How to improve healthcare data ingestion and analytics with OMOP

Healthcare data exists in many formats, which often presents a challenge to researchers as they attempt to aggregate and analyze data. At times, data normalization can put projects at a standstill - the research cannot move forward if the team doesn't have all the data they need in a way that they can easily derive insights from. That's where the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) comes in. This new data standard is transforming healthcare data analytics.

Common challenges of healthcare data ingestion

Healthcare data ingestion is a daunting task for most research teams. If it's not because of factoring for patient privacy and data security, it may be due to data quality concerns or questions on linking disparate data. At times, just figuring out how to get all the right data together can feel like its own research project!

Some of the challenges research teams encounter with data ingestion include:

Data quality

Quality assurance must occur before any data ingestion work begins, and it isn't uncommon for the process to stop there. If data is incomplete, inaccurate, or in the wrong format, it can't be used. It's better for research teams to find a new real-world data source than to try to make sense of bad data.

Unfortunately, this leaves many organizations in a repetitive cycle of data collection, quality assurance, and more collection until they're sure they have the best data for the task at hand.

Linking disparate data

Once a team confirms they have good data, it's time to link everything together.

This is not as simple as just combining all the data into one file or repository, especially if the various samples are in unique formats. Data must first be standardized and deidentified.

Deidentification can be done via the Safe Harbor or Expert Determination method. Both are time consuming and have unique requirements - adding more barriers to insights.

Time to incorporate new datasets

Between quality assuring new data and determining how to link disparate datasets, time adds up quickly. And this is all before the real research begins!

While traditional data ingestion methods may have been good enough in the past, the industry is starting to shift toward greater data standardization. With one universal format for data collection and ingestion, healthcare organizations can proactively solve for these common challenges and get to the real work at hand.

Solving for data challenges and enhancing analytics with the OMOP Common Data Model

As the industry moves towards a more collaborative data delivery format, data ingestion and analytics can run at a much greater speed.

When everyone "speaks" the same data language, there's less noise in the data collection and aggregation process. Research teams don't have to be delayed by data quality concerns or questions on linking disparate data. Instead, they can focus on analyzing the data at hand to uncover rich insights and advance patient outcomes and healthcare economics.

We've already started to see this impact with the OMOP Common Data Model, which empowers organizations to:

• Be more collaborative during research
• Improve communications across the globe
• Conduct large-scale analytics
• Share tools and methodologies
• Answer healthcare questions with real-world evidence

Access OMOP-ready data extracts

Inovalon offers primary source data in the OMOP format through our Real-World Data extracts. Extracts include data dictionaries that follow the standards many organizations are adopting to improve data ingestion and quality.

Discover how easy healthcare data ingestion can be when working with a standard format. Ask our team about OMOP-ready data extracts.

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