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ECONOMICS, EDUCATION, AND HEALTH SYSTEMS RESEARCH

The Effects of Surgical Case Duration and Type of Surgery on Hourly Clinical Productivity of Anesthesiologists

Amr E. Abouleish, MD MBA^*, Donald S. Prough, MD^*, Charles W. Whitten, MD, and Mark H. Zornow, MD^*

*Department of Anesthesiology, University of Texas Medical Branch, Galveston, Texas; and Department of Anesthesiology and Pain Management, University of Texas Southwestern Medical Center at Dallas, Dallas, Texas

Address correspondence and reprint requests to Amr E. Abouleish, MD, MBA, Department of Anesthesiology, University of Texas Medical Branch, 301 University Blvd., Galveston, TX 77555-0591. Address e-mail to aaboulei{at}utmb.edu

	Abstract

Top Abstract Introduction Methods Results Discussion Conclusion References

 
Surgical duration (hours per case; h/case) and type of surgery (ASA base units per case; base/case) determine the hourly clinical productivity (total ASA units per hour of anesthesia care; tASA/h) for anesthesiology groups. In previous studies, h/case negatively influenced tASA/h, but base/case did not differ significantly. However, when cases are grouped by surgical service, the mean base/case varies. In this study we evaluated the effect of h/case and base/case on tASA/h when these are grouped by surgical services. Data from one calendar year were collected from an academic anesthesiology department’s billing database. All surgical cases for which the anesthesiology department provided care were included. Cases performed outside the main operating room, e.g., remote sites or obstetrics, were excluded. Any care not billed with ASA units was also excluded. Mean base/case and h/case were determined. For each service, tASA/h was calculated by dividing the sum of base/case and (4 x h/case) by h/case. A total of 12,769 cases were performed by 19 different surgical services. Mean base/case was 6.1 U, with a range of 4.0 U (orthopedics) to 16.0 U (cardiothoracic). Mean h/case was 2.9 h, with a range of 0.9 h (otolaryngology pediatric) to 5.4 h (orthopedic spine). Mean tASA/h was 6.35 U/h, with a range of 5.01 U/h (plastic surgery) to 9.71 U/h (otolaryngology pediatric). The services with high base/case did not necessarily have high tASA/h because of the longer h/case. The services with the shortest h/case had the highest tASA/h. The accurate prediction of both clinical and billing productivity requires inclusion of both base/case and surgical duration data. Anesthesiology groups should consider surgical duration when making strategic decisions.

IMPLICATIONS: Although type of surgery (base units per case) and surgical duration determine hourly clinical productivity, our study results demonstrated that surgical duration has more influence on the hourly clinical productivity.

	Introduction

Top Abstract Introduction Methods Results Discussion Conclusion References

 
Calculating billed units for anesthesia care incorporates both base units, which are fixed and are determined by the type of surgery, and time units, which are variable and are determined by the duration of anesthesia care. Therefore, both base units and time units influence estimates of productivity that are based on billed units. In comparisons of clinical productivity between anesthesiology groups, significant differences of surgical duration strongly influenced overall productivity (1–3), with longer-than-average surgical durations resulting in an economic disadvantage for academic anesthesiology departments (4). In these studies, hourly clinical productivity, defined as total ASA units billed per hour of care (tASA/h), was negatively influenced by surgical duration, defined as billed hours per case (h/case). The other component to tASA/h is the type of surgery, defined as base units per case (base/case). In previous work, base/case did not influence tASA/h, but there was no significant difference in the base/case for the groups in two of the studies (2,3), and base/case was kept constant in another study (4). In contrast, within an individual practice, base/case will vary and should differ when the cases are grouped by the surgeon’s primary specialty (e.g., plastic surgery, otolaryngology, or gynecology). The purpose of this study was to evaluate the effects of surgical duration (h/case) and type of surgery (base/case) on hourly clinical productivity (tASA/h) when anesthesia care is provided for different surgical services.

