{
  "abstract": "Background Patient complaints provide a complementary lens on surgical safety, yet prior analyses have been small and specialty specific. We aimed to characterise themes, perioperative processes, system factors and outcomes in surgical complaints using a scalable, large language model (LLM)-assisted approach.Methods We performed a national, retrospective, cross-sectional, sequential methods study of publicly available investigation reports from the Aotearoa New Zealand Health and Disability Commissioner. All reports published since 1998 were retrieved and screened for relevance across all surgical specialties. A fixed-parameter LLM workflow extracted demographics, clinical context, complications, breached patient rights, system factors and outcomes. An inductive LLM-supported thematic synthesis generated complaint themes, followed by rescoring of theme relevance across reports. Human validation supported the LLM-assisted process (Cohen’s κ >0.85).Results Of 1827 reports screened, 650 involved surgical care. Postoperative complications were frequent (84.2%), commonly accompanied by permanent disability (33.2%) or death (24.2%). Delays in recognition of deterioration (76.1%), escalation (58.3%) and definitive management (49.5%) of complications were common and associated with higher mortality. Prominent themes concerned postoperative clinical management/monitoring, communication/informed consent and professional conduct/competence. Technical/procedural errors and medication errors were comparatively less common. Breaches are most often related to reasonable care and skill, and to informed consent, with marked variation across specialties.Conclusions Complaints about surgical care predominantly reflect postoperative monitoring, escalation and communication rather than intraoperative technical errors. System priorities should include robust deterioration recognition and response, reliable handover and escalation pathways, and strengthened consent and communication processes. An LLM-assisted pipeline can scale complaint analysis to support organisational learning and quality improvement.",
  "authors": [
    {
      "affiliations": [
        "Department of Surgery, The University of Auckland, Auckland, New Zealand"
      ],
      "name": "Cameron Wells"
    },
    {
      "affiliations": [
        "Faculty of Engineering, The University of Auckland, Auckland, New Zealand"
      ],
      "name": "Allan Han"
    },
    {
      "affiliations": [
        "Department of Surgery, North Shore Hospital, Auckland, New Zealand"
      ],
      "name": "Nejo Joseph"
    },
    {
      "affiliations": [
        "Department of Surgery, The University of Auckland, Auckland, New Zealand",
        "Department of Surgery, Mayo Clinic, Rochester, New York, USA"
      ],
      "name": "Chris Varghese"
    },
    {
      "affiliations": [
        "Department of Surgery, The University of Auckland, Auckland, New Zealand"
      ],
      "name": "Greg O’Grady"
    },
    {
      "affiliations": [
        "Department of Surgery, The University of Auckland, Auckland, New Zealand"
      ],
      "name": "Ian Bissett"
    }
  ],
  "title": "Learning from complaints about surgical care: a large language model-assisted sequential methods analysis",
  "uid": "83ab9366-26d3-505d-a76b-722753176cbc"
}