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

Patrick Moynagh; Áine Mannion; Ashley Wei; Barbara Clyne; Frank Moriarty; Caroline McCarthy

doi:10.12688/hrbopenres.13909.2

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Study Protocol

Revised

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

[version 2; peer review: 4 approved]

Patrick Moynagh ¹, Áine Mannion¹, Ashley Wei¹, Barbara Clyne², Frank Moriarty³, Caroline McCarthy¹

Patrick Moynagh ¹, Áine Mannion¹, [...] Ashley Wei¹, Barbara Clyne², Frank Moriarty³, Caroline McCarthy¹

PUBLISHED 19 Feb 2025

Author details Author details

¹ Department of General Practice, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland
² Department of Public Health & Epidemiology, School of Population Health, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland
³ School of Pharmacy and Biomolecular Sciences, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland

Patrick Moynagh
Roles: Conceptualization, Methodology, Project Administration, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Áine Mannion
Roles: Writing – Review & Editing

Ashley Wei
Roles: Writing – Review & Editing

Barbara Clyne
Roles: Methodology, Writing – Review & Editing

Frank Moriarty
Roles: Methodology, Writing – Review & Editing

Caroline McCarthy
Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

Abstract

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.

Keywords

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

Corresponding author: Patrick Moynagh

Competing interests: No competing interests were disclosed.

Grant information: 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.

Copyright: © 2025 Moynagh P et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Moynagh P, Mannion Á, Wei A et al. Effectiveness of interactive dashboards to optimise prescribing in primary care: a protocol for a systematic review [version 2; peer review: 4 approved]. HRB Open Res 2025, 7:44 (https://doi.org/10.12688/hrbopenres.13909.2) First published: 03 Jul 2024, 7:44 (https://doi.org/10.12688/hrbopenres.13909.1) Latest published: 19 Feb 2025, 7:44 (https://doi.org/10.12688/hrbopenres.13909.2)

Revised Amendments from Version 1

In response to reviewer feedback, some revisions have been made to the manuscript. The introduction section has been updated to strengthen the rationale for this systematic review, additional references have been added. Key terms such as implicit and explicit medication appropriateness tools and effectiveness have been elaborated on. Clarity on the population of interest has also been provided, with minor changes made to table 1 to reflect this. In the study selection section, the fact that full text articles will be screened independently has been clarified. The analysis section has been updated to clarify under which criteria a meta-analysis will be performed. Information around sensitivity analyses has been added.

See the authors' detailed response to the review by Rainer Tan and Nina Emery
See the authors' detailed response to the review by Heike Vornhagen
See the authors' detailed response to the review by Denis O'Mahony
See the authors' detailed response to the review by Augustino Mwogosi

Introduction

Reducing medication-related harm has been identified within the World Health Organisation’s (WHO) third global patient safety challenge in 2017, ‘Medication without Harm’¹. In the United States alone it is estimated that adverse drug reactions represent the third leading cause of death². While English data shows 1.5% of hospital admissions are as a result of an Adverse Drug Reaction (ADR)³. In Ireland ADRs are the third most common type of reported adverse event in the health care system⁴. Most prescribing occurs in primary care⁵ and qualitative data from general practitioners (GPs) indicates prescribing has become more challenging, particularly for patients with multimorbidity and polypharmacy⁶. 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 feedback⁷. There is evidence that audit and feedback is effective in improving professional behaviour and that ongoing or repeated feedback is more effective^8–10.

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 processes¹¹. 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 recommendations¹². Evidence suggests CDSS probably have a small effect on practitioner performance but the effect on patient reported and clinical outcomes is less clear^13,14. 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^15,16. However, audit and feedback data typically provide a snapshot at one time point and therefore improvements may be temporary¹⁷. 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 peers¹⁸.

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 levels¹⁹. 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. Explicit tools are focused on drugs, measuring how they fit pre-defined criteria whereas implicit tools are based on clinical guidelines and clinical evaluation criteria²⁰. Examples of explicit measures of medication appropriateness include the United States (US) Beers criteria²¹ and the European Screening Tool for Older People’s potentially inappropriate Prescriptions (STOPP) criteria²². 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^23–25. In addition, specific research groups have identified high-risk and low-value prescribing criteria and evaluated the effectiveness of interventions utilising these criteria^26–28. 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 data²⁹. 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 record²⁶. 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 time³⁰, and this intervention resulted in a reduction of potentially hazardous prescribing by 27.9% (95% CI 20.3% to 36.8%, p < 0.001)³¹.

In line with national and international campaigns to reduce medication-related harm and recent developments in e-prescribing infrastructure, this systematic review aims to explore the effectiveness of interactive dashboards in improving prescribing-related outcomes in primary care. These outcomes include potentially inappropriate prescribing (PIP) and drug utilisation rates (e.g., reducing prescribing volumes where lower rates are preferable or optimising prescribing patterns in line with guidelines). Additionally, it aims to describe the characteristics of these interventions to inform 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 Interventions³², and reported in adherence to PRISMA-P reporting guidelines³³. 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 chaser³⁴. 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 independently 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 outcome measures detailed in Table 1 will be considered, we do anticipate however, based on scoping searches, that explicit criteria and utilisation patterns will feature more prominently. 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)³⁵.

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). Patients within primary care settings (No restrictions on patient characteristics)	Primary care prescribers working in a secondary care setting. Dentists Patients in other settings not considered a primary care setting.
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

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) checklist³⁶. 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.

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 tool³⁵. 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. We defined homogeneity based on several key factors, (i) study design (ii) outcome definition (iii) level of measurement.

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. As we believe that heterogeneity exists regardless of whether we happen to detect it using a statistical test, we will focus less on significance tests and instead further investigate the sources and impact of the heterogeneity (e.g Risk of Bias) through sensitivity analysis. In the event of substantial clinical or methodological heterogeneity, we will not report study results as pooled effect estimates and will synthesise study findings using the approach suggested in the Synthesis Without Meta-analysis (SWIM) guidance³⁷.”

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 individuals living with multimorbidity and polypharmacy, prescribing has become more challenging with a greater propensity for adverse outcomes^6,19. Preventable drug related morbidity (PDRM) has significant economic and social consequences at both the individual patient-level and for the wider healthcare system³⁸, 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 systems³⁹.

Interactive dashboards have demonstrated varied effects on prescribing-related outcomes, such as antibiotic prescribing rates and appropriate statin use^40,41. Current evidence suggests they are most effective when combined with additional strategies which include education and/or behavioural components⁴¹. 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.

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.v1⁴²

fighare: Supplementary file 2: PRISMA-P 2015 checklist https://doi.org/10.6084/m9.figshare.25887193.v1⁴³

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/)

Acknowledgments

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

Faculty Opinions recommended

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Version 2

VERSION 2 PUBLISHED 03 Jul 2024

Author details Author details

Patrick Moynagh
Roles: Conceptualization, Methodology, Project Administration, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Áine Mannion
Roles: Writing – Review & Editing

Ashley Wei
Roles: Writing – Review & Editing

Barbara Clyne
Roles: Methodology, Writing – Review & Editing

Frank Moriarty
Roles: Methodology, Writing – Review & Editing

Caroline McCarthy
Roles: Conceptualization, Funding Acquisition, Methodology, Project Administration, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

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.

Article Versions (2)

version 2

Revised

Published: 19 Feb 2025, 7:44

https://doi.org/10.12688/hrbopenres.13909.2

version 1

Published: 03 Jul 2024, 7:44

https://doi.org/10.12688/hrbopenres.13909.1

© 2025 Moynagh P et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Moynagh P, Mannion Á, Wei A et al. Effectiveness of interactive dashboards to optimise prescribing in primary care: a protocol for a systematic review [version 2; peer review: 4 approved]. HRB Open Res 2025, 7:44 (https://doi.org/10.12688/hrbopenres.13909.2)

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Version 2

VERSION 2

PUBLISHED 19 Feb 2025

Revised

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Reviewer Report 27 Feb 2025

Rainer Tan, University of Lausanne, Lausanne, Switzerland

Nina Emery, Unisante Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland

Approved

https://doi.org/10.21956/hrbopenres.15476.r45918

We thank the authors for their answers. The revised manuscript shows more comprehensiveness and clarity, and all our comments have been addressed. Particularly, the outcomes of interest and their prioritization is clearer, as well as the distinction between explicit and ... Continue reading

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Reviewer Report 20 Feb 2025

Heike Vornhagen, University of Galway, Galway, Ireland

Approved

https://doi.org/10.21956/hrbopenres.15476.r45919

I am happy with the revised version ... Continue reading

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Reviewer Report 20 Feb 2025

Augustino Mwogosi, The University of Dodoma, Dodoma, Dodoma Region, Tanzania

Approved

https://doi.org/10.21956/hrbopenres.15476.r45916

The revised manuscript demonstrates significant improvements in clarity, methodological transparency, and justification for the study. The authors have effectively addressed the primary concerns and enhanced the protocol's overall rigour. The introduction has been strengthened by providing a clearer rationale for conducting the systematic review, particularly about medication-related harm, polypharmacy, and the challenges of prescribing in primary care. Including additional references and clarifying key concepts, such as the distinction between implicit and explicit medication appropriateness tools, further contributes to the manuscript’s clarity.

