SBIR Phase I: An Algorithm that Predicts the Readmission Risk of a Hospital Patient

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be the availability of a patient readiness formula, an algorithm that will improve outcomes by engaging patients in their own healthcare from hospital admission through discharge. The societal impact includes (1) reducing the cost related to readmissions for Congestive Heart Failure, Acute Myocardial Infarction, and pneumonia, a total of $5.8 billion annually paid by U.S. taxpayers, (2) decreasing the cost of each hospital stay by $5-10K per Medicare beneficiary by using proven treatment and discharge planning models, (3) reducing the burden on post-acute providers (primary care physician, long-term care, home health, ambulatory care) by achieving optimized clinical stability prior to discharge, and (4) providing a method for a patient population (studies show who are eager to participate) to actively engage in their own treatment decisions. The proposed project will create a proprietary algorithm that produces a patient-specific hospital readmission-risk score. The score is based on the algorithm's dynamic adjustment of all known readmission risk factors determined by the patient's unique profile that includes clinical and other factors. The goal of the project is to accurately predict a patient's readmission-risk using the dynamic algorithm. The results of the algorithm will be verified via the facility's claims system. Technical challenges to be addressed by Phase I include the compilation of known risk factors, development of the algorithm, and creating multi-tract data input methods to obtain live data. The research and development plan will focus on identifying risks, development of the algorithm, and determining methods for capturing live data needed to execute the algorithm.