	Methods

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Data were collected for one calendar year (1999) from the billing database of an academic anesthesiology department in the United States. All anesthesia care performed by the department in the main operating room (OR) of the primary hospital was included. Care performed outside the OR—including remote sites (e.g., radiology), obstetric anesthesia, pain management services, and critical care services—was excluded. For each included case, the data collected consisted of the anesthesia codes billed, minutes of anesthesia care, faculty surgeon name, date of procedure, and OR number. Relative value unit-based charges performed in the OR and modifiers were excluded because not all payers recognized these charges or modifiers. Hence, these cases were not billed out consistently. The base units billed were determined by using the 1999 ASA relative value guide (5).

Assignment of the surgical service to each case was performed on the basis of the faculty surgeon’s academic department (for members of the department of surgery, the specific division was used). In addition, pediatric otolaryngology, pediatric orthopedics, orthopedics spine, and ophthalmology retina were separately identified from their respective departments. The total number of minutes of anesthesia care was divided by 15 to determine the time units billed. The total number of hours of anesthesia care was determined by dividing minutes by 60.

For each surgical service, averages and standard deviations for base/case and h/case were determined. From these means, the average tASA/h for each service was determined using the following formula:

	Results

Top Abstract Introduction Methods Results Discussion Conclusion References

 
In the calendar year 1999, the particular anesthesiology department in question provided care for 19 surgical services, which combined to perform 12,769 cases. The surgical services were burn surgery, cardiothoracic surgery, general surgery, gynecology, neurosurgery, ophthalmology (nonretina), ophthalmology retina, oral surgery, orthopedics (nonpediatric, nonspine), orthopedics pediatric, orthopedics spine, otolaryngology (nonpediatric), otolaryngology pediatrics, pain management, pediatric surgery, plastic surgery, transplant surgery, urology, and vascular surgery.

For all cases performed, the base/case, h/case, and tASA/h data are shown in Table 1. More than 50% of cases had 3, 4, or 5 base units; 90% of the cases had 9 base units. The median h/case was 2.25 h (mean, 2.9 h), and <10% of the cases were <1 h. Almost half of the cases (40%; between the 50th and 90th percentile) had h/case values of 2.25–5.5 h. The tASA/h median was 6.3 U/h, and 75% of the cases had 7.4 U/h. For each of the services, the number of cases, base/case, h/case, and tASA/h were determined as shown in Table 2. Four services—cardiothoracic surgery, neurosurgery, vascular surgery, and orthopedics spine—had the largest mean base/case (16.0, 9.8, 7.8, and 7.4 U, respectively). Nine services had a mean base/case between 5.0 and 6.0 U, and six others had a base/case between 4.0 and 4.6 U. The mean surgical duration was also high in the four services with high base units, but several services (plastic surgery, orthopedics pediatrics, ophthalmology retina, burn surgery, and orthopedics general) with a base/case <6.0 U had a mean surgical duration of 3.0 h. Four services (otolaryngology pediatric, pain management, ophthalmology, and urology) had a mean surgical duration <2.0 h.

In Figure 1, the mean base/case and h/case are plotted. The mean tASA/h for all services (6.35 U/h) is also plotted. Figure 2 illustrates the relationship between base/case, h/case, and tASA/h. In both figures, the services above the tASA/h line each had a higher-than-average tASA/h. These services also had shorter surgical durations, higher base units, or a combination of the two. The services below the tASA/h line each had a lower-than-average tASA/h because of longer surgical durations, lower base units, or a combination of the two.

View larger version (10K): [in this window] [in a new window]   Figure 1. Surgical duration (h/case) and type of surgery (base/case) for anesthesia care for surgeries performed over 1 yr grouped by 19 surgical services in an academic medical center. Hourly clinical productivity (tASA/h) is equal to the sum of base/case + (h/case x 4) divided by h/case. The average tASA/h for all the services was 6.35 U/h and is designated by the solid line. Below the line are services with a tASA/h <6.35 U/h, and above the line are services with a tASA/h >6.35 U/h. All data are from the billing database of the anesthesiology group. h = hour of anesthesia care; case = surgical case for which anesthesia was provided; base = base units; tASA = total ASA units.

View larger version (25K): [in this window] [in a new window]   Figure 2. The effect of surgical duration (h/case) and type of surgery (base/case) on hourly clinical productivity (tASA/h) is shown. Hourly clinical productivity (tASA/h) is equal to the sum of base/case + (h/case x 4) divided by h/case; h/case is inversely proportional to tASA/h. For example, if h/case is high and base/case is average, then tASA/h is below average. h = hour of anesthesia care; case = surgical case for which anesthesia was provided; base = base units; tASA = total ASA units.

