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Questions & Answers

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What is
in these Quality Reports?
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Limitations of the Data
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How were
these indicators selected?
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How do I use these reports?
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What does
"risk-adjusted" mean?
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How often
will the data in this report be updated?
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Does this
quality report display data about individual physicians?
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Why isn't my hospital on this
report?
- Where can I
find the technical specifications for the Quality Indicators?
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How do I read the Graph?
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Two statistical
concepts you should understand
Glossary
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1. What is
in these Quality Reports? |
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The Kentucky Hospital
Association is displaying the Inpatient Quality Indicators defined by the
Agency for Healthcare
Research & Quality (AHRQ). These indicators
are a set of
measures that provide a perspective on hospital quality of care using
hospital administrative data. These indicators reflect quality of care
inside hospitals and include inpatient mortality for certain procedures and
medical conditions; utilization of procedures for which there are questions
of overuse, underuse, and misuse; and volume of procedures for which there
is some evidence that a higher volume of procedures is associated with lower
mortality.
Each
Indicator can be grouped by bedsize or Acute/Critical Access status for hospitals in the state based on
their results. If a hospital has a statistically significant
number (the variation is not due to random chance) - it will be highlighted
- Red is significantly
worse than the National average, Green is significantly
better. If national data are
available for the indicator, that National Rate is listed at the top of the
report. Statewide rates are listed as the first row of the report.
Please note the following exceptions:
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Rehabilitation
and Psychiatric hospitals were excluded from the input dataset.
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Hospitals that had less than 20 total
cases were not shown.
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If an Indicator had less than
20 cases statewide, it was not included.
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2.
Limitations of the Data |
Users of these reports must understand
that the data are
administrative
data collected for billing purposes, not clinical data so
there are significant limitations to using this data for quality purposes
including but not limited to the following:
- Hospitals are required to submit data within 90 days
after the close of a calendar quarter (hospital data submission vendor
deadlines may be sooner). Depending on hospitals' collection and billing
cycles, not all discharges may have been billed or reported. Therefore,
data for each quarter may not be complete. This can also affect the
accuracy of source of payment data, particularly self-pay and charity
categories, where patients may later
qualify for Medicaid or
other payment sources.
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Hospitals record as
many as twenty-five diagnosis codes and twenty-five procedure codes for
each patient for billing purposes. Data submitted to KHA are limited to
nine diagnosis codes and six procedure codes. Therefore, the data
submitted may not fully represent all diagnoses treated by the hospital
or all procedures performed. A consequence may be that sicker patients
with more than nine diagnoses or undergoing more than six procedures are
not accurately reflected. This may also result in total volume and
percentage calculations for diagnoses and procedures not being complete.
- Another critical limitation is that
diagnosis codes do not distinguish between conditions present at the
time of the patient's admission to the hospital and those occurring
during hospitalization. This makes it difficult to obtain accurate
information regarding things such as complication rates. It
has been proposed to Medicare to add flags on secondary diagnostic codes
to distinguish conditions present upon admission. The "present on
admission" code is currently used in California and New York and has
been helpful in distinguishing conditions and infections that occurred
during the patient’s stay or prior to the hospitalization. There is
space reserved on the electronic 837 claim and the paper UB-04 form for
this "present on admission" field.
- AHRQ assigns the Risk of Mortality and Severity of
Illness scores using the APR-DRG methodology designed by 3M Corporation.
These scores may be affected by the limited number of diagnosis and
procedure codes collected by KHA and may be understated.
- Conclusions drawn from the data are subject to errors
caused by the inability of the hospital to communicate complete data due
to reporting form constraints, subjectivity in the assignment of codes,
system mapping, and normal clerical error. The data are submitted by
hospitals as their best effort to meet statutory requirements.
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3.
How were these indicators selected? |
| National organizations have
endorsed lists of indicators and safe practices. In 2005, KHA formed a
joint committee comprised of hospital members who serve on our Data
Committee and our patient Safety Committee. The committee agreed to publish
these nationally recognized indicators as a starting place on reporting of
hospital quality.
