The complexity of ICD-10 is familiar to anyone who works in HIM. This complexity has always been both an opportunity – but more often a challenge – for professionals that work with the tens of thousands of codes included in the classification system.
There is light on the horizon, however. Our evolving ability to capture, store, and analyze huge streams of data from a diverse array of sources is enhancing the capacity of HIM professionals to utilize the depth of ICD-10 to its fullest. Using the techniques of big data analytics, the overwhelming scope of ICD-10 is becoming a tool for improving outcomes across the healthcare spectrum.
What is Big Data Analytics?
As computing power has grown, our ability to save huge streams of information has become exponentially larger. The sum of this information is commonly called “Big Data,” and it is having a profound effect on just about every pursuit imaginable, from designing safer cars, to improving the online shopping experience.
Collecting this data is just the first step, however. The term “Big Data Analyitcs” refers to our ability to analyze this data to give it form, function, and utility. Without the analytic side, it is just a mountain of information. But once we have the means to analyze the substance of this data to identify trends and further predict outcomes, we essentially have a tool that can take unfathomable complexities and make them digestible and relevant. The impact this could have on healthcare generally, and ICD-10 usage specifically, should make all HIM professionals excited.
What Role Can Big Data Play in ICD-10?
The capabilities of big data are still developing, but one healthcare system is already finding innovative ways to integrate existing techniques with ICD-10. California-based Baptist First and Dignity Health wanted to analyze the most effective way to manage the transition to ICD-10. They were considering an ICD-10 true code approach, a DRG-level analysis, and an encounter-level approach. Ultimately, they determined definitively that the encounter-level approach provided a richer base of information at a lower cost than the two alternatives.
How did they make this determination? They recoded old charts into ICD-10 to determine their exposure to risk based on individual physicians and individual diagnostic categories. The test ultimately involved analyzing over 1 million claims, a large enough data set to extrapolate truly-actionable information about the effects of the ICD-10 transition.
Using the same data set, the provider was able to analyze the efficiency of its internal coders compared to some of its outsourced contract coders. The test revealed that the outsourced coders displayed some startling inefficiencies, resulting in huge cost inflations.
Ultimately, when the techniques of big data analytics were applied in advance of the IC-10 conversion, they revealed both opportunities that could be seized with early enough preparation, and challenges that could be avoided if they were addressed before the conversion went into full effect. That will save Baptist First and Dignity Health money while improving outcomes for all involved. In the past, this kind of broad and deep analysis would have been impossible. Thanks to big data analytics, it’s a tool that all providers can utilize.
Prepare yourself for the ICD-10 transition and add professionals to your workforce who understand the opportunities of big data by working with the health information management staffing specialists at MedPartners HIM.