Actionable Data Trumps Big Data

By: Phil Suiter, President and CEO – Aegis Health Group

Executives understand the value of data intelligence tools, but they’re having a hard time finding ones that are easy to use. That is the main takeaway from the 2015 Information Week Analytics and BI Survey.

Many executives share this sentiment and nearly half of those surveyed felt that “ease-of-use challenges with complex software” was a key barrier to success. And bear in mind that these are large organizations being polled. Sixty percent of those surveyed work for organizations employing 5,000 or more people – including numerous hospitals and health systems.

Most large health care organizations are swimming in vast amounts of data: Medicare/Medicaid information, hospital discharge data, insurance claims data, rapidly changing patient demographic information, and much more. Hospital leaders and strategic planners simply don’t have enough hours in the day to evaluate and organize hundreds of online spreadsheets. They’re increasingly relying on data mapping and analytics tools to help them identify trends and make wise decisions.

The first generation of data visualization tools consisted primarily of Geographic Information System (GIS) technologies to help correlate geographic and demographic information. These tools helped decision-makers see at a glance where opportunities existed for new facilities and clinics. For example, some U.S. suburbs are rapidly attracting younger people, so starting a gerontology practice there would be fruitless.

Now there’s a second wave of innovation: web-based intelligence tools that use data graphs or diagrams and analytics to provide actionable data. While Big Data analyzes extraordinarily large datasets (like those used for clinical trials of new cancer drugs), these easy-to-use intelligence tools are designed for specific business purposes and their ability to help leadership focus on areas that will make the biggest impact

Now, more than ever, it’s important for hospital and health system leaders to get accurate, easy-to-understand information on the relationships driving value-based care and population health management.  The Information Week survey also examined two approaches to addressing ease-of-use in software with one camp favoring simplicity and the other focusing on intelligence.  With next generation physician data analytics, it is possible to benefit from both, gaining a unique view to the movement of patient populations as never before.

An effective data intelligence tool needs to:

• Provide ease of use that doesn’t require assistance from a data analyst – The Information Week survey noted that health care organizations want tools tailored to “less technically savvy employees.” No clinician wants to learn a complex query language to use the software.
• Proactively identify and measure competitive threats
• Capitalize on geographic opportunities, market trends, and mergers/acquisitions
• Optimize payer mix and spur the growth of key service lines
• Visually map physician referral patterns and leakage trends
• Support strategies for reducing referral leakage and enhancing physician loyalty
• Guide the development of narrow networks
• Offer cloud-based availability that eliminates the need for extra on-site hardware

Next-generation data intelligence must also provide a clear picture of an organization’s population health management programs. For example, these tools should make it easy to generate a Clinician Care Team graphic showing every provider who has helped treat an individual patient (or patient population). The software should also offer a hospital flow diagram that visually shows how patients got to the hospital and exactly where they go following acute care.

Gaining Meaningful Insights Faster

Here are some examples of how these advanced data intelligence tools can improve both clinical and business decision-making:

• This year, the tiny town of Austin, Indiana (population 4,200) went from almost zero to 150 HIV-positive patients in a matter of a few months. Data visualization tools could have pinpointed the hardest hit neighborhoods and guided clinical interventions.
• A hospital in Arkansas conducted a data analytics and visualization implementation centered around market opportunities related to their employed physicians, medical staff and community referring physicians (not on staff). They were able to promptly /graph and quantify these care relationships, identifying potential revenue lift of $470K in staff coding improvements and addressable leakage prospects of $106M going to other hospitals.
• Data intelligence tools can also help lower hospital readmissions because health system leaders can visually determine each patient’s location post-discharge in order to better coordinate care.

In today’s health care environment – where margins are often razor-thin – the newest data intelligence tools can both simplify and speed important decisions that can greatly improve care while increasing revenue