Quality of the estimate. December, p. 47 - Health Care Compliance ...

Confidence and
precision in claims
audits: Quality of the
Editor’s note: Cornelia M.
Dorfschmid is Executive Vice
President with Strategic Management
in Alexandra, Virginia. She may be
contacted by e-mail at cdorfschmid@
strategicm.com or by telephone at
703/683-9600, ext 419.
The contractor reform in
health care brought a
consolidation of Medicare
contractors and new contractors,
as exemplified by the Medicare
Administrative Contractors
(MACs), Medicaid Integrity
Contractors (MICs), Medicare
Recovery Audit Contractors
(RACs), and Zone Program
Integrity Contractors (ZPICs).
These government contractors
have different objectives, some are
more fraud oriented (e.g., ZPIC),
and others are focused on detecting
payment errors (e.g., MACs,
RACs). They conduct pre- and
post-payment audits. However,
no matter what their charge and
CMS-assigned tasks are, these
contractors have aggressively
been monitoring and auditing
claims that were paid to health
care organizations by the federal
and state health care programs. In
estimate
By Cornelia M. Dorfschmid, PhD
their claims audits, the contractors
typically assess whether there were
inappropriate payments received
by a health care organization and,
if so, they determine the recovery
amount. Oftentimes the totality of
cases (e.g., charts, claims, line item
of claims, beneficiaries, or whatever
the unit of observation may be),
which may potentially be affected
by a suspected billing error, cannot
be reviewed. Time and cost
constraints and benefit/cost considerations
make a sample a much
more viable alternative. If the sample
is a statistically valid random
sample (SVRS), such as a “probability
sample” as set forth in the
Centers for Medicare & Medicaid
Services (CMS) Medicare Program
Integrity Manual (PIM), then the
contractor may draw conclusions
from the sample to the universe
(total number) of cases. Simply
put, one can estimate the total
overpayment in the total number
of cases by projecting overpayments
from a relatively small sample
to the universe at large.
Similar considerations, which
weigh the possibility of using the
universe of cases affected by a
Health Care Compliance Association • 888-580-8373 • www.hcca-info.org
potential payment error pattern
versus a sample with appropriate
projection, are increasingly also
part of many providers’ internal
auditing and monitoring strategies.
So what does it take to develop a
good estimate Three aspects can
be considered.
n Correct interpretation of the
projected estimate
To begin with, it requires that the
estimate is projected from a random
sample that was based on the correct
interpretation and application
of the various medical documentation
requirements and payer coverage
rules. If the medical review, the
application of coverage criteria, and
case-by-case review findings can be
challenged in an appeal or a quality
assurance process, the overpayment
estimate derived from the sample
would not be tenable.
n Statistically valid random
sample
Another aspect of a good estimate
is that it must be generated from
a statistically valid random sample
that was selected. If there is no
statistically valid sample, then
the validity of the projection of
the total overpayment estimate is
difficult to defend.
n Confidence and precision
If each sampled case was reviewed
correctly and the sample was a
statistically valid random sample,
acceptable confidence (i.e, degree
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December 2011
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