“Health care is the most information intensive industry in the economy, but it uses IT the least.” – David Cutler, Harvard Kennedy School economics professor
I think that executives of health and human service organizations think they spend a lot of time with data, but it’s all relative. Generally, the industry-wide and enterprise-wide data in the health care field is pretty limited – in quantity, quality, and utility. And, the “time with data” that executives refer to, is the time trying to get electronic health records (EHRs) up and running, or figuring out web optimization, or how to integrate telehealth into their operational processes. This is time spent wrestling with the information management process – and not using data.
One argument is that while the data practices (and rapid innovation) in other industries – or at giants like Amazon and Google – is a great target for forward thinking executives, it is just a target. There are some unique data challenges for making data work in the health and human service field. I had a chance to learn more about that perspective last month from Ravi Ganesan, President & CEO at Core Solutions, Inc. at The 2016 OPEN MINDS Performance Management Institute in his session, Why Health Care Is Not Like Google Or Amazon: The Challenges Of Fitting Ideal Data Models Into The Real World.
Organizations spend a lot of time learning about data, learning what it actually is, how to make data-driven decisions, and how to build a data culture. But the problem is putting that discussion into action. Part of the problem is the immense complexity of health care data. We just have so much of it – and so many requirements that go along with how we manage and report it. Another big problem is that there aren’t any standard data models that are used across the health care system. We all use different EHRs and because of system incompatibility, regulations, and lack of care coordination, it’s difficult to share data.
So how do you turn your data into knowledge? According to Mr. Ganesan, you need to create a “data journey” – moving from simple data modeling, to big data for analytics:
Data modeling – A data model organizes data elements and standardizes how the data elements relate to one another. By representing real things (people, places, services, etc.), the model represents “reality.” It allows you to document real life things, make connections between those things, and organize that data in an easy to understand format. If you have already decided what data to track, make reports of that data, and makes decisions with those reports, you are data modeling (see How Do We Measure ‘Impact’?).
Data maturity – A data maturity model is a framework to help organizations identify and quantify how “sophisticated” their data modeling is – from creation to decisionmaking. There are four stages of data maturity: Undisciplined, Reactive, Proactive, and Governed (for more on those four stages, check out Best Practice Leadership Is Leadership With Analytics).
Data-driven decisions – Data‐driven decision management (DDDM) is an approach to business governance that values decisions that can be backed up with data, which is supplied by your data modeling and reliant on your data maturity. Ask yourself – What was the source of your data? How well do the sample data represent the population? Why did you decide on that particular analytical approach? What alternatives did you consider? What assumptions are behind your analysis? These questions need answers (see The ‘Five-Step Formula’ For Making Data-Driven Decisions).
Big data – This term describes the large volume of data – structured and unstructured – that is extremely prevalent in the health care industry, and the result of the data collected throughout the above described journey. But it’s not the amount of data that’s important. It’s the ability to analyze that data for even better strategic decisions (see Big Data To Survive, Sustain & Succeed and ‘Big Data’ For Dummies).
When looking for organizations that are setting the bar for data best practices, it’s hard to look past Amazon and Google. The OPEN MINDS team has taken a close look at the operations of those organizations — and the lessons health and human service executives can learn from their strategies. You may want to check these out:
- Lessons From Walmart Vs. Amazon
- Preparing For The Amazon Effect
- The Amazon Leadership Principles & The Amazon Flywheel – What They Could Mean For Your Organization
- The Footprint Of The Elephant In Health Care
- Stop Integrating Data & Start Liberating Data
The bottom line is that we need to think about how to use the information we gather, make sure it’s clean, understandable, and do modeling around those data elements. This is no easy task, but many organizations are already on their way in their data journey. As Mr. Ganesan noted, “We sort of have this inferiority complex that we don’t use data as well as everyone else. But we aren’t as far behind as we think we are.”