Pegwin Patient Safety Innovation — Posts

The Memorandum of Understanding between Pegwin and Prism Care is regarding joint efforts to provide integrated data analytic services and Total Care Management services to health care organizations (HCOs). \r\nThe objective is to create an alliance that can support improvement in patient health outcomes and generate incremental revenue. \r\nPrism Care is a provider of remote patient monitoring (RPM), and chronic care management (CCM) that encompasses a full scope of services. This includes an advanced RPM digital momitoring platform, the Prism Care Patient Engagement Center (PEC), and allied services designed to improve patient outcomes. \r\nPegwin is a visionary Digital Health IT company revolutionizing patient care with its groundbreaking product, Pegwin Insights. It provides predictive analytics using AI and Machine Learning to give providers (MD/DO, NP/PA) actionable information obtained from Prism Care (through chronic care management and RPM). \r\n

1. Harnessing the power of machine learning models to accurately predict risks, provide early alerts and reduce the incidence of critical postoperative pulmonary complications in a tertiary cancer center.\r\n \r\n2. Pegwin has developed and tested the Pegwin Insights Platform prototype that agnostically sits on top of any Electronic Health Record (EHR) to display each patient's Risk Profile to support nurses to do the right thing, to the right patient at the right time. This will help to continue timely, quality care even with the nursing shortages.\r\n\r\n3. Pegwin has a signed agreement with the #1 Cancer Center in the U.S. to conduct a feasibility and usability study of our Machine Learning and medical AI algorithm. They have asked for the rights to be the first to publish the results.\r\n\r\n\r\n\r\n

The preliminary study performed on >15M randomized observations (SMART DST2; SMART STU3; MIT, Harvard, Boston Children's Hospital) showed completeness, computability, and accuracy of the raw data sources (Fast Healthcare Interoperability Resources - FHIR). \r\n \r\nRESULTS: Cross-validated (70/30) AI-Driven model demonstrated good discrimination for unplanned ICU Admission - AUC 0.90; 95% CI, 0.76-0.94) and performed as well, or better than the current models (C statistic ranges: 0.56– 0.74). \r\n \r\nThe model calibration across different risk categories showed a close association of predicted and observed outcomes (PSI11; PSI04). \r\n