Performance Indicators in Emergency Medical Services: A Scoping Review of Dashboard Applications in the Context of Crisis Management

Abstract

Background:

Emergency medical services (EMS) are critical for responding to crises and mass casualty incidents (MCIs). Rapid decision-making and effective patient flow management are essential for improving survival. Digital technologies—particularly dashboards—enable real-time monitoring of performance indicators and data-driven decision-making. This scoping review aimed to identify and classify key performance indicators (KPIs) used in EMS and to examine the role of dashboards in monitoring these indicators in both routine and crisis conditions.

Methods:

A systematic search was conducted in PubMed, Scopus, Embase, and Web of Science from inception to January 2026, supplemented by gray literature. Two independent reviewers screened titles/abstracts and full texts. Data were extracted using a standardized form and synthesized narratively in accordance with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines.

Results:

Twenty-six studies were included. KPIs were categorized using the Donabedian model into structure (resources), process (response time, triage, coordination), and outcome (survival, satisfaction). Time-based indicators were most frequent. Eight studies (31%) reported dashboards, all in routine operations. Eight studies (31%) used real-time feedback devices (not classified as dashboards), and ten studies (38%) employed other digital tools. Only one study addressed crisis conditions, and it did not use a dashboard.

Conclusion:

Dashboards are predominantly applied in routine EMS operations, with very limited evidence in crisis contexts. Developing crisis-specific, context-adaptive dashboards and standardized indicators should be prioritized to support decision-making and coordination in emergencies and disasters.

1. Introduction

The Emergency Medical Services (EMS) are a critical part of the healthcare system globally and the first responders to Medical Emergencies, Mass-casualty Incidents (MCIs) and Disasters (1, 2). Scene assessment, triage, and prompt administration of lifesaving interventions are crucial in these high-stakes environments (3). This environment is complex and cognitively demanding, forcing the personnel to make quick decision-making choices in a physically and psychologically stressful manner (4, 5). Poor clinical decision making and triage can lead to adverse patient outcomes (6, 7).

Routine and crisis EMS operations differ substantially in their operational requirements. Routine EMS involves predictable demand, standardized procedures, and established resource allocation, emphasizing response times, high-quality cardiopulmonary resuscitation (CPR), and appropriate patient flow (8, 9).

However, crises, such as MCIs, active shooter events, or disasters, are associated with a surge in patient volume and demand, lack of resources, environmental uncertainty, and a rapidly changing situation (10, 11). These circumstances require dynamic triage, inter-organizational coordination, and flexible allocation of personnel, equipment, and infrastructure (12-14).

Performance indicators are quantifiable measures that can be used to evaluate the effectiveness, efficiency and quality of EMS services (15, 16). They offer a consistent measurement and identification of performance gaps (17). According to the Donabedian model, indicators can be classified into three areas: structure (resources and infrastructure), process (care delivery and response processes), and outcome (patient outcomes) (6, 18). Time-based indicators, including response and transport times, are often reported and are more regularly assessed, while outcome indicators, such as neurological status and survival, are less routinely assessed (19-22).

Digital technologies have increased the ability of EMS systems to collect, share and make decisions in real time (23, 24). Documentation, information sharing and quality improvement are facilitated by electronic patient care reporting, computer-aided dispatch and telemedicine platforms (25, 26). Dashboards offer visual representations of metrics for aggregated performance and other key information, which helps to foster situational awareness and data-informed decision making (27-29). Their design can be modified based on user needs, such as managers needing information on the system and clinicians needing information in real time for their clinical needs (28, 30).

Dashboards have been used for service quality, patient flow, response times and resource utilization in pre-hospital and emergency care (31, 32). Real time dispatch dashboards have helped manage the capacity of ambulances and hospitals, and clinical dashboards have been applied to time sensitive care like ST-Segment Elevation Myocardial Infarction (STEMI) management (32, 33). Real-time feedback technologies have also been shown to enhance CPR process measures but have yet to show improvements in patient outcome (34-40). But, the existing literature is not very extensive on dashboard applications during crises and most of it relates to routine EMS operations.

This evidence gap is salient because a standardized way to measure the performance of crisis management and a real-time awareness of the situation are needed. Earlier studies have identified problems with the availability and standardization of performance measures in MCIs and disasters (10, 12, 15). During such events, dashboard technologies can help to integrate and visualize information that changes over time for dynamic decision making and resource allocation (32, 41, 42).

Thus, the purpose of this scoping review was to identify and categorize performance indicators used by the prehospital EMS, classify performance indicators based on the Donabedian Structure–Process–Outcome and map the digital technologies and dashboards used for real-time monitoring in normal and emergency settings. In particular, we explored the key performance indicators (KPIs) employed in routine and crisis EMS, their categorization in the framework of Donabedian, and their application in dashboards and other digital tools to enable real time monitoring, especially during the crisis and during MCIs.

2. Methods

2.1 Research Design

The current study was designed and executed as a scoping review and followed the methodological framework as suggested by Arksey and O'Malley (43) and the strict guidelines as proposed by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) (44). A checklist of questions and answers based on PRISMA is included as an additional file for transparency and completeness.

Donabedian model was used as the common ground to classify the types of indicators (18). This well-established model divides indicators into three areas: structure (resources, equipment, and infrastructure), process (care delivery, response time, accuracy of triage, and coordination), and outcome (survival rates, outcomes for the nervous system, and patient satisfaction). Consistency was achieved in the classification of data throughout the data extraction, synthesis, and reporting process to ensure methodological coherence and consistency with the objectives of the review.

Operational definition: A dashboard is a visual display system that consolidates and integrates data from various data sources into a single coherent, user-friendly interface that displays and presents KPIs in graphical or tabular format to facilitate real-time or near-real-time system-level decision-making (28). In this review, only tools that offered aggregated, system-level results and not device-specific, unidimensional results were considered to be dashboards. Importantly, real-time feedback devices integrated into defibrillators or monitors (e.g., AccuVent®, ZOLL QCPR, or other CPR feedback devices) were NOT included in the definition of dashboards. These sensors provide real-time visual feedback on a specific parameter, but they present unidimensional data sources that are device-specific and fail to aggregate multiple data sources into a system-level view for managerial and/or operational control (34, 40).

2.2 Information Sources and Search Strategy

All identified evidence and extracted KPIs were systematically organized and analyzed under three interrelated overarching dimensions - (1) operational context (routine EMS operations compared with crises , such as mass casualty incidents, disasters , active shooter events, etc.), (2) crisis phase (before, during, and after the crisis event), and (3) indicator type (based on a standardized quality assessment framework).

