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<p>As the numbers of older people (65 +) rise globally, the pressure on acute hospitals to provide efficient and effective care while addressing resource inequities increases. In this study we introduce Recognising Episodes of Acute Complexity in Health (REACH), a novel Automatic Machine Learning (AutoML)-based predictive model that prioritises older patients and assigns them to complex or non-complex care pathways using routinely collected electronic health record (EHR) and interRAI data. Existing triage and patient-flow approaches are rarely designed for equity-sensitive deployment in settings where underserved populations experience systematically different access and outcomes; in our context, this gap motivated the development of REACH. Designed to be flexible and adaptable, REACH was developed and stress-tested in New Zealand, where Māori, Pasifika and other underserved older populations face inequitable access and outcomes, and is intended as a transferable approach for other health systems with similar equity and capacity challenges. The model estimates individual complexity probabilities and incorporates resource and capacity constraints to support decisions about patient prioritisation, bed allocation and placement into older-person-specific pathways. Applied to seven years of hospital data, REACH demonstrates improved identification of complex geriatric cases and more efficient use of specialised ward capacity compared with traditional FIFO and triage-based assignment. Using New Zealand data as a real-world case study, we demonstrate that equity-aware, capacity-constrained pathway assignment can be operationalised with routine EHR/interRAI inputs, with a design that can be retrained and recalibrated for other jurisdictions that use comparable assessment standards. The application of the model demonstrates its potential to improve patient prioritisation and placement processes while addressing challenges in healthcare access for underserved populations, highlighting the importance of data-driven decision support systems in older persons' care. The findings hold significant implications for healthcare practitioners, managers, policymakers and researchers seeking to design equitable and efficient models of acute care for older people.</p>

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Reach: Recognising episodes of acute complexity in health: a predictive older patient prioritisation machine learning model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Abtin Ijadi Maghsoodi,
  • Valery Pavlov,
  • Paul Rouse,
  • Cameron Graham Walker,
  • Matthew Parsons

摘要

As the numbers of older people (65 +) rise globally, the pressure on acute hospitals to provide efficient and effective care while addressing resource inequities increases. In this study we introduce Recognising Episodes of Acute Complexity in Health (REACH), a novel Automatic Machine Learning (AutoML)-based predictive model that prioritises older patients and assigns them to complex or non-complex care pathways using routinely collected electronic health record (EHR) and interRAI data. Existing triage and patient-flow approaches are rarely designed for equity-sensitive deployment in settings where underserved populations experience systematically different access and outcomes; in our context, this gap motivated the development of REACH. Designed to be flexible and adaptable, REACH was developed and stress-tested in New Zealand, where Māori, Pasifika and other underserved older populations face inequitable access and outcomes, and is intended as a transferable approach for other health systems with similar equity and capacity challenges. The model estimates individual complexity probabilities and incorporates resource and capacity constraints to support decisions about patient prioritisation, bed allocation and placement into older-person-specific pathways. Applied to seven years of hospital data, REACH demonstrates improved identification of complex geriatric cases and more efficient use of specialised ward capacity compared with traditional FIFO and triage-based assignment. Using New Zealand data as a real-world case study, we demonstrate that equity-aware, capacity-constrained pathway assignment can be operationalised with routine EHR/interRAI inputs, with a design that can be retrained and recalibrated for other jurisdictions that use comparable assessment standards. The application of the model demonstrates its potential to improve patient prioritisation and placement processes while addressing challenges in healthcare access for underserved populations, highlighting the importance of data-driven decision support systems in older persons' care. The findings hold significant implications for healthcare practitioners, managers, policymakers and researchers seeking to design equitable and efficient models of acute care for older people.