; Mobeena Naz, Medical Student, RCSI.

Funding Statement

PM is funded by an ICGP Post CSCST Fellowship award. CMC is funded by a HRB Clinician Scientist Fellowship Award (CSF- 2023-012).

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

[version 1; peer review: 1 approved, 3 approved with reservations]

Data availability

Underlying data

No underlying data are associated with this article.

Extended data

This project contains the following extended data:

figshare: Supplementary file 1: Electronic search reports https://doi.org/10.6084/m9.figshare.25859506.v1 38

fighare: Supplementary file 2: PRISMA-P 2015 checklist https://doi.org/10.6084/m9.figshare.25887193.v1 39

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0) ( https://creativecommons.org/licenses/by/4.0/)

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HRB Open Res. 2024 Dec 28. doi: 10.21956/hrbopenres.15255.r43666

Reviewer response for version 1

Rainer Tan 1, Nina Emery 1

The authors highlight the challenge of appropriate prescribing for patients with multimorbidity and polypharmacy, which is a growing problem in the context of populational aging. More specifically, they focus on primary care, where the majority of prescribing takes place. Interactive dashboards are identified as a potential solution to help primary healthcare workers improve prescribing safety. They propose a protocol for a systematic review with the aim of evaluating the effectiveness of interactive dashboards on prescribing outcomes. Overall, the protocol is clear, well-developed, and aligned with the PRISMA-P and Cochrane guidelines.

However, some changes could improve overall clarity and completeness:

Major issue:

  1. In line with section 13 of PRISMA-P guidelines, more details on the different outcomes of interest, the type of measures and their prioritization should be provided. It is unclear if the outcomes listed in table 1 are an exhaustive list of the outcome of interest in the review, or the outcome upon which studies will be selected for the review. Moreover, in line with Cochrane guidelines ( MECIR Box  3.2.d C17), authors should define which measure of prescribing quality will be prioritized, if multiple measures are reported (eg, explicit vs implicit criteria) or provide a rationale if not possible. Finally clarifying the difference between implicit and explicit tools would add clarity.

Minor issues:

  1. Last paragraph of introduction: the concept of “effectiveness” of interactive dashboards on prescribing related outcomes would benefit from further clarification. How is effectiveness defined and measured? Is it, for example, the dashboards’ effect on the rate of prescriptions aligned with Beers or STOPP criteria?

  2. Study selection: In line with section 8 of PRISMA-P guidelines, the authors report the study characteristics to be used as eligibility criteria for the review, in a clear manner as part of a table (table 1), but the described population of interest would benefit from more details. Indeed, in the description of the population of interest, the authors mention primary care prescribers. However, there is no mention of the type of patients targeted by the intervention. Given the focus given in the introduction on the elderly population, and given that tools such as Beers and STOPP are designed for elderly patients, does the review focus on geriatric patients? Children? Studies looking at patients with particular illnesses (e.g. COVID-19, HIV)?

  3. Study selection: The authors state that full text will be reviewed by two reviewers, however not completely clear whether this will be done independently or subsequently.

  4. Analysis: In line with PRISMA-P section 15a, the protocol is clear that a meta-analysis will be performed if there are sufficient studies examining the same outcome, but it is unclear what other criteria will be used to define «homogenous studies».

  5. Analysis: Will any subgroup or sensitivity analyses be performed? If so, they should ideally be described.

Is the study design appropriate for the research question?

Partly

Is the rationale for, and objectives of, the study clearly described?

Partly

Are sufficient details of the methods provided to allow replication by others?

Yes

Are the datasets clearly presented in a useable and accessible format?

Yes

Reviewer Expertise:

Digital Health, antibiotic stewardship

We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above.

HRB Open Res. 2024 Dec 26. doi: 10.21956/hrbopenres.15255.r43664

Reviewer response for version 1

Heike Vornhagen 1

A potentially interesting study. However, it is unclear to me if the focus is on prescribing in primary care generally as indicated in the title, or on older people and multi-morbidity / polypharmacy. This confusion is carried forward into the introduction which starts with CDM and older people but then exclusively focuses on dashboards, feedback and prescribing with no further mention of older people. I would suggest to clarify the focus of the systematic review and update the article accordingly.

This paper describes a protocol for carrying out a systematic review of interactive dashboards and prescribing in primary care. It gives an overview of the context for the research and details the search strategy. However, the context veers between care of older people especially regarding multi-morbidity and polypharmacy and the use of interactive dashboards to improve prescribing. It is not clear if there will be a link established in the full review, however, as the search terms do not include specificities regarding elder care, it seems unlikely.

My main concerns are as follows:

1) Lack of clarity

What is the focus of this review? Is it prescribing generally of prescribing in the context of elder care? This is not clear.

2) Search strategy

This lack of clarity impacts the search strategy. While search terms are sufficient for general prescribing, there are no specific search terms (or inclusion / exclusion criteria) regarding elder care.

3) Overall

I am slightly confused as the systematic review is already available as a preprint with a focus solely on prescribing in primary care (ie no mention of older people). So it seems that the study purpose has been changed but the protocol was not updated.

These are the reasons why I approve this protocol with reservations as this issue needs to be clarified.

Is the study design appropriate for the research question?

Yes

Is the rationale for, and objectives of, the study clearly described?

Partly

Are sufficient details of the methods provided to allow replication by others?

Yes

Are the datasets clearly presented in a useable and accessible format?

