Observational studyInternal medicine journal2025
What does 'complex' mean for the general medicine team? Exploring the characteristics, outcomes and clinician perspectives of complex patients: an observational cohort and cross-sectional survey.
Observational study in Internal medicine journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundPatients who are 'complex' experience poorer outcomes during and after hospitalisation. At our health service, patients identified as complex are referred to a specialist transdisciplinary allied health pathway, but this process is subjective and predominantly based on clinical judgement.
aimsTo characterise patients referred to the complex pathway by describing their characteristics and outcomes and by developing a list of words clinicians associate with complexity to generate an electronic health record (EHR) complexity phenotype.
methodsWe performed a retrospective observational cohort study of all patients admitted to General Medicine at a metropolitan hospital in Melbourne over a 10-month period and a cross-sectional survey of clinicians (allied health, medical, nursing). We compared the demographics, clinical features and outcomes of the complex patients to their non-complex peers. Cohort outcomes included length of stay, readmissions, discharge destination, mortality and adverse event rates. The survey data scored the likelihood of words suggesting complexity from a clinician's perspective.
resultsIn the cohort (n = 3061), 328 (11%) were complex. Complex patients were older, frail and more multimorbid. This group stayed longer in hospital, and more required rehabilitation, with increased mortality and readmissions (P < 0.01). Eighty clinicians (allied health (50%), medical (31%) and nursing (19%)) generated a library of 18 words that described a complex patient.
conclusionFrailty, age and high hospital utilisation were associated with complexity across both studies. Combining clinical and demographic data with natural language processing of complexity words may allow prospective digital prediction of patients likely to benefit from complex care pathways.
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