Methodological improvements are evident in the study selection criteria, which now explicitly state that all patient cohorts within primary care settings will be considered. This revision resolves prior concerns about whether the review was specifically focused on older adults or had a broader scope. The authors have also refined the analysis section by specifying the criteria for determining study homogeneity in meta-analysis, which includes study design, outcome definitions, and measurement levels. This level of detail ensures a more transparent and reproducible methodology. Moreover, the clarification that two independent reviewers will conduct full-text screening further strengthens the rigour of the study selection process.

The manuscript now provides a more precise definition of “effectiveness” concerning interactive dashboards, linking it to prescribing-related outcomes such as potentially inappropriate prescribing (PIP) and adherence to guidelines. Sensitivity analyses are now explicitly discussed, although no specific subgroup analyses are planned due to the anticipated aggregate nature of the data. While this limitation is acknowledged, the authors may consider briefly mentioning whether any exploratory subgroup analyses (e.g., based on intervention type or setting) could be considered if the data allow. This minor addition would further strengthen the interpretability of the findings.

Competing Interests: No competing interests were disclosed.

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.

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Version 1

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PUBLISHED 03 Jul 2024

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Reviewer Report 28 Dec 2024

Rainer Tan, University of Lausanne, Lausanne, Switzerland

Nina Emery, Unisante Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland

Approved with Reservations

https://doi.org/10.21956/hrbopenres.15255.r43666

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:

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:

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

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

Partly
Is the study design appropriate for the research question?

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

Competing Interests: No competing interests were disclosed.

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.

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Author Response 19 Feb 2025

Patrick Moynagh, Department of General Practice, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland

19 Feb 2025

Author Response

Thank you very much for reviewing our manuscript. Your comments are very helpful and appreciated. Please see our replies below.

Major issue:
In line with section 13 of PRISMA-P ... Continue reading Thank you very much for reviewing our manuscript. Your comments are very helpful and appreciated. Please see our replies below.

Major issue:
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.

In the protocol, we intentionally kept the definition of prescribing-related outcome measures broad, encompassing measures such as utilisation patterns and prescribing quality assessed through explicit and/or implicit criteria. This approach was informed by scoping searches, which suggested that explicit criteria and utilisation patterns were likely to be the predominant method in these studies which typically analyse large populations at aggregate levels. While implicit criteria were not explicitly excluded, they did not emerge in the included studies during the review process, likely due to the operational constraints of applying implicit tools in such contexts. We acknowledge that further detail on the prioritisation of outcomes would be helpful and have now added clarification that while both explicit and implicit criteria were considered, we anticipated, based on our scoping searches, that explicit criteria and utilisation patterns would feature more prominently. Additionally, we have expanded on the distinction between explicit and implicit tools in the revised text to provide clarity. We have also clarified that all prescribing-related outcome measures that meet our inclusion criteria will be included.
We have updated the manuscript as follows:
Introduction: “Explicit tools are focused on drugs, measuring how they fit pre-defined criteria whereas implicit tools are based on clinical guidelines and clinical evaluation criteria”

Methods: Study Selection: “All outcome measures detailed in Table 1 will be considered, we anticipate however, based on scoping searches, that explicit criteria and utilisation patterns will feature more prominently.”

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?

Thank for you highlighting this point. the introduction paragraph has been updated as follows:

“In line with national and international campaigns to reduce medication-related harm and recent developments in e-prescribing infrastructure, this systematic review aims to explore the effectiveness of interactive dashboards in improving prescribing-related outcomes in primary care. These outcomes include potentially inappropriate prescribing (PIP) and drug utilisation rates (e.g., reducing prescribing volumes where lower rates are preferable or optimising prescribing patterns in line with guidelines). Additionally, it aims to describe the characteristics of these interventions to inform future intervention development and e-prescribing infrastructure.”

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)?

We agree this needs clarification. As this intervention is targeted at clinicians but outcomes measured on patients/populations there are two populations of interest. We have clarified this within Table 1.

3) 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».

In the protocol, we outlined that meta-analysis would be performed if there were sufficient studies examining the same outcome. To further clarify, we defined homogeneity based on several key factors:
Study Design: Studies needed to share a similar design (e.g., observational studies, interventional studies) to ensure comparability.
Outcome Definition: Outcomes needed to be defined and measured in a similar way across studies.
Level of Measurement: Outcomes measured at comparable levels, such as practice-level prescribing rates versus patient-level prescribing rates, were grouped separately to ensure consistency.
These criteria aimed to balance comparability while maintaining a sufficiently broad scope to include a wide range of prescribing-related outcomes. Where studies differed in any of these aspects, meta-analysis was not performed, and results were synthesised narratively.

We have updated the manuscript as follows:
“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. We defined homogeneity based on several key factors, (i) study design (ii) outcome definition (iii) level of measurement”

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

Given the nature of the intervention we anticipated that outcomes will be measured at aggregate level, thus population level subgroup analyses were not planned as they were unlikely to be feasible.
We have noted in the last sentence of the analysis:

“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.”

Sensitivity analysis can be performed. The manuscript was updated as follows.

“As we believe that heterogeneity exists regardless of whether we happen to detect it using a statistical test, we will focus less on significance tests and instead further investigate the sources and impact of the heterogeneity (e.g Risk of Bias) through sensitivity analysis. In the event of substantial clinical or methodological heterogeneity, we will not report study results as pooled effect estimates and will synthesise study findings using the approach suggested in the Synthesis Without Meta‐analysis (SWIM) guidance.”

5) 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

Thank you for highlighting this omission in the manuscript. The manuscript was updated as follows:
“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 independently by two reviewers who will assess their suitability for inclusion.”
Thank you very much for reviewing our manuscript. Your comments are very helpful and appreciated. Please see our replies below.

Major issue:
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.

In the protocol, we intentionally kept the definition of prescribing-related outcome measures broad, encompassing measures such as utilisation patterns and prescribing quality assessed through explicit and/or implicit criteria. This approach was informed by scoping searches, which suggested that explicit criteria and utilisation patterns were likely to be the predominant method in these studies which typically analyse large populations at aggregate levels. While implicit criteria were not explicitly excluded, they did not emerge in the included studies during the review process, likely due to the operational constraints of applying implicit tools in such contexts. We acknowledge that further detail on the prioritisation of outcomes would be helpful and have now added clarification that while both explicit and implicit criteria were considered, we anticipated, based on our scoping searches, that explicit criteria and utilisation patterns would feature more prominently. Additionally, we have expanded on the distinction between explicit and implicit tools in the revised text to provide clarity. We have also clarified that all prescribing-related outcome measures that meet our inclusion criteria will be included.
We have updated the manuscript as follows:
Introduction: “Explicit tools are focused on drugs, measuring how they fit pre-defined criteria whereas implicit tools are based on clinical guidelines and clinical evaluation criteria”

Methods: Study Selection: “All outcome measures detailed in Table 1 will be considered, we anticipate however, based on scoping searches, that explicit criteria and utilisation patterns will feature more prominently.”

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?

Thank for you highlighting this point. the introduction paragraph has been updated as follows:

“In line with national and international campaigns to reduce medication-related harm and recent developments in e-prescribing infrastructure, this systematic review aims to explore the effectiveness of interactive dashboards in improving prescribing-related outcomes in primary care. These outcomes include potentially inappropriate prescribing (PIP) and drug utilisation rates (e.g., reducing prescribing volumes where lower rates are preferable or optimising prescribing patterns in line with guidelines). Additionally, it aims to describe the characteristics of these interventions to inform future intervention development and e-prescribing infrastructure.”

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)?

We agree this needs clarification. As this intervention is targeted at clinicians but outcomes measured on patients/populations there are two populations of interest. We have clarified this within Table 1.

3) 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».

