Unstructured data such as free text found in clinician
notes and PDF documents
Healthcare organizations are increasingly trying to draw in-
sights from all this information, but much of it remains locked
in silos due to communication barriers between systems. To
achieve a foundation of accurate analytics, all this data must be
represented by a standard terminology—content that makes up
an organization’s reference data—to establish a single source of
truth that allows information to flow easily.
While this equation may seem simple on the surface, the reality is that many organizations struggle because they lack an
effective RDM strategy. This state of affairs leads to poor data
quality and notable downstream consequences including cost
duplications, negative impacts to reimbursement, and ineffective approaches to care and disease management.
The RDM Opportunity
RDM plays an important role in an organization’s data governance strategy. When healthcare organizations centralize management of their data assets through RDM, they can simplify
complex data governance processes, establish a single source of
terminology truth to optimize analytics, and reduce overhead.
The key is having systems in place that ensure reference data
is current and accurate and used consistently across an enterprise. Otherwise, inconsistencies can diminish the opportunity.
A comprehensive RDM strategy addresses five components:
Governance, Acquisition and Promotion, Content Authoring,
List or Value Set Management, and Integration and Distribution.
Governance. Healthcare organizations must ensure alignment of a RDM strategy across people, processes, and technology. Some questions to ask include: How will my team govern
terminology definition, intended use, versioning, and implementation across data domains? How do I align my enterprise
around a single source of terminology truth?
Acquisition and Promotion. Identifying all code sets used
across an enterprise and defining what it takes to maintain them
in an optimal way is the next step in designing an RDM strategy. For instance, CPT is a commonly used code set throughout healthcare today. It is found in many disparate systems
such as EHRs, admission and registration, billing, and financial
systems. Each of these systems is frequently managed on the
department level, requiring CPT to be acquired departmentally. By adopting a centrally managed data acquisition process,
healthcare organizations can reduce cost duplications related
to the acquisition, updating, and maintenance of CP T while also
ensuring that all systems are operating on the same version of
the code set. Some questions organizations can ask include: Are
we sourcing the same thing from different vendors? Are we pay-ing duplicate fees? How do we keep up to date with changes in
our reference data as new versions are released?
Content Authoring. Once sourcing is addressed, an RDM
strategy should consider an organization’s needs around en-
riching and authoring data. As such, the next step requires de-
fining what that information model looks like and supporting it
with toolsets and services. Some questions to consider are: How
can custom content be authored in a consistent manner? How
do we notify downstream users of any changes to both stan-
dards and local data?
List or Value Set Management. Moving beyond sourcing
and authoring data, the next step is the management of groups
or lists of codes. Code groups or lists are often used as building blocks for business rules that help inform such initiatives
as population health and quality measures. For example, how
does your organization identify patient cohorts for research,
create preference lists for provider workflows, and know which
patients need additional intervention for their chronic conditions? It is important to not only curate these lists in a logical
and data-driven way, but to ensure that once created, lists are
kept up to date as the underlying standards update. A good
RDM platform can keep these processes automated, accurate,
and available throughout the enterprise.
Integration and Distribution. Finally, RDM must address the
distribution of content and how an organization will integrate
data into its infrastructure either manually or automatically via
APIs. This part of RDM should be supported by processes that
handle communication and change management across an organization. Often, this begins with a question: How do I ensure
systems are receiving the updates they need?
Implementing the above five steps lays the groundwork for effective RDM. Many resource-strapped healthcare organizations
find that the business case for leveraging a framework of automation to do the heavy lifting is an easy one to make. The good news
is that technological infrastructures exist that can help healthcare
organizations extract the greatest value from reference data.
The best strategies draw on the right data, software, and services to advance RDM. First, healthcare organizations should
consider single sourcing reference data to reduce costs and
overhead that can result from managing multiple suppliers.
Once content is in place, advanced infrastructures can be deployed to overcome the burden of managing reference data on
spreadsheets. Healthcare organizations can consider applications that provide tools for modeling, grouping, and searching
data, as well as automating the distribution of updates.
Even with an advanced technological infrastructure in place,
many organizations are still challenged to allocate resources to
RDM initiatives. In these cases, organizations can lean on third-party informaticists, clinicians, and coders with intimate knowledge of reference data to help augment staff as necessary. ¢
1. Densen, Peter. “Challenges and Opportunities Facing
Medical Education.” Transactions of the American Clinical
and Climatological Association 122 (2011): 48-58. https://
Cheryl Mason ( email@example.com) is director of clinical
informatics consulting at Wolters Kluwer, Health Language.