The search strategy was collaboratively developed by three researchers (SK, HE, FA) and was designed to capture studies related to: (1) dashboards or similar digital visualization tools such as performance measurement systems, real-time monitoring platforms, or data visualization; and (2) EMS, prehospital care, dispatch centers, or emergency medical services; and (3) crisis management, disasters, mass casualty incidents, or related emergent situations. The full search string is displayed in Table 1, with the query strings modified to suit each database. A combination of controlled vocabulary Medical Subject Headings (MeSH) and Emtree terms and free-text terms was used, and the search terms were modified to suit the syntax of each database. To complement the main electronic search and minimize publication bias, grey literature was sought using manual searching of the references of included studies and related review articles, as well as Google, Google Scholar, and MSQ. The first 100 hits on each of the grey literature sources were selected for preliminary screening for relevance.

Table 1.

Systematic Search Strategy for Electronic Databases

Database	Search Query
PubMed	("Dashboard"[Mesh] OR "dashboard"[tiab] OR "data visualization"[tiab] OR "performance measurement system"[tiab] OR "visualization platform"[tiab] OR "status board"[tiab] OR "electronic whiteboard"[tiab]) AND ("Emergency Medical Services"[Mesh] OR "EMS"[tiab] OR "prehospital emergency care"[tiab] OR "ambulance"[tiab] OR "emergency medical dispatch"[tiab] OR "dispatch center"[tiab]) AND ("performance indicator"[tiab] OR "Key Performance Indicators"[tiab] OR KPI[tiab] OR "quality assurance"[tiab] OR "performance measure"[tiab] OR "quality indicator*"[tiab] OR "Mass Casualty Incident"[tiab] OR "Disaster"[tiab] OR "Multiple Casualty Incident"[tiab])
Scopus	TITLE-ABS-KEY("dashboard" OR "performance measurement system" OR "visualization platform" OR "status board" OR "electronic whiteboard" OR "data visualization") AND TITLE-ABS-KEY("Emergency Medical Services" OR EMS OR "prehospital emergency care" OR ambulance OR "emergency medical dispatch" OR "dispatch center") AND TITLE-ABS-KEY("performance indicator" OR "Key Performance Indicators" OR KPI OR "quality assurance" OR "performance measure" OR "quality indicator*" OR "Mass Casualty Incident" OR Disaster OR "Multiple Casualty Incident")
Embase	('dashboard'/exp OR dashboard:ti,ab OR 'data visualization':ti,ab OR 'performance measurement system':ti,ab) AND ('emergency health service'/exp OR 'EMS':ti,ab OR 'prehospital emergency care':ti,ab OR ambulance:ti,ab) AND ('performance indicator':ti,ab OR 'KPI':ti,ab OR 'quality assurance':ti,ab OR 'performance measure':ti,ab OR 'quality indicator*':ti,ab OR 'mass casualty incident':ti,ab OR disaster:ti,ab)
Web of Science	TS=("dashboard" OR "performance measurement system" OR "visualization platform" OR "status board" OR "electronic whiteboard" OR "data visualization") AND TS=("Emergency Medical Services" OR EMS OR "prehospital emergency care" OR ambulance OR "emergency medical dispatch") AND TS=("performance indicator" OR "Key Performance Indicators" OR KPI OR "quality assurance" OR "performance measure" OR "quality indicator*" OR "Mass Casualty Incident" OR Disaster)

2.3 Screening methods and study selection criteria

All identified records from the electronic database searches were exported to EndNote X8 (Clarivate Analytics, Philadelphia, PA, USA) for reference management and deduplication. After removing duplicate records, the remaining citations were imported into Rayyan QCRI (Rayyan Systems Inc., Cambridge, MA, USA) (18), a web-based systematic review screening tool, to facilitate collaborative and efficient title and abstract screening.

Two independent reviewers (SK, HE) conducted the screening process in two phases:

Title and abstract screening: Both reviewers independently screened the titles and abstracts of all retrieved records against the predefined eligibility criteria. This phase was performed using Rayyan QCRI, which allowed for blinding of reviewers' decisions and resolution of conflicts.

Full-text review: The full texts of potentially eligible studies were retrieved and independently assessed by the same two reviewers. Disagreements regarding study inclusion were resolved through discussion and consensus between the two reviewers; when consensus could not be reached, a third reviewer (FA) acted as an arbiter. Inter-rater agreement was assessed using Cohen's Kappa statistic (calculated within Rayyan QCRI), which yielded a value of 0.85, indicating substantial agreement and confirming the reliability of the screening process.

2.4 Inclusion Criteria

Studies were included if they met all of the following criteria:

1. Study type: Original peer-reviewed research articles or official grey literature reports (e.g., government or institutional technical reports, dissertations) published in English or Persian.

2. Population/Setting: Focused on prehospital Emergency Medical Services (EMS), emergency medical dispatch systems, or ambulance services.

3. Intervention/Tool: Described or utilized digital technologies such as dashboards, electronic patient care reporting systems (ePCR), real-time feedback devices, or other data visualization or monitoring tools.

4. Outcomes: Reported at least one measurable KPIs related to structure, process, or outcome of EMS operations.

5. Publication date: No restriction on publication date.

2.5 Exclusion Criteria

Studies were excluded if they met any of the following criteria:

1. Conference abstracts without full text.

2. Editorials, commentaries, opinion pieces, or letters to the editor.

3. Focused exclusively on in-hospital emergency care without a prehospital component.

4. Duplicate publications of the same dataset.

5. Lacking measurable performance indicators or digital technology implementation.

The full-text screening was conducted independently, and reasons for exclusion were documented for transparency. The complete study selection process is presented in the PRISMA-ScR flow diagram (Figure 1), and the PRISMA-ScR checklist is provided as a supplementary file to ensure reporting completeness and adherence to established guidelines (44).

Figure 1.

PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) flow diagram for the paper screening approach.

2.6 Data Collection and Extraction Protocol

The extracted information included: (1) bibliographic details (first author, publication year, country, journal); (2) study design and methodology; (3) operational context (routine or crisis); (4) identified performance indicators categorized according to the Donabedian model; (5) type and description of digital technology used (dashboard, ePCR, feedback device, etc.); (6) key findings and outcomes; and (7) limitations as reported by study authors. The data extraction form was structured according to the population, intervention, comparison, outcomes and study (PICOS) framework where applicable: Population (prehospital EMS settings), Intervention (digital tools including dashboards, ePCR, and feedback devices), Comparison (not applicable in this scoping review), Outcomes (performance indicators related to structure, process, and outcome), and Setting (routine or crisis EMS operations). No assumptions or simplifications were made during data extraction; all data were extracted as reported in the original studies.

