How Does Your Coding Measure Up?: Analyzing Performance
Data Gives HIM a Boost in Managing Revenue
by Kurt Price, MS, and Dean Farley, PhD
As they take on greater responsibility helping manage their facilities’ revenue
cycles, HIM professionals benefit from incorporating coding, compliance, and reimbursement
performance data into their improvement efforts.
Accurate and complete coding ensures that a hospital receives the
reimbursement it deserves—full reimbursement that is fully compliant
with coding and payment rules. As the payment environment grows more
complex, HIM professionals are being asked to play a more active and
formal role in their hospitals’ revenue
cycle management. Successfully assuming these new responsibilities includes
employing a tool that may also be new to many HIM professionals—data
analysis. Monitoring coding performance data is an important method for
improving coding productivity and effectiveness and advancing the HIM
role in revenue management.
Tracking HIM performance must go beyond typical
operational measures that document performance in the narrow context
of the hospital’s own
experience. To be an effective participant in revenue and compliance
management, HIM professionals must have access to—and take advantage
information that allows them to answer the following questions:
- Does our
coding appear out of compliance according to the Office of Inspector
General’s (OIG) upcoding measures?
- Do our complication and comorbidity
(CC) coding rates appear overly aggressive compared to peer hospitals?
our CC coding rates suggest a potential for improved reimbursement
through more accurate and complete coding?
- Do our Ambulatory Payment
Classification (APC) coding patterns suggest a compliance risk or revenue
- Do I know the financial risk and reimbursement
impacts of these and other coding patterns at our hospital?
profiling results, this article illustrates how HIM professionals can
incorporate coding, compliance, and reimbursement performance data
into their monitoring and improvement efforts.
Evaluating Current Performance,
Identifying Future Improvements
Profiling HIM performance allows hospitals
to go beyond narrow operational metrics and evaluate themselves in a
broad industry context, identifying areas where they appear to be out
of alignment with industry averages. This focuses efforts on improving
coding, compliance, and reimbursement management in areas where the hospital
stands out as a compliance risk or where incomplete billing may be undermining
reimbursement. By establishing a routine and replicable evaluation process,
profiling can be used as a basis for evaluating current performance and
future changes in performance.
Ideally then, HIM can become at least in
part a data-driven process. Typical steps in this process should enable
the HIM department to:
- Establish quantitative baseline performance levels
for the hospital overall, as well as for specific clinical areas (e.g.,
CC coding rates for selected DRGs)
- Conduct qualitative documentation,
coding, and billing audits to identify deficiencies and vulnerabilities
and implement remedial strategies
- Assess progress in quantitative terms
- Calculate financial implications
of deficiencies and improvements
These steps can be repeated and expanded as necessary to provide an
effective ongoing monitoring and improvement program.
The federal government’s
emphasis on compliance underscores the importance of profiling documentation
and coding performance. Medicare compliance programs and directives of
the Centers for Medicare and Medicaid Services (CMS) and OIG directly
influence compliance monitoring in hospitals. OIG identifies benchmarking,
longitudinal studies, and regular reporting as key elements of an effective
compliance program.1,2 CMS
has incorporated these perspectives in its Medicare review programs managed
by quality improvement organizations (QIOs), in particular, the Hospital
Payment Monitoring Program (HPMP) implemented in 2003. [Editor's note: For more on HPMP and its studies on error-prone codes, see "The Codes to Watch" (Journal of AHIMA, July-Aug 2005).]
Both internal and external data are
important for performance evaluation. External data, such as Medicare
inpatient and outpatient claims data, ground the evaluation in measurable
industry norms and force hospitals to evaluate performance beyond “local
data establish what is typical and how much variation there is around
normal performance. Further, external data encourages hospitals to assess
how they differ from the norm and how those differences might affect
Internal data provide other advantages and can add significant
depth to a performance profiling program. Using a hospital’s own
historical data enables consistent comparison of performance measures
over time and injects local factors into the evaluation process. It also
gets around the typical limitations of external data: timeliness, uncertain
data collection processes, and privacy considerations. In contrast with
external data, using internal data encourages evaluators to ask and assess
where the organization is now, how far it has come, and what else has
changed that might affect results.
Profiling in Action
The following examples illustrate the process of
performance profiling in the HIM professional’s role of managing
revenue and compliance. The process is one of discovery, investigation,
and action: discovery of risk and opportunity areas through profiling
are investigated using focused reviews, and appropriate corrective strategies
can be developed and implemented. Industry coding guidelines and expertise
(Coding Clinic for ICD-9-CM, for example) support this process.
documentation and coding areas, both inpatient and outpatient, should
be incorporated in a performance profiling program, including:
- Key OIG
and QIO/HPMP target areas
- CC coding rates (DRGs)
- Medicare casemix index (DRGs)
- Outpatient Code Editor failure rates
- Outpatient coding into APC levels
- Medicare Discounted Service Index
- DRG and APC outlier payment rates
The examples provided here come from the first two areas comparing rates
and historical trends and estimating the financial implications.
performance results in these examples are derived from a commercially
available profiling tool using publicly available data for all hospitals
in the US. These analyses are for illustration purposes, and alternative
profiling approaches or benchmark datasets are available or could be
developed to serve this purpose.
