Data-driven quality improvement is propelling healthcare transformation. The ability to strategically leverage healthcare data is essential, making highly effective data analysts more valuable than ever. So, what attributes differentiate a good data analyst from a great analyst?
Stephen Covey’s well-known book “The 7 Habits of Highly Effective People,” has long had far-reaching impacts in the business world. These same principles are relevant today and applicable in the world of healthcare analytics. Learn how Covey’s second habit, “Begin With the End in Mind,” drives great healthcare data analysts.
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While healthcare leaders can easily use current literature to delve into the skills, knowledge, and experience data analysts need to perform their jobs at a basic level, learning what makes a healthcare data analyst highly effective is more difficult. A key habit that a data analyst needs is often missing, Begin With the End in Mind (one of Stephen Covey’s seven habits of highly effective people), can help data analysts bring significantly more value to their health systems’ improvement work. This article explains how that habit fits into the healthcare industry and how data analysts can leverage it in their work.
Data-driven quality improvement is at the heart of healthcare transformation. However, an organization’s healthcare analytics are only as effective as its ability to leverage that data. This is where the value of highly effective data analysts comes in.
Data analysts help turn hunches into insights. They partner with domain experts in many aspects of a health system to make sure there’s evidence behind theories. On a daily basis, data analysts run analyses on measures to detect variations or trends that point toward an organization’s performance. They also provide opportunity analysis to see which areas are ripe for improvement or additional focus. These analyses can help supply hospital leaders with critical information as they make decisions. Ultimately, data analysts provide access to information that supports better decision making.
As data analysts manipulate, process, and analyze healthcare data, the best tools and technical acumen in the world don’t automatically create the best analyst. To fully leverage those skills, an analyst must think about what value that analysis brings to the organization.
What separates an average data analyst from a truly great one? While there are many things that make a great data analyst, two traits stand out:
When an analyst is starting her career, she may have great SQL skills but have difficulty partnering with domain experts who have more insight into the problem at hand. An inability to build relationships can create dissatisfaction, encourage siloed thinking, and slow down the process, resulting in wasted resources. A good relationship between an analyst and a subject matter expert (SME) facilitates two-way communication and allows the person with the technical chops to use an SME’s domain expertise. It’s the difference between just handing over a report request, or, with a little bit of collaboration, providing something much more valuable.
Steven Covey’s widely known book, The 7 Habits of Highly Effective People, offers valuable insights into the traits of successful business and healthcare leaders. The second habit (Begin With the End in Mind), helps create a highly effective healthcare data analyst by building on the idea mentioned above: the ability to look deeply into a problem.
As Covey says of this habit, “Begin With the End in Mind means to begin each day, task, or project with a clear vision of your desired direction and destination, and then continue by flexing your proactive muscles to make things happen.” As it relates to healthcare data analysts, this habit helps them relate the small decisions to the bigger picture and ensure that what’s produced at the end of the day will be valuable.
Covey goes on to say, “Sometimes people find themselves achieving victories that are empty—successes that have come at the expense of things that were far more valuable to them. If your ladder is not leaning against the right wall, every step you take gets you to the wrong place faster.” There’s so much focus on process measures in healthcare; if those measures are not tied to something bigger and used in an appropriate context, they won’t get the organization where it wants to go.
Questions such as, “Was the patient turned appropriately?” and “Are hand hygiene practices being followed?” are good examples. Analysts are frequently asked about one specific step of the process, and they can easily to get lost in the minutia and never reach a valuable answer. Someone might ask about hand washing, and an analyst returns the compliance data for the last 30 days.
To add value with actionable analytics, even after answering the compliance rate question completely and accurately, an analyst needs to ask key questions:
This understanding allows analysts to make some decisions themselves. For instance, if an analyst is asked to run a query about something for a department, but he knows that it’s part of a hospital-wide initiative, he might decide to pull the data for the entire system. A great data analyst understands the what and the why behind what he’s doing. This pragmatic innovation around beginning with the end in mind makes for a highly effective healthcare data analyst.
As someone who oversees a team of healthcare data analysts, I’ve experienced many great examples of beginning with the end in mind. For example, a data analyst working with a large healthcare system in the Midwest put together a referral analysis to share among the top leaders of the system.
A number of surgeons from one service line were concerned that their patient volume decreased over the past year and were worried they were losing patients to competitors. The analyst for that service line put together a referral analysis to answer two questions: “Are we losing market share?” and “If so, where are our patients going?”
The analyst could see the business needs and then put data together in a way that allowed leaders to make informed decisions. Given little information upfront, she worked with the SMEs to understand the workflow and documentation practices that provided context to the data. She also had to confirm, rather than assume, that staff documented items in a certain way. This partnership led to an insightful analysis that allowed the health system to improve an actionable area that would decrease leakage.
In a large system like this, it takes a lot of relationship building to understand how service lines, teams, and, ultimately, data tie together. This type of engagement takes bravery to ask the right questions and curiosity to take the extra step. An analyst stands out by asking great questions. If she has an interest in this type of data, she can go past having purely technical skills and bring the additional value of being a thought partner.
Developing this type of relationship takes time—even where an analyst feels comfortable enough to ask the difficult questions. When the relationship develops to this point of trust, however, stakeholders know this process will produce a better outcome. This type of trust and mutual respect fosters more than a purely transactional relationship of “I want to know the total cost of care for this disease,” to “What’s your opinion of what you’re seeing in this data?” A great analyst can provide a different lens to interpret the data even though they may not have clinical expertise.
While the type of curiosity and bravery shown by the analyst in the example above can take more time up front, it helped her deliver much more useful results. Here are some examples of questions and a statement she might ask when applying the principle of Beginning with the End in Mind:
This type of thinking helps analysts understand a problem deeply, so they can begin with the end of mind. If they don’t understand the end goal, they need to be brave enough to ask. It is one of the clear divides between the people who are good at this job and the people who are great.
The benefits of having highly effective data analysts are clear:
Although there may be more back and forth at the outset, getting to the heart of the question saves time in the long run.
A good data analyst will provide the results of their analyses in a way that points to clear conclusions and helps create informed decisions.
By both saving time and avoiding unnecessary work, a highly effective data analyst ultimately saves the organization money.
How can an organization encourage data analysts to start with the end in mind? The most effective way is to have supervisors frequently ask questions about the end goal an analyst’s work. When a manager checks in with an analyst and her work, they should contemplate together where the analysis is going and what value added is. From a habit-building perspective, these frequent touchpoints help promising data analysts become highly effective.
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