My last two posts addressed a general overview of the healthcare analytics continuum, and the importance of foundational data integrity to healthcare analytics. For today's post I'd like to dig a little deeper into the difference between descriptive, predictive and prescriptive analytics, with a focus on prescriptive analytics, as I've found it has different meanings for different users.
Ever tried to create a portrait image of another person using push pins? Neither have I, but it looks hard. Few have mastered the art.
Deerwalk is proud to announce enhanced features of Plan Analytics, the flagship population health analytics and reporting application, with version 7.6 released earlier this week. With 7.6, the application will now report age and gender adjusted benchmark values to give customers higher quality comparisons of patient populations and provider performance.
My last post addressed a general overview of the entire healthcare analytics continuum - from foundational data integrity to true prescriptive analytics. At the end of that post I promised to dive into each layer with a bit more detail. So today's post will deal with the most important layer in the continuum - foundational data integrity. After all, without high quality, fully integrated and enriched healthcare data, any analytics exercise will be flawed at best.
Recent polls by GE and Accenture show that big data analytics is a priority for almost 90% of organizations within healthcare. Data is an essential tool for market participants that want to increase share while improving the quality of customer deliverables. And, in the not too distant future, healthcare providers and stakeholders that implement an analytics strategy will excel in clinical quality and prevention of operational bottlenecks.
In many fields, it is common to hear the terms "platform" and "application" tossed around interchangeably. The confusion is understandable because, for a non-technical person, platforms and applications can provide similar initial results. The two concepts are quite different, though, and it's important to understand which one your organization needs.
If I were going to design the worst possible health care analytics department in a health plan, TPA, or benefits consultancy, this is what I would do. First, I would go out and buy all the hardware and software licenses I needed, not to mention the database administrators and IT personnel necessary to support it. Why use the cloud? That's just a trend, right? And what about data security? The cloud can’t be nearly as secure as my data warehouse, right?
There has been so much hysteria about potential changes to the healthcare law that I feel compelled to attempt a level-headed view of things and suggest an idea. There are a lot of moving parts here, but I am going to stick to the pre-existing conditions portion as that one seems to get the most angst or at least the most television news coverage. The acronyms for the ACA and the ACHA are so close, let me simply call them Obamacare and Trumpcare for clarity. I heard Warren Buffett the[...]
I was listening to the news on the radio and heard a commercial from the AARP about proposed repeal and replace changes in the American Health Care Act that will allow health insurers to charge older Americans up to 5 times more than younger Americans for health insurance. The bill is referred to as the State Age Rating Flexibility Act of 2017. In the words of the AARP, this is an unfair age tax. I do not want to misrepresent the position of the AARP, an organization that helps millions[...]