The profiling examples present the actual
findings for a large teaching hospital (fictitiously named Metro Medical
Center [MMC] for purposes of this article), with its performance compared
against the benchmark performance for all large teaching hospitals in
the US. MMC’s story
for each example, however, is for illustration purposes only.
OIG has identified DRG 416 as one of the DRGs at high risk for
upcoding, and CMS has included DRG 416 as one of its HPMP target areas.
The compliance review method employed by CMS tabulates the frequency
with which high-risk DRGs occur among a hospital’s cases as a fraction
of cases in DRGs with the potential to be grouped into the high risk
category. As specified by CMS, this means the number of DRG 416 cases
as a percentage of cases in DRGs 416, 320, and 321.
(DRG 416) from Urinary Tract Infections (UTIs) with and without CCs (DRGs
320 and 321, respectively) has been a major issue for this hospital.
The most recent profiling results (shown in figure 1) suggest that MMC
codes cases into DRG 416 more frequently than benchmark hospitals: 52.5
percent of MMC cases in DRGs 416, 320, and 321 are coded into DRG 416,
compared to 47.2 percent for benchmark hospitals, for a variance of 5.3
Figure 1. DRG 416 Snapshot: Above the Benchmark
Several years ago, however, the same profiling analysis
uncovered an even larger variance of more than 15 points. This represented
a significant compliance risk, and MMC initiated focused coding and documentation
reviews of septicemia and UTI cases. These reviews revealed that, for
many physicians, urosepsis refers to a urinary tract infection only.
The ICD-9-CM system similarly classifies urosepsis in this way, and code
599.0 (Urinary Tract Infection, NOS) was appropriately assigned. However,
there was also a substantial proportion of physicians who believe that
urosepsis means sepsis (a systemic infection that involves bacteria in
the bloodstream) that originated in the urinary tract.
resulted when MMC coders assumed that all physicians meant “sepsis” when
they documented “urosepsis.” The clinical presentation, treatment,
and severity of the patient are very different between the two conditions,
and the medical record documentation must support assignment of a code
for sepsis (038.X, 995.91). The MMC HIM department implemented changes,
working to improve the accuracy of coding for these DRGs.
trend shown in figure 2 illustrates that MMC’s compliance
risk for DRG 416 has decreased dramatically. In 2000 and 2001 the hospital’s
rate for DRG 416 was 15.2 points higher than the benchmark, and in these
years MMC’s rates were among the highest 10 percent of all benchmark
hospitals (as indicated by the “Zone” flagged red for those
years). The rate difference has decreased in the last two years to reach
Figure 2. DRG 416 Trend: Decreasing Risk
DRG 14 is another DRG identified by OIG as a high risk for upcoding;
it is also targeted by HPMP. The example shown in figure 3 analyzes the
frequency of DRG 14 cases as a percentage of cases in DRGs 14, 15, and
524. Rather than representing a compliance risk for MMC, this error-prone
DRG may actually reflect an opportunity for improved revenues. The profiling
results suggest that MMC codes cases into DRG 14 much less frequently
than do benchmark hospitals: 70.4 percent of MMC cases compared to 84.4
percent for benchmark hospitals. MMC’s rate for DRG 14 is among
the lowest 10 percent of all benchmark hospitals, as indicated by the
indicator flagged blue.
Figure 3. DRG 14 Snapshot: Lowest 10 Percent
A documentation and coding review of these cases
reveals that the issue for DRG 14 involves the appropriate coding of
the term “stroke” as
documented in a medical record without further physician substantiation
of the presence of an infarction. For many years “stroke” was
indexed in ICD-9-CM to code 436 (Acute, but ill-defined, cerebrovascular
disease), which was grouped to DRG 14 (Intracranial Hemorrhage or Cerebral
Infarction). Effective October 2002 (FY 2003), the DRG grouping method
was changed so that if “stroke” only was documented without
an infarction, code 436 was then regrouped to DRG 15 (Nonspecific CVA
and Precerebral Occlusion without Infarction).
The five-year trend for
MMC shown in figure 4 confirms a dramatic drop in DRG 14 cases in 2003
compared to previous years. Thus MMC suspected and confirmed with profiling
data that beginning in 2003 many cases were grouped to DRG 15 that should
have been grouped to DRG 14 if more complete documentation and coding
had occurred for these cases.
Figure 4. DRG 14 Trend: Sudden Drop
Much public comment has since indicated
that in many physicians’ minds
(and inherent in the documentation), a stroke was considered an infarction.
As of October 2004 (FY 2005), the word “stroke” is now indexed
in ICD-9-CM to code 434.91 (Cerebral artery occlusion, unspecified, with
infarction). The same documentation of “stroke” that grouped
cases to DRG 15 for two years will now group the same cases to DRG 14.
