Editor’s Note: George Dealy is the Vice President of Healthcare Solutions at Dimensional Insight, a healthcare data analytics platform that allows healthcare providers to make data-driven decisions, empowering organizations to analyze both financial and clinical data to quickly improve efficiency and patient outcomes.
A recent contributor to HIT Consultant made a bold, yet not too far fetched statement about the data analytics space. He noted that applied artificial intelligence (AI) is here to stay, and that the more common form of healthcare analytics will slowly but surely be replaced. For a quick IT lesson, applied AI refers to the intersection of artificial intelligence and machine learning, where the best of human intelligence is brought together with the best of computing capabilities.
These technologies are then cohesively implemented across an enterprise to bring meaningful answers to end-users before they even have thought to pose a question. As an executive in the healthcare analytics space, I must admit that I do believe that this prediction will come to fruition, though with a few qualifications. Let me explain…
Leveraging the capabilities of AI and machine learning will undoubtedly become an increasingly important aspect of healthcare analytics. Tech giants like IBM, GE and Microsoft have already put a heavy foot on the gas in efforts to capitalize on the technology’s potential to revolutionize diagnostic, treatment and prevention strategies.
However, what seems to have been overlooked is that capabilities like machine learning and other AI technologies require well-understood, intensively curated information to produce optimally useful results – something that those working in the healthcare analytics space have worked hard to achieve.
So while applied AI and machine learning can, and will, transform the way we gather, analyze and ultimately benefit from data in the healthcare field, there are components of healthcare analytics that will remain essential for AI’s success, particularly as it relates to data governance.
As a quick refresher, data governance refers to a set of processes that ensures that important data assets – also known as master data — are formally managed across the enterprise. With such a vast amount of data coming in from multiple disparate systems, it is critical that organizations have an effective data governance strategy in place to truly maximize the full potential of their information assets – and, by extension, the potential of AI.
Practicing effective governance in a disciplined matter and applying logic that will transform raw data from individual source systems, such as EHRs or ERPs, into meaningful information for AI to work with will be essential for realizing greater impact of the technology. Simply put, AI will only produce results as good as the data available to it.
An effective analytics strategy requires the fundamentals to be addressed up front, and while they may seem less “exciting” than AI, they will form the foundation necessary for AI to deliver on its full potential. Here are a few considerations for how to get a data governance strategy off the ground:
1. Start with the basics
Building an effective data governance foundation doesn’t need to be a massive undertaking that is done all at once. Start by determining which pieces of information are the most critical to achieving your organization’s goals and objectives. Because regulatory compliance does tend to consume an excessive portion of data management, reporting and analytics resources, it’s critical that your organization’s leaders determine key priorities for the information most helpful to managing their operations and guiding strategy.
2. Build a knowledge-driven culture
It’s the day-to-day execution of effective data governance that actually leads to results, so it’s essential that your organization gets up to speed as soon as possible. Create opportunities to connect staff members who are more well-versed in applying governance practices with those who are coming up the learning curve. Organizations that have already made progress advancing their governance strategy are often enthusiastic to share what they’ve learned — and even help other organizations avoid the same mistakes they’ve previously made.
3. Share the wealth
Once your organization has gained momentum with its governance efforts, make sure your colleagues across departmental boundaries know how to take full advantage of the information to help them guide decisions. That way, they’ll have a good understanding of what data they can use, what it means and how it can help them make better informed decisions.
Over time, AI will likely impact our health in ways they we can barely imagine now. However, this may also mean that the foundation provided by traditional healthcare analytics will become even more important than ever before. Thankfully, when data is made trustworthy and accurate through strong governance, emerging technologies like AI will reach their full potential.