Canada
Puerto Rico
USA
Belgium
France
UK
India
Client Background
A therapy services provider delivering uncompromised quality and service excellence across 18 states in the U.S. Their facilities treat patients with diverse diagnoses, functional abilities, and clinical needs, and require a systematic way to identify similar patient profiles, understand effective treatment plans, and predict the likelihood of re-hospitalization at the time of admission.
Client Need
Prevent the re-hospitalization of patients post-treatment to maintain facility ratings and ensure patient stability
Enable efficient use of nursing staff, clinical teams, and available resources
Build a model to predict the likelihood of re-hospitalization at the time of admission
Recommend treatment plans based on hospitalization risk and the effectiveness of treatments used for similar patients in the past
Suggest timely interventions during treatment based on the latest patient evaluation and assessment data
Solution
Extracted and analyzed patient admission data, including demographic details, prior hospitalization history, Section GG scores, medical conditions, and associated diagnoses
Identified important predictive features and engineered new variables to enhance model performance
Developed clustering algorithms to find clinically similar patients with 95% accuracy
Built classification models using advanced machine learning techniques to predict patient re-hospitalization likelihood
Realized Benefits
Higher Predictive Accuracy—Achieved a 77% ROC-AUC score for re-hospitalization prediction, enabling proactive clinical decisions
Optimized Care Pathways—Provided evidence-based treatment plans from outcomes of matched patient cohorts
Targeted Interventions—Surfaced timely recommendations during therapy, improving patient stability and reducing likelihood of readmission
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