	Discussion

Top Abstract Introduction Methods Results Discussion Conclusion References

Mathematically, tASA/h is calculated by using base and time units (see the formula in Methods). When base/case is held constant, tASA/h is an inverse function of h/case, as shown below:

where y is tASA/h, x is h/case, and k is base/case. Therefore, for short surgical durations, the tASA/h will be high, and for long surgical durations, the tASA/h will be low. In Figure 3, "isobars" for different base units illustrate the effect of h/case on tASA/h. Along each isobar, the base/case is constant. Therefore, when surgical duration decreases to less than one hour, tASA/h increases rapidly. As surgical duration increases, differences in tASA/h become less and approach the same asymptote of 4 U/h as h/case becomes very long. In other words, the influence of base/case on tASA/h is most when the h/case is short, especially when h/case is one hour or shorter. However, when h/case is two hours or longer, the isobars begin to merge together, and the extreme approximates 4.0 U/h. In previous studies in which base/case did not differ between groups (1–4), h/case correlated highly (r ranged from -0.56 to -0.60). Because Figure 3 is a mathematical calculation of the relationship between varying base/case, h/case, and tASA/h, correlations cannot be calculated.

View larger version (23K): [in this window] [in a new window]   Figure 3. The effect of surgical duration (h/case) and hourly clinical productivity (tASA/h) at varying levels of base units (base/case). Each line represents a constant value for base/case, as noted above each line. tASA/h = (base/case)/(h/case) + 4; therefore, at h/case <1 h, tASA/h increases. At a surgical duration of 1 h, tASA/h = base/case + 4. As h/case increases, tASA/h decreases and approaches 4 U/h. Below 1 h, the tASA/h varies depending on the base/case. At h/case 2 h, the differences in base/case influence tASA/h less. h = hour of anesthesia care; case = surgical case for which anesthesia was provided; base = base units, tASA = total ASA units.

If an anesthesiology group provides care for a facility that performs many short cases (h/case less than one hour), then the base/case becomes more influential in determining differences in tASA/h. One example of such a facility is an ambulatory surgical center (1), in which base units may not vary greatly because of the limitation of what types of surgeries can be performed in the center. In contrast, another example would be a nonambulatory surgical center where only private-practice surgeons practice (1,2). In this situation, a greater variety of surgeries are performed with varying base units, and the type of surgery (base/case) may influence the tASA/h.

In this study, tASA/h will always be less than or equal to tASA per hour staffed because the time units and the "hour of care" examined are limited to billed time. The actual hours staffed measurement includes both billed and nonbilled time. Nonbilled time includes turnover time, delays in starting cases, and other times when no anesthesia is provided during the day. To obtain an overall approximation of tASA per hour staffed, tASA/h can be divided by the raw utilization of regular hours for the OR suite. Unfortunately, the actual tASA per hour staffed may vary tremendously depending on surgical service, surgical duration, availability of OR staff, and utilization of specific ORs. In lengthy surgical procedures, tASA per hour staffed and tASA/h may be similar because little time is spent in room turnover. In contrast, operating suites with many short surgical procedures should have a larger difference between tASA per hour staffed and tASA/h because considerable time is spent in room turnover (6).

In this study, turnover time and actual hours staffed were not examined for several reasons. First, the purpose of the study was to examine the influence of base/case and h/case on tASA/h—a question that was raised by our previous group productivity studies, which showed that groups that reported longer h/case had lower tASA/h (1–4). Therefore, the methodology focused on billed time and excluded nonbilled time (e.g., turnover time). Second, the previous studies, which were designed to compare productivity between anesthesiology groups, used survey data. The survey included only data that were readily available to anesthesiology groups from their billing databases. Although nonbilled time and raw utilization of the OR suite are important in analyzing actual hours worked, those data are not available from a billing database. Finally, when clinical productivity or financial productivity (i.e., clinical productivity x payer conversion factor) is evaluated, a comparison of billable time provides a more useful comparison between surgical services or individual surgeons. In individual practices, analysis of nonbillable time is confounded by OR allocation of block time, scheduling, transportation, cleanup services, and availability of OR staff.

