Kaufman Hall: 5 Key Learnings from HFMA’s Financial Analytics Leadership Council
The Healthcare Financial Management Association (HFMA) engaged Kaufman Hall to spearhead the Financial Analytics Leadership Council—a dynamic and dedicated group charged a little less than a year ago with advancing industry thinking and practices around the role of data analytics in managing financial performance. The group's members are passionate about doing more to leverage financial and operational data to inform strategic decisions in their hospitals and health systems.
Council discussions to date have covered several important topics, ranging from using analytics to manage performance improvement initiatives to determining the value and applicability of rolling forecasting. The most recent meeting focused on data governance and strategies for launching a framework. Stephanie Lenzner, chief analytics officer at Froedtert Health and the Medical College of Wisconsin, and Melanie Zickgraf, associate vice president financial planning at Geisinger Health System, led the discussion.
Below, from the meeting, are five key takeaways, which can serve to inspire and guide healthcare organizations in their efforts to advance data analytics use.
#1—Encourage Responsible Data Citizenship
We all need information to do our jobs effectively, but often we don't think about how the data we receive, use, and generate may be helpful to someone else. We inadvertently overlook the idea that our data could inform decisions outside of our immediate focus areas and departments. Instead, we complete tasks and check them off the list, moving on to the next item on the agenda.
However, successful organizations have adopted a fundamentally different way of thinking about data-use behaviors. It starts with recognizing the value of a give-and-get mindset, embracing the idea that everyone has a role in data governance, no matter who you are and what level you're at in the organization.
#2—Stop the Finger Pointing
Organizations that realize meaningful performance also discourage finger pointing. Instead of saying, "Your data is wrong," they redirect the conversation to ask: "How did you pull that? What's your definition? What data source did you use? Which analyst pulled that for you?" Nine times out of 10, the data is not wrong—it's just different based on the methodology used to generate it.
#3—Build a Hub-and-Spoke Model
To fully appreciate the context of various data, analysts should be planted in the areas and communities they serve, listening, analyzing, understanding, and teaching their clinical, business, and operations colleagues. That said, they also need to be connected to an analytics center of excellence where they can come together, share ideas, access new tools, and create standards. A hub-and-spoke model facilitates these two concepts, fostering the best of both worlds.
The ultimate goal, of course, is that clinicians and operations leaders will get to the point where they can understand the data without having an analyst explain it to them. In these scenarios, the analyst's role morphs over time to focus on helping colleagues drill down into anomalies so they may see and understand why a particular change occurred.
#4—Create a Data Adoption Program
Healthcare organizations should appreciate the fact that many of their staff, including clinicians, operations leaders, and other decision-makers are not accustomed to seeing sophisticated analytics. As such, the pace of information delivery and how the data is presented can be overwhelming. If there is not a safe place for people to learn how to interpret and respond to information, then the organization may not be effective at compelling staff to use the data to drive decisions. As a result, the organization could miss key opportunities or fail to learn about concerning risk points.
With that in mind, it's important to create a dedicated data-adoption program that teaches and reinforces how to use the new information being generated. As part of this effort, organizations should consider establishing a program manager position. This would be someone whose entire job is to manage the data citizenship and governance program. Responsibilities could include everything from facilitating various governance meetings to teaching people how to interpret data to driving data analytics adoption.
#5—Don't Let Perfect Be the Enemy of Good
We all want perfect data, but rarely does it exist. Instead of waiting for your data to be flawless, it is wise to start working with the information you have and try to find ways to further refine it. For example, if you can get it to a level of 80 percent confidence, put it in the hands of your decision-makers, help them understand what gaps exist, and incentivize them to assist in cleaning it up, you can make meaningful progress on the road to consistent data use. The cost of waiting to act until the data is perfect is far greater than the cost of working with and refining a somewhat imperfect data set and then responding to the trends it reveals.
Establishing a strong data-governance foundation is an essential step in starting to reliably and effectively employ data to improve decision-making and meet strategic goals. Committing to this work can move an organization farther along the path toward data-driven improvement and performance.
About Kaufman Hall