(OPPE) Using Automatically Captured Electronic Anesthesia Data

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The Joint Commission Journal on Quality and Patient Safety
matter—does not actually occur in 100% of cases, it would appear
to be a valid quality measure with relevance to physician
performance and patient care.
■ End Tidal CO 2 Monitoring. The primary purpose of the
end tidal CO 2 query, was to determine whether or not it was
measured during a given general anesthesia case. Whenever it is
measured, the AIMS creates a unique database entry every 60
seconds, which includes a number of data elements (for example,
time stamp, value, data source). Because most cases contain
hundreds of end tidal CO 2measurements, we programmed our
SQL query to simply count the number of measurements per
case and associate that count with a single unique case identifier.
The ASA national standard for end tidal CO 2 monitoring is
that “continual monitoring for the presence of expired carbon
dioxide shall be performed unless invalidated by the nature of
the patient, procedure or equipment.” 10 However, there are circumstances
in which CO 2 monitoring is not applied at all when
patients undergo general anesthesia (particularly when a MAC
case is converted to a general anesthetic). We therefore targeted
use of end tidal CO 2 monitoring at any point during a case as
our first version of this metric, but we expect that a future version
might evaluate the frequency and duration of monitoring.
■ Timely Documentation of Compliance Statements. To
create the data needed for the query regarding timely documentation
of compliance, the “End of Anesthesia Care Time”—a
particular time stamp recorded by the anesthetist at case conclusion—was
obtained from the AIMS database. In addition, all
time stamps associated with each of the required compliance
statements were also obtained and compared. We chose this metric
because we believe that timely documentation facilitates communication.
Downstream care providers need complete
documentation to make the best clinical decisions for their patients.
Given this metric’s direct effect on patient care, it is important
to assess as an aspect of the quality of care provided
patients—just as, say, outstanding dictation reports is also frequently
used as a compliance metric. The time frame of two
hours was selected because the availability of complete electronic
charts to providers of downstream care (for example, postanesthesia
care unit, intensive care unit [ICU], and general floors) is
an important goal for our department. Although billable aspects
of care might not necessarily reflect quality of care, they do influence
a hospital’s ability to provide care. In addition, it is a
growing concern that a caregiver’s economic performance, which
is often influenced by billing metrics, is in fact a valid criteria to
determine credentialing or appointment of staff. 14
It is important that criteria reflect expectation of failure rates.
Any criterion that has a near-perfect passing rate might be a poor
measure of performance—and might not contribute to a potential
for quality improvement. 15
CASE EXCLUSIONS
Several case types were excluded from the baseline assessment.
Because of clinical practice patterns, pediatric cases were excluded
from the BP metric, as were all cases in which patients
were noted to arrive in the OR already intubated—such patients
are almost universally transported with sedation/general anesthesia
while on a transport monitor. Cases in which the anesthetic
delivered did not include a significant inhalational agent
concentration (see the footnote on page 75) were also excluded.
Therefore, cases in which total intravenous anesthesia were excluded
from capture. Finally, we elected to exclude cases in which
a transfer of care occurred, so as to not penalize physicians who
had performed a portion of the case but transferred the case to
another physician who failed to document compliance appropriately.
As such, a separate SQL query was written to find and
demarcate cases in which a transfer of care occurred.
BASELINE DATA ANALYSIS
Data collected by the SQL query were analyzed using a
spreadsheet program. Cases were determined to either pass or
fail each of the three metrics on the basis of our predefined standards.
For each of the cases, which were counted on a per-physician
basis, passing/nonpassing percentages were calculated. If an
individual physician performed fewer than 60 cases during the
assessment, he or she was removed from the analysis because of
low case volume and underwent a different evaluative process. A
summary was then created, ranking each physician by his or her
percentage of passing cases. As established by the OPPE credentialing
committee, the bottom 5% of the group was flagged as
“Not Passing the Metric” for each of the three parameters.
ONGOING PHYSICIAN PERFORMANCE REPORTING
In February 2009, after establishing passing thresholds for
each of our three metrics, we provided the information on the
metrics to the department’s staff members and began our ongoing
evaluation process. No major educational intervention was
performed. Three months of data for each physician were then
evaluated according to the three metrics, and a confidential
report was provided to each physician.
To present individual reports to clinicians and use our administrative
staff efficiently, an automated data reporting system
was implemented using a spreadsheet program with macros. Two
types of confidential reports were produced—(1) final reports
for physicians being credentialed within the given three-month
February 2012 Volume 38 Number 2
Copyright 2012 © The Joint Commission