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4. How
do I use these reports? |
While the
indicators provided here can be useful for choosing a hospital, the
reality is that these quality indicators are only one source of
information. Other factors that need to be considered include the
patient’s health plan coverage, place of residence, location of the
patient’s physician, and recommendations from family and friends. Also keep in mind that doctors direct and oversee the medical care in
hospitals, prescribing the tests, medications, and treatments. These
reports do not separate the effect of the doctor from the effect of the
hospital. The quality of care provided in a hospital is influenced by
how well its doctors, nurses, support staff, and management team work
together, as well as by the availability of technology and other
resources. If a major change occurs that affects any of these—for
example, the departure of a key surgeon or the addition of new
technology—the indicators for a given hospital may change dramatically
and rapidly. Medical practice and standards of care also change as new procedures and
medicines become available and as research studies demonstrate the
effectiveness of specific treatments or procedures. Patients should
talk with their doctors and hospitals about their care and ask questions
about what changes, if any, have occurred that could affect that care.
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5. What
does "risk-adjusted" mean? |
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The risk of a complication or
death varies by patient and by procedure. For example, an older surgical
patient who has complicating illnesses such as kidney failure and diabetes
is at greater risk of developing complications than a young, healthy
patient. Open heart surgery has a greater risk of a collapsed lung than knee
surgery does. The same care, given by the same physician, in the same
hospital, might have very different effects on a patient who is
healthier than another patient. For example, a patient requiring a heart
valve repair who also has an infection resistant to antibiotics is more
severely ill entering surgery than a valve repair patient who is
otherwise healthy. The severely ill patient may not respond as well to
treatment or surgery and, therefore, may have to stay in the hospital
longer or may not recover at all.
Risk adjustment mathematically takes into
account differences in patient and procedure risk factors, so that
comparisons are more meaningful. Risk adjustment allows for comparison of
actual performance with predicted performance, based on the average U.S.
hospital.
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6. How often will the data on this
report be updated?
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| This report will be updated annually as new data become
available.
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7.
Does
this Quality Report display data about individual physicians?
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| No. We are publishing hospital
data only.

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8.
Why isn't my hospital on
this report? |
There are a number of reasons that a
hospital may not appear on the report
- The Hospital does not perform that procedure
- The Hospital is a specialty hospital (Psych or
Rehab)
- The Hospital had a low volume (< 20) of the
procedure
- The Hospital did not report their data for that
time period
Low volume (small cell
size) could impact patient confidentiality and also limit the ability to
reliably identify quality differences. Small cell size is a frequent
problem in performance measurement, especially when using measures of
rare events such as mortality or foreign body left after procedure.
Small cell size refers to the occasion when there is a small number of
cases within any individual unit of analysis. For example, a single
hospital (location unit of analysis) may only have one death (small cell
size, number of patients who died = 1) in a year (time unit of
analysis). It would be difficult to ensure protection of patient
confidentially in this instance.

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9.
Where can I find the
technical specifications for the Quality Indicators?
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The technical specifications for
the indicator definitions can be found in each
http://www.qualityindicators.ahrq.gov/iqi_download.htm. Detailed specifications, including specific ICD-9-CM codes, DRGs,
and/or patient age or sex are included.
The technical specifications for the
QI
software can be found in
each QI module’s software documentation. Detailed specifications, including
input file formats, software files, and data processing instructions are
provided.
The User Guide for
each QI module (PQI, IQI, and PSI) may be downloaded from the same QI Web site at
http://www.qualityindicators.ahrq.gov/iqi_download.htm.

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Glossary |
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All-Patient
Refined DRG's (APR-DRG's)
- The APR-DRGs are used to
risk-adjust the IQIs for patient clinical
condition and severity of illness or risk of
mortality. If the patient APR-DRG is not
available, the software will risk-adjust using
information on age and gender only, which is
less desirable than using the APR-DRGs. Although
the AHRQ QIs program modules are free, the APR-DRGs
is a commercially licensed software package that
may be obtained from the
3M Corporation. The
3M
Corporation did not have any affiliation with
development of the AHRQ QIs. Future versions of
the AHRQ QIs software may incorporate
alternative clinical classification systems.
