Think about the technology inside your average hospital—at any given time there are dozens of devices collecting data of all sorts, from patient intake information to vital-sign monitoring; prescription requests to hospital bed availability status. The types of data hospitals collect on a continuous basis goes on and on. On the patient side, hospitals aim to capitalize on this data to optimize the care they’re providing. On the business side, hospitals are trying to maximize their revenue while reducing wasteful practices.

Now zoom out. Within the overall healthcare landscape, there are other types of companies similarly aiming to use data to improve their operations—like pharmaceutical companies, insurance providers, etc. This is an industry rife with valuable data but still figuring out how to best use it to drive better decision-making. 

A recent survey found that more than three-fourths (79 percent) of healthcare executives said “they have a greater urgency for investing in big data and artificial intelligence (AI) initiatives.”

But, despite the majority of executives who feel their organization needs advanced data analytics and AI to remain competitive and perform well, only 31 percent of survey respondents believe their organization is data driven. Even fewer say their company has achieved a “data culture.”

This points to a disconnect between intent and reality—healthcare leaders are obviously seeing the need for better analytics, but are still in the process of actually implementing the technology they need to reap full value from stored data.

Is Data Analytics in Healthcare an IT Issue?

A few challenges have traditionally plagued data analytics in healthcare. One major hurdle is that anything data-related has often been relegated to the realm of IT—meaning organizations using legacy systems saw their data heavily siloed. Business users with questions would have to work through data specialists, meaning it took a certain amount of time and hassle to get data-driven insights.

However, the advent of self-service analytics from providers like ThoughtSpot has bridged the gap between business users—like executives, administrators, physicians, pharmacists, nurses, etc.—and ad hoc insights. The latest wave of data analytics allows a wide range of users outside the IT team to ask questions and get answers from within stored data, complete with an automatic chart. The AI-driven analytics tools available also allow these users to launch advanced insight-detection algorithms with a click, helping them divulge insights that would have otherwise stayed buried deep in stored data until a data scientist went digging for it.

The latest wave of data analytics technology prioritizes accessibility for all, speed and user-friendliness—three attributes that make it very valuable within the context of a fast-paced healthcare organization.

The Transition to Value-Based Care

Healthcare organizations are increasingly gauging performance instead of value-based care as opposed to volume-based care. Instead of looking at the volume of patients served, organizations are looking at the outcomes they’re achieving. This means healthcare providers have more incentive than ever to offer the best care at the lowest cost.

Healthcare organizations serious about providing value-based care to patients need data to know how they’re doing. Data-driven insights help decision-makers within healthcare figure out what’s working and what isn’t; it helps uncover new ways to improve the services they’re providing. It has the potential to optimize daily workflows, streamline best processes, enhance the hiring and training process, reduce wastage, fine-tune the supply chain and fortify patient outcomes. Utilizing data can even help hospitals reduce readmissions and help patients recover from major procedures faster! The potential is there, but the payoff depends on deploying the right tools.

In today’s competitive healthcare landscape, organizations need every advantage on their side. This is why leaders anticipate healthcare IT spending will focus on AI and data analytics: because it can revolutionize how organizations operate.