Developing and validating an electronic health record-embedded AI model for managing multimorbid hospitalisation risk in patients with chronic RESpiratory disease (AiRES): a study protocol

Clinical Snapshot

Key Findings

• P Value: Not yet reported - protocol stage

• Effect Size: Not yet reported - protocol stage

• Primary Outcome: Not yet reported - protocol stage

• Nnt Or Sensitivity: Discrimination and calibration metrics planned but not yet reported

• Confidence Interval: Not yet reported - protocol stage

Clinical Application

High feasibility in systems with robust EHR infrastructure and administrative data capture Highly relevant to Australian practice - similar healthcare structure, PBS data could enhance model, aligns with RACGP chronic disease management guidelines Adults with asthma and COPD in integrated healthcare systems with comprehensive electronic health records

Abstract

BACKGROUND: Chronic respiratory diseases (CRDs), such as asthma and chronic obstructive pulmonary disease (COPD), are heterogeneous conditions with a high multimorbidity burden. However, existing risk assessment instruments prioritise physiological measures while overlooking systemic comorbidities. We aim to develop and validate an electronic health record (EHR)-embedded artificial intelligence (AI) model-AiRES (AI in patients with RESpiratory disease)-to predict the 30-day, 90-day and 180-day risks of all-cause and index-disease hospitalisations. This model represents a first step towards a clinical decision support tool for personalised multimorbidity management in patients with CRD. METHOD AND ANALYSIS: Patients aged ≥18 years with a validated case definition of asthma and COPD will be identified from Singapore health administrative data (2012-2020). Candidate predictors will include age, sex, ethnicity, housing type, and comorbidities, measured across multiple care settings as visit frequency, grouped at quarterly intervals in Year 1 and annually for Years 2 and 3 over a 3-year lookback window. We will predict 30-day, 90-day, and 180-day risks of (1) all-cause and (2) asthma/COPD-specific hospital admissions using up to five randomly selected index dates per individual. Three machine learning algorithms-logistic regression (LR) with Lasso regularisation, eXtreme Gradient Boosting, and Categorical Boosting-will be trained using 10-fold cross-validation (CV) with an ensemble feature selection strategy. The optimal model, selected based on performance and feature importance, will be benchmarked against two reference models: a full LR and a Zero-Inflated Negative Binomial regression with hospitalisation history as the sole predictor. Discrimination and calibration will be assessed using internal-external cluster-based and temporal CV. Clinical utility will be evaluated using decision curve analysis. ETHICS AND DISSEMINATION: This study obtained ethics approval from the National University of Singapore (NUS-IRB-2024-849). Results will be published in international peer-reviewed journals.

References

1. 1.Tan, W. Y., Lee, T. Y., Tan, K. B., Koh, M. S., Abisheganaden, J. A., Lam, S. S. W., Chotirmall, S. H., Yadav, C. P., Yii, A. C. A., Tiew, P. Y., Liew, M. F., Sun, Q., & Chen, W. (2026). Developing and validating an electronic health record-embedded AI model for managing multimorbid hospitalisation risk in patients with chronic RESpiratory disease (AiRES): a study protocol. BMJ Open. https://doi.org/10.1136/bmjopen-2025-116086