Trust is the foundation of all good relationships—and not just between people. Trust in data is essential for healthcare organizations. This is especially true as the move toward value-based care demands increased, high-level collaboration among different constituencies within a healthcare enterprise.
As care migrates away from straight fee-for-service, healthcare organizations must weigh investments, risks, and tradeoffs objectively with quantitative, trustworthy data. This kind of data driven decision-making will be critical to shaping the initiatives and high stakes choices required by value-based care. In this blog, I will detail three steps you can take to build a trusted data foundation.
1. Keep subject matter experts close to the data
Programmers and data engineers likely will be the ones to extract data from the source systems, but it is the subject matter experts who best understand the data and how it will be used. Subject matter experts should be integrally involved in designing and implementing the systems that provide information critical to decision making. They are also in the best position to determine the context for presenting information to the user community that best fosters understanding—and ultimately builds trust.
In a September 2018 report, “Progress in Healthcare Analytics Lies in Leveraging Data,” Gartner analyst Laura Craft notes that many clinicians currently doubt the integrity of their organizations’ data and discount insights that might improve patient care and costs. To mitigate this, organizations can work toward “shared ownership in data integrity.” Craft writes:
“Clinical data is almost always involved, so clinician involvement is critical. Involve clinicians who know how the data needs to be represented for any type of consumption from simple reports and dashboards to complex advanced analytic algorithms.”
Remember that, for better or worse, the organization will respond to what is measured—so it is essential to measure the things that matter most. Subject matter experts can help ensure the right choices are made.
2. Automate business logic transformations
More automation is better when it comes to the often-complex logic required to transform raw data into meaningful information. Analysts need to be able to define this logic at a level that makes sense to them and then have the computer do the heavy lifting. And they need help in understanding how the impact of a single, seemingly simple change could cascade through a system of interrelated data and measures. This is only possible with an analytics framework that was designed with this approach in mind. Automation helps to avoid the mistakes of error-prone humans and makes a system more agile and adaptable to change.
3. Promote visibility and transparency
The best way to make sure data is right is to let people—the front-line information consumers—at it. Once you get past the fear of throwing open the doors, you may be surprised at how engaged the organization becomes in both using information and continually improving it. The key to achieving this lies in the combination of awareness and access.
- Awareness means knowing the information exists and having insight into what it represents. Effective “catalogs and dictionaries” will help members of your organization discover what is available.
- Access is the ability to easily get to the specific information required for a particular decision. This typically demands more than summarized overviews, but rather the “nitty gritty” behind those summary numbers. The opportunity to perform deeper analysis is key to making optimally informed decisions. When there are big dollars at stake, details matter.
Of course, there are limits to how much visibility an organization will want to provide, especially given regulatory implications in healthcare. That’s where a robust analytics architecture comes in, helping your organization to be selectively transparent and protective of data at the same time.
Vision of the future
Imagine a future where everyone in the organization who needs information to make decisions—from bedside to back office to corner office—has the data they need at their fingertips and can use it to collaborate with one another, whether that’s asking for clarification on what a specific measure (KPI) means or sharing an observation on what signal a data point may be sending. Speed of decisions could increase, as would confidence that they are the right ones. And it all stems from a foundation of trust—both in the data and one another.
The right analytics architecture provides a trusted data foundation while ensuring speed and agility to adapt to shifting requirements. And there will be plenty of those to come as the velocity of change in healthcare accelerates. In this time of transition, a static data environment will not cut it. Staying one step ahead of the competition will require both having the right information as well as the know-how and confidence to use it to make crucial decisions.
Read about three smart moves for healthcare organizations that are Navigating the Path to Value-Based Care.
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