Austin Health

Title
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
Publication Date
2025-09-15
Author(s)
Gerstman, Elena
Jones, Jennifer R A
Michael, Chris
Berney, Susan C
Thursky, Karin
Berlowitz, David J
Subject
comorbidity
complexity
frailty
general internal medicine
health services research
Type of document
Journal Article
OrcId
0000-0001-6484-5182
0000-0002-9443-3426
#PLACEHOLDER_PARENT_METADATA_VALUE#
0000-0003-1633-805X
0000-0002-7400-232X
0000-0003-2543-8722
DOI
10.1111/imj.70203
Abstract
Patients 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. To 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. We 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. In 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. Frailty, 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.
Link
Citation
Internal Medicine Journal 2025-09-15
Jornal Title
Internal Medicine Journal

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