In my last blog post, I talked about the two things you need to get started on your path to improving health care: “big” data (that’s also quality data) and advanced analytical tools.
Big data is only possible in health care because of the vast amount of claims, clinical and additional data generated and shared through practice management and billing systems, electronic medical records, and data warehouses.
Provider organizations that want to use big data need infrastructure, including:
- Claims data, which can be found within a provider organization’s billing system as well as augmented from de-identified sources. Claims data provides a holistic view of a patient’s interaction with the health care system
- An electronic medical record that is fully installed and adopted. EMRs include a broad spectrum of data that isn’t included on claims, which are mostly used for reimbursement purposes
- The ability to connect data across the organization through an EMR, health information exchange or other platform
Different kinds of data have strengths and weaknesses; no one kind of data provides a complete view of patients, making it all the more important to use all of the data available to you.
Let’s start with claims data. Most people who follow baseball recognize its traditional metrics, such as batting average, runs batted in, strikeouts, walks and steals. These have been useful stats for decades, most of them dating back to 19th century. Claims data is the traditional source of data for health care. Because claims data is based on abstracted data, it is most useful for finding overarching care patterns—like the stats one would find on the back of a baseball card.
Because claims data are based on abstracted data, it’s useful for finding overarching care patterns such as who receives care, where they receive their care, the kinds of care they receive and their demographics. This kind of information is great for population health discovery, research studies and for retrospective analyses of patterns in care and costs.
But there are drawbacks to this data, too. The purpose of claims is reimbursement, which means only the information that is necessary for payment is included. And, because claims are processed up to 60 days after a service is provided, the data is dated by the time it’s available.
Next is the clinical data found in EMRs, which are found throughout the continuum of care. Unlike claims data, they reflect how medicine is actually practiced. EMR data is timely, specific and can show a full picture of the conditions a patient has exhibited. The data within EMRs, however, may be incomplete or unstructured since they are not standardized. In fact, structured data makes up only about 20 percent of the EMR.
Marrying claims and clinical data provides distinct advantages for providers, combining the strengths of each while making up for each type’s limitations. And, weaving both socio-demographic and care management data into both sets makes the picture even more powerful. As providers take on clinical and financial risk, combining these data streams can help providers turn risk into opportunity. something that needs to be done now rather than later in order to provide accuracy. .
In my next blog, we’ll look at how higher quality and larger samples of health care data open the door for better comparative analytics.
For more on using analytics to turn big data into good data, download this Optum eBook, “Moneyball Analytics: Connecting and leveraging the best data across the health care continuum”
–Jeremy Orr, MD, MPH, Chief Medical Officer, Optum Analytics