In the protocol, we outlined that meta-analysis would be performed if there were sufficient studies examining the same outcome. To further clarify, we defined homogeneity based on several key factors:
Study Design: Studies needed to share a similar design (e.g., observational studies, interventional studies) to ensure comparability.
Outcome Definition: Outcomes needed to be defined and measured in a similar way across studies.
Level of Measurement: Outcomes measured at comparable levels, such as practice-level prescribing rates versus patient-level prescribing rates, were grouped separately to ensure consistency.
These criteria aimed to balance comparability while maintaining a sufficiently broad scope to include a wide range of prescribing-related outcomes. Where studies differed in any of these aspects, meta-analysis was not performed, and results were synthesised narratively.

We have updated the manuscript as follows:
“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. We defined homogeneity based on several key factors, (i) study design (ii) outcome definition (iii) level of measurement”

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

Given the nature of the intervention we anticipated that outcomes will be measured at aggregate level, thus population level subgroup analyses were not planned as they were unlikely to be feasible.
We have noted in the last sentence of the analysis:

“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.”

Sensitivity analysis can be performed. The manuscript was updated as follows.

“As we believe that heterogeneity exists regardless of whether we happen to detect it using a statistical test, we will focus less on significance tests and instead further investigate the sources and impact of the heterogeneity (e.g Risk of Bias) through sensitivity analysis. In the event of substantial clinical or methodological heterogeneity, we will not report study results as pooled effect estimates and will synthesise study findings using the approach suggested in the Synthesis Without Meta‐analysis (SWIM) guidance.”

5) 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

Thank you for highlighting this omission in the manuscript. The manuscript was updated as follows:
“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 independently by two reviewers who will assess their suitability for inclusion.”
Competing Interests: No competing interests were disclosed. Close
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COMMENTS ON THIS REPORT

Author Response 19 Feb 2025

Patrick Moynagh, Department of General Practice, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland

19 Feb 2025

Author Response

Thank you very much for reviewing our manuscript. Your comments are very helpful and appreciated. Please see our replies below.

Major issue:
In line with section 13 of PRISMA-P ... Continue reading Thank you very much for reviewing our manuscript. Your comments are very helpful and appreciated. Please see our replies below.

Major issue:
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.

In the protocol, we intentionally kept the definition of prescribing-related outcome measures broad, encompassing measures such as utilisation patterns and prescribing quality assessed through explicit and/or implicit criteria. This approach was informed by scoping searches, which suggested that explicit criteria and utilisation patterns were likely to be the predominant method in these studies which typically analyse large populations at aggregate levels. While implicit criteria were not explicitly excluded, they did not emerge in the included studies during the review process, likely due to the operational constraints of applying implicit tools in such contexts. We acknowledge that further detail on the prioritisation of outcomes would be helpful and have now added clarification that while both explicit and implicit criteria were considered, we anticipated, based on our scoping searches, that explicit criteria and utilisation patterns would feature more prominently. Additionally, we have expanded on the distinction between explicit and implicit tools in the revised text to provide clarity. We have also clarified that all prescribing-related outcome measures that meet our inclusion criteria will be included.
We have updated the manuscript as follows:
Introduction: “Explicit tools are focused on drugs, measuring how they fit pre-defined criteria whereas implicit tools are based on clinical guidelines and clinical evaluation criteria”

Methods: Study Selection: “All outcome measures detailed in Table 1 will be considered, we anticipate however, based on scoping searches, that explicit criteria and utilisation patterns will feature more prominently.”

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?

Thank for you highlighting this point. the introduction paragraph has been updated as follows:

“In line with national and international campaigns to reduce medication-related harm and recent developments in e-prescribing infrastructure, this systematic review aims to explore the effectiveness of interactive dashboards in improving prescribing-related outcomes in primary care. These outcomes include potentially inappropriate prescribing (PIP) and drug utilisation rates (e.g., reducing prescribing volumes where lower rates are preferable or optimising prescribing patterns in line with guidelines). Additionally, it aims to describe the characteristics of these interventions to inform future intervention development and e-prescribing infrastructure.”

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)?

We agree this needs clarification. As this intervention is targeted at clinicians but outcomes measured on patients/populations there are two populations of interest. We have clarified this within Table 1.

3) 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».

In the protocol, we outlined that meta-analysis would be performed if there were sufficient studies examining the same outcome. To further clarify, we defined homogeneity based on several key factors:
Study Design: Studies needed to share a similar design (e.g., observational studies, interventional studies) to ensure comparability.
Outcome Definition: Outcomes needed to be defined and measured in a similar way across studies.
Level of Measurement: Outcomes measured at comparable levels, such as practice-level prescribing rates versus patient-level prescribing rates, were grouped separately to ensure consistency.
These criteria aimed to balance comparability while maintaining a sufficiently broad scope to include a wide range of prescribing-related outcomes. Where studies differed in any of these aspects, meta-analysis was not performed, and results were synthesised narratively.

We have updated the manuscript as follows:
“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. We defined homogeneity based on several key factors, (i) study design (ii) outcome definition (iii) level of measurement”

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

Given the nature of the intervention we anticipated that outcomes will be measured at aggregate level, thus population level subgroup analyses were not planned as they were unlikely to be feasible.
We have noted in the last sentence of the analysis:

“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.”

Sensitivity analysis can be performed. The manuscript was updated as follows.

“As we believe that heterogeneity exists regardless of whether we happen to detect it using a statistical test, we will focus less on significance tests and instead further investigate the sources and impact of the heterogeneity (e.g Risk of Bias) through sensitivity analysis. In the event of substantial clinical or methodological heterogeneity, we will not report study results as pooled effect estimates and will synthesise study findings using the approach suggested in the Synthesis Without Meta‐analysis (SWIM) guidance.”

5) 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

Thank you for highlighting this omission in the manuscript. The manuscript was updated as follows:
“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 independently by two reviewers who will assess their suitability for inclusion.”
Thank you very much for reviewing our manuscript. Your comments are very helpful and appreciated. Please see our replies below.

Major issue:
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.

In the protocol, we intentionally kept the definition of prescribing-related outcome measures broad, encompassing measures such as utilisation patterns and prescribing quality assessed through explicit and/or implicit criteria. This approach was informed by scoping searches, which suggested that explicit criteria and utilisation patterns were likely to be the predominant method in these studies which typically analyse large populations at aggregate levels. While implicit criteria were not explicitly excluded, they did not emerge in the included studies during the review process, likely due to the operational constraints of applying implicit tools in such contexts. We acknowledge that further detail on the prioritisation of outcomes would be helpful and have now added clarification that while both explicit and implicit criteria were considered, we anticipated, based on our scoping searches, that explicit criteria and utilisation patterns would feature more prominently. Additionally, we have expanded on the distinction between explicit and implicit tools in the revised text to provide clarity. We have also clarified that all prescribing-related outcome measures that meet our inclusion criteria will be included.
We have updated the manuscript as follows:
Introduction: “Explicit tools are focused on drugs, measuring how they fit pre-defined criteria whereas implicit tools are based on clinical guidelines and clinical evaluation criteria”

Methods: Study Selection: “All outcome measures detailed in Table 1 will be considered, we anticipate however, based on scoping searches, that explicit criteria and utilisation patterns will feature more prominently.”

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?

Thank for you highlighting this point. the introduction paragraph has been updated as follows:

“In line with national and international campaigns to reduce medication-related harm and recent developments in e-prescribing infrastructure, this systematic review aims to explore the effectiveness of interactive dashboards in improving prescribing-related outcomes in primary care. These outcomes include potentially inappropriate prescribing (PIP) and drug utilisation rates (e.g., reducing prescribing volumes where lower rates are preferable or optimising prescribing patterns in line with guidelines). Additionally, it aims to describe the characteristics of these interventions to inform future intervention development and e-prescribing infrastructure.”

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)?

We agree this needs clarification. As this intervention is targeted at clinicians but outcomes measured on patients/populations there are two populations of interest. We have clarified this within Table 1.

3) 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».

In the protocol, we outlined that meta-analysis would be performed if there were sufficient studies examining the same outcome. To further clarify, we defined homogeneity based on several key factors:
Study Design: Studies needed to share a similar design (e.g., observational studies, interventional studies) to ensure comparability.
Outcome Definition: Outcomes needed to be defined and measured in a similar way across studies.
Level of Measurement: Outcomes measured at comparable levels, such as practice-level prescribing rates versus patient-level prescribing rates, were grouped separately to ensure consistency.
These criteria aimed to balance comparability while maintaining a sufficiently broad scope to include a wide range of prescribing-related outcomes. Where studies differed in any of these aspects, meta-analysis was not performed, and results were synthesised narratively.

