Technology Application to Enhance Discharge Referral Decision Support

DESCRIPTION provided by applicant Decreasing readmissions through better discharge planning DP and transitional care is a national healthcare priority RightCare Solutions has leveraged over years of interdisciplinary academic research led by a nurse researcher and through our highly successful phase one SBIR grant demonstrated market value for the D S product and the technical expertise of our team The D S is a six item decision support tool installed by RightCare Solutions in the hospital EHR to assist discharge planners to identify high risk patients upon admission allowing time and focus to target appropriate transitional and post acute care to prevent readmissions We have achieved outstanding outcomes from our phase award and are proposing further technological developments to enhance our commercial launch The market potential for the D S tool is significant since discharge decision support is estimated to be applicable to roughly U S hospitals with a census that is older adults equaling million discharges per year The phase results indicate a significant impact on and day readmissions giving us strong evidence as to the value of the product However the results and our experience using the software indicate there is opportunity to enhance the productandapos s accruacy and functionality We propose to enhance our predictive accuracy through innovative data mining and machine learning techniques and to improve the functionality by electronically connecting the acute and post acute care settings Due to implementation in the three hospitals of the University of Pennsylvania Health System we have data on over patients and through the continued live implementation we will accumulate data on over patients by the start of the phase grant Using existing and new data generated from continued operations this proposed SBIR grant will advance the product in two major ways to enhance the commercial benefit to its users Aim Develop test and scale SMART capabilities a dynamic process for improving prediction accuracy by using hospital specific and patient level characteristics D S variables and additional clinical and non clinical characteristics and modern data mining machine learning techniques AIM Operationalize the D S recommendations by electronically connecting high risk patients with post acute care PAC providers and stakeholders known as CONNECT capabilities Called a andquot learning health systemandquot the end product of this SBIR grant will provide continuous evaluation and improvement to the end user measured against their goals Our innovative design produces a closed loop system that will use data from the D S and hospital databases over time to andquot get smarter andquot Further our enhanced product will link acute care to post acute providers giving them advanced warning of patients who will shortly come to them for care PUBLIC HEALTH RELEVANCE This decision support software identifies high risk patients upon admission and alerts the clinicians in acute and post acute care This project will enhance the toolandapos s SMART capabilities to improve its predictive accuracy and will CONNECT patients and providers with post acute settings to coordinate the care transition