A few months ago, I had the honor of speaking at Optum’sand enjoyed the opportunity to engage with a broad spectrum of professionals and innovators. In talking with one young data scientist about the definition and types of AI, he commented that “yesterday’s AI is often today’s common technology”.
There are many great things coming in healthcare for 2019, and before diving into the last part of the “Cutting Through the Hype” series, here is what we have covered so far:
Cutting Through the Hype, Part 4 of 5 – Leveraging Governance to Prioritize Clinical and Business Objectives
In the Marine Corps, one of the first things I learned about how to prioritize and follow various orders I received was the phrase “Stars over Bars”. “Stars” refers to the rank of generals and “bars” indicates the silver or gold bars that makeup the insignia for captains and lieutenants. In the corporate world, we might refer to this as “C’s over VPs”.
Cutting through the Hype, Part 3 of 5 – Data Governance; How to Start, Organize and Build to Sustain
In Part 2 of the “Cutting through the Hype” series, we discussed the importance, especially in healthcare, of getting the data matched, merged, cleansed, mastered and enriched/tagged with the proper HLI code sets and clinical ontologies. This is the work represented in the first 4 columns (Connect, Move, Fix, and Relate) of the “Data Value Chain” diagram below:
Cutting Through the Hype, Part 2 of 5 – Real Life Illustrations That Help Define Healthcare’s Unique Data Challenges
In my previous blog post, it was determined that data silos in healthcare require utilization of technology with capabilities in three core competencies: Integration, Integrity (Data Quality), and Intelligence. Since these are not core competencies of the EHR, we should define the problem in more detail before considering further options.
In my introductory blog, I spoke of the importance of getting the foundational aspects of data correct before moving into more technologically advanced areas. Getting our “data house in order” is the first step to avoiding a lot of rework, which reminds me of a cautionary cartoon a co-worker had pinned in his cube when I first started as a software developer; “Don’t worry about the mule going blind, just load the cart.”
When it comes to Business Intelligence and Analytics, people tend to think about big data, artificial intelligence, and data mining. This “silver bullet thinking” tends to be driven by much of the hype out there. The challenge is that none of it works without two key requirements: