nicciaraujoFrom abacus to analytics, you’ve come along way, baby.


The abacus, used centuries before the adoption of the modern numeral system, is a calculating tool still widely used in Asia, Africa and elsewhere by merchants and traders for performing arithmetic processes. Did you notice it’s not used in healthcare?


No, we have come a long way and today possess sophisticated technology that allows us to perform a process commonly referred to as data analytics. What is data analytics? It’s the process whereby you inspect, scrub, clean, transform and model data in order to obtain information to gain insight, suggest conclusions to problems and hopefully support decision-making.


Now that our data no longer resides in binders on a shelf in finance, compliance or health information management, but instead in an electronic format, each of you has the key that unlocks the door to enlightenment. Through a process known as data mining, this electronic data can be transferred from its various locations to a platform that allows it to be inspected, grouped and modeled to provide us with many of the answers we need to self-identify opportunities for improvement within our healthcare systems. This process is what each of the revenue auditors currently are using to pinpoint our weaknesses, swoop in and take back millions of dollars.


If we take a moment to step back and look at what the revenue audit process truly entails, it involves an external agency taking our 837 claims data and putting it through a batch of algorithms and filters to identify potential opportunities for denials. This is data we have, and we are providing to them in an electronic format. What precludes us from looking at this data ourselves, whether concurrently or retrospectively? Nothing.


The level of granularity you use to inspect your data may vary depending on the tools you use, but before we discuss tools, we need to discuss data integrity. All of us have used the well-worn phrase “garbage in, garbage out,” and, well, it still holds true today. Reviewing the quality of your data is the first step in the analytics process. Who or what is the source of the data? Are there multiple sources? Do they use the same classification system (i.e. ICD-9-CM)? How do they apply the classification methodology (i.e. manual intervention or automated), and what is the source document they use to ascertain the classification (i.e. physician documentation, charge ticket, etc)?  Also, who is the “owner” of the process, and how often does that person utilize quality measures to check the integrity of the process as well as the source documents? While this might seem a bit complex, a flow diagram will help to chart the origin and route  of this data, plus what obstacles are standing between it and its final destination.


Before you call out for help, take a moment to document what types of filters you might want to put your data through, what groupings of data you might want to be able to isolate and what the outcomes of these analyses should look like, plus how they should function. What types of “if this, then that” statements do you require? Are you looking to compare your data to other data stored elsewhere? For example, are you comparing your 837 data to contract terms? How would you like to search and view the results of your searches? The “hot buttons” of the revenue auditors are posted on their websites for all to see. We know that a patient admitted as an inpatient with a final diagnosis of chest pain will be “suspect” for audit. Should the outcomes of your searches be in the form of canned reports and custom reports with numbers only, or would graphic displays such as dashboards also be beneficial?


Armed with your data flow diagram and your list of reporting requirements, you now are ready to tackle the final question: how do you analyze the data? This is where your organization’s information technology department or an external vendor comes in. Some organizations have made the long-term commitment and financial investment necessary to develop internal business intelligence tools, and have data warehouses where massive amounts of information can reside and be scrutinized without the process impacting the day-to-day operations. Other healthcare organizations have not put these provisions in place, but that’s ok. There are several cost-effective tools on the market specifically designed to help you evaluate this data and thereby allow you to dive headlong into the world of data analytics. One such tool allowed a director of patient financial services in the Midwest first to identify a potential for more than $3 million in takebacks for just one year, and secondly to put new safeguards and improved processes in place to prevent future recurrences. This is just one of many examples of the immediate return on investments being realized through technology adoption.


Know that this is just the beginning. Pilots already are in place to begin tying quality outcomes data to reimbursement. Websites are utilizing quality outcomes measures and other data to help patients “in the market” to decide what facility is the best fit for them. Payer contracts are being proposed and sometimes even renegotiated with negative returns through forecasting on the part of the payers. What you can do, however, is to take disparate data and model it in order to obtain relevant and timely information to allow you to gain insight on potential vulnerabilities and hopefully support critical decision-making that leads to improved processes. The technology to perform data analytics is out there, and we, both healthcare professionals and consumers, will benefit from its application.


About the Author

Ms. Araujo received her degree in health information administration from Loma Linda University. She has extensive experience in management of organizational change projects with an emphasis on best practices with a fiscal focus. Currently Ms. Araujo is Vice President, Sales and Consulting Services for the national firm SOURCECORP, focused on their healthcare division, SOURCECORP HealthSERVE.

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