Thought Leadership

How Data Simplifies Problem Solving and Creates Better Outcomes

There is a condition affecting the way behavioral health organizations make decisions and drive change. It does not have a DSM code yet, but we’re calling it the “It Feels Like” syndrome. Onset usually occurs during leadership meetings when someone asks a question related to outcome improvement, cost containment, or strategic planning.

“It feels like” is easily identified in the following conversation:

  • CEO: “How are the overall outcomes from our jail diversion program compared to our targets?”
  • VP:  “Well . . . it feels like we are making progress.”

After our last article on Finding Insights From Your Data, our readers expressed interest in understanding why data-driven decision making has so quickly moved to the forefront of behavioral health technology discussions. Initiatives like Integrated Health and Value-Based Care are the most likely culprits. However, in the larger picture, behavioral health organizations are beginning to feel more pressure from competition and reimbursement reform than they ever have in the past.  This is forcing executive leadership to move to a more “corporate” mindset to thrive (or survive), and the “it feels like” mentality is no longer applicable to decision making.

Behavioral healthcare organizations need the ability to know answers based on data, instead of relying solely on individual stories. This is where data analytics and business intelligence platforms come into play.

To explain how analytics and business intelligence support better decision making, below are two specific case studies. These scenarios show the power of having objective, measurable information to power data-driven decisions.

Case Study 1 – Productivity

The dreaded productivity word. While many organizations hate to talk productivity, it is an important measure to understand for long-term financial health. Meeting best practice productivity standards is also one of the most consistent issues that we see within Community Mental Health (CMH) organizations across the country.

In this instance, we were working with an organization that “felt like” their staff were not seeing enough consumers each day, and they were feeling the financial effects. Once we analyzed the data, we identified that they had average productivity levels in the teens for their clinicians. As you can imagine, this was worse than they expected and ultimately unsustainable for their business model. After identifying the issue, we created a multi-tiered plan:

  1. Utilize the data to determine programs or teams that were experiencing extremely low productivity
  2. Interview and shadow the staff and supervisors to understand the issue
  3. Design the workflow and organizational changes to improve staff efficiency
  4. Implement the changes and monitor their effectiveness over time with the data

From these steps, you can see the importance of weaving together workflow re-design, large-scale project management, and data analytics to create real process improvement. Once the effort was underway, we could slice and dice the data to identify the teams that were improving and where additional training or discussions were needed. Within months of beginning this effort, the organization doubled their average productivity and created time to focus on improved consumer care.

Case Study 2 – Hospitalizations

Reducing unnecessary hospitalizations for people with mental illness is a significant focus for almost every behavioral health organization and health system. While working with a large CMH to create their data analytics platform, hospitalizations became a high priority initiative. They felt like they were taking the right steps to divert hospitalizations, but the volume was still much higher than their goal.

After implementing their data warehouse, we began digging into the data to find trends or outliers. In doing so, we noticed there were spikes of reported hospitalization requests around 4 pm. We brought this information back to the governance team, and after some discussion determined that this could be occurring because the office-based crisis staff were only working until 4 or 5 pm. Calls after 3 pm were sent to a hospital-based team, and the admit rate in these events was almost 98%.

The CMH used this data to change their scheduling to ensure that staff would be available until 6 pm each evening and that there was walk-in availability after 3 pm. These subtle, but critical, changes resulted in a 14% reduction in hospitalizations year-over-year, with an incredible 30% reduction for children’s services.

Can Data Analytics Help Your Organization?

The short answer is, of course, yes. However, what we are trying to help bring attention to for behavioral healthcare organizations are the lingering issues that are left unsolved. Think about the items that are on every leadership meeting agenda.

Ask yourself:

  1. What organizational decisions did we make in 2017 based on feeling instead of knowing?
  2. Are there areas where supervisors are operating without reliable information?
  3. What piece of data would change the way you operate if it was available tomorrow?

Usually, those questions can help point you in the right direction, but sometimes it can be difficult to get to the actual data set that you are after. We’re happy to help investigate, troubleshoot, or discuss your current data story if the conversation gets complex, but make sure you can answer those questions for your organization at your next meeting. We find they drive important dialogue, shine light on unexamined areas, and ultimately create the action that your organization needs.

 

 

All Thought Leadership
Archive