To ensure consistency and minimize bias, the extraction form was pilot-tested on three randomly selected articles before full-scale extraction. Reference management for all included studies was performed using EndNote X8.

2.7 Data Synthesis and Presentation

The extracted data were synthesized narratively, as a scoping review typically does not involve quantitative meta-analysis due to the heterogeneous nature of study designs, settings, and outcome measures. In accordance with scoping review methodology, formal quality assessment (risk of bias) of the included studies was not performed. This decision is consistent with the PRISMA-ScR guideline (Item 12), which lists critical appraisal as an optional item, and the JBI Manual for Evidence Synthesis, which recommends that scoping reviews do not typically include quality assessment since the primary aim is to map the available evidence rather than to evaluate its methodological quality (45, 46).

The findings are presented in structured tables to facilitate comparison and interpretation. Table 1 presents the search strategy used across the electronic databases. Table 2 summarizes the characteristics of the included studies, including study design, setting, operational context, digital technologies, dashboard utilization, and key findings. Table 3 summarizes the dashboard characteristics, performance indicators, and main findings for each included study, while Table 4 provides detailed operational definitions and data sources for each identified KPIs. Table 5 integrates these findings by mapping indicators to required dashboard features and their applicability across routine and crisis contexts.

Table 2.

Characteristics of Included Studies (N = 26)

Author (Year) /	Country	Study Design	Population / Setting	Context	Dashboard / Digital Tool
Mears et al. (2010) / (52)	USA	Mixed-methods descriptive	Statewide EMS (100 counties)	Routine	PreMIS, SMARTT, integrated statewide data system
Rahman et al. (2015) / (67)	Multiple Asian cities	Quantitative descriptive (PAROS)	EMS systems in 7 cities	Routine	ePCR, electronic PAROS forms
Coster et al. (2018) / (22)	UK	Mixed-methods (Delphi + consensus)	Ambulance services, clinicians, patients	Routine	Electronic voting system, SPSS
Redlener et al. (2018) / (54)	USA	Quantitative survey	National EMS agencies (45 states)	Routine	SurveyMonkey, SAS
Bachman et al. (2019) / (11)	USA	Quantitative (simulation)	Urban/suburban ALS EMS (active shooter)	Crisis	Microsoft Access, Excel
Azami-Aghdash et al. (2021) / (68)	Iran	Mixed-methods (Delphi)	Pre-hospital EMS for RTIs	Routine	Email (Delphi)
Alem et al. (2021) / (69)	France	Mixed-methods (Delphi)	Emergency call centres (CRRAs)	Routine	Operational dashboard in Excel
Eun et al. (2013) / (53)	South Korea	Quantitative analytical	Regional EMS systems	Routine	NEDIS, SAS, SPSS
Chegini et al. (2023) / (19)	Iran	Quantitative cross-sectional	Pre-hospital EMS missions	Routine	Asayar electronic registration system
McLeod et al. (2010) / (32)	Canada	Quantitative before-after	Calgary EDs and EMS	Routine	REPAC real-time dashboard
Christen et al. (2020) / (50)	Switzerland	Qualitative descriptive	EMS team at hospital	Routine	Modular dashboard prototype
Hermans et al. (2017) / (33)	Netherlands	Quantitative before-after	STEMI patients	Routine	H2H real-time dashboard
Drennan et al. (2024) / (40)	Canada	Quantitative before-after	Adult OHCA patients	Routine	AccuVent® real-time feedback device
Hanf et al. (2024) / (41)	France	Quantitative (dashboard development)	Regional ED/EMS	Routine	Near real-time interactive dashboard
Salvi et al. (2024) / (49)	USA	User-centered design	Overdose Fatality Review teams	Routine	Real-time overdose touchpoints dashboard
Heaton et al. (2019) / (42)	USA	System development	High-acuity ED patients	Routine	TeleEM real-time dashboard
Guigues et al. (2024) / (51)	Brazil/USA	Platform development	EMS operations	Routine	Visualization and simulation platform
Ghanbari et al. (2025) / (20)	Iran	Quantitative cross-sectional	Prehospital EMS	Routine	Electronic Information Management System (EIMS)
Schröder et al. (2024) / (48)	Germany	Quantitative observational	Aachen EMS system	Routine	Telemedicine system (TM-Doku)
Smart et al. (2015) / (47)	Europe	Prospective observational	Prehospital EMS professionals	Routine	SimPad SkillReporter
Hostler et al. (2011) / USA/ (38)	USA/Canada	Cluster-randomized trial	Prehospital cardiac arrest	Routine	Philips MRx (QCPR software)
Lakomek et al. (2020) / (39)	Germany	Prospective cohort	Out-of-hospital cardiac arrest	Routine	corpus³ defibrillator with feedback
Lyngby et al. (2021) / (36)	Denmark	Randomized controlled trial (simulation)	Simulated OHCA	Routine	AccuVent™ real-time feedback devices
Charlton et al. (2021) / (34)	UK	Before-after (simulation)	Ambulance clinicians	Routine	AccuVent™ real-time feedback devices
Lyngby et al. (2023) / (35)	USA	Before-after (simulation)	Simulated resuscitation	Routine	Real Vent Help feedback devices
Gould et al. (2020) / (37)	USA	Prospective observational study (simulation)	20 EMTs (10 teams of 2) performing simulated cardiac arrest CPR on an intubated manikin	Routine	AccuVent™ real-time visual feedback system (differential pressure-based flow sensor) displayed on ZOLL X Series® monitor/defibrillator

EMS, Emergency Medical Services; PreMIS, Prehospital Medical Information System; SMARTT, Stroke, Myocardial Infarction, Appropriate Response Time, and Trauma; PAROS, Pan-Asian Resuscitation Outcomes Study; SPSS, Statistical Package for the Social Sciences; SAS, Statistical Analysis System; ALS, Advanced Life Support; NEDIS, National Emergency Department Information System; RTI, Road Traffic Injury; ED, Emergency Department; H2H, Hospital-to-Hospital; STEMI, ST-Segment Elevation Myocardial Infarction; OHCA, Out-of-Hospital Cardiac Arrest; QCPR, Quality Cardiopulmonary Resuscitation; EMT, Emergency Medical Technician; ePCR, Electronic Patient Care Report.

