Just about every organization in every industry is rolling in data—and that means an abundance of opportunities to use that data to transform operations, improve performance and compete more effectively.
"Big data" has caught the attention of many—and perhaps nowhere more than in the healthcare industry, which has volumes of fragmented data ready to be converted into more efficient operations, bending of the "cost curve" and better clinical outcomes.
But, despite the big opportunities, for most healthcare organizations, big data thus far has been more of a big dilemma: What is it? And how exactly should we "do" it?
Not surprisingly, we've talked to many healthcare organizations that recognize a compelling opportunity, want to do something and have even budgeted accordingly. But they can't seem to take the first step forward.
Why is it so hard to move forward?
First, most organizations lack a clear vision and direction around big data. There are several fundamental questions that healthcare firms must ask themselves, one being whether they consider data a core asset of the organization. If so, then what is the expected value of that asset, and how much will the company invest annually toward maintaining and refining that asset? Oftentimes, we see that, although the organization may believe that data is one of its core assets, in fact the company's actions and investments do not support that theory. So first and foremost, an organization must decide whether it is a "data company."
Second is the matter of getting everyone on the same page. Big data projects are complex efforts that require involvement from various parties across an organization. Data necessary for analysis resides in various systems owned and maintained by disparate operating divisions within the organization. Moreover, the data is often not in the form required to draw insight and take action. It has to be accessed and then "cleansed"—and that requires cooperation from different people from different departments. Likely, that requires them to do something that is not part of their day jobs—without seeing any tangible benefit from contributing to the project until much later. The "what's in it for me" factor is practically nil for most such departments.
Finally, perception can also be an issue. Big data projects often are lumped in with business intelligence and data warehouse projects. Most organizations, and especially healthcare organizations, have seen at least one business intelligence and data warehouse project fail. People understand the inherent value but remain skeptical and un-invested to make such a transformational initiative successful. Hence, many are reticent to commit too deeply until it's clear the organization is actually deriving tangible benefits from the data warehouse.
A more manageable approach
In our experience, healthcare organizations make more progress in tapping their data by starting with "small data
"—that is, well-defined projects of a focused scope. Starting with a small scope and tackling a specific opportunity can be an effective way to generate quick results, demonstrate potential for an advanced analytics solution and win support for broader efforts down the road.
One area particularly ripe for opportunity is population health. In a perfect world with a perfect data warehouse, there are infinite disease conditions to identify, stratify and intervene for to improve clinical outcomes. But it might take years to build and shape that perfect data warehouse and find the right predictive solution for each disease condition and comorbidity. A small-data project could demonstrate tangible results—and do so quickly.
A small-data approach focuses on one condition—for example, behavioral health, an emerging area of concern and attention. Using a defined set of data, it allows you to study sources of cost and derive insights from which you can design and target a specific intervention for high-risk populations. Then, by measuring the return on the intervention program, you can demonstrate value of the small data solution; for example, savings of several million dollars over a one-year period. That, in turn, can help build a business case for taking action, possibly on a larger scale and gaining the support of other internal departments.
While this approach helps build internal credibility, which addresses one of the biggest roadblocks to big data, it does have some limitations. There is a risk that initiating multiple independent small-data projects can create "siloed" efforts with little consistency and potential for fueling the organization's ultimate journey toward using big data. Such risks can be mitigated with intelligent and adaptive data architecture and a periodic evaluation of the portfolio of small-data solutions.
Building the "sandbox" for small-data projects
To get started, you need two things: 1) a potential opportunity to test and 2) tools and an environment that enable fast analysis and experimentation.
It is important to understand quickly whether a potential solution has a promising business case, so that you can move quickly to implement it—or move on to something else without wasting further investment.
If a business case exists, proceed to find a solution. Waiting to procure servers for analysis or for permission to use an existing data warehouse will cost valuable time and money. So that leaves two primary alternatives for supporting data analysis: leveraging Software-as-a-Service solutions such as Hadoop with in-house expertise, or partnering with an organization that provides a turnkey solution for establishing analytics capabilities within a couple of days.
You'll then need a "sandbox" in which to "play" with those tools. The "sandbox" is an experimentation environment established outside of the organization's production systems and operations that facilitate analysis of an opportunity and testing of potential intervention solutions. In addition to the analysis tools, it also requires resources with the skills and availability to interpret the analysis, design solutions (e.g., a behavioral health intervention targeted to a specific group), implement the solution and measure the results.
Then building solutions
For building a small-data initiative, it is a good idea to keep a running list of potential business opportunities that may be ripe for cost-reduction or other benefits. Continuing our population health example, this might include areas as simple as finding and intervening for conditions that lead to the common flu and reduced employee productivity, to preventing pre-diabetics from becoming diabetics, to behavioral health. In particular, look at areas where there is no competing intervention solution already in the marketplace and where you believe you can be a unique solution provider.
It is important to establish clear "success criteria" up front to guide quick "go" or "no-go" decisions about potential projects. These should not be specific to the particular small-data project opportunity but rather generic enough to apply across topics—as they become the principles guiding small data as a journey to broader analytics initiatives. Examples of success criteria might include:
- Cost-reduction goals
- Degree to which the initiative changes clinical outcomes
- Ease of access to data
- Ease of cleansing data so that it is in a form needed for analysis
For example, you might have easy access to data, but it requires a lot of effort to "clean" it for analysis—so it isn't actually easy to use.
Another important criterion is presence of operational know-how for turning insight into action that will create outcomes. For example, if you don't have behavioral health specialists who can call on high-risk patients and deliver the solution (or a partner that can provide those services), then there is little point in analyzing the issue to start with. There must be a high correlation between data, insight and application.
Finally, you will need to consider the effort required to maintain a specific small-data solution over time. For instance, a new predictive model to help identify high-risk behavioral health patients or high-risk pregnancies. Will that require a lot of rework each year to adjust the risk model as more data becomes available? If so, that affects the solution's ease of use. Small-data solutions need to be dynamic and able to adjust easily to the market needs.
Just do it
Quick wins can accelerate progress toward realizing the benefits of big data. But realizing those quick wins requires the right focus—"small data"—and the right environment for making rapid decisions about when to move forward with a solution or when to abandon it and move on to something else. If in a month or two, you haven't produced a solution that is translating into tangible benefits, it is time to get out and try something else.
A small-data approach requires some care and good governance, but it can be a much more effective way to make progress toward the end goal of leveraging big data for enterprise advantage.
This article first appeared at Becker's Hospital Review.