Healthcare Data Quality and Management

Learn about data quality standards, research guidelines from the FDA, and how healthcare organizations can use data to improve care.


Real-world data IRL: Accelerate your research with new FDA guidelines 

Incorporating EHR and claims data into studies can lead to fast, informed results, especially when you follow the FDA’s guidelines.


Using real-world data for real-world evidence

Here's how researchers are using clinically rich real world data to get new products to market faster and accelerate outcomes for patients.

4 metrics you need to ensure your data yields usable, useful insights

Could your data quality use a check-up?

Your data should be as healthy as the populations you serve. Mary Kuchenbrod, Arcadia’s Senior Director of Data Operations, explains how quality data can yield useful insights for patients.

Press Releases

Arcadia partners with Datavant to accelerate biopharma research

Datavant will enable connectivity to Arcadia’s de-identified electronic health record data; Arcadia’s diverse, rapidly growing data asset and comprehensive, longitudinal insights to drive innovations in patient care


More meaningful care through data

When Arcadia partnered with Nuvance Health, we found a small, agile team that needed more efficient processes for better patient care. We provided.


3 data quality questions you should be asking according to the FDA

New FDA guidance highlights how crucial it is to have a vetting process that ensures data suppliers meet your data quality needs. As you collaborate with RWD suppliers, here are three critical questions to ask.


Practical challenges of EHR data for RWE

On-demand webinar recording | The lack of data quality standards in clinical research creates challenges — this is how Mary Kuchenbrod curates EHR data create meaningful patient views.

Product Info

Healthcare database optimization to improve efficiency and care

Ensure a smooth healthcare database optimization journey with data-backed decisions and real, human support every step of the way.

White Paper

Three strategies for reducing implicit bias

Implicit bias and health inequities can easily get baked into Artificial Intelligence (AI) and predictive tools. To prevent this, we need an intentional approach to development that specifically addresses these issues. In this white paper, you’ll learn three important strategies for reducing implicit bias and improving the equity and diversity of your predictive outputs, while still optimizing performance against your organizational and financial objectives.