Welcome @Bill_Johns!
If I understand correctly, your interest in OMOP and OHDSI might have a few pieces to it.
- standard representation of claims
- standard representation of costs associated with claims
- standard analytics for cost-effectiveness
The first - representation of claims - is the area where the most prior work has been done. You should be able to build on prior claims ETLs to some degree in mapping the data you have to OMOP. Since NPN seems to have claims from multiple sources, there might be needs that aren’t already covered by that prior work. Others who are bringing their data into OMOP from similarly complex sources might want to partner with you on this. See this recent thread calling for collaboration on those issues: Mapping multiple payers claims files into OMOP. And there are several very experienced ETL firms in the community that can assist you.
Re the second piece - representation of costs: @Christian_Reich’s list of improvements and call for use cases for costs is excellent. You could help drive a set of improvements like those he describes or others that your use cases require. Outcome Insights’ @Mark_Danese and @jenniferduryea led some of the prior work on costs and might be worth reaching out to.
Re the third - standard analytics: this is a great community to work with in evaluating and standardizing analytic approaches. The methods library and the workgroups on population level estimation and methods evaluation are important resources. Adding cost effectiveness packages to the Methods Library would be a great contribution!
If you and NPN want to take a leadership role beyond development of informatics and analytics, you might look to build the OHDSI community’s partnerships with important entities that need to do this kind of work. The OHDSI community’s data standardization, curation, and standardized support for best analytic practices make it an excellent partner for entities like CMS and the FDA that need reliable evidence for their regulatory decisions.
You could keep that tractable and focused by working on a clearly defined area where this need is obvious and the amount of work is manageable. For example, coverage of genomic tests. The decision last year by CMS to begin coverage of FDA-approved genomic testing panels increased access to those tests here in the US. Their uptake and benefits aren’t well understood. As noted in this paper there is a well developed model for using new coverage decisions like this to develop the evidence needed to evaluate cost effectiveness. That coverage with evidence development model has long been espoused by Sean Tunis and others at the Center for Medical Technology Policy. It is highly regarded, but one reason it hasn’t been widely adopted is that it requires a set of partners with the ability to efficiently and reliably assess cost-effectiveness. Such partners are scarce.
OHDSI might be able to fill that gap. There is excellent work in the Oncology Genomics subgroup, building out the capture of genomic panel data. There is excellent work on data curation in the Data Quality Dashboard, and ACHILLES tools and workgroups. There is excellent work on study design and analyses in the methods code libraries and workgroups mentioned above. You could complement these with work on costs and cost-effectiveness analysis to meet CMS’ evidence development needs.
In short, you might be able to develop NPN’s business by building a collaboration between CMS and OHDSI for coverage with evidence development in a narrow area like genomic tests. To investigate that possibility you might seek out other private, public, governmental, or academic groups in the community who are interested in this and have the relevant data or expertise and start a workgroup.
That’s all just food for thought - one person’s take on the opportunities a group like yours might have in collaborating with OHDSI. Good luck.