Healthcare organizations are making significant investments in data analytics – an estimated $11 billion last year, forecast to surpass $30 billion by 2022. But spending does not necessarily bring success. Healthcare organizations need to put thoughtful, collaborative work into laying the foundation for analytics success.
There are three crucial areas to address before any healthcare analytics project starts rolling down the tracks:
- User community perspectives, opportunities, and imperatives
- Data governance
- Business rules
Let’s take a closer look at each one.
#1: User perspectives, opportunities, and imperatives
If there is one thing a healthcare organization can do to ensure analytics success, it is getting inside its users’ heads. Here are some steps to help understand users’ domains, empathize with their challenges and priorities, and comprehend information from their perspective:
- Identify the most critical problems users are trying to solve and determine if and how analytics can help.
- Understand what data is necessary for a useful analytics solution. Determine what transformations may be necessary for the data to be meaningful. Data from transactional systems often does not include useful measurements. Healthcare organizations must figure out which measurements are most valuable and how to define them.
- Decide what visual contexts will most effectively tell the story of the data to each user community.
Typically, the most important information is highly summarized and presented in a visually intuitive way. Different users may require different information arrayed in different ways. For example, Doctors Centers Hospital in Puerto Rico used Dimensional Insight’s Diver Platform to create two different dashboards to support two sets of decision makers. The hospital’s president and other managers monitor an executive dashboard that tracks key metrics including census, admissions, and discharges at all four of its hospitals. Another dashboard conveys key emergency department metrics, such as turnaround time and total visit time, to decision-makers in each of the four EDs.
#2: Data governance
Data governance refers to processes that dictate how data is managed in an organization. It is critical to address data governance prior to kicking off a business intelligence project. This does not need to be bureaucratic or complicated. In fact, much of it is common sense. Consider the essence of the problem, which is that there can be multiple versions of the truth, especially in a healthcare organization. But a single version of the truth is necessary to make data useful and gain insights from it.
How do you create a single, agreed-upon version of the truth? It requires:
- Bringing together the user community, including leadership
- Reaching consensus – or agree to disagree on certain issues
- Formalizing the process
Healthcare organizations must commit to doing the hard work of creating shared definitions and measures and then applying them consistently across the enterprise. But the pay-offs are huge. First, consistent definitions and measures create enterprise-wide confidence in the data. Second, once definitions and measures are agreed upon, an organization saves time by using them again and again.
In addition, the process of establishing data governance sometimes reveals unexpected things. Michigan’s Covenant Healthcare spent a significant amount of time on data governance when implementing Diver and Measure Factory, a business rules engine that provides automated data governance and hundreds of standard definitions of measures. When Covenant examined its readmissions, Measure Factory generated information that seemed implausible. So the IT department went back to the source – the EHR system. The IT team demonstrated that this implausible readmission data was exactly what had been entered into Epic in the first place. As a result, Covenant re-examined its Epic workflow and improved the way users input information.
#3: Business rules
Intertwined with data governance is the issue of business rules, which refer to the transformations that are applied to your data between the original data source and the user presentation.
In healthcare, a helpful example is how to measure “length of stay.” There are many different ways to define this measure and often there is a legitimate need for multiple definitions that help answer different types of questions. A healthcare organization might ask itself, are we focused on certain populations such as acute v. non-acute, adult v. pediatric, elective v. emergent? Do we need to stratify by disease type? Are we considering patients that have not yet been discharged from the hospital? Before an organization can begin meaningfully analyzing its data, it needs to define and implement these rules. Then, most importantly, it needs to circle back to users to ensure that everyone understands what measure they are working with, specifically how they are defined, and what they are intended to measure.
Done well, business rules are the foundation of that “single version of the truth” that is so essential to effective healthcare analytics. Healthcare organizations that focus on business rules, data governance, and user communities from the very start increase their probability of success with data analytics both near term and in the long run.
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