Tomás Barry

Background: The current landscape of emergency care (EC) is marked by high demand, leading to issues such as emergency department boarding, overcrowding, and subsequent delays that impact the quality and safety of patient care. Integrating data science into EC can enhance decision-making with predictive, preventative, personalized, and participatory approaches. However, gaps in adherence to fairness, accountability, interpretability, and responsibility are evident, particularly due to barriers to data-sharing, which often result in a lack of transparency and robust oversight in these applications. Objective: The FAIR-EC (Fair, Accountable, Interpretable, and Responsible-Emergency Care) collaboration adapts the existing Fair, Accountable, Interpretable, and Responsible principles to address emerging challenges as data science integrates with EC. This initiative aims to transform EC by establishing ethical artificial intelligence standards specifically tailored for this integration. By bridging the gap between EC professionals, data scientists, and other stakeholders, the collaboration promotes international cooperation that leverages advanced data science techniques to enhance EC outcomes across different care settings. Methods: We propose a federated research design to analyze extensive datasets from various global institutions without compromising patient privacy. This approach transforms epidemiological research with advanced data science techniques, emphasizing the harmonization of data for comprehensive analyses across different health care systems. Results: The FAIR-EC initiative has facilitated the identification and harmonization of datasets from diverse geographical regions, enabling the examination of regional variations in EC practices. As of paper submission, participating sites have identified retrospective EC datasets totaling >2 million records (eg, Duke Health >400,000 and Singapore General Hospital >1.7 million records). Initial projects have demonstrated feasibility and operational readiness, including implementation of federated workflows and ongoing development of a federated scoring system, cross-site evaluation, and adaptation of association studies and predictive models across various regions. Cross-site harmonization and pilot analyses are underway (with local ethics approvals in progress), and first multisite results are expected to be submitted in mid-late 2026, with additional project-level publications anticipated in 2027. These efforts highlight the feasibility of leveraging advanced data science techniques to address the complexities of EC while preserving patient privacy without centralizing individual-level data. This project was funded from September 1, 2022, to August 31, 2023. Conclusions: FAIR-EC integrates data science ethically and effectively into EC, addressing challenges such as fragmented data, real-time handoffs, and public health crises. Its federated design harmonizes diverse data streams while preserving privacy, and its emphasis on ethical artificial intelligence aligns with the dynamic nature of EC. Despite challenges in data variability and system complexity, FAIR-EC establishes a strong foundation for innovation in global EC.

Background: Expansion of paramedic roles in the community could reduce non-urgent emergency department (ED) attendances. In Ireland a knowledge gap exists regarding the development of alternative care pathways that avoid ED conveyance. The aim of this research was to explore inter-rater agreement amongst advanced paramedics (AP) about the ‘appropriateness’ of attendances to the ED in Ireland utilising data from the Better Data, Better Planning (BDBP) Study. Methods: The BDBP study was a multi-centre study investigating factors influencing ED utilisation in Ireland. For this secondary data analysis, a chart review was conducted and adult patient summary files ( n = 306) were assessed for measures of appropriateness by a national panel of six AP, including whether each presentation was considered an inappropriate use of ED resources and potentially could be managed by a General Practitioner (GP) within 24–48 h. A subset analysis compared outcomes for patients transported by ambulance with those that were transported to the ED by other means. Results: The AP panellists determined that between 12% to 73% of ED attendances were considered an inappropriate use of ED resources (Inter-rater Agreement 19%, κ = 0.182, p < .001) and that between 12% to 79% of cases were suitable for management by a GP within 24–48 h (Inter-rater Agreement 11%, κ = 0.120, p < .001). Inter-rater agreement was higher for patients transported by ambulance who tended to be older ( p < .001), live alone ( p ≤ .01), have higher triage scores ( p ≤ .01), and be admitted to hospital ( p ≤ .01). Conclusions: APs did not reach agreement on what constitutes an ‘inappropriate’ attendance to the ED. The reasons for this are multifactorial but it highlights the complexities in developing alternative care pathways for ED avoidance in the community setting. The potential for development of alternative care pathways in Ireland is significant but requires appropriate supervision and governance to support decision-making for paramedic roles with an expanded scope of practice.

