- Healthcare data is challenging to manage efficiently due to its complexity and sheer volume.
- Data quality is a key consideration and should be prioritized.
- Self-service capabilities with pre-built content help accelerate analytics.
- Using a project mindset allows organizations to solve problems incrementally, achieve wins and keep moving.
- Talent is in short supply, and the solution may lie in your own ranks for those with the innate ability to tell stories, even if they're not analytics professionals or data scientists.
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Healthcare data is challenging to manage efficiently because of its complexity and sheer volume. Its annual compounded growth rate is 36 percent — more than any other industry. Despite this astounding wealth of information, the quality of data is lacking, which can make putting it to good use extremely difficult.
Below are five key steps to effectively launch a healthcare analytics program that benefits your entire organization.
1. Start with clean, standardized data.
When it comes to data, everyone wants to do the modeling. It's a lot more exciting than the down-and-dirty work of making sure the information you're relying on is solid. Yet, more and more healthcare organizations are feeling the adverse effects of poor-quality data. Data analysts report spending up to 80 percent of their time attempting to access data sources, and cleansing and preparing the data for use.
Think about how you link your data together and introduce simple quality rules to make sure that your data is as standardized as possible when you're planning a project. Start with a clear understanding of which data can be used for what purpose and its limitations with key stakeholders.
Your analytics strategy does not need to be complex or require complicated tools. What dataset(s) does your organization use most? Maybe it’s your billing system or a clinical system. Work on standardizing and improving its quality.
2. Create a self-service organizational environment.
Content is increasingly generated by business intelligence users more than by data scientists, making the self-service model essential. The self-service approach can help close data science and analytics gaps. When planning your analytic strategy, consider non-proprietary technologies. Industry-leading, cloud-based business intelligence tools such as Tableau or Power BI don't require a lot of technical skill which is essential when you're enabling self-service.
Technology is evolving rapidly, and this is especially true in the analytics space. Keeping up with changes and learning new analytics tools can take a significant amount of time. How can your team stay focused on process and strategy instead? Consider these tips:
- Avoid embedding all your logic into spreadsheets or BI tools. Consider using a measure library or measure management strategy instead.
- As you center your strategy and simplify information, keep your audience in mind. Work to streamline and document the data. Investments like this have a longer shelf life than technology alone, and they're essential to self-service analytics.
- Take time to ensure that your models are easy to use and understand. If the lay businessperson can get in and do the analytics through self-service, the analytics team is free to work on more advanced analytics.
- Automate updating of key measures so you don't keep reinventing the wheel.
- Grow your analytics team's skills using non-proprietary or industry-leading technology. Invest in learning and teaching new skills such as structured query language (SQL), Python, and artificial intelligence (AI) skills. Though this may take more effort than learning the latest propriety analytics tool, these skills are portable regardless of how often the technology changes.
3. Adopt a product management mindset.
When it comes to analytics, "Go big or go home" does not apply. As much as 70 percent of big, transformative projects within organizations fail. Large, sweeping plans are often more than an organization can pull off, and most are better served by implementing a sequence of incremental changes to build upon as the analytics program evolves.
You may wonder, "How do we turn our data into actionable insights?" Consider that question and focus on small steps that can get you there. Center your approach around pre-cleansed data and standard measures that act as building blocks for other small but essential wins. Foster an environment that allows and incentivizes rapid iteration rather than transforming the whole system at once.
It’s important to consider your data and analytics with a product management mindset. A business map model (BMM) can help you plan your roadmap by prompting you to answer central questions upfront. A BMM includes questions such as:
- Who are the key stakeholders?
- What will the project be used for?
- How will it drive value?
Methods like this focus your project and ensure that you get critical business value out of it. Other strategies? Name subject matter experts within your team, even in small ones. Plus, don't be afraid to look to outside sources for expertise.
4. Recruit and build talent from within.
There's a shortage of training and talent which could limit the implementation of analytics, particularly AI. Because everyone is competing for the same small talent pool, organizations must develop alternatives, such as turning to untapped internal resources.
Instead of competing for the small pool of talent available, focus on nurturing data storytellers within your own ranks. In many cases, it's not statistics that drive performance improvement but how you tell the data story. Few forms of communication are as persuasive as a compelling narrative paired with visuals — and data storytelling is a skill that can be taught and learned. Look for the natural storytellers within your organization, even if they're not analytics professionals. Consider a project-based learning program and give the teams projects that require visualizing the data and how it might impact your organization.
- Provide building blocks such as reusable templates for everyone.
- Take advantage of tool-related communities and their visualization examples along with public examples from Tableau and Power BI.
- Ensure that environments are preconfigured and provide your team with as much storytelling time as possible.
Finally, track how much time you spend readying data for a project. Work to keep it to a minimum while maximizing the time left for storytelling.
5. Utilize templates for a rapid start.
Analytics teams spend most of their time preparing data for use rather than analyzing it. Reducing that preparation time is foundational to a successful analytics program. Work to ensure that environments are preconfigured, cutting down data prep time and increasing the time your analytics team can spend on the work of storytelling.
Instead of reinventing the wheel, provide building blocks. Reusable templates might be ideal for specific items where you'll need to tell the same stories, like patient lists or variability. Communities centered around tools have good visualization examples, so visit them and evaluate some of your favorite examples.
PINC AI's approach is to ensure that data is continuously loaded in the environment and that it's linked to utilize across different data domains. Clean, standardized, mapped data has been leveraged for analytics, so you can start analyzing your data immediately. A centralized high-performance analytics environment where data, measures and shared content is available with visualization tools prevents duplication of work.
These five points are a guide to establishing a successful analytics strategy. Healthcare organizations can leverage the richness of their data with the right tools. It’s time to make PINC AITM INsights part of your strategy. INsights breaks down data silos, streamlines processes and makes self-service analytics accessible across your organization. Transformation takes time; INsights provides the structure your organization needs to take the first step and accelerate your path to analytics success.
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