Hospitals: How to Turn Your Overburdened Analytics Operation Into a High-Performance Enterprise

Data analytics has become as essential to healthcare organizations as staff and medical supplies. And for good reason: a robust analytics operation is helping hospitals across the country solve many longstanding challenges, from lowering sepsis mortality rates to improving patient satisfaction.

In other hospitals, however, resources are seriously overtaxed. Even requests that can be fulfilled leave data consumers unsatisfied, questioning the data’s validity and complaining that it takes too much back and forth to interpret. With that, here are the five core fundamentals to rebuild an analytics operation into an undeniable success.

Define the Customer(s)

In most analytics operations, this will include several different types of customers, each with different needs and abilities to consume data. A helpful way of segmenting customers follows:

Self-service. Typically, sophisticated users or consumers of data who just need some training and orientation to access the data themselves.

Guided tours. These are not everyday users and essentially need a “tour guide” to supply analytics expertise and be responsive to requests as they arise.

Information monitor. What this person needs is a detailed but simple-to-comprehend view of a particular story. Often an executive or other frontline leader, the information monitor appreciates a dashboard that, at a glance, shows “the big picture.”

Regulatory measure submitters. These customers will require significant assistance in assembling reports that meet the meticulous requirements of regulatory bodies.

While not all of these categories may apply to some organizations, the key is to understand the fundamental goals of your different customer types.

Define the Offering

Both the analytics operation and its customers must have a clear understanding of which services are provided by the analytics teams. Another sample list follows, highlighting the services increasingly expected by data-driven organizations.

• Data access. Data must be reliable, timely and compatible with the customer’s needs.
• Toolset alignment. Some customers have different comfort levels with different data instruments.
• Education and stewardship. Customers will expect help in understanding and trusting the data.
• Data source plan. This ties into defining what the customer is trying to do. Once the data sources are prioritized, there needs to be a plan for acquiring the data.
• Data modeling. Once data is prioritized from various sources, what can be done to strategically and pragmatically eliminate repetitive tasks that every user would have to do when they access the raw data?

Measure Success

Healthcare analytics teams should be proactive in showing their value. The cost of analytics is highly visible to leadership — and as such, the value provided must be equally, if not more, visible to prove the investment’s ROI.

To that end, an analytics director will need to determine what defines success in a healthcare analytics operation and how to measure it. Be aware that if these definitions aren’t formed clearly and confidently by the analytics director, someone else will do so. A few common metrics include:

• Customer satisfaction. Regularly check how satisfied customers are in a measurable way, such as by administering a satisfaction survey each time a request is filled or through another system that produces quantifiable results.
• Team member satisfaction. Similarly, it is important to monitor and measure the engagement and satisfaction levels of your team and be open to adjusting.
• Number of customers. Is this number going up or down over time? Keeping track of these simple statistics can help analytics directors know how they’re doing or if they need to make changes.
• Analytics project results. When it’s time to cut costs and the only thing visible is the expense, analytics teams are likely to see cuts if they can’t effectively communicate the value they’re providing to the organization. Thus, it is critical to regularly track the status of various improvement projects and measure the results.

Define the Process

In an efficient operation, both the intake and fulfillment processes are clearly defined. The intake process, for example, addresses simple questions regarding how the customer will access the data. Fulfillment is more nuanced and will depend on establishing decision-making rights and process through effective governance.

Define the Operation

This step establishes the best way to organize your analytics team. It is recommended to err on the side of generalization and only add specialized team members if and when there’s enough demand for that specialization to be self-sustaining.

Lastly, give careful consideration of who leads and holds accountability for the analytics operation. This person must be a visionary who is closely connected to the customer and has strong executive sponsorship. With these elements in place, along with incorporating the other steps outlined above, the data analytics enterprise will prove itself as an indispensable asset for the healthcare organization’s viability.

Dan LeSueur is senior vice president of client services at Health Catalyst, a leading provider of data and analytics technology and services to healthcare organizations.

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