Everyone
knows the old adage about data: “garbage in – garbage out”. Now, however, the
meaning of the phrase is magnified because the volume, quality, and impact of
data has reached unprecedented levels. With increasing importance and reliance
on analytics and predictive initiatives, as well as the promise of metadata
analysis, the importance of quality data is paramount.
This is
not intended as a doomsday message. It’s more of a not-so-gentle nudge to
change business practices regarding data management because doing otherwise
will lead to significant financial disadvantages. Unfortunately, the people who
have the power to change, frequently think the problem belongs elsewhere.
Not an IT problem!
The
misconception is if it’s data, it must be an IT problem. However, only management
has the power to change data quality, not IT. It is a management decision and
responsibility to hold people and organizations accountable for data quality.
The following is an excerpt from and email I received recently from our IT
describing one client’s data. Unfortunately, the problem it describes is common.
“There is a field for NPI number in the data
feeds but it is not often populated. When it is populated we can
definitely use that information to derive the specialty and possibly to
determine the individual provider rather than the practice or facility.”
This
example highlights a widespread problem in Workers’ Compensation data. Even
though a field is available to capture a specific data element, in this case,
NPI (National Provider Identification) number, it is not populated. This number
is derived from medical bills and the reason for the omission should be
thoroughly investigated.
The information trail
The first
place to look is upstream in the information trail, to the submitting provider
or entity. Standard billing forms such as the HCFA 1500 contain a field for
NPI, but it may not be filled. Second along the information hand-off line is
the bill review company. Is the NPI number being captured from the bill?
If the
provider is submitting the NPI, is the bill review company capturing it? Then,
if the bill review company is capturing the NPI, is it included in the data set
transmitted to the payer? Once the source of the problem is discovered,
management must require the necessary process changes.
Management intervention
If the
submitting provider is not including the data needed, in this case the NPI
number, the best management intervention is refusing to pay incomplete bills.
Likewise, if the bill review company or system is not capturing the data or is
not passing it on to the payer, management must demand the data needed.Seemingly trivial data omissions can lead to multiple other problems. Another common data problem is the submitting provider or entity entering a facility, group, or practice name while excluding that of the individual treating physician. Management should insist upon using the individual treating physician name and NPI number rather than the entity name only. Systems should capture all three pieces of information.
Bad
data comes in many forms beyond missing data in existing fields. Other kinds of
bad data include erroneous data and duplicate records in the data. Regardless
of the form and source of bad data, the challenges on the horizon are
significant. The simple fact is, benefits from analytics to gain cost
advantages are not accessible to those with poor data quality.
Management owns data quality
Accurate
and complete data is the only affordable and practical resource on the horizon
to advance to the next levels of medical management and measureable cost
control. Only management can insure data quality.
Karen Wolfe is the founder and President of MedMetrics, LLC, an Internet-based Workers’ Compensation analytics
company. MedMetrics offers online apps that intensify medical management,
including Provider Performance Analysis, Predictive Intelligence Profiles with
Alerts, ICD-9 Predictive Scores, and Ask-the-Data Query Library. MedMetrics
will import and analyze your data to identify omissions and opportunities for data
quality improvement. karenwolfe@medmetrics.org
No comments:
Post a Comment