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

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.