We have updated the manuscript as follows:
“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. We defined homogeneity based on several key factors, (i) study design (ii) outcome definition (iii) level of measurement”

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

Given the nature of the intervention we anticipated that outcomes will be measured at aggregate level, thus population level subgroup analyses were not planned as they were unlikely to be feasible.
We have noted in the last sentence of the analysis:

“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.”

Sensitivity analysis can be performed. The manuscript was updated as follows.

“As we believe that heterogeneity exists regardless of whether we happen to detect it using a statistical test, we will focus less on significance tests and instead further investigate the sources and impact of the heterogeneity (e.g Risk of Bias) through sensitivity analysis. In the event of substantial clinical or methodological heterogeneity, we will not report study results as pooled effect estimates and will synthesise study findings using the approach suggested in the Synthesis Without Meta‐analysis (SWIM) guidance.”

5) 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

Thank you for highlighting this omission in the manuscript. The manuscript was updated as follows:
“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 independently by two reviewers who will assess their suitability for inclusion.”
Competing Interests: No competing interests were disclosed. Close
Report a concern

Views

Reviewer Report 26 Dec 2024

Heike Vornhagen, University of Galway, Galway, Ireland

Approved with Reservations

https://doi.org/10.21956/hrbopenres.15255.r43664

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

Partly
Is the study design appropriate for the research question?

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

Competing Interests: No competing interests were disclosed.

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.

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Author Response 21 Feb 2025

Patrick Moynagh, Department of General Practice, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland

21 Feb 2025

Author Response

Many thanks for taking the time to review our manuscript. Your comments are very helpful and appreciated. Please see our reply below.

We agree the focus of the systematic ... Continue reading Many thanks for taking the time to review our manuscript. Your comments are very helpful and appreciated. Please see our reply below.

We agree the focus of the systematic review lacks clarity in the introduction of the protocol. This systematic review aims to assess the effectiveness of interactive dashboards on prescribing for all patient cohorts within generalist primary care settings. The content in the introduction on older people and polypharmacy was to emphasise that medication-related harm is becoming a more pressing problem due to the aging population and rising levels of polypharmacy.

However, our focus as described in the methods was and remains as all patient cohorts, and hence the search strategy does not include specific terms relating to older adults. This is consistent with the pre-registered PROSPERO protocol (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=481475) and preprint, and thus there has been no change to the focus after commencing the systematic review.

We have restructured the first paragraph of the introduction for clarity:

“Reducing medication-related harm has been identified within the World Health Organisation’s (WHO) third global patient safety challenge in 2017, ‘Medication without Harm’. In the United States alone it is estimated that adverse drug reactions represent the third leading cause of death. While English data shows 1.5% of hospital admissions are as a result of an ADR. In Ireland ADRs are the third most common type of reported adverse event in the health care system. Most prescribing occurs in primary care and qualitative data from general practitioners (GPs) indicates prescribing has become more challenging, particularly for patients with multimorbidity and polypharmacy. 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.There is evidence that audit and feedback is effective in improving professional behaviour and that ongoing or repeated feedback is more effective.”
Many thanks for taking the time to review our manuscript. Your comments are very helpful and appreciated. Please see our reply below.

We agree the focus of the systematic review lacks clarity in the introduction of the protocol. This systematic review aims to assess the effectiveness of interactive dashboards on prescribing for all patient cohorts within generalist primary care settings. The content in the introduction on older people and polypharmacy was to emphasise that medication-related harm is becoming a more pressing problem due to the aging population and rising levels of polypharmacy.

However, our focus as described in the methods was and remains as all patient cohorts, and hence the search strategy does not include specific terms relating to older adults. This is consistent with the pre-registered PROSPERO protocol (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=481475) and preprint, and thus there has been no change to the focus after commencing the systematic review.

We have restructured the first paragraph of the introduction for clarity:

“Reducing medication-related harm has been identified within the World Health Organisation’s (WHO) third global patient safety challenge in 2017, ‘Medication without Harm’. In the United States alone it is estimated that adverse drug reactions represent the third leading cause of death. While English data shows 1.5% of hospital admissions are as a result of an ADR. In Ireland ADRs are the third most common type of reported adverse event in the health care system. Most prescribing occurs in primary care and qualitative data from general practitioners (GPs) indicates prescribing has become more challenging, particularly for patients with multimorbidity and polypharmacy. 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.There is evidence that audit and feedback is effective in improving professional behaviour and that ongoing or repeated feedback is more effective.”
Competing Interests: No competing interests were disclosed. Close
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COMMENTS ON THIS REPORT

Author Response 21 Feb 2025

Patrick Moynagh, Department of General Practice, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland

21 Feb 2025

Author Response

Many thanks for taking the time to review our manuscript. Your comments are very helpful and appreciated. Please see our reply below.

We agree the focus of the systematic ... Continue reading Many thanks for taking the time to review our manuscript. Your comments are very helpful and appreciated. Please see our reply below.

We agree the focus of the systematic review lacks clarity in the introduction of the protocol. This systematic review aims to assess the effectiveness of interactive dashboards on prescribing for all patient cohorts within generalist primary care settings. The content in the introduction on older people and polypharmacy was to emphasise that medication-related harm is becoming a more pressing problem due to the aging population and rising levels of polypharmacy.

However, our focus as described in the methods was and remains as all patient cohorts, and hence the search strategy does not include specific terms relating to older adults. This is consistent with the pre-registered PROSPERO protocol (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=481475) and preprint, and thus there has been no change to the focus after commencing the systematic review.

We have restructured the first paragraph of the introduction for clarity:

“Reducing medication-related harm has been identified within the World Health Organisation’s (WHO) third global patient safety challenge in 2017, ‘Medication without Harm’. In the United States alone it is estimated that adverse drug reactions represent the third leading cause of death. While English data shows 1.5% of hospital admissions are as a result of an ADR. In Ireland ADRs are the third most common type of reported adverse event in the health care system. Most prescribing occurs in primary care and qualitative data from general practitioners (GPs) indicates prescribing has become more challenging, particularly for patients with multimorbidity and polypharmacy. 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.There is evidence that audit and feedback is effective in improving professional behaviour and that ongoing or repeated feedback is more effective.”
Many thanks for taking the time to review our manuscript. Your comments are very helpful and appreciated. Please see our reply below.

We agree the focus of the systematic review lacks clarity in the introduction of the protocol. This systematic review aims to assess the effectiveness of interactive dashboards on prescribing for all patient cohorts within generalist primary care settings. The content in the introduction on older people and polypharmacy was to emphasise that medication-related harm is becoming a more pressing problem due to the aging population and rising levels of polypharmacy.

However, our focus as described in the methods was and remains as all patient cohorts, and hence the search strategy does not include specific terms relating to older adults. This is consistent with the pre-registered PROSPERO protocol (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=481475) and preprint, and thus there has been no change to the focus after commencing the systematic review.

We have restructured the first paragraph of the introduction for clarity:

“Reducing medication-related harm has been identified within the World Health Organisation’s (WHO) third global patient safety challenge in 2017, ‘Medication without Harm’. In the United States alone it is estimated that adverse drug reactions represent the third leading cause of death. While English data shows 1.5% of hospital admissions are as a result of an ADR. In Ireland ADRs are the third most common type of reported adverse event in the health care system. Most prescribing occurs in primary care and qualitative data from general practitioners (GPs) indicates prescribing has become more challenging, particularly for patients with multimorbidity and polypharmacy. 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.There is evidence that audit and feedback is effective in improving professional behaviour and that ongoing or repeated feedback is more effective.”
Competing Interests: No competing interests were disclosed. Close
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Reviewer Report 26 Dec 2024

Denis O'Mahony, University College Cork, Cork, Ireland

Approved

https://doi.org/10.21956/hrbopenres.15255.r41536

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

Yes
Is the study design appropriate for the research question?

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

Competing Interests: No competing interests were disclosed.

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.

CITE

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Author Response 08 Feb 2025

Patrick Moynagh, Department of General Practice, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland

08 Feb 2025

Author Response

Many thanks for taking the time to review our manuscript. It is most appreciated.
Competing Interests: No competing interests were disclosed.
Many thanks for taking the time to review our manuscript. It is most appreciated.
Many thanks for taking the time to review our manuscript. It is most appreciated.
Competing Interests: No competing interests were disclosed. Close
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COMMENTS ON THIS REPORT

Author Response 08 Feb 2025

Patrick Moynagh, Department of General Practice, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland

08 Feb 2025

Author Response

Many thanks for taking the time to review our manuscript. It is most appreciated.
Competing Interests: No competing interests were disclosed.
Many thanks for taking the time to review our manuscript. It is most appreciated.
Many thanks for taking the time to review our manuscript. It is most appreciated.
Competing Interests: No competing interests were disclosed. Close
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Reviewer Report 09 Dec 2024

Augustino Mwogosi, The University of Dodoma, Dodoma, Dodoma Region, Tanzania

Approved with Reservations

https://doi.org/10.21956/hrbopenres.15255.r43659

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:

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

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

The rationale and objectives could be more explicitly linked to the research question.
The description of outcomes (e.g., "effectiveness") lacks specificity.
Plans for managing heterogeneity in the meta-analysis need to be more detailed.
Accessibility of data and presentation formats could be better clarified.

