Making decisions with data in healthcare is still a largely unfulfilled promise
Healthcare data has incredible potential. It can improve patient experiences and outcomes. It can increase operational productivity and efficiency. Data can help optimize revenue cycle management. Analytics offers great promise to improve accountable care and population health management. Leading healthcare organizations turn information across these areas into insight and then act on that insight. But the potential in healthcare overall is largely unfulfilled. There’s no argument that making decisions with data is desirable. So, why are more healthcare organizations not able to do it? Here’s why. It takes more than just data to empower your organization. It takes more than just technology or software tools to uncover insights. You need to develop a data culture. This post highlights the four key steps to take to transform your healthcare organization.
“Health care payers and providers have access to more data than the vast majority of organizations. So why hasn’t more been done with that data to slow the rapid climb in health care spending and begin competing on outcomes rather than expenditures? The answer is that, despite the promise of electronic medical records, much of the data that reveals what works in health care has been inadequate and unusable—or is missing altogether. What’s more, organizational silos have made it difficult to link together pieces of information to show health-related patterns for any given patient group.” - The Boston Consulting Group
Take these 4 steps to transform how you make decisions with data
Tableau offers a Blueprint for building the capabilities to create a successful Data Culture in your organization. These principles apply to healthcare as well. So, with thanks and credit to Tableau for the framework, here are the keys to develop a culture that makes decisions with data in your healthcare organization.
1. Analytics strategy
Develop a vision for analytics in one area of your organization to get started. Don’t try to solve every problem at once. Create an analytics strategy. Begin with the outcome you desire. Determine the metrics that will confirm your strategy is working. Put plans in place to move from your current state toward your desired future state. It’s Strategy 101. Like any other change initiative, quick wins in one area will lead to demand for something similar in other departments. And like other change initiatives, you need support from the top for your analytics strategy.
2. Agility
It’s critical to have a secure and stable environment for your data that evolves as you do. Did you catch that? I just said the key to agility for analytics is a secure and stable environment. Those concepts are not at odds with each other. Security and stability are enablers of agility for analytics. A common challenge in healthcare is messy data that is neither easy to access nor fully trusted. Organizations often get stuck in this step because they try to gather up all possible sources and sets of data before doing any analysis. Don’t make that mistake. It’s best to pick a couple of key data sets that will yield results for the analysts. And by doing so, get a quick win for the organization. As I’ve discussed elsewhere, healthcare data that drives better outcomes for patients and lower costs for payers and providers can live in spreadsheets kept by providers, governmental population health data, payer data sets (which could be data feeds or simply file extracts), etc. Whatever the source and type of data, there’s a way to handle it. The key is to design for agility up front, so new data sets can be added as needed to the master database for the organization.
3. Proficiency
It’s one thing to access data. It’s another to be proficient with data either individually or as an organization. For an organization to be successful making decisions with data, their people need to know how to use data. It’s surprising to me how often the leaders of an organization have a vision to bring data together and make it accessible. But at the same time, they’ve given little to no thought to the question of who is going to do the analysis. Proficiency with data requires more than a smart intern who is an Excel wizard. Proficiency with data requires education for the team, measurement of engagement and adoption (for example, what data is being accessed, who’s engaged with the data), and development of organizational best practices. As the Analytics Strategy is laid out, attention must be paid to the organization’s proficiency with data. Do you have a plan to educate your team? Do you have experts who can train and coach your people? How will you measure the effectiveness of the new capability? Are you ensuring people use analytics best practices? Let’s move on to the next element.
4. Community
Community is the supercharger of a data culture. Your community can inspire, support learning, and drive excitement around data. Keys to this include creating easy communication channels, opportunities for the community to get together and collaborate, and strong support processes to make sure that roadblocks get removed as quickly as possible. Let’s face it, making decisions with data is a change initiative and most healthcare organizations are struggling to implement this change. They don’t ensure proficiency (see above) and they don’t engage people as a community to create a data driven culture.
Summary
Making decisions with data in healthcare starts with people. Every employee has the potential to generate a new insight that could be a breakthrough in cost, quality, or experience for patients.
Map out your analytics strategy. Ensure you plan for data agility, proficiency, and community to transform your organization into a data culture.
Data agility results from secure and stable data (trusted data) for your people to access for analytics. Data proficiency requires education, measurement, and best practices. For example, physicians need to know what metrics yield the best results for their patients and their practice. They need access to this data to make adjustments in real time rather than wait for the next monthly or quarterly report. And, people need to be part of a community that engages them where learning and best practice sharing occur. You can be the catalyst. What are you waiting for?
Decisions with data in healthcare start with people.
Read our article about creating a community around your organization's data.
