A recent article in the HealthIT Analytics newsletter noted that most healthcare organizations are looking to launch Big Data analytics projects to “improve clinical quality and reduce inefficiencies.” That’s only natural considering the buzz surrounding the benefits of Big Data analytics.
However, the article goes on to say that many healthcare organizations face major challenges in ramping up their analytics program - specifically lack of qualified talent, issues with system interoperability and the basic question of where to begin the process.
There’s no doubt that Big Data analytics initiatives have proven valuable in uncovering actionable information in nearly every industry. That can also happen in healthcare, but hospitals and physician groups should understand that massive Big Data projects are not the only route to gaining the critical knowledge you need to improve the operation of your organization. You can also obtain significant insights from “little data” information that is readily available if you use the right tool.
The data that can yield the most benefits already exists in the form of your claims and billing transactions and mining the data in these systems provides information which leads to insight. Leveraging the proper tools and tactics can get you the actionable intelligence you need to initiate corrective actions to improve efficiency, enhance productivity, ensure revenue integrity and minimize risk.
Big Data analytics uses statistics and data science tools to dig deeply into all levels of an organization’s data. Intelligent analysis, on the other hand, is a detailed examination of the elements of your data. While there is certainly a place for Big Data analytics in healthcare, intelligent analysis of day to day transactions can be a quicker and far less expensive way to obtain crucial insights. You may not need Big Data analytics, but you definitely need intelligent data analysis.
Intelligent data analysis that relies on best practices from thought leaders can provide the valuable information you need to make changes. Your data is there. Start taking advantage of it with the proper tool and processes.
Here is how leveraging an automated analyzer tool can overcome the three biggest challenges to data analytics and enable you to conduct critical intelligent data analysis.
Lack of qualified data experts
The HealthIT Analytics report reveals that 60 percent of organizations cite lack of qualified data experts as the single biggest obstacle to Big Data analytics. With intelligent analysis, you don’t need to be stymied by a lack of expensive experts. The answers to your most significant issues lie in your claims and billing data. An analyzer tool can reveal these issues and insights without the need for an expensive data scientist.
Unlocking the value of your billing and claims data through intelligent analysis can be empowering for your organization. Pooling this data with other sources of information provides an invaluable source for analyzing potential risk areas. Parsing of the data can provide a more comprehensive view of risks and issues as well as presenting a way to benchmark your data against regional and national data. It can also reveal issues with your revenue cycle that you can act on to improve revenue integrity.
Nearly 57 percent of respondents named interoperability issues among their multiple systems as a major challenge to Big Data analytics. They noted that data siloes, EHR optimization, and workflow issues present significant barriers to leveraging their data.
Using a data analyzer tool to conduct intelligent analysis overcomes these issues by getting to the heart of your data – the claims and billing transactions that are being collected and documented every day. An analyzer tool not only automates your auditing function, it also helps you quickly identify your key risk areas. This allows you to home in on these areas for further investigation and initiate the appropriate corrective action. The right tool also enables continuous monitoring so you can quickly uncover trends and deal with them before they seriously impact the organization.
Where to begin
A third of those responding to the HealthIT Analytics report identified not being sure where to start as a serious challenge to initiating a Big Data analytics program. Implementing intelligent data analysis with an analyzer tool makes this decision simple: begin with your own claims data.
You don’t need a qualified guru. You only need a subject matter expert who understands your data. By using an analyzer tool, a subject matter expert (SME) can quickly get you to the key intelligent insight you need to begin your program. You likely have highly qualified people who have already spent time thinking about your issues. Providing them with the proper tool will enable them to uncover issues and recommend actions to improve your overall operation.
On a recent plane trip, I was talking to someone from one of the Big 6 consulting firms. He mentioned that they had 200 data scientists in Boston. For a high figure price, they go in and analyze your data and hope to provide you with information that might help you. That’s what you get with Big Data analytics. You’re talking at the detailed, bit level and statistically analyzing numbers.
While that level of in-depth analytics is no doubt helpful, you don’t always need such a comprehensive program to gain valuable insight. With intelligent analysis, you can uncover similarly valuable information directly from your daily transaction data. You don’t need a data scientist to interpret the results. You just need the right analyzer tool with the insight already applied.
There are clearly obstacles to Big Data analytics in healthcare. But that shouldn’t stop you from getting the insights you need from the data that is right in front of you. Intelligent analysis with an effective analyzer tool can be the right solution for your organization.
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 Lack of Talent, Direction Afflict Healthcare Data Analytics Plans, HealthIT Analytics, March 22, 2017.