Recommendations to Make the Article Scientifically Sound
Essential Points:

link the rationale to the research question and specify how the review will address identified gaps.
Define key terms such as "effectiveness" and elaborate on how the characteristics of dashboards will be analysed.
Expand on handling heterogeneity in studies, including thresholds and criteria for conducting a meta-analysis.

Optional Enhancements:

Include mock-up tables or figures to illustrate how data will be presented.
Provide a clear plan for making datasets accessible through repositories or supplementary materials.

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

Partly
Is the study design appropriate for the research question?

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?

Partly

Competing Interests: No competing interests were disclosed.

CITE

Report a concern

Author Response 19 Feb 2025

Patrick Moynagh, Department of General Practice, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland

19 Feb 2025

Author Response

Many thanks for taking the time to review our manuscript and for your helpful comments. Please note our replies below.

Essential Points:
1) Link the rationale to the research ... Continue reading Many thanks for taking the time to review our manuscript and for your helpful comments. Please note our replies below.

Essential Points:
1) Link the rationale to the research question and specify how the review will address identified gaps.
We agree the rationale for this systematic review could be strengthened we have amended the first paragraph of the introduction as follows

“Reducing medication-related harm has been identified within the World Health Organisation’s (WHO) third global patient safety challenge in 2017, ‘Medication without Harm’. In the United States alone it is estimated that adverse drug reactions represent the third leading cause of death. While English data shows 1.5% of hospital admissions are as a result of an ADR⁴². In Ireland ADRs are the third most common type of reported adverse event in the health care system. Most prescribing occurs in primary care and qualitative data from general practitioners (GPs) indicates prescribing has become more challenging, particularly for patients with multimorbidity and polypharmacy. 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. There is evidence that audit and feedback is effective in improving professional behaviour and that ongoing or repeated feedback is more effective”.

Additionally the final paragraph of the introduction has been updated for clarity as follows:

“In line with national and international campaigns to reduce medication-related harm and recent developments in e-prescribing infrastructure, this systematic review aims to explore the effectiveness of interactive dashboards in improving prescribing-related outcomes in primary care. These outcomes include potentially inappropriate prescribing (PIP) and drug utilisation rates (e.g., reducing prescribing volumes where lower rates are preferable or optimising prescribing patterns in line with guidelines). Additionally, it aims to describe the characteristics of these interventions to inform future intervention development and e-prescribing infrastructure.”

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

Effectiveness is a commonly used in health services research to describe the extent to which an intervention achieves its intended objectives in real-world settings. For the purposes of this review, effectiveness will be assessed based on whether dashboards improve prescribing quality. These outcomes will be evaluated using the measures reported in the included studies, such as changes in prescribing rates or reductions in potentially inappropriate prescribing.
Regarding the analysis of intervention characteristics, we will examine factors such the level of interactivity and whether the dashboards were delivered as part of a multi-faceted intervention or whether they included clinical decision support. We will explore how these characteristics relate to the reported outcomes, as detailed in the included studies.

The introduction paragraph has been updated as follows:

“In line with national and international campaigns to reduce medication-related harm and recent developments in e-prescribing infrastructure, this systematic review aims to explore the effectiveness of interactive dashboards in improving prescribing-related outcomes in primary care. These outcomes include potentially inappropriate prescribing (PIP) and drug utilisation rates (e.g., reducing prescribing volumes where lower rates are preferable or optimising prescribing patterns in line with guidelines). Additionally, it aims to describe the characteristics of these interventions to inform future intervention development and e-prescribing infrastructure.”

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

This review will be conducted in line with guidance set out in the Cochrane Handbook for Systematic Reviews of Interventions. As such we outlined that meta-analysis would be performed if there were sufficient studies examining the same outcome. To further clarify, we defined homogeneity based on several key factors:
Study Design: Studies needed to share a similar design (e.g., observational studies, interventional studies) to ensure comparability.
Outcome Definition: Outcomes needed to be defined and measured in a similar way across studies.
Level of Measurement: Outcomes measured at comparable levels, such as practice-level prescribing rates versus patient-level prescribing rates, were grouped separately to ensure consistency.
These criteria aimed to balance comparability while maintaining a sufficiently broad scope to include a wide range of prescribing-related outcomes. Where studies differed in any of these aspects, meta-analysis was not performed, and results were synthesised narratively.

We have updated the analysis section of the manuscript as follows:

“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. We defined homogeneity based on several key factors, (i) study design (ii) outcome definition (iii) level of measurement.”

“As we believe that heterogeneity exists regardless of whether we happen to detect it using a statistical test, we will focus less on significance tests and instead further investigate the sources and impact of the heterogeneity (e.g. Risk of Bias) through sensitivity analysis. In the event of substantial clinical or methodological heterogeneity, we will not report study results as pooled effect estimates and will synthesise study findings using the approach suggested in the Synthesis Without Meta‐analysis (SWIM) guidance.”

Optional Enhancements:

1) Include mock-up tables or figures to illustrate how data will be presented.
We will present extracted data using a modified TIDier checklist similar to Table 2. If meta-analysis is performed the results will be displayed graphically using a forest plot.

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

No new primary data will be collected for this study, as the review will synthesise publicly available data from published studies. All data generated or analysed during this study will be included in the published review and its supplementary information files. This will include the search strategies used for each database, details of the included/excluded studies, and any additional data extracted during the review process.
Many thanks for taking the time to review our manuscript and for your helpful comments. Please note our replies below.

Essential Points:
1) Link the rationale to the research question and specify how the review will address identified gaps.
We agree the rationale for this systematic review could be strengthened we have amended the first paragraph of the introduction as follows

“Reducing medication-related harm has been identified within the World Health Organisation’s (WHO) third global patient safety challenge in 2017, ‘Medication without Harm’. In the United States alone it is estimated that adverse drug reactions represent the third leading cause of death. While English data shows 1.5% of hospital admissions are as a result of an ADR⁴². In Ireland ADRs are the third most common type of reported adverse event in the health care system. Most prescribing occurs in primary care and qualitative data from general practitioners (GPs) indicates prescribing has become more challenging, particularly for patients with multimorbidity and polypharmacy. 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. There is evidence that audit and feedback is effective in improving professional behaviour and that ongoing or repeated feedback is more effective”.

Additionally the final paragraph of the introduction has been updated for clarity as follows:

“In line with national and international campaigns to reduce medication-related harm and recent developments in e-prescribing infrastructure, this systematic review aims to explore the effectiveness of interactive dashboards in improving prescribing-related outcomes in primary care. These outcomes include potentially inappropriate prescribing (PIP) and drug utilisation rates (e.g., reducing prescribing volumes where lower rates are preferable or optimising prescribing patterns in line with guidelines). Additionally, it aims to describe the characteristics of these interventions to inform future intervention development and e-prescribing infrastructure.”

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

Effectiveness is a commonly used in health services research to describe the extent to which an intervention achieves its intended objectives in real-world settings. For the purposes of this review, effectiveness will be assessed based on whether dashboards improve prescribing quality. These outcomes will be evaluated using the measures reported in the included studies, such as changes in prescribing rates or reductions in potentially inappropriate prescribing.
Regarding the analysis of intervention characteristics, we will examine factors such the level of interactivity and whether the dashboards were delivered as part of a multi-faceted intervention or whether they included clinical decision support. We will explore how these characteristics relate to the reported outcomes, as detailed in the included studies.

The introduction paragraph has been updated as follows:

“In line with national and international campaigns to reduce medication-related harm and recent developments in e-prescribing infrastructure, this systematic review aims to explore the effectiveness of interactive dashboards in improving prescribing-related outcomes in primary care. These outcomes include potentially inappropriate prescribing (PIP) and drug utilisation rates (e.g., reducing prescribing volumes where lower rates are preferable or optimising prescribing patterns in line with guidelines). Additionally, it aims to describe the characteristics of these interventions to inform future intervention development and e-prescribing infrastructure.”