Background Significant workforce challenges are evolving in Irish general practice, which limit all aspects of practice, especially out-of-hour care. Community Paramedics can support general practitioner (GP) out-of-hour care and potentially improve the system efficiency in Ireland. Methods Qualitative semi-structured interviews were conducted with purposively recruited participants using a schedule informed by the existing research. A thematic analysis was undertaken based on the Braun and Clarke approach and supported by the NVivo software. Themes were developed based on the concept of preliminary key model components. The study was conducted in accordance with Standards for Reporting Qualitative Research. Results Comprehensive data analysis generated four major themes: Workforce Management, Synergies in Healthcare, Progressive Futuristic Care, and Safe Practice. These themes formed the basis for the further development of the preliminary key model components. Conclusion This study suggests that the integration of Community Paramedics into GP out-of-hour care in Ireland can be successfully launched if workforce planning is carefully considered. The role of the Community Paramedic must be defined, regulated, and governed in conjunction with the National Ambulance Service, the Pre Hospital Emergency Care Council, and the Department of Health in Ireland. Patient care must be underpinned by safe and effective care practices that are continuously audited and evaluated, and the scope of practice must be defined and maintained by each Community Paramedic through a guided educational system that incorporates continuous professional development. GPs can provide support to community paramedics, including the use of information technology, as required. GPs and Community Paramedics can work together to synergize healthcare and provide safe and effective care to patients in the domiciliary setting during out-of-hour hours, thus reducing emergency department attendance and increasing the GP’s capacity to provide face-to-face consultations.

Background: Redirecting avoidable presentations to alternative care service pathways (ACSPs) may lead to better resource allocation for prehospital emergency care. Stratifying emergency department (ED) presentations by admission risk using diagnosis codes might be useful in identifying patients suitable for ACSPs. Objective: We aim to cluster ICD-10 (International Statistical Classification of Diseases, Tenth Revision) diagnosis codes based on hospital admission risk, identify ED presentation characteristics associated with these clusters, and develop an exploratory classification to identify groups potentially suitable for ACSPs. Methods: Retrospective observational data from a database of all visits to the ED of a tertiary care institution for over 5 years (2016-2020) were analyzed. K-means clustering grouped diagnosis codes according to admission outcomes. Multivariable logistic regression was performed to determine the association of characteristics with cluster membership. ICD-10 codes were grouped into blocks and analyzed for cumulative coverage to identify dominant groups associated with lower hospital admission risk. Results: A total of 215,477 ambulatory attendances classified as priority levels 3 (ambulatory) and 4 (nonemergency) under the Patient Acuity Category Scale were selected, with a 17.3% (0.4%) overall admission rate. The mean presentation age was 46.2 (SD 19.4) years. Four clusters with varying hospital admission risks were identified. Cluster 1 (n=131,531, 61%) had the lowest admission rate at 4.7% (0.2%), followed by cluster 2 (n=44,347, 20.6%) at 19.5% (0.4%), cluster 3 (n=27,829, 12.9%) at 47.8% (0.5%), and cluster 4 (n=11,770, 5.5%) with the highest admission rate at 78% (0.4%). The four-cluster solution achieved a silhouette score of 0.65, a Calinski-Harabasz Index of 3649.5, and a Davies-Bouldin Index of 0.46. Compared to clustering based on ICD-10 blocks, clustering based on individual ICD-10 codes demonstrated better separation. Mild (odds ratio [OR] 2.55, 95% CI 2.48-2.62), moderate (OR 2.40, 95% CI 2.28-2.51), and severe (OR 3.29, 95% CI 3.13-3.45) Charlson Comorbidity Index scores increased the odds of admission. Tachycardia (OR 1.46, 95% CI 1.43-1.49), hyperthermia (OR 2.32, 95% CI 2.25-2.40), recent surgery (OR 1.31, 95% CI 1.27-1.36), and recent inpatient admission (OR 1.16, 95% CI 1.13-1.18) also increased the odds of higher cluster membership. Among 132 ICD-10 blocks, 17 blocks accounted for 80% of cluster 1 cases, including musculoskeletal or connective tissue disorders and head or lower limbs injuries. Higher-risk categories included respiratory tract infections such as influenza and pneumonia, and infections of the skin and subcutaneous tissue. Conclusions: Most ambulatory presentations at the ED were categorized into low-risk clusters with a minimal likelihood of hospital admission. Stratifying ICD-10 diagnosis codes by admission outcomes and ranking them based on frequency provides a structured approach to potentially stratify admission risk.

