Unscheduled Care Presentations increased from 1.1m in 2010 to 1.59m in 2022


Unscheduled Care Presentations in Irish hospitals increased from 1.1m in 2010 to 1.59m in 2022, according to an Analysis of Unscheduled Care Activity 2017 – 2022, compiled by Conan Shine and Mark Hennessy, Irish Government Economic and Evaluation Service Research Services & Policy Unit, Department of Health and published in September 2023.

Cumulatively, this represented a 45% increase in demand over this period, with the largest periods of growth between 2010 and 2012 (an increase of 200,000 presentations) and an increase of 250,000 presentations between 2021 and 2022. ▪

Per capita utilisation of emergency departments was concentrated among those aged 0-5 (54 visits per 100) and those aged 76+ (53 per 100 visits, or greater) relative to a presentation rate of under 30 visits per 100 for persons aged 6-75.

The researchers say admission probability had a direct relationship with age. For example, a person aged 20 had a 14% rate of admission, compared to someone aged 80 with a 50% rate of admission. Probability of admission was directly related to triage score. Less than 10% of patients at triage score 4 and 5 (standard and non-urgent) were admitted. Triage category 3 patients (urgent) were admitted 23% of the time. This has implications for which patients are likely best suited to treatment within community settings rather than in the ED.

There was a strong relationship between wait times and age. For example, median wait times for under 5s was 3.5 hours, compared to wait times of 9 hours for persons over 85. This was likely driven by the triage scores of patients by age, as persons of a younger age were less likely to be admitted than older patients and were therefore able to leave an Unscheduled Care environment after initial assessment (which takes place within a maximum of four hours from arrival).

Hour of Arrival: There was some evidence that probability of admission and waiting times in ED were dependent on hour of arrival. Patients who arrived with a triage score of immediate (patients of highest acuity) waited on average one hour longer for admission/discharge if they arrived at night than during the day. Equally, patients with a triage score of immediate were 20% less likely to be admitted when arriving during the day, versus a nighttime arrival.

Mode of Arrival – Ambulance: Arrival by ambulance versus through other pathways was associated with a 20-25% increase in admission probability. This was likely attributable to the higher acuity of patients arriving by ambulance, and the pre-triaging of presentations via this pathway by paramedics prior to conveyance to hospitals.

Referral Type – GP vs Self: 73% of referrals to Unscheduled Care settings from GPs did not result in an admission. This compared to 77% of presentations not resulting in admission for patients who self-referred. GP Out of Hours referrals were not admitted 75% of the time. This might indicate that GP referrals were not sufficiently effective at triaging patients for treatment in the community versus treatment in an acute setting. The even lower rates of admission of patients with Out of Hours GP referrals suggested inappropriate referrals to ED from this pathway.

Triage Score Attendances by Health Region: There was substantial variation in the triage level (acuity) of patients arriving in each Health Region, especially low acuity patients. For example, just 8% of presentations at Health Region Midwest were triage category 4 (standard) or 5 (non-urgent). This compared to Health Region Dublin Southeast (DSE), where 33% of presentations were of the same category. This might be the result of the availability of alternative treatment options in some regions, with for example Health Region Midwest having three operating Local Injury Units, versus other regions having more limited LIU coverage.


Alignment of Acute Care Capacity Resourcing with Demographic Change

The analysis has shown that the utilisation rate and admission probability of older patient cohorts was substantially higher than for other groups. This had direct implications for forecasting future Unscheduled Care capacity requirements at a national and regional level, as population ageing would likely increase demand pressures in both Unscheduled Care settings and Inpatient facilities.

Wait Times for Older Age Cohorts/Patient Flow

The analysis demonstrated the long waits patients experienced in ED prior to admission. Further work was required to understand the barriers to timely admission for these patients, especially those from older age cohorts, to better align outcomes with HSE general and age specific (over 75) wait time performance targets. Policymakers and practitioners could explore a range of options to improve in this area, including more efficient assignment, management and discharge of patients to beds, the provision of dedicated treatment pathways such as Medical Assessment Units for older patient cohorts, the treatment of additional patients in the community or the provision of additional beds in some hospitals to alleviate capacity pressures.