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

This review will be conducted in line with guidance set out in the Cochrane Handbook for Systematic Reviews of Interventions. As such we outlined that meta-analysis would be performed if there were sufficient studies examining the same outcome. To further clarify, we defined homogeneity based on several key factors:
Study Design: Studies needed to share a similar design (e.g., observational studies, interventional studies) to ensure comparability.
Outcome Definition: Outcomes needed to be defined and measured in a similar way across studies.
Level of Measurement: Outcomes measured at comparable levels, such as practice-level prescribing rates versus patient-level prescribing rates, were grouped separately to ensure consistency.
These criteria aimed to balance comparability while maintaining a sufficiently broad scope to include a wide range of prescribing-related outcomes. Where studies differed in any of these aspects, meta-analysis was not performed, and results were synthesised narratively.

We have updated the analysis section of the manuscript as follows:

“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. We defined homogeneity based on several key factors, (i) study design (ii) outcome definition (iii) level of measurement.”

“As we believe that heterogeneity exists regardless of whether we happen to detect it using a statistical test, we will focus less on significance tests and instead further investigate the sources and impact of the heterogeneity (e.g. Risk of Bias) through sensitivity analysis. In the event of substantial clinical or methodological heterogeneity, we will not report study results as pooled effect estimates and will synthesise study findings using the approach suggested in the Synthesis Without Meta‐analysis (SWIM) guidance.”

Optional Enhancements:

1) Include mock-up tables or figures to illustrate how data will be presented.
We will present extracted data using a modified TIDier checklist similar to Table 2. If meta-analysis is performed the results will be displayed graphically using a forest plot.

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

No new primary data will be collected for this study, as the review will synthesise publicly available data from published studies. All data generated or analysed during this study will be included in the published review and its supplementary information files. This will include the search strategies used for each database, details of the included/excluded studies, and any additional data extracted during the review process.
Competing Interests: No competing interests were disclosed. Close
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Respond or Comment

COMMENTS ON THIS REPORT

Author Response 19 Feb 2025

Patrick Moynagh, Department of General Practice, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland

19 Feb 2025

Author Response

Many thanks for taking the time to review our manuscript and for your helpful comments. Please note our replies below.

Essential Points:
1) Link the rationale to the research ... Continue reading Many thanks for taking the time to review our manuscript and for your helpful comments. Please note our replies below.

Essential Points:
1) Link the rationale to the research question and specify how the review will address identified gaps.
We agree the rationale for this systematic review could be strengthened we have amended the first paragraph of the introduction as follows

“Reducing medication-related harm has been identified within the World Health Organisation’s (WHO) third global patient safety challenge in 2017, ‘Medication without Harm’. In the United States alone it is estimated that adverse drug reactions represent the third leading cause of death. While English data shows 1.5% of hospital admissions are as a result of an ADR⁴². In Ireland ADRs are the third most common type of reported adverse event in the health care system. Most prescribing occurs in primary care and qualitative data from general practitioners (GPs) indicates prescribing has become more challenging, particularly for patients with multimorbidity and polypharmacy. 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. There is evidence that audit and feedback is effective in improving professional behaviour and that ongoing or repeated feedback is more effective”.

Additionally the final paragraph of the introduction has been updated for clarity as follows:

“In line with national and international campaigns to reduce medication-related harm and recent developments in e-prescribing infrastructure, this systematic review aims to explore the effectiveness of interactive dashboards in improving prescribing-related outcomes in primary care. These outcomes include potentially inappropriate prescribing (PIP) and drug utilisation rates (e.g., reducing prescribing volumes where lower rates are preferable or optimising prescribing patterns in line with guidelines). Additionally, it aims to describe the characteristics of these interventions to inform future intervention development and e-prescribing infrastructure.”

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

Effectiveness is a commonly used in health services research to describe the extent to which an intervention achieves its intended objectives in real-world settings. For the purposes of this review, effectiveness will be assessed based on whether dashboards improve prescribing quality. These outcomes will be evaluated using the measures reported in the included studies, such as changes in prescribing rates or reductions in potentially inappropriate prescribing.
Regarding the analysis of intervention characteristics, we will examine factors such the level of interactivity and whether the dashboards were delivered as part of a multi-faceted intervention or whether they included clinical decision support. We will explore how these characteristics relate to the reported outcomes, as detailed in the included studies.

The introduction paragraph has been updated as follows:

“In line with national and international campaigns to reduce medication-related harm and recent developments in e-prescribing infrastructure, this systematic review aims to explore the effectiveness of interactive dashboards in improving prescribing-related outcomes in primary care. These outcomes include potentially inappropriate prescribing (PIP) and drug utilisation rates (e.g., reducing prescribing volumes where lower rates are preferable or optimising prescribing patterns in line with guidelines). Additionally, it aims to describe the characteristics of these interventions to inform future intervention development and e-prescribing infrastructure.”

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

This review will be conducted in line with guidance set out in the Cochrane Handbook for Systematic Reviews of Interventions. As such we outlined that meta-analysis would be performed if there were sufficient studies examining the same outcome. To further clarify, we defined homogeneity based on several key factors:
Study Design: Studies needed to share a similar design (e.g., observational studies, interventional studies) to ensure comparability.
Outcome Definition: Outcomes needed to be defined and measured in a similar way across studies.
Level of Measurement: Outcomes measured at comparable levels, such as practice-level prescribing rates versus patient-level prescribing rates, were grouped separately to ensure consistency.
These criteria aimed to balance comparability while maintaining a sufficiently broad scope to include a wide range of prescribing-related outcomes. Where studies differed in any of these aspects, meta-analysis was not performed, and results were synthesised narratively.

We have updated the analysis section of the manuscript as follows:

“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. We defined homogeneity based on several key factors, (i) study design (ii) outcome definition (iii) level of measurement.”

“As we believe that heterogeneity exists regardless of whether we happen to detect it using a statistical test, we will focus less on significance tests and instead further investigate the sources and impact of the heterogeneity (e.g. Risk of Bias) through sensitivity analysis. In the event of substantial clinical or methodological heterogeneity, we will not report study results as pooled effect estimates and will synthesise study findings using the approach suggested in the Synthesis Without Meta‐analysis (SWIM) guidance.”

Optional Enhancements:

1) Include mock-up tables or figures to illustrate how data will be presented.
We will present extracted data using a modified TIDier checklist similar to Table 2. If meta-analysis is performed the results will be displayed graphically using a forest plot.

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

No new primary data will be collected for this study, as the review will synthesise publicly available data from published studies. All data generated or analysed during this study will be included in the published review and its supplementary information files. This will include the search strategies used for each database, details of the included/excluded studies, and any additional data extracted during the review process.
Many thanks for taking the time to review our manuscript and for your helpful comments. Please note our replies below.

Essential Points:
1) Link the rationale to the research question and specify how the review will address identified gaps.
We agree the rationale for this systematic review could be strengthened we have amended the first paragraph of the introduction as follows

“Reducing medication-related harm has been identified within the World Health Organisation’s (WHO) third global patient safety challenge in 2017, ‘Medication without Harm’. In the United States alone it is estimated that adverse drug reactions represent the third leading cause of death. While English data shows 1.5% of hospital admissions are as a result of an ADR⁴². In Ireland ADRs are the third most common type of reported adverse event in the health care system. Most prescribing occurs in primary care and qualitative data from general practitioners (GPs) indicates prescribing has become more challenging, particularly for patients with multimorbidity and polypharmacy. 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. There is evidence that audit and feedback is effective in improving professional behaviour and that ongoing or repeated feedback is more effective”.

Additionally the final paragraph of the introduction has been updated for clarity as follows:

“In line with national and international campaigns to reduce medication-related harm and recent developments in e-prescribing infrastructure, this systematic review aims to explore the effectiveness of interactive dashboards in improving prescribing-related outcomes in primary care. These outcomes include potentially inappropriate prescribing (PIP) and drug utilisation rates (e.g., reducing prescribing volumes where lower rates are preferable or optimising prescribing patterns in line with guidelines). Additionally, it aims to describe the characteristics of these interventions to inform future intervention development and e-prescribing infrastructure.”

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

Effectiveness is a commonly used in health services research to describe the extent to which an intervention achieves its intended objectives in real-world settings. For the purposes of this review, effectiveness will be assessed based on whether dashboards improve prescribing quality. These outcomes will be evaluated using the measures reported in the included studies, such as changes in prescribing rates or reductions in potentially inappropriate prescribing.
Regarding the analysis of intervention characteristics, we will examine factors such the level of interactivity and whether the dashboards were delivered as part of a multi-faceted intervention or whether they included clinical decision support. We will explore how these characteristics relate to the reported outcomes, as detailed in the included studies.