BACKGROUND Redirecting avoidable presentations to alternative care service pathways (ACSPs) may lead to better resource allocation for prehospital emergency care. Stratifying emergency department (ED) presentations by admission risk using diagnosis codes might be useful in identifying patients suitable for ACSPs. OBJECTIVE We aim to cluster ICD-10 (International Statistical Classification of Diseases, Tenth Revision) diagnosis codes based on hospital admission risk, identify ED presentation characteristics associated with these clusters, and develop an exploratory classification to identify groups potentially suitable for ACSPs. METHODS Retrospective observational data from a database of all visits to the ED of a tertiary care institution for over 5 years (2016-2020) were analyzed. K-means clustering grouped diagnosis codes according to admission outcomes. Multivariable logistic regression was performed to determine the association of characteristics with cluster membership. ICD-10 codes were grouped into blocks and analyzed for cumulative coverage to identify dominant groups associated with lower hospital admission risk. RESULTS A total of 215,477 ambulatory attendances classified as priority levels 3 (ambulatory) and 4 (nonemergency) under the Patient Acuity Category Scale were selected, with a 17.3% (0.4%) overall admission rate. The mean presentation age was 46.2 (SD 19.4) years. Four clusters with varying hospital admission risks were identified. Cluster 1 (n=131,531, 61%) had the lowest admission rate at 4.7% (0.2%), followed by cluster 2 (n=44,347, 20.6%) at 19.5% (0.4%), cluster 3 (n=27,829, 12.9%) at 47.8% (0.5%), and cluster 4 (n=11,770, 5.5%) with the highest admission rate at 78% (0.4%). The four-cluster solution achieved a silhouette score of 0.65, a Calinski-Harabasz Index of 3649.5, and a Davies-Bouldin Index of 0.46. Compared to clustering based on ICD-10 blocks, clustering based on individual ICD-10 codes demonstrated better separation. Mild (odds ratio [OR] 2.55, 95% CI 2.48-2.62), moderate (OR 2.40, 95% CI 2.28-2.51), and severe (OR 3.29, 95% CI 3.13-3.45) Charlson Comorbidity Index scores increased the odds of admission. Tachycardia (OR 1.46, 95% CI 1.43-1.49), hyperthermia (OR 2.32, 95% CI 2.25-2.40), recent surgery (OR 1.31, 95% CI 1.27-1.36), and recent inpatient admission (OR 1.16, 95% CI 1.13-1.18) also increased the odds of higher cluster membership. Among 132 ICD-10 blocks, 17 blocks accounted for 80% of cluster 1 cases, including musculoskeletal or connective tissue disorders and head or lower limbs injuries. Higher-risk categories included respiratory tract infections such as influenza and pneumonia, and infections of the skin and subcutaneous tissue. CONCLUSIONS Most ambulatory presentations at the ED were categorized into low-risk clusters with a minimal likelihood of hospital admission. Stratifying ICD-10 diagnosis codes by admission outcomes and ranking them based on frequency provides a structured approach to potentially stratify admission risk.