Theoretically, this could be a continuing issue for hospitals if their
coders are not using the updated index carefully. If they continue to
assign code 436 whenever “acute CVA” is documented, they
will continue to group an inappropriately high proportion of cases into
DRG 15 that should be grouped into DRG 14.
If MMC completed similar hospital
profiling analyses on a real-time basis using its own data, it could
have identified this issue as it was occurring, thus preventing the hospital
from losing reimbursement. For many of the cases during 2003 and 2004,
patients did suffer a CVA, but the documentation may not have documented
the word “infarction.” With timely
profiling results, coding and HIM department managers could have queried
physicians and requested that this more specific information be added
to the medical record in order that it be coded more appropriately to
the CVA DRG 14.
CC Coding Rates
CC coding rates are analyzed for DRG combinations (or “DRG
that are distinguished solely by the presence or absence of a CC. The
analyses tabulate the number of cases assigned to DRGs requiring a CC
as a percentage of discharges assigned to either DRG in the CC pair.
These types of evaluations are used to identify DRGs with potential upcoding
that could place the hospital at compliance risk or could suggest incomplete
coding of CCs.
To analyze potential revenue opportunities in the documentation
and coding of CCs, MMC focused on the top 10 DRG pairs where its CC coding
rates were lower compared to benchmark hospitals (see figure 5). As shown,
DRG pair 110-111 is the largest-volume pair, accounting for 1.4 percent
of MMC’s cases, and its CC coding rate is nearly 15 percentage
points lower than benchmark hospitals—70.4 percent compared to
Figure 5. Top DRG Pairs below Benchmark
The five-year trend shown in figure 6 demonstrates that
the lower CC coding rate for DRG pair 110-111 has been a consistent pattern
for MMC. However, the disparity in CC rates has greatly increased over
time and has remained among the lowest 10 percent of benchmark hospitals
for the last three years.
Figure 6. DRG Pair 110-111 Trend: Increasing Disparity
Thus MMC appears to have a persistent and growing
undercoding problem for DRG pair 110-111. If further review reveals that
there are true documentation and coding deficiencies, MMC has been losing
revenue and corrective action is necessary. The revenue opportunity may
indeed be significant. Figure 7 shows that if MMC’s CC coding rate
for DRG pair 110-111 matched the benchmark rate, its average reimbursement
would increase by more than $1,300 per case, totaling roughly $215,000
for its 162 cases. The reimbursement implications of CC coding for DRG
pair 110-111 identify it as a high-priority area.
Figure 7. DRG Pair 110-111: Potential Reimbursement Implications
While profiling results
for specific DRG pairs will identify specific areas that should be considered
for review, the comparison of overall CC coding rates could point to
more systematic documentation and coding concerns for the HIM department.
For example, a major barrier to accurate and complete documentation for
coding is the requirement that cases be coded within the “bill
hold” period, which is typically three
to five days after discharge. The accounts receivable report deadlines
and goals drive HIM department managers to push coding as quickly as
possible, sometimes with negative effects on optimal coding and corresponding
reimbursement. In this situation, routine monitoring of CC coding rates
would detect such a systematic pattern of undercoding and help HIM department
managers identify the necessary remedial action.
Making It Happen
In spite of the advantages of employing performance
profiling data in HIM, there can be barriers to doing so. From an institutional
perspective, there are issues of ownership and control that may limit
the HIM department’s
ability to define the types of management reporting and operating guidelines
for the hospital. Obviously, the larger the role the HIM department plays
in the compliance and revenue management processes, the more opportunity
there is for the HIM department to bring about more effective use of
Given the opportunity, HIM professionals should be ready
and willing to lead the way in using profiling data. HIM departments
that perceive quantitative profiling measures as an opportunity, and
whose goals and objectives support the use of the data, will do so.
operational changes can promote more effective use of data. Management
objectives and rewards can establish measurable performance standards
relating to clinical data quality, even tying compensation and bonuses
to performance. Providing adequate resources for data analysis and proper
alignment of responsibilities is crucial. It is also crucial that HIM
departments develop and articulate data-driven management strategies
that include clear links between performance profiling data and existing
monitoring and improvement activities (e.g., record reviews and audits).
In the end, successful use of performance profiling requires a long-term
commitment to improve data quality throughout the organization. Just
as data can be the source of problems, it can be a big part of the solution.
The benefits of performance profiling encompass both improved data quality
and stable and enhanced revenues with reduced compliance risk.
- “Compliance Program Guidance for Hospitals.” Federal
Register 63, no. 35 (1998):8987–98.
- “Supplemental Compliance Program Guidance for Hospitals.” Federal
Register 70, no. 19 (2005):
Kurt Price (firstname.lastname@example.org) is vice president of
reimbursement management and Dean Farley is senior vice president
of healthcare policy and analysis at HSS, Inc. The authors thank Melinda
Stegman, MBA, CCS, manager of clinical HIM services at HSS, for her
coding input and guidance in preparing this article.
Price, Kurt, and Dean Farley. "How Does Your Coding Measure Up?: Analyzing Performance Data Gives HIM a Boost in Managing Revenue." Journal of AHIMA 76, no.7 (July-August 2005): 26-31.