For several reasons, it may be important for anesthesiology groups to understand the influence of h/case on tASA/h and, thus, to determine (and track) tASA/h. First, hourly revenue productivity is determined by multiplying the tASA/h by the conversion factor (determined by the payer mix for each service). In addition, tracking and trending the expected revenue productivity allows a group to predict when providing care for a service may result in a net loss or profit by comparing expected revenue with the staffing costs. Second, this determination of net profit or loss is important for determining the economic effect on an anesthesiology group if (or when) the group considers providing additional clinical coverage. This may be the case when the hospital the group serves wants to open additional ORs, add a Saturday elective schedule, or request that remote sites be covered. It is also important for evaluating the profitability of providing care at a new facility, such as an ambulatory surgical center or another hospital (7). With the expected revenue (based on tASA/h, payer mix conversion factor, h/case, and expected number of cases) determined, some groups have requested and received financial support from the hospital if the staffing costs exceed the revenue (8,9). The staffing costs will be based on the number of ORs to be staffed (10). Hence, the same expected revenue in one model may cover staffing costs when the number of OR sites is small (high OR utilization) but may not cover such costs in a second model where the number of OR sites is large (low OR utilization). Third, the hourly revenue productivity could be significantly different from the tASA/h because the payer mixes for each service or hospital may be significantly different. Finally, the results of this study show that making decisions only on the basis of which services have the highest base/case leads to inaccurate conclusions because base/case alone cannot be used to predict tASA/h.

For academic anesthesiology programs, the implications of the results of this study are significant. Academic groups provide care for cases with longer surgical duration, which results in lower tASA/h. When compared with private-practice groups, this lower tASA/h means that the academic group must work more billable hours to bill the same tASA per OR (2–4). In addition, if academic groups have lower payer mix conversion factors, then the expected hourly revenue will also be less than in private-practice groups.

This study’s methodology focused on ASA units billed and excluded modifiers and other charges that might occur in the OR (e.g., the insertion of arterial or central venous catheters). These additional charges were excluded because not all payers recognize these charges and modifiers. For instance, in Texas, for the time period of the data collection (1999), in contrast to commercial (nongovernment third-party) payers, Medicare did not allow for the billing of central venous or arterial catheter placement for cardiac surgery and did not recognize ASA physical classifications of III or higher or the emergency modifier. Similarly, Texas Medicaid also excluded charges for central venous or arterial catheters and modifiers for ASA physical class III or higher; they did, however allow for billing with the emergency modifier. It should be noted that the inclusion of these additional charges in total anesthesia charges does not change intergroup comparisons based only on ASA units (11).

This study also focused only on care provided in the main OR suite. Nonobstetric care outside the OR was excluded for several reasons. For the institution studied, unlike the cases performed in the main OR suite, the remote sites consistently did not have a full day of cases. However, if the caseload (i.e., the number of billed hours) were larger, these remote cases could be among the highest in terms of clinical productivity. Although anesthesia for diagnostic or interventional radiology or gastrointestinal endoscopy has case durations longer than 30 minutes (or 1 hour), some remote cases, such as electroconvulsive therapy (ECT) or cardioversion, have extremely short durations. With a large caseload, an anesthesiology group may find that even several hours of anesthesia care per day for ECT may result in higher tASA billings than twice as many hours performed in the main OR suite. Specifically, the tASA/h for an ECT case is estimated to be 24 U/h (by using a case duration of 15 minutes and base units of 5). For a case with a 10-minute duration, tASA/h would be 34 U/h.

The services identified in the study were defined by the surgeon’s academic appointment. In other words, a transplant surgeon performing a general surgery procedure (e.g., incisional hernia) would have had this procedure grouped with the transplant service. An alternative grouping would be by surgical procedure or anesthesia procedure code. The reason for grouping by surgeon rather than procedure was that the OR block allocation was performed by the surgeon’s service rather than by the surgical procedure scheduled. Hence, an anesthesiology group attempting to predict the effects of changing OR block allocation or adding additional ORs would need to be evaluated on the basis of service.

Several services were separated into more specific groupings (e.g., otolaryngology into pediatric and nonpediatric groups). This more focused grouping was done when a surgeon was identified as performing only subspecialty specific surgical cases. For instance, although pediatric plastic surgery was performed primarily by one surgeon, that surgeon did not exclusively perform pediatric plastic surgery. Hence, for the plastic surgery service, no subgrouping could be done.