Table 3.

Summary of Dashboard Characteristics, Performance Indicators, and Main Findings

Author (Year) /	Dashboard Function	Performance Indicators (KPIs)	Donabedian Domain	Main Findings
Mears et al. (2010) / (52)	Statewide integrated data system	Response time, data quality, linkage to registries	Structure, Process, Outcome	Successful statewide EMS data system; significant improvements in service delivery and patient care.
Rahman et al. (2015) / (67)	Electronic data collection (PAROS)	Structure, process, outcome uniformity	Structure, Process, Outcome	Non-uniformity across Asian cities; data gaps in developing systems.
Coster et al. (2018) / (22)	Consensus on measures	Pain, patient experience, dispatch accuracy, survival	Process, Outcome	Prioritized 20 measures for ambulance performance.
Redlener et al. (2018) / (54)	National QI survey	QI staff, clinical metrics tracking	Structure, Process	Significant variability in QI adoption across US EMS agencies.
Bachman et al. (2019) / (11)	Simulation data analysis	Time intervals, tourniquet placement	Process	Effective integrated RTF model for active threats.
Azami-Aghdash et al. (2021) / (68)	Delphi-based indicator development	Quality indicators for RTIs	Structure, Process, Outcome	37 QMIs developed for road traffic injury EMS.
Alem et al. (2021) / (69)	Operational quality dashboard	Call handling, resources, quality approach	Structure, Process	70 prioritized indicators for emergency call centres.
Eun et al. (2013) / (53)	EMSI composite index	Composite performance score	Structure, Process, Outcome	EMSI validated as useful evaluation tool.
Chegini et al. (2023) / (19)	Electronic registration system	Time indicators (response, scene, transport)	Process	Most indicators below standards; room for improvement.
McLeod et al. (2010) / (32)	Real-time capacity dashboard	Favorable status time, EMS avoidances	Process	Reduced ambulance diversion and improved ED flow.
Christen et al. (2020) / (50)	Modular operational dashboard	Staff/vehicle allocation, traffic, news	Structure, Process	Improved information access replacing whiteboard.
Hermans et al. (2017) / (33)	H2H time monitoring dashboard	Call-to-balloon, FMC-to-balloon times	Process	Significant reduction in ischaemic times for STEMI.
Drennan et al. (2024) / (40)	Real-time ventilation feedback	Ventilation rate/volume compliance	Process	Improved ventilation quality during OHCA.
Hanf et al. (2024) / (41)	Near real-time demand dashboard	ED visits, EMS calls, predictions	Process, Outcome	High access; improved situational awareness.
Salvi et al. (2024) / (49)	Overdose touchpoints dashboard	Prior system interactions	Outcome	Enhanced OFR recommendations via touchpoint data.
Heaton et al. (2019) / (42)	TeleEM real-time dashboard	High-acuity patient alerts	Process	Proactive engagement for high-acuity patients.
Guigues et al. (2024) / (51)	Visualization & simulation platform	Response times, ambulance movements	Process	Comprehensive tool for dispatch and training.
Ghanbari et al. (2025) / (20)	Electronic Information Management System	Prehospital time indicators	Process	Mixed impact; most times below standards.
Schröder et al. (2024) / (48)	Telemedicine documentation system	Teleconsultation rates, quality indicators	Structure, Process	Long-term improvements in EMS quality.
Smart et al. (2015) / (47)	Real-time CPR feedback	Compression/ventilation quality	Process	Feedback + competition improved manikin CPR performance.
Hostler et al. (2011) / USA/ (38)	Real-time CPR feedback	CPR metrics, ROSC, survival	Process, Outcome	Improved CPR performance but no clinical outcome benefit.
Lakomek et al. (2020) / (39)	Real-time CPR feedback sensor	Compression fraction, rate, depth	Process	Improved pauses and rate; depth stable.
Lyngby et al. (2021) / (36)	Real-time ventilation feedback	Ventilation rate & tidal volume	Process	Increased guideline compliance in simulation.
Charlton et al. (2021) / (34)	Real-time ventilation feedback	Ventilation rate & tidal volume	Process	Improved compliance; baseline often non-compliant.
Lyngby et al. (2023) / (35)	Real-time ventilation feedback	Ventilation rate & volume	Process	Improved ventilation quality in simulation.
Gould et al. (2020) / (37)	Real-time visual feedback on ventilation rate and tidal volume	Ventilation rate (target: 8–10 breaths/min) and tidal volume (target: 500 ± 75 ml / 425–575 ml)	Process	Mean percentage of ventilations in target increased significantly with feedback: rate (41% → 71%, p < 0.01), volume (31% → 79%, p < 0.01), and rate + volume combined (10% → 63%, p < 0.01). Compression quality also improved simultaneously.

PAROS, Pan-Asian Resuscitation Outcomes Study; QI, Quality Improvement; RTI, Road Traffic Injury; EMSI, Emergency Medical Services Index; H2H, Hospital-to-Hospital; STEMI, ST-Segment Elevation Myocardial Infarction; OHCA, Out-of-Hospital Cardiac Arrest; OFR, Overdose Fatality Review; EMS, Emergency Medical Services; CPR, Cardiopulmonary Resuscitation; ROSC, Return of Spontaneous Circulation.

Table 4.

EMS key performance indicators (Operational Definitions & Data Sources by Donabedian Model)