The introduction paragraph has been updated as follows:

“In line with national and international campaigns to reduce medication-related harm and recent developments in e-prescribing infrastructure, this systematic review aims to explore the effectiveness of interactive dashboards in improving prescribing-related outcomes in primary care. These outcomes include potentially inappropriate prescribing (PIP) and drug utilisation rates (e.g., reducing prescribing volumes where lower rates are preferable or optimising prescribing patterns in line with guidelines). Additionally, it aims to describe the characteristics of these interventions to inform future intervention development and e-prescribing infrastructure.”

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

This review will be conducted in line with guidance set out in the Cochrane Handbook for Systematic Reviews of Interventions. As such we outlined that meta-analysis would be performed if there were sufficient studies examining the same outcome. To further clarify, we defined homogeneity based on several key factors:
Study Design: Studies needed to share a similar design (e.g., observational studies, interventional studies) to ensure comparability.
Outcome Definition: Outcomes needed to be defined and measured in a similar way across studies.
Level of Measurement: Outcomes measured at comparable levels, such as practice-level prescribing rates versus patient-level prescribing rates, were grouped separately to ensure consistency.
These criteria aimed to balance comparability while maintaining a sufficiently broad scope to include a wide range of prescribing-related outcomes. Where studies differed in any of these aspects, meta-analysis was not performed, and results were synthesised narratively.

We have updated the analysis section of the manuscript as follows:

“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. We defined homogeneity based on several key factors, (i) study design (ii) outcome definition (iii) level of measurement.”

“As we believe that heterogeneity exists regardless of whether we happen to detect it using a statistical test, we will focus less on significance tests and instead further investigate the sources and impact of the heterogeneity (e.g. Risk of Bias) through sensitivity analysis. In the event of substantial clinical or methodological heterogeneity, we will not report study results as pooled effect estimates and will synthesise study findings using the approach suggested in the Synthesis Without Meta‐analysis (SWIM) guidance.”

Optional Enhancements:

1) Include mock-up tables or figures to illustrate how data will be presented.
We will present extracted data using a modified TIDier checklist similar to Table 2. If meta-analysis is performed the results will be displayed graphically using a forest plot.

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

No new primary data will be collected for this study, as the review will synthesise publicly available data from published studies. All data generated or analysed during this study will be included in the published review and its supplementary information files. This will include the search strategies used for each database, details of the included/excluded studies, and any additional data extracted during the review process.
Competing Interests: No competing interests were disclosed. Close
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VERSION 2 PUBLISHED 03 Jul 2024

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Augustino Mwogosi, The University of Dodoma, Dodoma, Tanzania
Denis O'Mahony, University College Cork, Cork, Ireland
Heike Vornhagen, University of Galway, Galway, Ireland
Rainer Tan, University of Lausanne, Lausanne, Switzerland

Nina Emery, Unisante Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland

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3 Views

27 Feb 2025 | for Version 2

Rainer Tan, University of Lausanne, Lausanne, Switzerland

Nina Emery, Unisante Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland

3 Views Cite this report Responses(0)

Approved

Competing Interests

No competing interests were disclosed.

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.

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12 Views

20 Feb 2025 | for Version 2

Heike Vornhagen, University of Galway, Galway, Ireland

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Approved

I am happy with the revised version and have no further comments to make.

Competing Interests

No competing interests were disclosed.

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.

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7 Views

20 Feb 2025 | for Version 2

Augustino Mwogosi, The University of Dodoma, Dodoma, Dodoma Region, Tanzania

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Approved

Competing Interests

No competing interests were disclosed.

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.

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21 Views

28 Dec 2024 | for Version 1

Rainer Tan, University of Lausanne, Lausanne, Switzerland

Nina Emery, Unisante Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland

21 Views Cite this report Responses(1)

Approved With Reservations

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:

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

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

Partly
Is the study design appropriate for the research question?

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

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Digital Health, antibiotic stewardship

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Responses (1)

Author Response

19 Feb 2025

Patrick Moynagh, Department of General Practice, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland

Thank you very much for reviewing our manuscript. Your comments are very helpful and appreciated. Please see our replies below.

Major issue:
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.

In the protocol, we intentionally kept the definition of prescribing-related outcome measures broad, encompassing measures such as utilisation patterns and prescribing quality assessed through explicit and/or implicit criteria. This approach was informed by scoping searches, which suggested that explicit criteria and utilisation patterns were likely to be the predominant method in these studies which typically analyse large populations at aggregate levels. While implicit criteria were not explicitly excluded, they did not emerge in the included studies during the review process, likely due to the operational constraints of applying implicit tools in such contexts. We acknowledge that further detail on the prioritisation of outcomes would be helpful and have now added clarification that while both explicit and implicit criteria were considered, we anticipated, based on our scoping searches, that explicit criteria and utilisation patterns would feature more prominently. Additionally, we have expanded on the distinction between explicit and implicit tools in the revised text to provide clarity. We have also clarified that all prescribing-related outcome measures that meet our inclusion criteria will be included.
We have updated the manuscript as follows:
Introduction: “Explicit tools are focused on drugs, measuring how they fit pre-defined criteria whereas implicit tools are based on clinical guidelines and clinical evaluation criteria”

Methods: Study Selection: “All outcome measures detailed in Table 1 will be considered, we anticipate however, based on scoping searches, that explicit criteria and utilisation patterns will feature more prominently.”

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?

Thank for you highlighting this point. the introduction paragraph has been updated as follows:

“In line with national and international campaigns to reduce medication-related harm and recent developments in e-prescribing infrastructure, this systematic review aims to explore the effectiveness of interactive dashboards in improving prescribing-related outcomes in primary care. These outcomes include potentially inappropriate prescribing (PIP) and drug utilisation rates (e.g., reducing prescribing volumes where lower rates are preferable or optimising prescribing patterns in line with guidelines). Additionally, it aims to describe the characteristics of these interventions to inform future intervention development and e-prescribing infrastructure.”

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)?

We agree this needs clarification. As this intervention is targeted at clinicians but outcomes measured on patients/populations there are two populations of interest. We have clarified this within Table 1.

3) 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».

In the protocol, we outlined that meta-analysis would be performed if there were sufficient studies examining the same outcome. To further clarify, we defined homogeneity based on several key factors:
Study Design: Studies needed to share a similar design (e.g., observational studies, interventional studies) to ensure comparability.
Outcome Definition: Outcomes needed to be defined and measured in a similar way across studies.
Level of Measurement: Outcomes measured at comparable levels, such as practice-level prescribing rates versus patient-level prescribing rates, were grouped separately to ensure consistency.
These criteria aimed to balance comparability while maintaining a sufficiently broad scope to include a wide range of prescribing-related outcomes. Where studies differed in any of these aspects, meta-analysis was not performed, and results were synthesised narratively.

We have updated the manuscript as follows:
“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. We defined homogeneity based on several key factors, (i) study design (ii) outcome definition (iii) level of measurement”

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

Given the nature of the intervention we anticipated that outcomes will be measured at aggregate level, thus population level subgroup analyses were not planned as they were unlikely to be feasible.
We have noted in the last sentence of the analysis:

“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.”

Sensitivity analysis can be performed. The manuscript was updated as follows.

“As we believe that heterogeneity exists regardless of whether we happen to detect it using a statistical test, we will focus less on significance tests and instead further investigate the sources and impact of the heterogeneity (e.g Risk of Bias) through sensitivity analysis. In the event of substantial clinical or methodological heterogeneity, we will not report study results as pooled effect estimates and will synthesise study findings using the approach suggested in the Synthesis Without Meta‐analysis (SWIM) guidance.”

5) 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

Thank you for highlighting this omission in the manuscript. The manuscript was updated as follows:
“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 independently by two reviewers who will assess their suitability for inclusion.”

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Competing Interests

No competing interests were disclosed.

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Reviewer Report

13 Views

26 Dec 2024 | for Version 1

Heike Vornhagen, University of Galway, Galway, Ireland

13 Views Cite this report Responses(1)

Approved With Reservations

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

Partly
Is the study design appropriate for the research question?

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

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Dashboards, Data Visualisation, UX Design

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Author Response

21 Feb 2025

Patrick Moynagh, Department of General Practice, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland

Many thanks for taking the time to review our manuscript. Your comments are very helpful and appreciated. Please see our reply below.

