The experience of organizations that have led the way in applying business intelligence and analytics technology to healthcare information suggests that the traditional ways of doing things haven’t worked particularly well. As I listen to presentations on healthcare analytics at various conferences, the “lessons learned” narratives seem very consistent:
- Be careful about taking on too much too fast
- Focus on delivering “early and often” to get constructive feedback
- Make sure that the user community will derive value from what you do deliver
Of particular note is the dissatisfaction with the data warehouse model. One of the biggest criticisms is that this model often represents a “build it and they will come” approach. IT implements it, assuming that once this vast store of information exists, the user community will line up to take advantage of it. In reality, however, after the lengthy, resource-intensive and complicated process typically required to build a warehouse, that huge “AH-HA!” moment from the user community that IT enthusiastically anticipates seldom arrives. Unfortunately, these types of data warehouse-focused projects are often not implemented with a focus on the problems that need to be solved and end up being “solutions looking for problems”. And what ends up as the result of the inordinate amount of time, money and organizational bandwidth invested? An 80% failure rate, according to Gartner statistics.
Healthcare organizations need a new way to think about analytics – an approach that at its core considers the goals of business, operational and clinical communities, and aims to help them reach those goals quickly and efficiently. In this two-part blog series, we will examine such an approach and discuss why it may not require a data warehouse.
Old way of thinking: data as static
A data warehouse is typically defined as a central repository of an organization’s most important data. It’s the place from which data can be conveniently and flexibly accessed independent of source transactional systems. In practice, this is a very stationary concept: once in the warehouse, most of the data will change very little. However, some of the most interesting and useful information is that which reflects what’s happening right now, and that’s very dynamic. So, most of the warehouse is devoted to data that will likely be used less and less over time.
New way of thinking: data in motion
An alternative view would be to think of data as being in constant motion. What’s most important in this model is a continuous and timely flow of information that represents the current state of the organization and its processes, as well as how that state is changing over time. For example, what is the trend in our hospital census from hour to hour and how is it being impacted by an outbreak of the flu? We may be able to use this type of information to make more optimal decisions if we can understand it from multiple perspectives. That’s analytics at its essence. But to be effective, this approach requires the most relevant information to be available when the decision needs to be made.
For many scenarios like this, we don’t necessarily need a comprehensive data warehouse. Instead the emphasis would shift to extracting data from its original sources and combining and transforming it into the information that’s most useful to the user community. That information could also be stored at an appropriate level of summarization to provide historical perspective. The result is similar to that provided by the data warehouse, but with an emphasis on the aspects of the problem that represent the highest value to the user community.
It’s important here to distinguish between data warehouses and operational data stores. Most EHRs and other transactional systems maintain their own operational data stores for offline reporting and archiving. Data warehouses in many respects duplicate the role of the operational data store. If you’re going to invest a lot of time, money and effort, doesn’t it make sense to focus on things that will add value rather than re-creating what already exists?
Other systems embrace this notion of data in motion. For example, consider the way that Google handles Internet data. It doesn’t store information from different websites all over again. That would take far too many resources, and it’s just not necessary. Rather, it indexes data so you can explore it in contextually relevant ways.
We can think about analyzing healthcare data in a similar manner. The real goal is to derive knowledge from the data. What’s arguably more important than the storage of data is how it can be transformed into new information that will guide you toward better decisions and actions. But the age-old challenge lies in making this quickly evolving knowledge readily and consistently accessible to the people who can best take advantage of it.
Where do you begin?
Adopting this alternative approach allows us to change the way we implement analytics projects. No longer do we need to start by “boiling the ocean” (tackling everything at once) as often happens with data warehouse projects. Rather, we can focus on a limited number of opportunities to produce new information and knowledge that will lead to high value, measurable outcomes.
This is a noble goal, but where should you focus your efforts first to make sure you get off to a good start? We’ll explore the possibilities in my next blog post. Stay tuned.
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