Indicator Name	Operational Definition	Primary Data Source(s)	Donabedian Category	References
Personnel and ambulance density	(1) EMS staff per 10,000 population; (2) Ambulances per 100 km²	HR records, fleet registries	Structure	(19, 20, 22, 67)
EMS organization type & readiness	(1) System type classification; (2) Existence of formal response protocols	Policy manuals, national EMS registry	Structure	(19, 22, 52, 67)
Training and competency level	(1) Percentage of staff certified in BLS/ACLS/ATLS; (2) Annual training hours per staff	HR training database, certification logs	Structure	(19, 22, 53, 67)
Infrastructure and communication systems	(1) Existence of interoperable ICT systems; (2) Real-time communication capacity	IT audit reports, network logs	Structure	(49, 67)
Availability of medical equipment	(1) Number of functional defibrillators per ambulance; (2) Equipment readiness index	Asset inventory, maintenance records	Structure	(19, 52, 54, 67)
Data interoperability level	(1) Data-sharing score across systems; (2) Use of standard exchange formats	IT integration docs, system interface reports	Structure	(22, 52, 53)
Response time	(1) Call receipt to arrival; (2) Dispatch to arrival	CAD logs, GPS timestamps	Process	(19, 20, 22, 34, 48, 54, 67)
Dispatch accuracy	(1) Percentage of correctly prioritized dispatches; (2) False dispatch rate	Dispatch QA records	Process	(19, 22, 52, 67)
On-scene time	Arrival to departure time	Mission documentation	Process	(19, 20, 22, 53, 67)
Transport time	Scene departure to hospital arrival	GPS and ADT data	Process	(19, 20, 22, 67)
Call processing time	Call receipt to dispatch initiation	Dispatch logs	Process	(19, 22, 49, 67)
Incident command establishment time	Alert to full command setup	Command center reports	Process	(49)
Triage efficiency	Correct triage classification rate (%)	Scene triage forms	Process	(49)
Protocol compliance	Compliance with clinical guidelines (%)	ePCR system	Process	(22, 34, 37, 42)
Chest compression depth	Mean depth in centimeters (target: 5-6 cm)	Defibrillator accelerometer data	Process	(11, 35, 36, 38-40, 47)
Chest compression rate	Mean rate per minute (target: 100-120/min)	Defibrillator accelerometer data	Process	(11, 35, 36, 38-40, 47)
Chest compression fraction (CCF)	Proportion of resuscitation time with compressions (%)	Defibrillator impedance data	Process	(11, 35, 36, 38-40, 47)
Ventilation rate	Breaths per minute (target: 8-10/min)	AccuVent™ or similar sensor	Process	(34-37, 40, 41, 50)
Ventilation volume (Tidal volume)	Tidal volume in ml (target: 500-600 ml)	AccuVent™ or similar sensor	Process	(34-37, 40, 41, 50)
Peri-shock pause time	Time between last compression and shock delivery (target: <10s)	Defibrillator ECG data	Process	(39)
Interorganizational coordination time	Time to first joint coordination	Communication logs	Process	(49)
Patient survival rate	Survival to hospital discharge (%)	Hospital outcome registries	Outcome	(42, 47, 49, 51)
Return of spontaneous circulation (ROSC)	Sustained ROSC (>20 min) or any ROSC	CPR registries	Outcome	(11, 35, 36, 38-40, 47)
Mortality rate during transport	Deaths during transfer per 1000 transports	Transport records	Outcome	(19, 52, 67)
Neurological outcome (CPC score)	CPC 1 or 2 at 1-month or discharge	Hospital outcome registries	Outcome	(47, 51)
Patient satisfaction level	Positive satisfaction responses (%)	Patient surveys	Outcome	(19, 20, 67)
Touchpoint prevalence	Percentage of decedents with prior system interaction	Linked administrative databases	Outcome	(36, 49)

EMS, Emergency Medical Services; HR, Human Resources; BLS, Basic Life Support; ACLS, Advanced Cardiac Life Support; ATLS, Advanced Trauma Life Support; ICT, Information and Communication Technology; IT, Information Technology; CAD, Computer-Aided Dispatch; GPS, Global Positioning System; QA, Quality Assurance; ADT, Admission, Discharge, Transfer; ePCR, Electronic Patient Care Report; CCF, Chest Compression Fraction; ECG, Electrocardiogram; ROSC, Return of Spontaneous Circulation; CPR, Cardiopulmonary Resuscitation; CPC, Cerebral Performance Category.

Table 5.

EMS Performance Indicators by Donabedian Domains, Dashboard Capabilities, and Context

No.	Indicator Name	Donabedian Category	Feature Required	Context	References	Classification
Structural Indicators
1	Personnel and ambulance density	Structure	GIS mapping, HR data integration	Routine	(19, 20, 22, 67)	Dashboard
2	EMS organization type & readiness	Structure	Registry dashboard	Routine	(19, 22, 52, 67)	Dashboard
3	Training and competency level	Structure	Training tracking dashboard	Routine	(19, 22, 53, 67)	Dashboard
4	Infrastructure and communication systems	Structure	IoT connectivity visualization	Crisis	(49, 67)	Dashboard
5	Availability of medical equipment	Structure	Asset monitoring dashboard	Routine	(19, 52, 54, 67)	Dashboard
6	Data interoperability level	Structure	API integration dashboard	Routine	(22, 52, 53)	Dashboard
Process Indicators
7	Response time	Process	Real-time GPS tracking	Routine	(19, 20, 22, 34, 48, 54, 67)	Dashboard
8	Dispatch accuracy	Process	CAD analytics dashboard	Routine	(19, 22, 52, 67)	Dashboard
9	On-scene time	Process	Live telemetry tracking	Routine	(19, 20, 22, 53, 67)	Dashboard
10	Transport time	Process	GPS tracking	Routine	(19, 20, 22, 67)	Dashboard
11	Call processing time	Process	CAD system timer	Routine	(19, 22, 49, 67)	Dashboard
12	Incident command establishment time	Process	Command dashboard	Crisis	(49)	Dashboard
13	Triage efficiency	Process	Decision-support triage tool	Crisis	(49)	Dashboard
14	Protocol compliance	Process	Compliance monitor	Routine	(22, 34, 37, 42)	Dashboard
15	Chest compression depth	Process	Real-time feedback display	Routine	(11, 35, 36, 38-40, 47)	Feedback Device
16	Chest compression rate	Process	Real-time feedback display	Routine	(11, 35, 36, 38-40, 47)	Feedback Device
17	Chest compression fraction (CCF)	Process	Real-time feedback display	Routine	(11, 35, 36, 38-40, 47)	Feedback Device
18	Ventilation rate	Process	Real-time feedback display	Routine	(34-37, 40, 41, 50)	Feedback Device
19	Ventilation volume (Tidal volume)	Process	Real-time feedback display	Routine	(34-37, 40, 41, 50)	Feedback Device
20	Peri-shock pause time	Process	Real-time feedback display	Routine	(39)	Feedback Device
21	Interorganizational coordination time	Process	Multiagency dashboard	Crisis	(49)	Dashboard
Outcome Indicators
22	Patient survival rate	Outcome	Outcome tracking dashboard	Routine	(42, 47, 49, 51)	Dashboard
23	Return of spontaneous circulation (ROSC)	Outcome	CPR performance tracker	Routine	(11, 35, 36, 38-40, 47)	Feedback Device
24	Mortality rate during transport	Outcome	Linked EMS-hospital database	Routine	(19, 52, 67)	Dashboard
25	Neurological outcome (CPC score)	Outcome	Outcome tracking dashboard	Routine	(47, 51)	Dashboard
26	Patient satisfaction level	Outcome	Feedback device	Routine	(19, 20, 67)	Dashboard
27	Touchpoint prevalence	Outcome	Surveillance dashboard	Routine	(36, 49)	Dashboard

