This is the introduction to a series exploring big data in healthcare. Each piece will break down an aspect of analytics and where it fits into healthcare needs. This first piece provides an overview of the issues to be discussed.
In March I attended the HIMSS 2013 event
To understand what was on the conference attendee's minds, I took the liberty of analyzing the Twitter chatter during the HIMSS days, and again, I found analytics subjects topping the tweets as shown in Figure 1. But much to my surprise, there was very low chatter on governance and interoperability, even though governance and interoperability were among the top three session tracks.
To dig deeper on audience sentiments on such topics, I found very few negative sentiments on analytics, but observed a large number of negative sentiments on governance and interoperability, as shown in the Figure 2. One reason is, both governance and interoperability force organizations/people to change, which is hard to do, so people resist.
Are we talking about analytics or simple arithmetic: Synthesizing the analytics bandwagon at HIMSS 2013
At HIMSS, analyzing Twitter activity revealed which topics were covered through sessions and workshops, and what sentiments vendors and audience members had regarding those topics.
Analytics topped that discussion. But, do we really mean analytics or do we mean arithmetic? I would say arithmetic. Here is why.
Under the umbrella of analytics, most vendors and presenters talked about CMS measurements as part of meaningful use and value-based purchasing dashboards. These measures are not analytics -- that's why they are called measures; they translate to simple arithmetic calculations based on rules defined by CMS for the measure numerator and denominator.
The reason why I call such analytics applications arithmetic is because 90% of the effort involves data acquisition from electronic health records (EHRs) and harmonization of the data before applying the measure rules for numerator and denominator -- and none of these processes are analytical in nature; none of such measures use any statistical techniques or other cognitive assessment other than very simple calculation, i.e., arithmetic.
Upcoming pieces in this big data series:
Wake-up call: Healthcare industry has lots of information and not much data
Here I will go against the universal fact that the healthcare industry has lots of data but little information. Here I will also spell domains of the healthcare industry and how respective content attributes differ.
Informatics or analytics: How big data fits in the healthcare enterprise
An architectural look at big data characteristics and how/where big data elements fit in managing healthcare content.
Analytics design strategies for healthcare provider organizations
Here I will shed some light on who/how/where clinical decisions are made, which determines where/how/what/when we should design informatics or analytics and methods to deliver insight to the right end user.
Analytics design strategies for healthcare payer organizations
Here, I will discuss how the role of payers is changing from payments alone to wellness programs, which will change their risk analytics and business model from pure claim payments to population health segment.
Challenges in implementing big data analytics for the healthcare provider
Here I will highlight key barriers to adoption of big data for providers.
This will include organizational culture, IT skills, patient concerns, compliance, etc. I will also cover issues related to unstructured data analytics.
Continue to the next piece in this series.
About the author:
Naeem Hashmi is chief research officer for Information Frameworks, as well as an expert in healthcare data analytics, information management and data exchange. He is an active member of HIMSS, CHIME and AMIA. Let us know what you think about the story; email firstname.lastname@example.org or contact @SearchHealthIT on Twitter.
This was first published in April 2013