Life Sciences

Sadiqa Mahmood, DDS, MPH

Using COVID-19 Value Sets for Patient Identification

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.

Elia Stupka, PhD

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?
It’s time to consider a progressive approach to patient data that keeps the patients involved by informing them when and how their data is used to earn trust and engagement, making patients partners in data-driven healthcare transformation.

Alyssa Antonopoulos
Adem Albayrak
Sadiqa Mahmood, DDS, MPH
Elia Stupka, PhD

Data-Driven Precision Medicine: A Must-Have for the Next-Generation of Personalized Care

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.

Dale Sanders

Bridging the Data and Trust Gaps: Why Health Catalyst Entered the Life Sciences Market

Why would a healthcare data warehousing and analytics company partner with the life sciences industry? Because trust and collaboration across the industry—between life sciences, healthcare delivery systems, and insurance—is the only path to real healthcare transformation.
Health Catalyst recognizes an industrywide improvement opportunity in collaborating with life sciences to build mutual trust, integrate data, and leverage analytics insights for a common interest (i.e., patient outcomes). By aligning themselves around human health fulfillment, Health Catalyst, their provider partners, and life sciences will advance important healthcare goals:

Improving clinical trial design and execution.
Stimulating clinical innovation.
Supporting population health.
Reducing pharmaceutical costs.
Improving drug safety and pharmacovigilance.

Elia Stupka, PhD

Extended Real-World Data: The Life Science Industry’s Number One Asset

The life science industry has historically relied on sanitized clinical trials and commoditized data sources (largely claims) to inform its drug development process—an under-substantiated approach that didn’t reflect how a new drug would affect broader patient populations. In an effort to gain more accurate insight into the patient experience and bring drugs to market more efficiently and safely, the industry is now expanding into extended real-world data (RWD).
To access the needed breadth and depth of patient-centric data, life science companies must partner with a healthcare transformation company that has three key qualities:

A broad and deep data asset.
Extensive provider partnerships.
An outcomes-improvement engine to support the next generation of drug development.