Aug 2026· Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care· Vol 15, pp. 45 - 49· 0 citations· 17 references
TL;DR
The Arrival Readmission Report (ARR) was designed to facilitate readmission risk assessment at emergency department admission using an interpretable, structured format and highlighted strengths in interpretability and structure and opportunities for improving contextualization and decision support for earlier intervention.
Abstract
Hospital readmission risk is typically assessed at or near discharge, limiting opportunities to address modifiable risk factors such as social determinants of health (SDOH). The Arrival Readmission Report (ARR), a proof-of-concept, AI-assisted, HIE-enabled tool was designed to facilitate readmission risk assessment at emergency department admission using an interpretable, structured format. The report incorporates 30-day readmission predictions from pre-admission variables, and SHapley Additive exPlanations (SHAP) to identify individual readmission risk drivers. Results are presented in structured modules, including a high-level risk snapshot, cohort comparisons, and patient-specific explanations. Preliminary formative evaluation highlighted strengths in interpretability and structure and opportunities for improving contextualization and decision support for earlier intervention.
BACKGROUND
The increasing prevalence of chronic illnesses among Medicare populations requires a shift in care delivery towards a more proactive and sustained surveillance and monitoring approach. Remote patient monitoring paired with technology-enabled, guideline based clinical assistance, so-called remote patient care...
Ahmad Jamal, Fatima Tauseef, Fahad Naseer et al.· International Journal of Med...· 0 citations
PURPOSE
To develop, validate, and implement an admission predictive model that estimates the likelihood and expected number of changes to the prior-to-admission (PTA) medication list, to help triage pharmacy-led medication histories.
METHODS
We performed a retrospective study of adult admissions at a large academic m...
S. Nelson, M. Hobensack, L. Fleenor et al.· American Journal of Health-S...· 0 citations
BACKGROUND
Hospital readmissions after asthma exacerbations contribute substantially to healthcare burden. Deployment of resource-intensive interventions to reduce readmission requires risk stratification, but few prediction models have been developed specifically for asthma-related hospital readmission.
OBJECTIVE
To...
T. Tay, M. H. Tun, A. Yii et al.· Respiratory Medicine· 0 citations
This paper presents a simple intervention program to reduce the risk of hospital readmission for people with type 2 diabetes (T2D). To this end, we deployed machine learning models to identify T2D patients who are most likely to be readmitted and assessed them using both a set of numerical metrics and graphical tools....
Y. S. Kim, Jie Kuang· Management Journal for Advan...· 0 citations
Background Readmission is an important indicator of healthcare quality, reflecting continuity of care, treatment outcomes, and the quality of discharge management. However, existing readmission prediction studies have primarily focused on 30-day readmission as the primary outcome, with limited attention to differences...
Yingyan Guan, Xian-Hao Chen, Ting Zhang et al.· Frontiers in Public Health· 0 citations
Manual clinical judgment or rule-based protocols are still widely used in patient triage for healthcare institutions, resulting in a lack of early identification of critically ill patients. The vast majority of current machine learning (ML) models are used to build a prediction for outcomes after the patient has been a...
A. P, Srikanth Cherukuvada, Padakanti Siri et al.· International Workshop on Ar...· 0 citations
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