{
  "abstract": "Objectives To develop a systematic, data-driven approach to map patient journeys and apply this to care pathways, assessing alignment with published guidance and highlighting potential areas to improve service delivery using the case study of first diagnosis of psychosis.Design Using document review, an idealised patient journey was developed. Event data were extracted from electronic health records using a systematic process to map real-world patient journeys across care settings.Setting North West London’s de-identified integrated care records system (Discover).Participants 3219 (mean age 37 years; 55% men) adult patients (18–64) registered in North West London in December 2024, with a first episode of psychosis recorded between 2016 and 2019.Main outcomes Mapping full patient journeys, including care transitions and service contacts (ie, psychiatric admissions, early intervention in psychosis (EIP) involvement and GP management). Comparison of real-world pathways against National Institute for Health and Care Excellence (NICE) guidelines.Results Case identification yielded demographic trends comparable with national and local level data. Two-thirds of cases had their diagnosis first recorded in primary care, with many already attending secondary care services (ie, records may not reflect where diagnosis was clinically established). Only 18% had a clearly identifiable record of an EIP service and the recorded duration of EIP involvement varied widely. Psychiatric admission, readmission and use of crisis and emergency services were frequent. A large proportion of patient records showed no recorded healthcare contact within either 30 days or 6 months following the index admission. The patient journey models highlighted the complexity and heterogeneity of psychosis care and were difficult to manage and visualise using available models (process maps and matrices).Conclusions Real-world patient journeys in psychosis, as represented in electronic medical records, appear to diverge widely from NICE-recommended care, particularly around diagnosis, EIP service provision and postdischarge follow-up. Interpretability is likely limited by incomplete and inconsistent recording, with ambiguities posing a challenge to utilisation and interpretation of this data to reliably inform clinical decision-making, service evaluation and policy. This work points to the need for more advanced analytical tools (ie, artificial intelligence and optimisation) to better interpret and use patient journey data.",
  "authors": [
    {
      "affiliations": [
        "Department of Primary Care and Public Health, School of Public Health, Imperial College London School of Public Health, London, UK"
      ],
      "name": "Katharine Collet"
    },
    {
      "affiliations": [
        "Department of Analytics, Marketing and Operations, Business School, Imperial College London, London, UK"
      ],
      "name": "Bowen Li"
    },
    {
      "affiliations": [
        "Department of Analytics, Marketing and Operations, Business School, Imperial College London, London, UK"
      ],
      "name": "Wolfram Wiesemann"
    },
    {
      "affiliations": [
        "Department of Primary Care and Public Health, School of Public Health, Imperial College London School of Public Health, London, UK"
      ],
      "name": "Alex Bottle"
    },
    {
      "affiliations": [
        "School of Public Health, Imperial College London, London, UK"
      ],
      "name": "Qian Gao"
    },
    {
      "affiliations": [
        "Department of Brain Sciences, Imperial College London School of Public Health, London, UK"
      ],
      "name": "Ryan Williams"
    },
    {
      "affiliations": [
        "Department of Primary Care and Public Health, School of Public Health, Imperial College London School of Public Health, London, UK"
      ],
      "name": "Thomas Woodcock"
    },
    {
      "affiliations": [
        "Department of Primary Care and Public Health, School of Public Health, Imperial College London School of Public Health, London, UK"
      ],
      "name": "Paul Aylin"
    }
  ],
  "title": "Retrospective cohort study mapping real-world psychosis care pathways and guideline adherence using integrated electronic health records",
  "uid": "38088ee2-84be-500a-b853-82cbbeca37a7"
}