For the services studied, the subgrouping resulted in differences that would not have otherwise been apparent. For instance, for otolaryngology, the pediatric service had significantly shorter h/case than the rest of the otolaryngology service. Similarly, the orthopedics spine service had longer h/case than the other orthopedic services. However, the orthopedic pediatric service was not significantly different from the orthopedic service. In addition, the ophthalmology service was separated into two services: the retina surgery service and general service that performed all other types of surgery (including cataract, strabismus, and glaucoma surgeries).

	Conclusion

Top Abstract Introduction Methods Results Discussion Conclusion References

 
The ability to predict revenue and clinical productivity is important for strategic planning for anesthesiology groups. Simply relying on the base/case of a service to identify which surgical service will optimize billed units and revenue is inadequate. The h/case correlates better with tASA/h than base/case, but the mean tASA/h should still be determined for each service. Combining the mean tASA/h with several other factors (number of cases, h/case, and average payer mix conversion factor) allows for a group to determine its expected revenue. From this determination, comparisons with expected staffing costs (determined by the number of ORs to be covered) then allow for the group to determine the predicted net profit or loss that will come from providing anesthesia to a specific surgical service or surgical suite.

	Acknowledgments

 
The authors thank Jordan Kicklighter, BA, and Christy Perry in the Department of Anesthesiology at the University of Texas Medical Branch for preparing and editing this manuscript.

	Footnotes

 
Presented in part at the annual meeting of the American Society of Anesthesiologists, New Orleans, LA, October 15, 2001 (Anesthesiology 2001;95:A1107).

	References

Top Abstract Introduction Methods Results Discussion Conclusion References

 

1. Abouleish AE, Prough DS, Barker SJ, et al. Organizational factors affect comparisons of clinical productivity of academic anesthesiology departments. Anesth Analg 2003; 96: 802–12.[Abstract/Free Full Text]
2. Abouleish AE, Prough DS, Whitten CW, et al. Comparing clinical productivity of anesthesiology groups. Anesthesiology 2002; 97: 608–15.[Web of Science][Medline]
3. Abouleish AE, Prough DS, Zornow MH, et al. Designing meaningful industry metrics for clinical productivity for anesthesiology departments. Anesth Analg 2001; 93: 309–12.[Abstract/Free Full Text]
4. Abouleish AE, Prough DS, Hughes J, et al. The impact of longer than average anesthesia times on the billing of academic anesthesiology departments. Anesth Analg 2001; 93: 1537–43.[Abstract/Free Full Text]
5. American Society of Anesthesiologists. 1999 Crosswalk: a guide for surgery/anesthesia CPT codes. Park Ridge, IL: American Society of Anesthesiologists, 1999.
6. Abouleish AE, Hensley S, Zornow MH, Prough DS. Inclusion of turnover time does not influence identification of surgical services that over- and underutilize allocated block time. Anesth Analg 2003; 96: 813–8.[Abstract/Free Full Text]
7. Beirstein K. Evaluating a new ambulatory surgical center opportunity. ASA Newsl 2001; 65: 32–4.
8. Bierstein K. Hospital contracts, four years later. ASA Newsl 2001; 65: 25–7.
9. Tremper KK, Barker SJ, Gelman S, et al. A demographic, service, and financial survey of anesthesia training programs in the United States. Anesth Analg 2003; 96: 1432–46.[Abstract/Free Full Text]
10. Abouleish AE, Zornow MH. Estimating staffing requirements: how many anesthesia providers does our group need? ASA Newslett 2001; 65: 14–6.
11. Abouleish AE, Zornow MH, Levy RL, et al. Measurement of individual clinical productivity in an academic anesthesiology department. Anesthesiology 2000; 93: 1509–16.[Web of Science][Medline]

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This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Email this article to a colleague Similar articles in this journal Similar articles in Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via HighWire Citing Articles via Web of Science (13) Citing Articles via Google Scholar Google Scholar Articles by Abouleish, A. E. Articles by Zornow, M. H. Search for Related Content PubMed PubMed Citation Articles by Abouleish, A. E. Articles by Zornow, M. H. Related Collections Economics and Health Care Research Surgery