{
  "abstract": "Introduction Timely and accurate discharge communication is vital for patient safety, yet Emergency Department (ED) discharge summaries are often deprioritised due to clinical pressures. Artificial Intelligence (AI) tools offer a potential solution to streamline this process, but their feasibility and quality in ED workflows remain understudied.Methods In this prospective observational study, 13 ED healthcare providers used a purpose-built AI assistant (MedWrite™) to generate 132 anonymised discharge summaries. Time-to-completion, frequency and types of errors, and number of edits were recorded. Each summary was independently reviewed by a senior Emergency Medicine consultant and an experienced GP using a structured tool assessing accuracy, completeness, clarity, and clinical acceptability. Interrater agreement and subgroup analyses were performed.Results The median time to generate and approve a summary was 106 seconds (IQR 70–128). Errors were identified in 38% of letters (median 1 error per letter, mostly grammatical or formatting). Reviewer 1 deemed 90.2% of summaries clinically acceptable; Reviewer 2 deemed 76.5% acceptable. Letters were rated as better than typical historical letters in 90.2% (Reviewer 1) and 66.7% (Reviewer 2) of cases. However, interrater agreement on key quality domains was low, particularly regarding completeness and clarity of medication or disposition plans.Conclusion AI-generated discharge summaries were produced rapidly and rated as clinically acceptable in the majority of cases, showing potential to reduce documentation burden in the ED. However, inter-reviewer variability underscores the need for standardisation and oversight. Safe integration of AI tools into clinical workflows should maintain a human-in-the-loop approach with clear governance around quality benchmarks and discharge communication standards. *presenting author",
  "authors": [
    {
      "affiliations": [
        "Department of Emergency Medicine, University Hospital Galway, Ireland"
      ],
      "name": "Enda Hession"
    },
    {
      "affiliations": [
        "Department of Emergency Medicine, University Hospital Galway, Ireland"
      ],
      "name": "Siobhan McGrath"
    },
    {
      "affiliations": [
        "Department of Emergency Medicine, University Hospital Galway, Ireland"
      ],
      "name": "Sinead McDonnell"
    },
    {
      "affiliations": [
        "General Practitioner Partner, Galway City Medical Centre, Galway, Ireland",
        "Irish College of General Practitioners, Ireland"
      ],
      "name": "John Lally"
    },
    {
      "affiliations": [
        "Department of Emergency Medicine, University Hospital Galway, Ireland"
      ],
      "name": "James Binchy"
    },
    {
      "affiliations": [
        "Information Technology Manager, Galway University Hospitals, Ireland"
      ],
      "name": "Kevin Collins"
    },
    {
      "affiliations": [
        "Department of Emergency Medicine, University Hospital Galway, Ireland",
        "School of Medicine, University of Galway, Ireland"
      ],
      "name": "John O’Donnell"
    },
    {
      "affiliations": [
        "Paediatric Emergency Research and Innovation (PERI), Department of Emergency Medicine, Children’s Health Ireland at Crumlin, Dublin, Ireland",
        "Women’s and Children’s Health, School of Medicine, University College Dublin, Dublin, Ireland"
      ],
      "name": "Michael Barrett"
    },
    {
      "affiliations": [
        "Department of Emergency Medicine, University Hospital Galway, Ireland",
        "School of Medicine, University of Galway, Ireland"
      ],
      "name": "James Foley"
    }
  ],
  "title": "#89 The use of artificial intelligence to generate discharge correspondence from the emergency department (the AIDED study)",
  "uid": "4a41f609-8803-5081-b853-e766d834ca50"
}