Sadiqa Mahmood, DDS, MPH

除了推动美国国内对COVID-19的了解外，一个全国性的疾病登记处正在为美国境外的研究提供信息。Clinicians with the Singapore Ministry of Healthcare Office for Healthcare Transformation (MOHT) have used Health Catalyst Touchstone^®利用COVID-19数据开发机器学习工具，帮助预测COVID-19死亡的可能性。有了这个利用深度聚合的电子病历数据的国家数据集，卫生部访问了它所需的研究级数据，以构建一个预测COVID-19死亡风险的机器学习算法。登记信息的预测模型足够准确，足以经得起发表文献的比较，并有望为疫苗研究提供信息，并最终在人群中分配疫苗。

除了推动美国国内对COVID-19的了解外，一个全国性的疾病登记处正在为美国境外的研究提供信息。新加坡卫生部医疗改革办公室(MOHT)的临床医生使用Health Catalyst Touchstone®COVID-19数据开发了一种机器学习工具，有助于预测COVID-19死亡的可能性。世界杯葡萄牙vs加纳即时走地
有了这个利用深度聚合的电子病历数据的国家数据集，卫生部访问了它所需的研究级数据，以构建一个预测COVID-19死亡风险的机器学习算法。
登记信息的预测模型足够准确，足以经得起发表文献的比较，并有望为疫苗研究提供信息，并最终在人群中分配疫苗。

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.