In addition to driving COVID-19 understanding within the United States, a national disease registry is informing research beyond U.S. borders. Clinicians with the Singapore Ministry of Healthcare Office for Healthcare Transformation (MOHT) have used Health Catalyst Touchstone® COVID-19 data to develop a machine learning tool that helps predict the likelihood of COVID-19 mortality. With this national data set that leverages deep aggregated EHR data, the MOHT accessed the research-grade data it needed to build a machine-learning algorithm that predicts risk of death from COVID-19. The registry-informed prediction model was accurate enough to stand up to comparisons in the published literature and promises to help inform vaccine research and, ultimately, allocation of vaccines within populations.
Learn more about Sadiqa Mahmood, DDS, MPH
Sadiqa Mahmood, DDS, MPH is the Senior Vice President and General Manager of the Life Sciences business at Health Catalyst and contributes to the overall vision and growth for the company. Sadiqa’s work focuses on identifying and addressing areas of high unmet need for therapeutic development through application of real-world data. She is an advisor to several healthcare organizations and global policy makers. Sadiqa is a dental surgeon and holds a master’s degree in public health from the Harvard School of Public Health. Passionate about improving access to care and patient outcomes by leveraging data and healthcare ecosystem, Sadiqa has spent her career at the intersection of medicine, policy, technology, and analytics. Previously she led clinical analytics, quality and safety, value-based contracting, and population health across healthcare organizations, including the Dana-Farber Cancer Institute, Partners HealthCare System, and Boston Medical Center. Sadiqa has been an advocate of collaborative learning system in healthcare and has steered cross-industry multi-stakeholder national and global collaborations to drive healthcare innovation. She joined Health Catalyst in 2019 as SVP of Medical Affairs. Sadiqa has lived and worked in Asia and UK in addition to the US. She is based in Boston, Massachusetts. Outside of work, she is a Formula 1 fan and race as a member of a local team.
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Comprehensive COVID-19 understanding is a critical asset for adapting to pandemic needs, directing resources, developing vaccines, and planning for surges in a timely, informed manner. Because common barriers have impeded the progress of comprehensive data repositories, researchers have relied on surveillance data from population-level viral testing, which has proven insufficient. To significantly advance COVID-19 understanding, the medical community needs a digital patient registry that captures national-level data on how the virus impacts individuals differently according to comorbidities, lifestyle factors, and more. These essential insights lie in real-world evidence, which a registry can only deliver when it applies value sets to leverage clinical and claims data from health systems across the United States.
With a lack of historical population-based information to steer COVID-19 research, pharmaceutical companies are struggling to understand the everchanging virus as they work tirelessly to develop a vaccine in less than one year. Research teams can access near real-time COVID-19 patient data with Touchstone® for COVID-19 National Data Sets and Registry from over 80 million patients across the United States and three national data sources: John Hopkins University, The New York Times, and The COVID Tracking Project.
The Registry offers up-to-date, comprehensive data with outcome analysis and clinical trial analysis so research teams can stay up to date through every stage of the vaccine development process.
The U.S. healthcare system was not prepared for a health crisis of the magnitude of the COVID-19 pandemic. Hospitals are working to facilitate widespread distribution of information within their organization and to local, state, and federal authorities to successfully manage this novel infection. EHRs and Lab Information Systems (LISs) have become public health tools for disease surveillance and management.
Due to signification 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.
Under a precision medicine approach, clinicians, academics, and pharma and biotech researchers and regulators aim to deliver the right drug for the right patient at the right time. Data, however, can present a challenge to precision medicine goals due to gaps in clinical care, research, and drug development when organizations don’t have the ability to capture and report on relevant real-world data. With the right systems to collect and share clinical and molecular data, the healthcare industry can realize the full benefits of precision medicine.