SBIR Phase I: SMARTCharts: A Rehabilitation Documentation Optimization Tool using machine learning (ML) and data visualization

This Small Business Innovation Research (SBIR) Phase I project aims to transform rehabilitation healthcare by developing software that standardizes and automates documentation processes, creating personalized progress visualizations and notes. By using machine learning, this innovation could provide care teams with easy-to-understand visuals that accurately show patient progress, improving communication and decision-making while reducing costly rehospitalizations, procedures, and surgeries.

It aims to improve productivity, quality of care, and patient experience by simplifying rehabilitation communication and documentation processes. The software aspires to be a go-to resource for patient and care team communications, impacting over 557,000 rehabilitation therapists who support over $550 billion in annual spending on speech, occupational and physical therapy services.

After completion, the software will serve as a business-to-business (B2B) Software as a Service (SaaS) healthcare system, benefiting providers in various rehabilitation settings.

This Small Business Innovation Research (SBIR) Phase I project addresses the challenge faced by healthcare providers in efficiently creating documentation that aids patient understanding, facilitates care team communications, and could ensure easy reimbursement by insurance companies. The research objectives include utilizing machine learning and data visualization to construct a data source that accurately represents patient therapeutic rehabilitation progress.

The proposed research synthesizes novel rehabilitation documentation data sources to produce datasets structured for visual representation of progress. The anticipated technical results involve establishing a database in Phase I that integrates data from various sources across rehabilitation settings, yielding reliable datasets for future modeling. The scope of research entails determining the feasibility of organizing novel data sources to create meaningful visual representations of patient progress in therapeutic rehabilitation.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.