How to improve data literacy within healthcare organizations

The healthcare industry has been turned on its head in the last two years, and is still reeling from supply chain disruptions, staffing shortages, high rates of physician burnout, and uncertainty over the continuing Covid-19 pandemic.

What has been clear to many healthcare leaders during the pandemic is that their organization’s data can be incredibly useful in helping them to figure out how to make better, more informed decisions in these areas. However, many organizations are failing in having a data-literate workforce. In fact, research by Accenture shows only 25% of employees believe they use data effectively, and only 21% are confident in their data literacy skills.

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So how can healthcare organizations go about improving data literacy among both their staff and their leadership? Here are some best practices that hospitals can implement to better empower their employees to make data-driven decisions.

It all starts at the top

To improve data literacy, it’s important that employees know they will be expected to make data-driven decisions. That means a data mindset needs to be embraced by leadership, and they need to create goals for employees that are tied into the data. If leadership is speaking the language of data, employees will be more driven to embrace that mindset.

Healthcare organizations can take this even further by tying analytics and data-driven decisions into their strategic goals. For example, if “improving the patient experience” is a strategic goal for the hospital, leadership should look at specific analytics-driven initiatives to help achieve that goal.

Make sure you’re talking about data in the same way

We all know it’s extremely challenging to communicate with a person who is speaking a different language than you are. Unfortunately, with data, many people within an organization talk about the data in different ways, making it hard to communicate and find common ground.

How can healthcare organizations ensure their employees are speaking the same language around data? First is to agree on common definitions across departments so everyone knows when they talk about a “patient day,” for example, it is consistent across the organization. Second is to provide access to those definitions and allow people to access the lineage so they understand and trust the definitions. Once people are confident they are talking about data in the same way as their peers, they will be more confident in making decisions based off of that data.

Provide access to data in a way that’s meaningful

In order for employees to be able to use data on a regular basis, they need access to that data. However, it’s not as simple as opening the gates and letting them in. Oftentimes, people won’t use data – even if they have access to it – if it’s not presented to them in a way that is meaningful to their role. All too often, a health system’s IT department will determine the paths that employees can take to access data without fully considering different users’ roles and expectations. Users need personalized paths to access data with minimal friction in order to increase adoption.

Provide ongoing training that meets user needs

The days of “big bang” training where users receive hours of training compressed into a short period are over. Inevitably, what happens is that users haven’t experienced real-world use of the product yet, and when they do, they forget everything they learned and fail to actually use it.

Healthcare organizations can overcome this in a couple of different ways. First, they should look for those individuals who are a little (or a lot!) more data savvy than others and involve them in peer-to-peer training efforts. These employees understand the culture and more intimately understand the goals of an organization, and can be incredibly effective in teaching others how to analyze and make decisions based on data.

Second, organizations should seek to provide ongoing training that meets users where they are through a combination of lessons, short tutorials, and hands-on project work. Whoever is conducting the training should make sure the environment is one where people can comfortably ask questions and receive guidance if they are not understanding the data. Every individual needs to be met where they are so they can receive the right support and feel comfortable navigating data.

Measure the impact of data literacy efforts

Healthcare leaders should apply the same data-driven thought processes to data literacy efforts that they apply to other areas of the organization. After desired outcomes have been defined and data literacy efforts aligned to those outcomes, organizations can gather some of the hard data on their efforts. This includes measuring factors such as the number of users that log in to analytics systems over a particular time period (daily, weekly, monthly, etc.) and how long they stay the system.

Ultimately, though, organizations won’t want to rely on these data points alone, but they will want to use those points to dive into areas of investigation. If people should be logging in daily or weekly in order to meet organizational objectives and they aren’t, what is keeping them from doing so? Is it that people don’t understand how to use the data to improve business outcomes or meet strategic objectives? Or do people need additional data training? Or does the data need to be presented in a different way more relevant to users’ roles within the organization?

Conclusion

Healthcare organizations know their data can help them make more informed and more impactful decisions that will benefit their operations, finances, and patient outcomes. But in order to make a difference, employees need to understand how the data is connected to the results they want to see. Having the right technology in place is important, but it’s also not enough. Healthcare organizations must also ensure they have a data-literate workforce that is confident in using the data and is empowered in using it to make decisions.

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