Dashboards were defined as visual display systems that aggregate and integrate data from multiple sources to present aggregated system-level KPIs. Indicators marked as "Feedback Device" were measured using unidimensional, device-specific tools (e.g., CPR feedback monitors, ventilation sensors) and do not meet the dashboard definition. This table categorizes performance indicators based on their measurability with dashboards or feedback devices, not the number of studies that used each tool. The actual number of studies that utilized dashboards (n = 8) and feedback devices (n = 8) is reported in Table 2 and Section 3.3. Abbreviations: EMS, Emergency Medical Services; GIS, Geographic Information System; HR, Human Resources; IoT, Internet of Things; API, Application Programming Interface; GPS, Global Positioning System; CAD, Computer-Aided Dispatch; CCF, Chest Compression Fraction; CPR, Cardiopulmonary Resuscitation; ROSC, Return of Spontaneous Circulation; CPC, Cerebral Performance Category.

3. Results

3.1 Study Selection and Summary of Included Articles

A systematic search of PubMed, Scopus, Embase, and Web of Science resulted in the identification of 1,354 records. A total of 908 articles were screened for title and abstract after excluding 446 duplicates. Of those records, 830 were removed because they did not fit the pre-established criteria for relevance to prehospital EMS, did not have measurable performance indicators, or were exclusively for in-hospital care. The full texts of the remaining 78 articles were then evaluated for eligibility, and 54 were excluded for being either not specific enough about digital technologies, lacking formal empirical data, or reporting duplicate results. Finally, a total of 26 studies were selected for this scoping review. The process of screening studies is shown in the PRISMA-ScR flow chart (Figure 1).

The features of the studies included are detailed in Table 2. The studies were carried out in different countries, healthcare systems, and a wide range of methods such as descriptive, observational, qualitative, and consensus-based, simulation, and interventional study designs. The majority of studies explored routine EMS operations, while one study focused on EMS performance in crisis. The reviewed studies also assessed a wide array of digital monitoring technologies, such as operational dashboards, clinical dashboards, public health surveillance platforms, telemedicine systems, real-time feedback devices, and other digital monitoring technologies.

3.2 Classification of Performance Indicators

A total of 27 distinct performance indicators were identified across the 26 included studies and categorized according to the Donabedian model into three domains: structure, process, and outcome. Indicators were further grouped based on operational context (routine versus crisis) and, where available, by crisis management phase (before, during, and after). Table 3 provides a summary of dashboard functions, performance indicators, Donabedian classification, and main findings for each included study. Detailed operational definitions and primary data sources for all 27 indicators are presented in Table 4.

Process indicators were the most frequently reported, comprising predominantly time-based metrics such as response time, dispatch time, call processing time, on-scene time, and transport time. Quality of care indicators were also commonly reported, including triage accuracy, protocol adherence, chest compression quality, and ventilation quality. Among process indicators, time-based measures appeared in 22 studies (85%), while quality-of-care indicators were reported in 14 studies (54%).

Structure indicators primarily focused on resource availability and operational capacity, including the number of available ambulances, personnel deployed, hospital bed capacity, staff training levels, and communication infrastructure. These indicators were reported in 11 studies (42%) and were more prevalent in consensus-based and survey studies.

Outcome indicators were reported less frequently than the other two domains and included return of spontaneous circulation (ROSC), survival to hospital discharge, neurological outcome (Cerebral Performance Category score), and patient satisfaction. These indicators appeared in nine studies (35%). Notably, outcome indicators were reported more inconsistently in studies using real-time feedback devices, whereas dashboard-based studies tended to report process and structure indicators more consistently.

The complete classification of indicators by Donabedian domain, along with operational definitions, data sources, and corresponding references, is provided in Table 4. This classification reveals that process indicators constitute the largest share, with 15 indicators (56%), followed by structure indicators with six indicators (22%) and outcome indicators with six indicators (22%).

3.3 Classification of Digital Tools and Dashboard Utilization

All studies in this review (100%) employed some form of digital technology, ranging from simple electronic record-keeping systems to sophisticated real-time feedback systems. Among the 26 included studies, eight (30.8%) utilized dashboards as defined in this review. Eight studies (30.8%) employed real-time feedback devices for CPR quality assessment, which were not classified as dashboards, and ten studies (38.5%) used other digital tools. A detailed summary of dashboard functions and performance indicators for each study is provided in Table 3.

Real-time feedback devices: The most commonly cited digital tools were real-time feedback devices integrated into defibrillators or monitors. Studies by Drennan et al. (40), Lyngby et al. (35, 36), Charlton et al. (34), Gould et al. (37), Hostler et al. (38), Lakomek et al. (39), and Smart et al. (47) identified these devices for assessing CPR quality. These tools were not considered dashboards in this review because they provide device-specific, unidimensional feedback and do not integrate multiple data sources or present system-level indicators.

Ambulance allocation and hospital capacity management dashboards: This category included real-time dashboards designed to manage resource allocation and hospital capacity. McLeod et al. (32) implemented a regional dashboard (REPAC) to reduce ambulance diversion time and improve hospital accessibility. Hermans et al. (33) developed a hospital-to-hospital (H2H) time monitoring dashboard that significantly reduced call-to-balloon times for STEMI patients.

Telemedicine and telehealth dashboards: These dashboards were designed for remote patient monitoring and consultation. Heaton et al. (42) developed a TeleEM real-time dashboard to increase awareness of high-acuity emergency department patients across an enterprise practice. Schröder et al. (48) examined the long-term effects of a prehospital telemedicine system on structural and process quality indicators.

Public health surveillance dashboards: This category included dashboards designed to track emergency care demand and epidemiological trends. Hanf et al. (41) developed a near-real-time interactive dashboard for monitoring and anticipating emergency care demand in the Île-de-France region. Salvi et al. (49) created a real-time dashboard for tracking overdose touchpoints to inform fatality review recommendations.

Visualization and simulation platforms: Christen et al. (50) developed a modular dashboard prototype that integrated staff and vehicle allocation, traffic, and news information for an EMS team at a hospital. Guigues et al. (51) created a comprehensive visualization and simulation platform for EMS operations, supporting both dispatch coordination and training activities.

