2016 Industry Insights: RWE and Centers of Excellence (CoE) Teams Growing

By Jack Fuller, MS

At BHE, we spend a lot of time speaking with leaders in the field of real world data about the ways in which they go about generating robust, reliable evidence for use with key stakeholders both internally and externally.  Fundamentally, it is the unique way that companies combine database experts, processes, and technology that provides the best indication of success.  Below are a few of my thoughts on this topic based on conversations with our clients and industry stakeholders in 2016.

Expansion of Internal Analytics Teams

Along with the growing trend of bringing large datasets in-house, many life science companies are expanding their internal analytics teams to better serve stakeholders who rely on advanced analytics for decision making.  The traditional outsourcing model, while still important, is no longer efficient or cost-effective enough to handle the large volume of requests that health economics, epidemiology, drug safety, and commercial analytics groups receive.  To deal with this paradigm shift, companies are building centralized functions in the form of Real World Evidence (RWE) or Centers of Excellence (CoE) teams. These teams typically have three main responsibilities:

  1. Centralize Real World Data (RWD) assets and tools for analysis
  2. Reduce the friction between groups to generate efficient results
  3. Develop and implement standard methodologies

Finding Talent Is a Challenge

The one thing all the companies I’ve spoken to agree on is that finding talent is extremely difficult. An effectively run data and analytics group requires prioritizing talent who know and understand data, technology, and informatics. However, these skill sets may not apply to many graduates of epidemiology and health economics programs.

Technology Is Key to CoE Success

Technology also plays a key role in any centralized analytics group, especially as expectations increase. One company I recently spoke with anticipates that the number of projects their analytics group is expected to complete will triple in the next three years. Traditional methods of programming become bottlenecks to productivity as the queues for analytics teams continue to grow. Scaling with hard-to-find analysts alone, may not be the entire solution to growth in demand for real-world evidence.

Looking Forward in 2017

Our work with RWE groups provides us with a unique window into industry trends.  We look forward to sharing our knowledge and also learning from others, as new opportunities arise with the 21st Century Cures Act and the proliferation of new, exciting data sources that can be brought to life via technologic advances.

ICD-10: The New Metric System?

At last, the 30+ year old ICD-9 code set has become outdated in the US. No longer considered usable for today’s treatment, reporting, and payment processes, it does not reflect advances in medical technology and knowledge, or provide accurate patient diagnoses.

On October 1, 2015, ICD-10-CM, the 10th revision of the International Statistical Classification of Diseases and Related Health Problems, became effective. Now including 68,000 codes for diseases, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or disease, ICD-10-CM has already had a profound impact on daily practice in terms of documentation challenges. In addition, the US also has the ICD-10 Procedure Coding System (ICD-10 PCS), a coding system that contains 76,000 supplementary codes not used by other countries.

So, what implications does ICD-10 have for healthcare analytics?

First, one must consider whether the full range of ICD-10 codes will be used, or just a subset based on convenience.  The clinical work necessary to accurately choose codes may be too overwhelming for busy practitioners, leading to time-saving short-cuts in the form of limited diagnosis code checklists.

Second, just like coding under ICD-9-CM, analysts need to be cautious about the clinical value and accuracy of ICD-10-CM codes. While ICD-9-CM has been using outdated codes that produce inaccurate and limited data, the hope here is that the new ICD-10-CM codes will make it easier to measure the results of treatment and the quality of care.

The structure of the ICD-10 code is as follows:

  • 1-3 (Category of disease)
  • 4 (Etiology of disease)
  • 5 (Body part affected)
  • 6 (Severity of illness)
  • 7 (Placeholder for extension of the code to increase specificity)

To make the conversion from ICD-9 to ICD-10, and sometimes vice versa easier, translation tables have been developed: https://www.cms.gov/medicare/coding/icd10/downloads/gems-crosswalksbasicfaq.pdf

Embrace the change, it’s time to jump on board with the rest of the world.