The Joint Commission’s performance measurement data is organized into core
measure sets, each of which relates to a condition of care. The core measure
sets included in the download are acute myocardial infarction (AMI), heart
failure (HF), pneumonia (PN), pregnancy (PR), and surgical infection prevention
(SIP). Hospitals are required to pick a subset of these measure sets to report
to the Joint Commission. Currently hospitals are required to pick a minimum of
three measure sets and submit data for all the measures within a measure set.
Hospitals submit data to an intermediary called a performance measurement
system. The measurement system aggregates a hospital’s data and sends this
aggregated data to the Joint Commission quarterly. The Joint Commission then
aggregates this quarterly data over the last four reported quarters for
reporting on Quality Check.
The Joint Commission uses two types of measures to report National Quality
Improvement Goal results: process measures and outcome measures.
Process measures describe how often a series of recommended activities, actions,
or steps are done (for example, a treatment such as aspirin at arrival) in a
patient population over a set time period. Process measures are expressed in
terms of a percentage, or rate. The denominator is the total number of patients
for whom the treatment or event was recommended.
Outcome measures describe the end results of a function or process in a patient
population over a set period of time. Outcome measures are expressed in terms
of a percentage or rate. The denominator is the total number of patients at
risk for the outcome.
Included in the download is the following:
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Hospital Results - Symbol
The symbol represents the comparison of the hospitals performance for the
measure to the national average at the measure and measure set level.
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Hospital Results – Number
The number of times as a percentage the hospital performed the measure during
the time period being reported.
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Total Patients
The total number of patients treated for the measure.
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Nationwide - Average Rate
The average rate for all Joint Commission accredited healthcare organizations
in the nation that provide results for a measure. The average rate is
calculated by dividing the total number of patients who had the recommended
care provided for a measure by the total number of patients who met the
inclusion and exclusion criteria for that measure in the timeframe being
reported.
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Nationwide - Top 10%
Scored at Least: The number of times, as a percentage, the top 10% of all Joint
Commission accredited hospitals in the nation followed the recommended
treatment/ procedure during the time period being reported.
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Statewide - Average Rate
The average rate for all Joint Commission accredited healthcare organizations
in the state that provide results for a measure. The average rate is calculated
by dividing the total number of patients who had the recommended care provided
for a measure by the total number of patients who met the inclusion and
exclusion criteria for that measure in the state for the timeframe being
reported.
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Statewide - Top 10%
Scored at Least: The number of times, as a percentage, the top 10% of all Joint
Commission accredited hospitals in the state followed the recommended
treatment/procedure during the time period being reported.
Uses of Quality Check Data
Quality Check data includes national rates, state rates, and hospital rates at
the measure level. Data can be analyzed in many ways. Comparisons can be made
from the hospital to national/state level. Comparisons between hospitals can be
made. Hospitals with known similar characteristics can have their rates
combined and compared to various benchmarks, either provided by the Joint
Commission data download or to an outside credible source. Valid comparisons
must be consistent using the same measures.
Misuses of Quality Check Data
Proper care needs to be taken into consideration when analyzing Quality Check
data. Analysis of the data should incorporate the proper distribution of the
data. Some analysis may require the use of a Binomial Distribution, Chi-Square
Distribution, or Normal Distribution. Using a wrong distribution will yield
incorrect inferences about a hospital’s level of care. Another source of error
is to use different measures in making comparisons from one hospital to
another. Process measures should be compared with process measures and not to
outcomes measures.