We agree the focus of the systematic review lacks clarity in the introduction of the protocol. This systematic review aims to assess the effectiveness of interactive dashboards on prescribing for all patient cohorts within generalist primary care settings. The content in the introduction on older people and polypharmacy was to emphasise that medication-related harm is becoming a more pressing problem due to the aging population and rising levels of polypharmacy.

However, our focus as described in the methods was and remains as all patient cohorts, and hence the search strategy does not include specific terms relating to older adults. This is consistent with the pre-registered PROSPERO protocol (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=481475) and preprint, and thus there has been no change to the focus after commencing the systematic review.

We have restructured the first paragraph of the introduction for clarity:

“Reducing medication-related harm has been identified within the World Health Organisation’s (WHO) third global patient safety challenge in 2017, ‘Medication without Harm’. In the United States alone it is estimated that adverse drug reactions represent the third leading cause of death. While English data shows 1.5% of hospital admissions are as a result of an ADR. In Ireland ADRs are the third most common type of reported adverse event in the health care system. Most prescribing occurs in primary care and qualitative data from general practitioners (GPs) indicates prescribing has become more challenging, particularly for patients with multimorbidity and polypharmacy. 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.There is evidence that audit and feedback is effective in improving professional behaviour and that ongoing or repeated feedback is more effective.”

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Competing Interests

No competing interests were disclosed.

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Reviewer Report

18 Views

26 Dec 2024 | for Version 1

Denis O'Mahony, University College Cork, Cork, Ireland

18 Views Cite this report Responses(1)

Approved

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

Yes
Is the study design appropriate for the research question?

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

Competing Interests

No competing interests were disclosed.

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.

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Reviewer Report

27 Views

09 Dec 2024 | for Version 1

Augustino Mwogosi, The University of Dodoma, Dodoma, Dodoma Region, Tanzania

27 Views Cite this report Responses(1)

Approved With Reservations

Explicitly articulate how the systematic review will address the gaps identified in the rationale.
Provide operational definitions for key terms like "effectiveness" and clarify the scope of "characteristics" being explored (e.g., usability, integration, user engagement).

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

The rationale and objectives could be more explicitly linked to the research question.
The description of outcomes (e.g., "effectiveness") lacks specificity.
Plans for managing heterogeneity in the meta-analysis need to be more detailed.
Accessibility of data and presentation formats could be better clarified.

Recommendations to Make the Article Scientifically Sound
Essential Points:

link the rationale to the research question and specify how the review will address identified gaps.
Define key terms such as "effectiveness" and elaborate on how the characteristics of dashboards will be analysed.
Expand on handling heterogeneity in studies, including thresholds and criteria for conducting a meta-analysis.

Optional Enhancements:

Include mock-up tables or figures to illustrate how data will be presented.
Provide a clear plan for making datasets accessible through repositories or supplementary materials.

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

Partly
Is the study design appropriate for the research question?

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?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Respond to this report

Responses (1)

Author Response

19 Feb 2025

Patrick Moynagh, Department of General Practice, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland

Many thanks for taking the time to review our manuscript and for your helpful comments. Please note our replies below.

Essential Points:
1) Link the rationale to the research question and specify how the review will address identified gaps.
We agree the rationale for this systematic review could be strengthened we have amended the first paragraph of the introduction as follows

“Reducing medication-related harm has been identified within the World Health Organisation’s (WHO) third global patient safety challenge in 2017, ‘Medication without Harm’. In the United States alone it is estimated that adverse drug reactions represent the third leading cause of death. While English data shows 1.5% of hospital admissions are as a result of an ADR⁴². In Ireland ADRs are the third most common type of reported adverse event in the health care system. Most prescribing occurs in primary care and qualitative data from general practitioners (GPs) indicates prescribing has become more challenging, particularly for patients with multimorbidity and polypharmacy. 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. There is evidence that audit and feedback is effective in improving professional behaviour and that ongoing or repeated feedback is more effective”.

Additionally the final paragraph of the introduction has been updated for clarity as follows:

“In line with national and international campaigns to reduce medication-related harm and recent developments in e-prescribing infrastructure, this systematic review aims to explore the effectiveness of interactive dashboards in improving prescribing-related outcomes in primary care. These outcomes include potentially inappropriate prescribing (PIP) and drug utilisation rates (e.g., reducing prescribing volumes where lower rates are preferable or optimising prescribing patterns in line with guidelines). Additionally, it aims to describe the characteristics of these interventions to inform future intervention development and e-prescribing infrastructure.”

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

Effectiveness is a commonly used in health services research to describe the extent to which an intervention achieves its intended objectives in real-world settings. For the purposes of this review, effectiveness will be assessed based on whether dashboards improve prescribing quality. These outcomes will be evaluated using the measures reported in the included studies, such as changes in prescribing rates or reductions in potentially inappropriate prescribing.
Regarding the analysis of intervention characteristics, we will examine factors such the level of interactivity and whether the dashboards were delivered as part of a multi-faceted intervention or whether they included clinical decision support. We will explore how these characteristics relate to the reported outcomes, as detailed in the included studies.

The introduction paragraph has been updated as follows:

“In line with national and international campaigns to reduce medication-related harm and recent developments in e-prescribing infrastructure, this systematic review aims to explore the effectiveness of interactive dashboards in improving prescribing-related outcomes in primary care. These outcomes include potentially inappropriate prescribing (PIP) and drug utilisation rates (e.g., reducing prescribing volumes where lower rates are preferable or optimising prescribing patterns in line with guidelines). Additionally, it aims to describe the characteristics of these interventions to inform future intervention development and e-prescribing infrastructure.”

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

This review will be conducted in line with guidance set out in the Cochrane Handbook for Systematic Reviews of Interventions. As such we outlined that meta-analysis would be performed if there were sufficient studies examining the same outcome. To further clarify, we defined homogeneity based on several key factors:
Study Design: Studies needed to share a similar design (e.g., observational studies, interventional studies) to ensure comparability.
Outcome Definition: Outcomes needed to be defined and measured in a similar way across studies.
Level of Measurement: Outcomes measured at comparable levels, such as practice-level prescribing rates versus patient-level prescribing rates, were grouped separately to ensure consistency.
These criteria aimed to balance comparability while maintaining a sufficiently broad scope to include a wide range of prescribing-related outcomes. Where studies differed in any of these aspects, meta-analysis was not performed, and results were synthesised narratively.

We have updated the analysis section of the manuscript as follows:

“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. We defined homogeneity based on several key factors, (i) study design (ii) outcome definition (iii) level of measurement.”

“As we believe that heterogeneity exists regardless of whether we happen to detect it using a statistical test, we will focus less on significance tests and instead further investigate the sources and impact of the heterogeneity (e.g. Risk of Bias) through sensitivity analysis. In the event of substantial clinical or methodological heterogeneity, we will not report study results as pooled effect estimates and will synthesise study findings using the approach suggested in the Synthesis Without Meta‐analysis (SWIM) guidance.”

Optional Enhancements:

1) Include mock-up tables or figures to illustrate how data will be presented.
We will present extracted data using a modified TIDier checklist similar to Table 2. If meta-analysis is performed the results will be displayed graphically using a forest plot.

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

No new primary data will be collected for this study, as the review will synthesise publicly available data from published studies. All data generated or analysed during this study will be included in the published review and its supplementary information files. This will include the search strategies used for each database, details of the included/excluded studies, and any additional data extracted during the review process.

View more View less

Competing Interests

No competing interests were disclosed.

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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

Abstract

Introduction

Methods

Discussion

Keywords

Revised Amendments from Version 1

Introduction

Methods

Search strategy

Study selection

Table 1. Study eligibility criteria.

Data extraction and management

Table 2. Intervention data extraction using adapted TIDieR checklist.

Discussion

Strengths and limitations

Potential implications for future research, policy and clinical practice

Data availability

Underlying data

Extended data

Acknowledgments

References

Comments on this article Comments (0)

Open Peer Review

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Open Peer Review

Reviewer Status

Reviewer Reports

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Are you a HRB-funded researcher?

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

Abstract

Introduction

Methods

Discussion

Keywords

Revised Amendments from Version 1

Introduction

Methods

Search strategy

Study selection

Table 1. Study eligibility criteria.

Data extraction and management

Table 2. Intervention data extraction using adapted TIDieR checklist.

Discussion

Strengths and limitations

Potential implications for future research, policy and clinical practice

Data availability

Underlying data

Extended data

Acknowledgments

References

Comments on this article Comments (0)

Open Peer Review

Comments on this article Comments (0)

Open Peer Review

Reviewer Status

Reviewer Reports

Comments on this article

Competing Interests Policy

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Are you a HRB-funded researcher?

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