Other digital tools: Ten studies (38.5%) employed other forms of digital technology that fell outside the dashboard definition. These included electronic patient care reporting systems (ePCR) such as North Carolina's PreMIS system (52), statistical software (SAS, SPSS) (53, 54), online survey platforms (SurveyMonkey) (54), computer-aided dispatch (CAD) systems, and telemedicine documentation platforms (TM-Doku) (48).

3.4 Distribution of Dashboard Utilization Across Routine and Crisis Contexts

The marked distinctions in the usage of the dashboard between the routine and crisis EMS were included among the results of this literature review. Out of eight studies mentioning the development of the dashboards, all (100%) had relevance to routine EMS tasks. The articles covered topics like real-time dispatching and capacity management (McLeod et al.)(32) ; clinical dashboards in STEMI treatment (33); telehealth and remote monitoring applications(41, 42); operational visualization and simulation tools (50, 51); and public health surveillance for overdose and demand for emergency care(41, 49). Additionally, we discovered real-time feedback devices for measuring the quality of CPR in the routine environment (34-40, 47); however, these were not regarded as dashboards as part of this review due to their device-oriented one-dimensionality.

However, the literature available does not report any use of dashboards during a mass casualty incident, disaster, or an active shooter incident, as was found in the only one study focused specifically on EMS operations during a crisis. Bachman et al. (11) used basic digital tools for data collection and analysis (Microsoft Access and Excel), but these were not dashboards, as described in this review. No other study in this review used any type of digital technology to explore crisis conditions. The result highlights a major gap in the literature: dashboards have been increasingly used in the daily operation of EMS to improve performance monitoring and decision-making, although there is little research on how they can be used in crisis and disaster management. The structured summary of EMS performance indicators by Donabedian domains, dashboard features necessary for implementation, and applicability in routine situations compared with crises is presented in Table 5.

3.5 Summary of Key Findings

The main findings of this scoping review can be summarized as follows:

Eight studies (31%) utilized dashboards as defined in this review (integrating multiple data sources and presenting aggregated KPIs for system-level decision support).

Eight studies (31%) employed real-time feedback devices for CPR quality assessment, which were not classified as dashboards due to their device-specific, unidimensional nature.

Ten studies (38%) used other digital tools (e.g., ePCR, statistical software, CAD) without dashboards.

Only one study examined crisis conditions using digital tools, and this study did not use a dashboard.

A total of 27 distinct performance indicators were identified, categorized into structure, process, and outcome domains using the Donabedian framework.

Process indicators were the most frequently reported domain overall, appearing in 22 studies (85%). Among these, time-based indicators (e.g., response time, dispatch time, transport time) were the most common sub-category, while CPR quality indicators (e.g., compression depth, rate, fraction, and ventilation volume) were reported in 14 studies (54%). Notably, CPR quality indicators were predominantly measured using real-time feedback devices, which were not classified as dashboards in this review.

Outcome indicators such as ROSC, survival, and neurological status were less consistently reported across studies.

4. Discussion

The typical scenario for routine EMS operations includes known call volumes, consistent clinical care pathways, and established performance metrics, which makes dashboard systems useful for operational monitoring and allocation of resources (32, 50), telemedicine (42, 48), and clinical quality improvement (33). Conversely, crises like disasters and Mass Casualty Incidents are dynamic, resource-limited, uncertain, and have evolving decision-making needs. However, despite all these operational differences, only one of the studies included considered the performance of the EMS in crises, and none reported the use of dashboards for real-time crisis management (11). Thus, the available literature does not reveal a significantly large body of evidence regarding the ineffectiveness of dashboard applications in a crisis, but rather an evidence gap concerning dashboard applications in crises.

4.1 The Crisis Gap and Routine Dashboards

The most important conclusion from this review is that there is little evidence of dashboard use in EMS studies related to crises. Of the 26 studies included, only one study, by Bachman et al. (11), was conducted in crisis conditions (active shooter events) using some type of digital technology. Basic digital tools (Microsoft Access and Excel) were used in this review for data collection and analysis, but not as dashboards.

This is limited evidence that may indicate some gaps in the literature. In a recently published scoping review, Markou-Pappas et al. analyzed 14 studies of prehospital disaster and MCI response KPIs, and all of the articles measured time indicators; only 2 of the 14 articles measured indicators of structure, process, and outcome (15). Interestingly, no study among the included studies reported using dashboards for real-time KPI monitoring.

Evaluating prehospital crisis response has been a problem for several decades now. Rüter and colleagues had previously started the process of developing performance indicators for prehospital command and control in training exercises (55), while Debacker et al. had suggested a uniform reporting system for disaster-related data (56), which is based on the Utstein form. However, most of these efforts have been limited to after-the-event documentation and reporting, not real-time dashboard monitoring. Recently, Kaim et al. created a real-time assessment tool featuring a dashboard functionality for Emergency Medical Technician (EMT) field hospitals, which is specific to field hospitals, not prehospital EMS response to MCIs (57).

The authors created a real-time assessment tool with dashboard functionality that includes six domains of KPIs, each validated. This tool was field-tested in a simulated outbreak response and reported high overall performance scores (4.52/5), but was developed specifically for EMT field hospitals and not for prehospital EMS responses to MCIs (57). Nonetheless, this work provides a promising model for developing similar tools for prehospital crisis settings.

The eight studies reporting the use of dashboards during routine EMS operations provide a positive example of both the possibility and the positive outcomes of using dashboards in these situations. Real-time dispatch dashboards (McLeod et al.) have proven to decrease ambulance diversion and enhance hospital access (32). Call-to-balloon times have significantly decreased in STEMI care with clinical dashboards (33). Quality assurance feedback devices for CPR have been consistently associated with better CPR adherence to resuscitation guidelines (34-40). These devices are not considered dashboards in this review because they provide feedback specific to the device, not incorporating multiple data sources or offering system-level indicators of performance.

However, there are some difficulties even in normal situations. A national survey of Dutch emergency departments (EDs) assessing the use of a real-time ambulance diversion dashboard revealed that 75.6% of EDs used the dashboard, but only 47.2% of respondents reported that it had a positive impact on the inflow of patients (58). The discovery highlights the importance of recognizing that even the best-designed dashboards have drawbacks and need to be embedded in larger system-level interventions.

