It’s really about how we make decisions
I heard author and data analyst Cathy O’Neil on the radio recently. She discussed her new book, Weapons of Math Destruction. Essentially, O’Neil talked about the misuse of data and the incorporation of biases when making decisions. She was especially concerned about using trends to stigmatize individuals.
I would like to reframe this and relate it to healthcare analytics. I’m renaming a few things, but it’s really about how we make decisions.
There are at least two ways to use data.
Big Data
I have heard a common pattern in big data success stories. We have an intent and a lot of data. Professional data analysts look at a very large set of data and discover a pattern, which leads us to an action, though usually indirectly.
This is a story of discovery and subsequent old-fashioned hard work. We don’t know what the problem is, so trained data analysts help us find some clues in the data. Sometimes the data indicates a broad stroke solution, but usually it’s vague. What we really see are trends across large groups. This approach promises, do more of this and you get more of that. It doesn’t promise individual results.
Directed Data
Contrast this with data products that are sold with the purpose and promise of generating value. Healthcare organizations use lots of reports and dashboards. The products promise us, buy this and you will get that. It is by its nature prescriptive. The data and the problem define one another. The value hoped for is usually lower cost and higher clinical quality.
There is nothing wrong with using data in this way. If the hospital data points to the infection rate on Fourth Floor East, or documentation fallouts by Dr. Williams, we know where to focus our efforts. The key is that the data tells us where the problem is.
We still need to discover more. We need qualitative data about the fourth floor or the practice of Dr. Williams. We need to understand what the problem is and develop a solution. Chances are good that our data vendor skipped talking about this step.
Both Approaches Have Limits
The promise of Big Data is to help us find and influence broad trends. A big data trend does not predict individual results. It also does not, as O’Neil argues in her book, justify action on an individual.
In contrast, we use Directed Data to see where attention is needed. We can’t forget that the metric is not the problem. We still need to understand the true problem.
With both approaches, further discovery through qualitative data helps us to understand the problems fully, and to synthesize effective solutions. There has been a lot of attention paid to quantitative data, and there’s been a lot of money poured into it. Recognizing how we intend to use the data, and supplementing it it with deeper understanding, is essential to getting value from that money.
