(OPPE) Using Automatically Captured Electronic Anesthesia Data

Recommendations

Info

76 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
The Joint Commission Journal on Quality and Patient Safety Sample Ongoing Professional Practice Evaluation (OPPE) Report Figure 1. A simple summary description of physician performance appears in the physician performance component of the sample report, below the summary data tables for each metric. period and (2) interim reports for all others. The reports were then disseminated to the appropriate parties for review. Each report includes a listing for each metric of the total number of compliant cases, the total number of noncompliant cases, and a comparison by percentage to the baseline departmental evaluation. A summary statement indicates whether the physician’s performance was within the group representing 95% of all department physicians. Noncompliant cases are listed by medical record number and case date at the bottom of the panel so that providers and reviewers can then examine individual cases. Reports INDIVIDUAL CONFIDENTIAL REPORTS The results of each metric measurement are continuously compiled for every attending clinician who practices in a clinical environment where the AIMS records clinical data and generates the anesthesia record. Individualized confidential reports include the total number of compliant and noncompliant cases, as well as the percentage of passing scores. A simple summary description of physician performance appears in the physician performance component of the sample report, below the summary data tables for each metric, as shown in Figure 1 (above). The presentation of aggregate perfor mance data for the entire department is intended to provide the individual clinician an opportunity to perform a comprehensive selfassessment against benchmark performance. COMPARISON OF INDIVIDUALS WITH THE GROUP Graphs—one graph for each of the three metrics—were also created to plot individuals against the group. These graphs, which were not included in the confidential individual reports, helped to show the overall trend of each metric. These data are intended to allow those managing the credentialing process to evaluate metrics over time and replace and/or modify them as necessary. A sample summary plot for Timely Documentation of Compliance Statements metric is shown in Figure 2 (page 78). BASELINE DATA FOR THE THREE METRICS The baseline data for the three metrics is shown in Table 2 (page 79), along with the average compliance scores for 2010 for comparison. Discussion We have created a novel, automated, and continuous reporting system for physician credentialing, which uses our existing clinical information system infrastructure, in an effort to address The Joint Commission’s OPPE requirements for quantitative metrics. This system is automated, continuous, objective, and relatively inexpensive to implement. Its basic framework can be refined and developed easily. The system can therefore serve as a key element of a comprehensive clinical performance evaluation that measures both technical and generalizable clinical skill sets. We avoided any metrics (such as reintubation rates, or unplanned ICU admissions) that might penalize practitioners in high-risk specialties or would require risk adjustment for case mix. The system requires no additional effort on behalf of the clinician being scrutinized and reduces the introduction of evaluation artifact in situations in which the presence of an observer February 2012 Volume 38 Number 2 Copyright 2012 © The Joint Commission 77
Page 1 and 2: Performance Measures The Joint Comm
Page 3: The Joint Commission Journal on Qua
Page 7 and 8: The Joint Commission Journal on Qua

(OPPE) Using Automatically Captured Electronic Anesthesia Data

Create successful ePaper yourself

Delete template?

Save as template?