Part 1 of this 3 part blog series discussed the various types of healthcare data available and why each type of data is important. In the second installment of this ongoing series, we discuss the data value chain to demonstrate how data and analytics can help create tangible value for healthcare organizations. The data value chain is complex and requires significant coordination among the various capabilities within the chain. A healthcare organization can gauge their maturity from a health analytics perspective by using the four A’s of Health Data Analytics.
Capabilities for Health Analytics : The Four A’s
Simply having healthcare data does not guarantee the ability to derive value from the data. When it comes to harnessing the power of data, healthcare organizations need four distinct capabilities known as the four A’s:
Access: Having access to the data is the first step. As mentioned earlier, there are four types of data (Claims, Clinical, Behavioral/Socioeconomic and Financial) and not all data is accessible. Claims data is perhaps the easiest to get and usually resides with payers. Clinical comes second and usually resides with providers. Accountable care organizations (ACOs) are beginning to have access to quality clinical data via electronic medical records (EMR). Payers generally have access to claims data but limited clinical data. No one player has sustainably greater access to behavioral/social data. Banks and Credit Unions have a competitive advantage when it comes to having access to financial data.
Aggregation: Aggregation of data is not as easy as it sounds. There are numerous factors – business, technological, data-matching algorithms, data-cleansing tools and processes, data governance organizational structures and software tools – that make this a daunting challenge. Doing this well requires a skillset different than what most health plans have. You need talent that has a strong ability to understand the health systems and processes AND strong technical and IT skills. Traditionally, payers have not housed such skills and must learn how to combine these capabilities into their current organization. In almost all of the mid-market payers (and even sometimes large market payers) we have worked with, there are significant organizational resources spent on the aggregation of data. And in each case, there’s always room for improvement.
Analytics: Big data is a big buzz word, but analytics is not a simple skill. There are plenty of statisticians, and even healthcare statisticians, but this field requires a fresh perspective and a familiarity with advanced tools in addition to knowing and understanding the healthcare data. Besides, there is a growing volume of data and doing large, complex calculations, and doing them quickly, is critical to commercializing healthcare analytics. A firm may have the skills to do complex modeling and predictive analytics, but it requires a whole new set of skills to automate and deploy such models and calculations in an enterprise setting. Both skills, complex modeling and deployment, fall within this larger capability of “analytics.” Building an analytic organization is a challenge in and of itself, one that most payers, providers, and in some cases, pharmaceuticals may face.
Application: Last, but not least, application of derived insights is a critical ability that many healthcare firms do not have today. The ability to engage large groups of patients and members in a sustainable way is quite challenging. Member engagement is an evolving science. Behavioral and incentive based segmentations and frameworks are used and no one model has proven to be dominant. The industry is still innovating when it comes to member engagement.
West Monroe helps clients develop the four capabilities mentioned above in an effort to better position organizations to turn data insights into real business impact – through meaningful and financially viable solutions. These are difficult pre-requisites to fulfill; otherwise, all payers would have all of these capabilities developed by now. But few indeed have.
Data and Analytics Maturity Landscape
The following table shows our point of view of the capabilities of payers, providers and pharmaceuticals/life science companies. Our view is based on our experience working with numerous healthcare organizations.
As can be seen above, the maturity of the market in general is pretty low. Especially going from left to right across the capabilities (from access to application), the maturity tends to drop with the exception of providers. This is because providers enjoy a unique channel and network of communicating with patients (sometimes via electronic media and most of the time through face to face physician-patient interactions). So, providers have the ability to communicate but, due to lack of access, aggregation and analytic capabilities, they lack the right content and insights to communicate.
An interesting pattern that can be noticed above is the combination of payer and provider can yield a greater maturity across the capabilities. This is one of the reasons we are seeing collaboration between payers and providers, and, in some cases, formal mergers.
Where do you fall on the data value chain maturity model? What are the business implications of your capabilities today? Stay tuned for part 3, the last part of this blog series, which will present a framework for how to think about your competitive advantage and potential M&A activity as it pertains to your analytics capabilities.