Given the evidence available, the reasons why dashboards were not identified in crisis settings are likely to be multifaceted. Firstly, the development and validation process of crisis-specific performance indicators is not well developed (56, 58). Second, technical and logistical constraints, such as connectivity and interoperability issues, might limit implementation (15). Thirdly, there is a lack of standardized instruments and benchmarks for prehospital crisis response, which is a huge barrier (59, 60). Fourth, crisis management is frequently seen as episodic and unpredictable, with ad hoc teams and small amounts of resources to collect and analyze information (50).

4.2 Summary of Main Findings and Implications

This scoping review identified the current evidence related to dashboard applications and prehospital EMS performance indicators. Most of the available evidence came from studies in the context of routine EMS operations, where dashboards are used to monitor operations, manage hospital capacity, provide telemedicine, and improve quality (32, 33, 40-42, 49, 51). By contrast, only one of the included studies specifically examined EMS performance in a crisis context (Bachman et al.); however, this study did not use a dashboard and instead relied on basic digital tools (Microsoft Access and Excel) for data collection and analysis (11). Some studies also showed that real-time feedback technologies have been associated with improvements in CPR process measures, but they did not satisfy the dashboard criteria defined in this review (34-40).

This review has implications for several stakeholder groups. Dashboard technologies can help EMS clinicians and managers monitor EMS performance, be aware of EMS operations, and implement quality improvement efforts. The scarcity of evidence that was found in disaster and mass casualty settings calls for additional investments in context-sensitive dashboard systems that aid emergency preparedness and response for healthcare organizations and policymakers. Finally, researchers and technology developers must consider and investigate future evidence gaps in the development of integrated dashboard solutions for crisis and disaster management.

4.3 Implications and Limitations

The findings of this review have several implications for EMS practice, research, and policy. First, EMS agencies and disaster management organizations should prioritize the development of crisis-specific dashboards that integrate data from multiple sources and provide real-time situational awareness to command centers and field personnel. The recent work by Kaim et al. (57) provides a promising model, but similar tools are needed specifically for prehospital EMS response. Second, standardized crisis KPIs should be established through multi-stakeholder consensus processes, such as Delphi studies or expert panels, building on existing frameworks like the Utstein-style template (56). Third, interoperability standards should be established to enable seamless data exchange between dispatch centers, ambulances, hospitals, and public health agencies (61). Fourth, training programs should be developed to enhance EMS providers' data literacy and ability to interpret dashboard outputs in high-stress environments (16).

Additionally, researchers and practitioners should consider the potential of low-tech, low-cost digital solutions for crisis settings. While sophisticated dashboard platforms offer significant advantages, basic data capture tools could provide meaningful improvements in situational awareness without requiring extensive technical infrastructure (42). Pilot studies in resource-constrained settings have demonstrated the feasibility of such approaches (62).

While direct evidence for dashboards in prehospital crisis management is limited, examples from other healthcare sectors may offer insights for future research. In hospital emergency departments, dashboards have been shown to improve patient flow, reduce crowding, and enhance team communication (31, 63). In disaster medicine, dashboards have been primarily confined to epidemiological surveillance and resource tracking (64). However, these examples do not constitute direct evidence for prehospital EMS crisis management, and further research is needed to determine whether similar benefits could be achieved in this context. The lack of operational dashboards for prehospital crisis response represents a critical unmet need, as highlighted by multiple reviews (15, 60, 65). Furthermore, military and tactical medicine communities have developed sophisticated command and control systems that integrate real-time data, but these are not typically available to civilian EMS providers (62).

Several limitations should be acknowledged. The restriction to English and Persian language studies may have introduced language bias. The heterogeneity of study designs, settings, and outcome measures precluded quantitative synthesis. In accordance with established scoping review methodology, a formal critical appraisal (risk of bias assessment) of the included studies was not undertaken. This approach is consistent with the PRISMA-ScR guideline, which considers critical appraisal an optional reporting item (Item 12), and with the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis, which states that critical appraisal is not mandatory for scoping reviews because their primary purpose is to map the available evidence and identify knowledge gaps rather than evaluate the methodological quality or effectiveness of the included studies (45, 46).

The search may have missed studies published in non-indexed journals or conference proceedings. Finally, the majority of studies were from high-income settings, limiting generalizability to low- and middle-income countries (66).

5. Conclusions

This scoping review of 26 studies identified limited evidence on the use of dashboards for monitoring prehospital performance indicators during mass casualty incidents, disasters, or active shooter events. Although dashboards have demonstrated potential benefits for operational monitoring and decision-making in routine EMS settings, only one included study addressed a crisis context and none reported the use of a dashboard for real-time crisis management. These findings indicate an important gap in the current evidence base, but should be interpreted cautiously given the small number of crisis-specific studies and the heterogeneity of the included evidence. Future research should focus on developing and rigorously evaluating context-adaptive dashboards that can support real-time performance monitoring, resource allocation, and decision-making during prehospital crises.

6. Declarations

6.1 Acknowledgments

No individual who does not meet the authorship criteria contributed to this manuscript. Therefore, there are no acknowledgments to declare.

6.2 Conflict of interests

The authors declare that they have no conflicts of interest.

6.3 Funding and supports

No specific funding or financial support was received for this study.

6.4 Authors' contributions

All authors contributed to the conception and design of the study. Shiva Karimi, Hassan Emami, Farkhondeh Asadi, Milad Ahmadi Marzaleh, Ali Delpisheh, and Mojtaba Pourmehdi contributed to the acquisition, analysis, and interpretation of the data. All authors contributed to drafting the manuscript or revising it critically for important intellectual content. All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work.

6.5 Ethical statement

Since this was a scoping review of previously published literature, the study did not involve primary data collection, any human subjects, or individual patient data; therefore, informed consent was not required. The selection of articles, extraction of data, interpretation, and reporting were performed with the utmost care in accordance with scientific ethical principles such as honesty, transparency, impartiality, and avoiding bias. The study was carried out following the Declaration of Helsinki and the guidelines for ethical conduct in health research.

6.6 Informed consent

Not applicable.

6.7 Availability of supporting data

All data analyzed in this scoping review were obtained from published studies cited in the manuscript. The extracted data are presented in the tables and supplementary materials, where applicable.

6.8 Using artificial intelligence chatbots statement

During the preparation of this manuscript, artificial intelligence-based tools were used solely to assist with language editing, grammar checking, and improvement of clarity and readability. The authors reviewed and verified all AI-assisted text and take full responsibility for the accuracy, originality, and integrity of the final manuscript. No artificial intelligence tool was used to generate, analyze, or interpret the study data or to make scientific or methodological decisions.