Executive Summary
In the continued quest to improve patient outcomes and lower costs, healthcare organizations (HCOs) are looking to technology, particularly advances in the field of artificial intelligence (AI) to spur exciting innovations. Such innovations have the potential to help with disease prediction and diagnosis, effective treatment selection and prognosis, life sciences and pharmaceutical research, epidemiology, public health, and precision health initiatives.
While these approaches hold great promise to fuel future breakthroughs in healthcare and care delivery, they require access to sufficient quantities of diverse data for the development and validation of models capable of consistent performance. Thanks to electronic health records (EHRs), medical devices and personal smart devices, as well as the data collected in groundbreaking research studies at different academic medical centers around the globe, more and more data is available than ever before. The problem, however, lies in how to safely and ethically access, integrate, and then analyze the information while preserving individual privacy.
Confidential computing platforms (CCPs), with memory encryption and privacy-preserving analytics, however, support HCOs in overcoming many of those traditional hurdles by helping protect data at rest and data in use. BeeKeeperAI has worked to validate three different clinical models using an Intel-based CCP, including a hemodynamic stability index, a COVID-19 detection tool, and a treatment stratification tool for diabetic retinopathy, but the possibilities for different clinical algorithms are endless.
Read “Privacy-Preserving Data Collaboration Methods that Accelerate Healthcare Innovation” ›