CTI Healthcare blog series: A Prescription for Improving Patient and Institutional Financial Wellbeing
Innovation: The “Now“ Modern Healthcare Data Architecture Part 5 of 5.
The healthcare industry produces massive volumes of data every day—from claims and cost data to clinical trials, electronic medical records, and even online patient sentiments. on-the-fly. most professionals within the industry, it’s a daily struggle to get access to the information they need fast enough. Static reports with stale data aren’t sufficient for making data-driven decisions, mainly when they affect patient outcomes. Overcome the data challenges in healthcare today around governed, self-service access for everyone.
We can take a cue from everyday life. In the past, we’d use physical libraries to investigate and learn. Then content moved online to a multitude of websites, and we replicated the library setup online (notably the Yahoo index of yore). Almost immediately, these online libraries couldn’t keep up, and the innovation of fast and accurate Search made them irrelevant (notably Google).
Now, imagine your analysts can “Google” all your healthcare data and immediately work with it to uncover clinical gaps and financial implications using a new way to discover, assemble, curate, collaborate and govern diverse healthcare data on-the-fly. This approach would require a data framework that ties together modern data tools that ingest, catalog, relate, search, share, secure, and govern data on-the-fly .
This search paradigm is compelling because:
We need a couple of other companion features: a way for users to share and collaborate on what they find (think Pinterest) and agree on meaning (think Wikipedia). How does this paradigm play out with your healthcare data? Its impact is dramatic in two ways.
First, it empowers clinical and revenue analysts to “Google” all your healthcare data and immediately work with it to uncover clinical and financial opportunities, share and collaborate on the results, all the while applying a grassroots governance process to develop consistently and approved definitions along the way.
Secondly, analysts search and mash together patient, claims, and related data no matter where it comes from; visualize and investigate the results; refine and pick the data until they “get it right”; and collaborate on the results. Iterative thinking and iterative doing working together. This is how people are genuinely productive.
This achieves the critical data goal of providing readily accessible and meaningful data to do things like:
There are many other use cases your healthcare analysts could come up with given this information freedom – kind of like data democratization by giving them “Google,” access to all the healthcare data in your ecosystem – on a data framework that supports team collaboration, grassroots governance, data protection, and interoperability with public, external and unstructured data.
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