How to Avoid the 8 Most Common Pain Points in Becoming a Data-Driven Healthcare Organization

data driven healthcare

A.C. Nielsen once said that the price of light is less than the cost of darkness. Said another way, the pursuit of knowledge often requires investment, but the costs of not having the information you need when you need it can be far greater. In this article, we’ll take a look the “investment” side, and explore some of the barriers and challenges many organizations overcome on the journey to becoming truly data-driven.

Data as a driver of organizational strategy and leadership isn’t just a recent trending topic. International business runs on data-driven leadership, and, of course, the world of finance has always been data rich and data dependent. is brimming with books on the subject and it’s easy to find dozens of scholarly articles and presentations from a simple online search. And the U.S. educational system has promoted data-driven instruction for the past decade.

What’s not as easy to detect is the pragmatic insight a healthcare organization needs to become data driven.

Advantages of Being Data Driven

The characteristic advantages of being data-driven are found across many areas of the organization. For example:

  1. Data-driven organizations look for data that supports the mission, thus ensuring its continuity and supporting the culture. If your mission is to care for patients, you want data that is in the best interest of patients.
  2. Data illuminates opportunities for improvement, and can help to avoid duplication of effort, unnecessary costs, and repeat performances of poor prior performance.
  3. A data-driven company realizes fewer disruptions of operations, such as those that can arise from internal politics.

And the clinical advantages are obvious. If medicine were not data driven, and clinicians routinely ignored the preponderance of evidence to show the benefits of the treatments they prescribe, we’d be back in the days of the Health Jolting Chair and Dr. Bonker’s Celebrated Egyptian Oil.

Leaders in a data-driven organization depend on the light of data, rather than just their own expertise and opinion, to drive the bus. They realize that the company runs best when its employees know where they have been, where they are, and where they are headed.

8 Pain Points toward Becoming a Data Driven Healthcare Organization

  1. Conflicting versions of the truth. Oftentimes, there is plenty of data, but it’s conflicting, so two groups from the organization will use similar sets of data, but arrive at different conclusions because of differences in the data. If they both present to the CEO, then, Houston, we have a problem.How many doctors do we have in our medical group? How many employees? How many ED visits or inpatient admissions did we process last fiscal year? Ask five different people in the organization these same questions and you might get five different answers.Definitions of data become very important. This boils down to leadership, governance, and executive sponsorship. Departments need to agree on the fundamental nouns or the “whats” of the organization in order to scope analyses and arrive at similar conclusions. There are certain data points that can early bind, like if we have 10 hospitals, then we know the names and locations of those hospitals. But is there agreement on how many nursing stations are in each hospital?
  1. Lacking a culture that supports data transparency. This is perhaps the single most important pain point to consider. To fully realize the power of data, it must be made available to those individuals within your company who need it. But it’s not always comfortable to share, especially if the data being requested may have data quality issues, or seem to point to opportunities for improvement. Our recommendation in these cases is to find ways to “start small” with data transparency, in the “safest” areas of your business, then use that success as an example to invite broader participation.Eventually, data analysts need to feel that they can provide insights – that they can “speak truth to power” – without being afraid of what they are going to expose. Leadership can lead through example; by embodying a non-punitive culture that is welcoming of new insights so staff feel empowered to deliver them.
  1. A lack of trust in the data. Transparency can also be a double-edged sword. As stated above, the culture has to be ready to accept it, and any issues of trust in the data must first be addressed, although not necessary “fixed.” For example, we recommend working with clinician leaders to validate key performance indicators, refine population definitions, and develop a level of trust around the data. There needs to be a process of getting the docs ready for transparency. Once that’s achieved, good things start to happen.One of our customers recently conducted a test of change involving a group of hospitalists at one of their hospitals. This group agreed to show all the other doctors their patient experience scores by physician name. Any doctor could look up individual names and scores of their peers, and just by doing this, patient satisfaction scores jumped by roughly 20%.
  1. Data volume and overload. A typical healthcare organization measures anywhere from 300 to 800 metrics across the system. Many are driven by governmental reporting. But many others are historical and it’s difficult to retire them. It’s hard to say ‘this isn’t the best metric for this process,’ so they become additive.
  1. Struggling to find an effective system. It’s challenging to make a decision about what’s important when selecting a healthcare analytics system. The variables to consider include speed of implementation, ease of use, proactive service, delivery of new technology, and, of course, cost. But this decision can be assisted through objective review sources, such as KLAS, Gartner, and Chilmark, which all provide impartial analyses of enterprise-data-warehouse and analytics-platform vendors that serve the unique needs of healthcare.Above all, seek to understand what customers of your potential analytic partners have accomplished to date. For example, KLAS has published a report that compares BI vendors by their ability to improve customer outcomes.Other excellent research and learning opportunities are healthcare analytics conferences. John Moore, the founder of Chilmark, in his review of the 2015 Healthcare Analytics Summit, described attendees as “arguably the most upbeat healthcare provider audience I’ve ever encountered in my eight plus years in the healthcare IT market. People were there to learn from one another and the positive energy was contagious.” Conferences like this offer exposure to what others are doing and what effective systems look like.
  1. “We already tried that.” There are a number of reasons why enterprise data warehouse implementations fail. Among them, a lack of executive sponsorship, not involving frontline healthcare information users from start to finish, and being unrealistic about what can be accomplished. Organizations that have been down this path know how difficult it is to change direction if asked to consider another solution.
  1. A dearth of data-savvy staff. There are a lot of smart analytical people, but many are doing work out of necessity that is “below their license”. They are stuck performing tasks which could be automated with the right governance and technology, and their potential to identify opportunities for organizational improvement goes underutilized. Once their roles are better defined, the organization can fully realize the power of their data analysts.
  1. Lack of executive sponsorship. Every successful data-driven organization has the backing of a well-organized governance structure with executive leadership at the top. Executive sponsors comprise the company’s top executives who are behind the wheel of data-driven healthcare. They provide guidance and decide on priorities. Best practice governance structures also include a leadership council of other top executives, and an advisory group of cross-functional executives.

Consequences of Maintaining the Status Quo

Everything changes, from our community, to our culture, to our economy, to ourselves. We are constantly learning more about medicine, people, and diseases. Not being tuned into this, and actively working to maintain the status quo means that you risk becoming irrelevant and incapable of providing a competitive service.

You Don’t Have to Go It Alone

When you are ready to start the journey toward being data driven, involve an organization that knows what’s worked before and has experience looking at the right metrics and indicators in the operational, clinical, and financial domains.

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