Specialised Treatment of Paediatric Patient Cohorts

Sixty one per cent of persons under 16 presented to Unscheduled Care settings outside of the Children’s Hospital Group in 2022. Given the relatively high utilisation rate and low triage scores of patients of this type, policymakers should consider whether these patients could be treated in alternative settings within the healthcare system, either in the community or segregated pathways for treatment within a hospital.

Factors Contributing to Variation in Outcomes by Hour of Arrival

The analysis demonstrated variation in waiting times and admission probability for patients by hour of arrival, even controlling for patient triage score.  ”This poses a potential patient safety and efficiency issue and is therefore worthy of further exploration. Potential contributors to this outcome could include differences in patient profiles by hour of arrival, reduced diagnostic availability during non-core hours, differences in staffing levels and the availability of senior decision-makers on a 24/7 basis.”

Targeted Intervention to reduce presentations from low-acuity patients

The analysis demonstrated the sizeable proportion of patients (29%) who presented to Unscheduled Care settings with low-acuity needs. These patients were largely of younger ages and were concentrated in certain Health Regions (Dublin Southeast, Southwest). This presented the opportunity for the development of targeted interventions to reduce the utilisation rate of Unscheduled Care settings by these groups, instead treating them in community or primary care settings. For example, investment in alternative care pathways such as Primary Care Centres or Local Injury Units could be concentrated in areas where lower triage presentations are most prevalent.

Explore strategies to reduce the level of unnecessary referrals from primary care settings into Unscheduled Care

The researchers said they demonstrated that patients who self-referred to ED were admitted at only a slightly lower rate (23%) than those who attended with a referral from a general practitioner (27%). This indicated unnecessary referrals to Unscheduled Care settings from primary care practitioners in some cases, and therefore represented an opportunity to reduce unscheduled care pressures through more effective referral decisions by primary care practitioners.

Continuous Evaluation of Target Investment in Unscheduled Care

The researchers said this paper was a novel application of the existing PET dataset to support improved monitoring and performance management within the healthcare system. Policymakers should ensure that analysis of PET data was used to maximise the impact of investments in unscheduled care, improving patient outcomes, value for money among other outcomes. “Interventions to improve unscheduled care performance should be the subject of continuous evaluation, with the objectives, inputs, outputs and outcomes associated with each measure analysed after their implementation to determine their relative impact.”

Data Improvements & Implementation of Urgency Related Groups (URGs)

“To further enable the use of PET for strategic policy development and evaluation, we advocate for further improvements to the Patient Experience Time dataset to be prioritised within short- and medium-term strategic planning in this area. First, we advocate for the development and implementation of Urgency Related Group classifications within Unscheduled Care settings in Ireland as this would allow identification of the resource requirement and reason for presentation to unscheduled care for each patient. Second, data improvements to existing reporting should be sought, including improvements to unclassified characteristics for some patients within the PET dataset, and the collection and reporting of PPSNs for each presentation.”


Spending Review 2023 Hospital Performance: An Analysis of Unscheduled Care Activity 2017 – 2022 provides an analysis of patient level Unscheduled Care data recorded in the Patient Experience Time dataset over the period 2017-2022.  It has been compiled by Conan Shine and Mark Hennessy Irish Government Economic and Evaluation Service Research Services & Policy Unit, Department of Health September 2023.

This paper provides an analysis of patient level Unscheduled Care data recorded in the Patient Experience Time dataset over the period 2017-2022. It provides numerous insights into patient characteristics and outcomes in Unscheduled Care environments, including in relation to their age, admission probability, clinical need, mode of arrival and whether they have a referral. The findings of the paper either have overt or indirect implications for strategic policy development for Unscheduled Care in Ireland.

The paper identifies data gaps for Unscheduled Care in Ireland, as well as opportunities for future research to further develop understanding. The authors say that where possible, the PET dataset should be leveraged to provide bespoke ex-ante evaluation of interventions to improve outcomes and reduce pressures in Unscheduled Care. In addition, the data environment should be improved with the provision of better data on patient clinical characteristics (via Urgency Related Groups), rectification of data gaps, and the provision of more extensive data on interventions and costs used within each Winter/ n-year Unscheduled Care plan.