Those providing data for these tables should consider, "What do you want those who seek to implement 'living guidance' to know about 'data-to-evidence' strategies, initiatives and tools to make their efforts more successful? Likewise, "What do you want to know about efforts of others working in the knowledge ecosystem to make your data-to-knowledge efforts more successful?"
Notes from 10/16-19/20 email exchange
The "Rosetta Stone"/ Holy Grail would be to use the same Concepts/Terms/Codes as appropriate throughout the entire cycle, NOT just the same Terminology Systems. And I won't even go down the path of using the same Information Model as both are needed to get truly accurate semantics (OM. CC-in Victor Lee and Andy Kanter in this regard."
Piggybacking onto Matt’s comments, the alignment on semantics is in fact the principal goal of the COVID-19 Interoperability Alliance (https://covid19ia.org). In collaboration with various community stakeholders, we’ve authored more than 600 value sets and have made them freely available from both the Alliance website (no registration required, just download away) and VSAC (registration for a free account is required by NLM). If there are specific inquiries or even requests for value sets, I’d be happy to discuss. It’s our way of giving back to the community and making an impact on the pandemic.
And to add further to this conversation, the 'data to evidence' portion is the key to precision medicine that we hope to achieve someday. So Big Data play a huge role here. IBM Watson was/is trying to move in this direction. I believe the oncology-focused Flatiron Health is also a player but would need to confirm that. The ASCO CancerLinQ would certainly be in this space.
Additionally, we should think about all registries. If we could get all registries using the same standards and terminologies in an interoperable way, we could really move the needle.
Also measures & common data elements for research (e.g., PhenX, NIH CDE Repository)… ideally aligning data standards across clinical care, registries, public health surveillance & research (to the extent possible).
Several NIH research efforts (e.g., RADx) are aiming to align research measures/CDEs for COVID 19.
While it’s not COVID-19-related, cancer reporting is one of the use cases we’re modeling in MedMorph, and mCODE already provides a head start towards semantic interoperability (it’s not quite there yet, but that’s part of the goal). Bouncing off of Sandy’s comments, there might be something there to explore further.
Indirectly COVID-19 related, cancer was one set of the chronic diseases that saw a dip in timely treatment especially earlier on in the pandemic, so there’s probably still a COVID-19 angle even with cancer… plus cancer is actually a lot of different diseases under that one umbrella. On the semantic interoperability end, we could look at cancer data elements that are also COVID-19 data elements (e.g., cross-disease semantic interoperability). Thinking out loud here, but seems like something we can at least discuss. Every state in the U.S. has a cancer registry with cancer reporting required by law.
There’s definitely plenty of opportunity just with COVID-19 too.
Absolutely agree with mobilizing the resources and bringing them on a common platform as suggested by all . I am happy to help out in any way.
Overarching Description of Data-to-Evidence Community Best Practices, Tools, Resources, Initiatives (see especially 'Apply Results' column at the end of these tables)