Clinical Research Organizations

Deliver accurate, robust clinical patient data and cohorts for reliable research

Support clinical research initiatives built on real world evidence with a solution that fills gaps, improves consistency, and adds specificity.

EHR integration
IMO Precision solutions standardize inconsistent patient condition and treatment data from diverse systems into consistent, structured, clinically validated terminology with the specificity required to identify accurate cohorts and power meaningful analytics.

Reliable insights require high quality clinical patient data

Whether your research or registry supports a specific disease, condition, procedure, or medical device, the reliable insights needed to support research, best practices, and care delivery must start with high quality data.

However, due to the differences in how EHRs collect and store information, the increasing number of data sources, and interoperability standards that have not been fully implemented, data consistency is highly variable and lacks specificity. These issues cause gaps in clinical patient data – gaps that require a great deal of manual intervention and can drain clinical resources.

How we help

Meaningful insights start with quality data

Ensure consistency in diagnosis, procedure, medication, and lab data extracted from disparate systems and sources

Reapply missing standard codes and add other metadata, like secondary codes, to gain deeper insights

Increase accuracy and processing speed with precise matching and data validation

Simplify the process required to ensure the right patients are included in patient cohorts

Ready for more robust and reliable clinical patient data?

Speak with an expert about how IMO can help standardize your data for more precise cohorts and accurate analytics.

Latest Ideas

Enhancing data quality initiatives across the health IT ecosystem

Getting all of your own medical information in one place can be a tough task. Now imagine gathering the data for a whole hospital, or even an entire state. Then, for good measure, throw in the fact that meaningful healthcare data analytics require that all these records be standardized before they can be useful. That’s where a normalization solution with a built-in clinical terminology layer can help.

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The new normal(ize): A primer on patient data standardization

You don’t have to work for a medical coding company to know that quality data is important – especially in healthcare. But patient information comes from a variety of sources, and it’s rarely all in the same format, or documented using the same clinical terminology. So, what does this mean? We’re glad you asked.

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