How to Overcome Challenges with Cardiovascular Data
If you’ve been trying to aggregate multisource data so that your organization can realize more impactful cardiology insights, your mission is worthy. I say this not because you don’t know it but because you could probably use an encouraging reminder right about now. Though the complexity may feel overwhelming and the roadblocks may seem never-ending, your aim is entirely achievable. As you read this, organizations are using advanced cardiology data and analytics to quickly identify trends, outliers, and root causes—insights that can influence how your healthcare organization operates and the outcomes it delivers.
To help technology leaders gain clarity going forward, I’ve enlisted the help of Dr. Jennifer Hall, chief of data science at the American Heart Association. My conversation with Dr. Hall revealed a few themes for technology leaders to focus on as they look to enhance their use of cardiology data. We also discussed how technology leaders should approach the future of cardiovascular data.
Start with a foundation of understanding and trust
Let’s start by addressing a factor that can doom cardiology data and analytics initiatives from the start. According to Dr. Hall, lack of understanding and trust among stakeholders can be the biggest barrier most organizations face. Because its absence creates a breeding ground for mistrust, understanding should never be assumed — though it often is.
“We are in the middle of an interesting period of time where data has gotten out in front of how it is managed. Data is such a large field now that the person making decisions about data may have specialties in many different areas,” says Dr. Hall. “The stakeholders of the data may be the chief information officer, or it may be someone in a leadership position whose expertise is not that of a data scientist or analyst, yet [that person] is increasingly expected to understand analytics and drive a data-driven culture.”
So, how can organizations do a better job of aligning an increasingly diverse group of stakeholders? Make sure that everyone has agreed upon basic definitions and standards. For example, “quality” is highly subjective, so it’s important to have stringent standards as to what quality data is and isn’t. Additionally, making data-entry processes as clear and as easy as possible can help protect the quality of your cardiology data. Consider how your workflows will align with your data, reporting, and analytics requirements. For example, if you want to do analytics around complications, make sure you’re capturing the data you need to produce a meaningful analysis.
Prioritize user experience when considering analytics tools
You want your analytics tools to be capable and scalable, of course, but not at the expense of usability. An analytics tool that is brilliantly designed but which has a less-than-optimal user interface will fall short of expectations. Which is why Dr. Hall maintains that the most valuable tools for providers are those which they find easy to use and that allow for providers to glean the most information in the shortest amount of time.
Whether buying software or building custom solutions, technology leaders should follow standards set by the industry and implement software that is user-friendly. Allowing your end users, who usually include members of the cardiovascular imaging departments, to have input and test different systems is critical to success.
Data consolidation holds the key to access and efficiency
We are living in a time when the power of data in the clinic is changing. Many providers are used to working without data at their fingertips; thus, it is challenging to help this group see what they may be missing when it comes to data. Having data in multiple locations that makes access challenging is a common hurdle.
Data consolidation is helpful for accessing the data, and this becomes more and more important every day. If data isn’t consolidated, organizations run the risk of duplicating efforts, spending time finding data, and hiring people to manage data, resulting in lost productivity and revenue. It also results in duplication of data, which creates a barrier to establishing a single source of truth and contributes to the aforementioned trust problem.
It makes no difference whether you’re using the data for hindsight, insight, or foresight. The important thing is to have data you can trust when you need it. Once this is accomplished, one can feel much more confident that the pipeline used to preprocess all the different imaging file types that feeds into the analysis software will do the job.
Advice for advancing your data science expertise
Factors like data literacy and analytics maturity are becoming more important to healthcare organizations each day. Hybrid models where data experts coexist with clinical experts are becoming more common. New techniques and approaches to managing and analyzing data are established each day. The sharing of code and data is also becoming more common, which opens the field to more individuals. Learn more about how Change Healthcare, a pioneer in cloud-native cardiology solutions, can help your oranization minimize complexity, cut costs, and increase productivity.