CODED DATA HAS a long and influential life span, and its
importance goes beyond that of just today’s revenue cycle.
It is important for health information management (HIM)
professionals to understand the micro and macro purposes
of coded data in relation to key healthcare initiatives such
as quality outcomes, risk adjustment, predictive analytics,
population health, medical research, fiscal integrity, and institutional longevity. Coded data also has a significant impact
on hospital ratings. This article will discuss this impact and
HIM’s role in hospital ratings while demystifying the healthcare rating process.
First, consider the impact of coding on the individual patient,
upon which healthcare ratings are built. Coded data follows
patients throughout their lives and directly influences their
safety and well-being. For example, inaccurate coding could
result in improper patient care during a future encounter or a
denial for a patient seeking life insurance. It is difficult to expunge an incorrect diagnosis code that is attached to a patient.
Despite the repetitive nature of coding and auditing, HIM professionals should pause long enough to ask themselves if they
would agree with the codes assigned to a claim if they were
the patient of record. By doing so, coding moves from the abstract to the tangible. Our national coded data is a vast collection of individual patient stories that help drive the business of
healthcare. HIM professionals’ mission is to make certain that
patients’ stories are correct for myriad reasons, including their
direct impact on healthcare ratings.
Why Healthcare Ratings Matter
Consumerism in healthcare has become the norm as patients have developed into sophisticated comparison shoppers who rely upon a plethora of publically available ratings to make informed decisions about where to seek care
and how to best spend their healthcare dollars. Marketplace
competition provides hospitals with greater incentive to improve quality, which can lead to fewer complications, lower
readmission rates, reduced length of stay, and lower mortality rates.
While many elements factor into health insurance contract negotiations between hospitals and payers, accurate
clinical outcomes data (obtained from coding) is foundational to the process. Payers are committed to managing
their risk and look to partner with organizations that can
objectively quantify their commitment to value-based and
high-quality care. Healthcare organizations that fall below
benchmarks for certain quality standards, including healthcare ratings, may be subject to financial penalties such as
a payer contract becoming null and void. 1 Conversely, organizations with excellent outcomes data and ratings have
greater leverage in negotiating favorable terms with payers.
Bottom line: payers want to do business with organizations
that do a good job managing their resources.
Savvy healthcare organizations utilize their ratings as an
opportunity to do a deep-dive into the integrity of their insti-
tutional data. This multi-factorial analytical process, which
involves stakeholders from across the continuum, should
provide an objective assessment of institutional data qual-
ity. The findings can be used to correct current deficiencies,
identify opportunities for improvement, and proactively
monitor data quality over time—which should ultimately im-
prove an organization’s ratings. It is important to remember
that the accuracy of healthcare ratings is dependent upon
the integrity of the supporting data, much of which is derived
from coding and clinical documentation.
Specific examples of how coding can negatively impact
healthcare ratings include:
Complication of Care Codes. As stated in the ICD-10-CM
Official Guidelines for Coding and Reporting, it is important
to note that not all conditions that occur during or following medical care or surgery are classified as complications.
There must be a cause-and-effect relationship between the
care provided and the condition and an indication in the
documentation that the condition is a complication. When
in doubt, query the provider for clarification. Incorrectly
assigned complication of care codes can negatively impact
Present on Admission (POA) Indicators. False positives
can occur in Patient Safety Indictor (PSI) rates if incorrect
POA indicators are assigned. Many PSIs have a coding exception that removes those cases from the PSI algorithm if
the condition was present on admission and did not develop subsequent to the admission. It is of particular importance to monitor POA indicators associated with patients
who are transferred from outside facilities. The receiving
facility does not want to take the hit for a PSI that occurred
at the transferring facility.
Resolved vs. Active Conditions. Although the practice
of cloned documentation (copy and paste) of clinical
information in electronic health records can be a timesaver for clinicians, it can pose a risk to documentation
integrity. Cloned documentation has the potential to
blur the distinction between current conditions and resolved (historical) conditions. If documentation of historical (resolved) conditions is misinterpreted as current
conditions—and coded as such—artificially inflated PSI
rates can occur.
Perceptions of Healthcare Ratings
Although ratings data is typically taken at face value, it is important to remember that healthcare ratings are not always reflective of the quality of care delivered, particularly if the data
behind them is flawed.
According to a 2016 article in Boston magazine, Elizabeth
Mort, Massachusetts General Hospital’s senior vice president
of safety and quality, says ratings have value, but she’s dubious of their simplicity. “Is there really a difference between
four and five [stars], three and four, two and three, one and
two?” she asks in the piece. “When you artificially slice a distribution curve of measures into categories, you run the risk
of misclassifying people.” 2 The opinion expressed by Mort of
the questionable significance between adjacent star ratings
further underscores the importance of accurate data. The
HIM’s Vital Role in