Although both APR-DRGs and
DRGs are based on a patient’s
International
Classification of Diseases, Ninth Revision,
Clinical Modification (ICD-9-CM) diagnostic and
procedure codes, they are different patient
classification schemes. The
3M Corporation
developed and sells a widely used software
product to define risk adjustment categories
called APR-DRGs, which has the advantage of
subclass groupings for Severity of Illness and
Risk of Mortality. DRGs are designated by the
Centers for Medicare and Medicaid Services (CMS)
and software groupers are available from various
vendors to assign DRGs.
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Diagnosis-Related Groups (DRGs) are a
patient classification system based on a
patient’s
International Classification of
Diseases, Ninth Revision, Clinical Modification
(ICD-9-CM) diagnostic and procedure codes, among
other variables. DRGs are designated by the
Centers for Medicare and Medicaid Services
(CMS) . Software groupers are available from
various vendors to assign DRGs.
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Confidence Interval
A range that depicts
the likelihood that a hospital's performance
could be influenced by random chance.
The confidence interval in
this report is a range of numbers (lower and
upper confidence limits) around the hospital’s
risk-adjusted rate. (Confidence intervals are
commonly reported in opinion polls: “plus or
minus 5%.”) The confidence interval reflects
how sure we can be that the risk-adjusted rate
reflects the hospital’s performance on the
quality indicator. For example, a rate based on
20 patients is a less reliable indication of the
hospital’s performance than a rate based on 100
patients is. The confidence intervals in this
report are the 95% risk-adjusted confidence
intervals from the Agency for Healthcare
Research and Quality’s software.
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Statistical Significance
A difference is “statistically
significant,” if it is unlikely to have happened
just by chance. (For example, having a coin
come up heads 6 times in 10 flips is not
surprising and is not “statistically
significant,” because that could happen just by
chance.) In this report, a hospital’s
risk-adjusted rate differs from the U.S. rate
more than is likely by random variation, if the
U.S. rate does not fall within the hospital’s
confidence interval. This report highlights (in
red or green) hospital risk-adjusted rates that
are statistically significantly different from
the U.S. rate.
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Mortality
Rate Indicators - The
mortality rate for patients with a specific
procedure or condition is the number of patients
who died, divided by the total number of
patients. However, because the patients’ age,
sex, or severity of condition may increase their
risk of death, the death rates for each hospital
are adjusted to account for these factors. Other
factors—for example, that some hospitals may
transfer out all but the most mild or most
severe cases—are not accounted for in the
risk-adjustment methods used here. Hence, while
death rates constitute a more sensitive
indicator of quality than mere procedure counts,
they too should be considered in tandem with
comments submitted by hospitals, as well as with
other information about quality of care.
- Observed Rate - The
observed rate is the raw rate from the data
provided by the hospital, or simply the
percentage of patients with a particular
condition or procedure who died. The observed
rate is not very useful, because it does not
adjust for differences in patient severity of
illness.
- Risk-adjusted
rate – This is the
rate that best reflects the hospital’s
performance on the quality indicator. The
risk-adjusted rate mathematically adjusts for
the severity and complexity of the hospital’s
patients and procedures. Risk adjustment is
important, because hospitals may differ in (for
example) the risk of death that their patients
have before they come to the hospital. Compare
the risk-adjusted rates in this report to the
U.S. rate. For example, if a hospital’s
risk-adjusted rate is five percent higher than
the U.S. rate, that means that the hospital’s
observed rate is five percent higher than would
be expected if the hospital were performing at
the U.S. average, adjusted for the hospital’s
mix of patients. The Agency for Healthcare
Research and Quality, an independent national
organization, developed the risk-adjustment
software used for this report.
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