Not applicable

Reviewer Expertise:

Dashboards, Data Visualisation, UX Design

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

HRB Open Res. 2024 Dec 26. doi: 10.21956/hrbopenres.15255.r41536

Reviewer response for version 1

Denis O'Mahony 1

This study protocol manuscript deals with a highly important issue i.e. preventable drug-related morbidity (PDRM) and the various interactive dashboard methods used in primary care settings. The protocol lays out clearly how the systematic review (SR) will be conducted, using an appropriate range of publicly available databases as well as other important sources for a thorough SR, including grey literature, trial registries and conference abstracts in order to avoid bias. The SR has already been registered on PROSPERO, the most internationally well-known registry for systematic reviews.

The protocol is clearly written and concise and includes all of the elements that one would expect to find in a high-quality SR. I can find no intrinsic weaknesses in the protocol as described in this manuscript.

Is the study design appropriate for the research question?

Yes

Is the rationale for, and objectives of, the study clearly described?

Yes

Are sufficient details of the methods provided to allow replication by others?

Yes

Are the datasets clearly presented in a useable and accessible format?

Not applicable

Reviewer Expertise:

Polypharmacy in older people; clinical pharmacology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

HRB Open Res. 2024 Dec 9. doi: 10.21956/hrbopenres.15255.r43659

Reviewer response for version 1

Augustino Mwogosi 1

Summary of the Article

The article outlines a systematic review protocol aimed at evaluating the effectiveness of interactive dashboards in optimizing prescribing in primary care. It also seeks to explore the characteristics of these dashboards to inform future developments in e-prescribing infrastructure. The protocol emphasizes the challenges of polypharmacy, multimorbidity, and inappropriate prescribing and positions interactive dashboards as innovative tools that combine real-time data visualization with feedback to improve prescribing practices.

The systematic review will include interventional studies assessing interactive dashboards in primary care. The methods align with PRISMA-P guidelines, with a detailed plan for study selection, data extraction, and quality assessment. A narrative synthesis and potential meta-analysis are proposed to evaluate the findings comprehensively.

Detailed Review

1. Is the rationale for, and objectives of, the study clearly described?

Answer: Partly

Evaluation:

The rationale for the study is generally clear, highlighting the importance of addressing challenges in prescribing through innovative interventions like interactive dashboards. The protocol contextualizes the need for such tools by discussing their potential to integrate the strengths of CDSS and audit-and-feedback mechanisms.

However, the link between the stated challenges (e.g., alert fatigue in CDSS, the transient impact of traditional feedback mechanisms) and the systematic review’s ability to address these gaps could be more explicitly developed.

The objectives assessing the effectiveness and characteristics of interactive dashboards are clear but could benefit from additional specificity. For example, defining "effectiveness" and detailing how the characteristics will be described would strengthen the objectives.

Suggestions for Improvement:

  1. Explicitly articulate how the systematic review will address the gaps identified in the rationale.

  2. Provide operational definitions for key terms like "effectiveness" and clarify the scope of "characteristics" being explored (e.g., usability, integration, user engagement).

4. Are the datasets presented in a usable and accessible format?

Answer: Partly

Evaluation:

While the protocol describes plans for data extraction and analysis, it does not provide examples or visualizations of how datasets or results will be presented in the review. Accessibility of data is implied but not explicitly addressed.

Suggestions for Improvement:

  1. Include mock-up tables or figures to illustrate how extracted data or synthesized results will be presented.

  2. Provide details on data-sharing plans, such as repositories or appendices where datasets will be made accessible.

General Comments

Strengths

  • The protocol is methodologically robust, adhering to Cochrane and PRISMA-P standards.

  • The systematic data extraction, quality assessment, and synthesis approach ensures reliability and transparency.

  • The rationale addresses a significant issue in primary care, with interactive dashboards positioned as a promising solution.

Limitations

  1. The rationale and objectives could be more explicitly linked to the research question.

  2. The description of outcomes (e.g., "effectiveness") lacks specificity.

  3. Plans for managing heterogeneity in the meta-analysis need to be more detailed.

  4. Accessibility of data and presentation formats could be better clarified.

Recommendations to Make the Article Scientifically Sound

Essential Points:

  1. link the rationale to the research question and specify how the review will address identified gaps.

  2. Define key terms such as "effectiveness" and elaborate on how the characteristics of dashboards will be analysed.

  3. Expand on handling heterogeneity in studies, including thresholds and criteria for conducting a meta-analysis.

Optional Enhancements:

  1. Include mock-up tables or figures to illustrate how data will be presented.

  2. Provide a clear plan for making datasets accessible through repositories or supplementary materials.

Is the study design appropriate for the research question?

Yes

Is the rationale for, and objectives of, the study clearly described?

Partly

Are sufficient details of the methods provided to allow replication by others?

Yes

Are the datasets clearly presented in a useable and accessible format?

Partly

Reviewer Expertise:

The core areas of research include Health Informatics and Decision-Support Systems, which focus on optimising the implementation and use of Electronic Health Record (EHR) systems to improve clinical decision-making in primary healthcare. Research also explores the integration of generative AI in EHR systems, particularly in low-resource settings.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Data Citations

    1. Supplementary file 1 Electronic search reports. figshare. 2024. 10.6084/m9.figshare.25859506.v1 [DOI]
    2. Supplementary file 2 PRISMA-P 2015 checklist. figshare. 2024. 10.6084/m9.figshare.25887193.v1 [DOI]

    Data Availability Statement

    Underlying data

    No underlying data are associated with this article.

    Extended data

    This project contains the following extended data:

    figshare: Supplementary file 1: Electronic search reports https://doi.org/10.6084/m9.figshare.25859506.v1 38

    fighare: Supplementary file 2: PRISMA-P 2015 checklist https://doi.org/10.6084/m9.figshare.25887193.v1 39

    Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0) ( https://creativecommons.org/licenses/by/4.0/)

    Articles from HRB Open Research are provided here courtesy of Health Research Board Ireland

    RESOURCES