Learn: Life Sciences
August 3, 2022
May 20, 2022
April 11, 2022
April 6, 2022
March 31, 2022
November 10, 2021
Network, Technology, and Data: Missing Pieces of the Puzzle for Clinical Trials Success
In this webinar, Health Catalyst’s Sadiqa Mahmood, Senior Vice President and General Manager for Life Sciences, and a guest industry expert will discuss the evolution and future of clinical trials. A presentation will follow by Emmanuel Cocodia, Vice President, Product Strategy, who will outline Health Catalyst’s strategy to address the challenges of clinical research through the Health Catalyst Research Network™ (HCRN) & Touchstone Match™.
Delivering Precision Medicine: How Data Drives Individualized Healthcare
Data access and interoperability barriers have often impeded the precision medicine transformation. However, current healthcare industry trends increase opportunities for researchers and clinicians to more comprehensively understand medical conditions and the patients in their care. These insights establish the foundation for precision medicine and support actionable pathways towards more efficient development of targeted treatments.
Using COVID-19 Value Sets for Patient Identification
Due to significant variation in EHR data, informatics tools are needed to define patients with suspected SARS-Cov2 Infection and confirmed COVID-19 infection. With the aim of building an extensible model for a COVID-19 database, Health Catalyst has built a detailed approach that leverages a heuristic methodology for capturing both confirmed and suspected cases.
Health Catalyst has proposed value sets that define two patient cohorts for the registry for confirmed and suspected COVID-19 patients, stratified further into three levels of confidence: high confidence suspected, moderate confidence suspected, and low confidence suspected.
Creating a Data-Driven Research Ecosystem with Patients at the Center
As patient data because one of the healthcare industry’s most valuable assets, organizations are establishing new practices around accessing and handling data. In question is the practice of de-identifying patient data for widespread cross-organizational data collaboration without compromising patient privacy. But because deeper and richer data drives better clinical understanding and, ultimately, better outcomes, does separating patients from their health data and how it’s used give researchers and developers the best insights? Or do data users risk losing critical connection with the patients and insights into therapies their lives, disease, treatments, and deaths that contribute to new therapeutic approaches?