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Economics, Education, and Policy: Research Report

A Mission-Based Productivity Compensation Model for an Academic Anesthesiology Department

Reich, David L., MD; Galati, Maria, MBA; Krol, Marina, PhD; Bodian, Carol A., DrPH; Kahn, Ronald A., MD

Section Editor(s): Dexter, Franklin

Author Information
doi: 10.1213/ane.0b013e31818ca31c
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Academic anesthesia practices are facing many challenges that threaten the core of their clinical and academic missions. Financial pressures from escalating costs and declining reimbursements impair an academic practice’s ability to compete for faculty and contribute to recruitment and retention problems that further erode a practice’s ability to perform the research and teaching activities that distinguish them from private practices. Increased call burdens and diminished nonclinical time have become more common in academic anesthesia practices, despite a trend toward increased annual stipends from hospitals to supplement clinical revenues.1 Further, changing provider demographics (e.g., working parents) and overarching societal changes have introduced the need to satisfy a broad range of lifestyle choices while assuring fairness in compensation and work assignments and offering flexibility in work hours and on-call responsibilities.

Compensation models can be designed to align the incentives of individual faculty members with those of an academic practice. Successful models deal with the need for transparency and consistency, and support perceptions of fairness among the participants.2

In order to maximize overall faculty job satisfaction and operational efficiency while preserving the academic mission, academic practices have begun to evolve from traditional models.3 In a general sense, traditional academic compensation models rewarded seniority and academic accomplishment with protected nonclinical time and guaranteed salaries, and therefore tended to pay lower salaries to more clinically productive entry-level faculty. These types of systems persisted in an era when faculty recruits were plentiful and resources were more abundant. Additionally, some of these models tied at least a portion of individual faculty compensation to clinical receipts. This type of linkage in a group academic practice creates inefficiencies and additional assignment complexities as well as fairness issues in practices with subspecialty divisions and a disparate range of reimbursement opportunities.4

We analyzed the financial effects of implementing a productivity model that was created according to the principle of mission-based budgeting. This system is analogous to those used by medical schools to allocate resources among departments.5 We hypothesized that the productivity-based model would improve clinical practice metrics, faculty salaries, and academic output at our institution.

METHODS

Determination of the Budgetary Division of Compensation Funds

In the authors’ medical center, the faculty are employees of the School of Medicine and base salary ranges are determined by academic rank. In the process of converting to a productivity-based model, we created a grid for base salary determination that was both rank- and experience-related. (Table 1) Experience was measured as cumulative years of academic or clinical anesthesia practice after completion of an accredited residency training program. The base salary system was budget neutral at the point of implementation.

Table 1
Table 1:
Grid for Base Salary Determination

The other component of anesthesia faculty compensation is commonly called the supplemental and/or bonus pay. This component currently averages approximately 70% of total faculty compensation at the authors’ institution. Before the implementation of the new model, the supplement was allocated in three ways:

  1. By guaranteed amounts that were determined as percentages of base salary;
  2. By year-end distributions that were determined by individual and group contributions to the missions of the department as assessed by the chairperson; and
  3. By receipt of approximately 8% of net practice revenues (representing approximately 6% of total annual income), which was influenced significantly by payer and case assignment mix of the individual practitioner.

The productivity-based compensation model described below is a reallocation of the dollars in the supplement pool according to mission-based contributions of the individual faculty member. The underlying principles of the reallocation included:

  1. Placing all of the supplemental income at risk by removing guarantees for all but the base pay (30% of average total compensation);
  2. Removing the dependency of compensation upon payer mix and receipt generation; and
  3. Removing mandatory call assignments.

The mission-based budgeting categories are listed in Table 2.

Table 2
Table 2:
Mission-Based Budgeting of Anesthesiology Faculty Supplemental Compensation

Assigning Value to the Mission-Based Categories

The basic principle for establishing the value of each mission-based activity was to consider its degree of effort in comparison to a benchmark, the average productivity of an attending anesthesiologist staffing an individual operating room (OR) for a single workday. From the anesthesia information system database, we determined that this average productivity during 2003 was very close to 50 American Society of Anesthesiologists Relative Value Units (ASA units).6 For greater transparency to the faculty and for simplicity, we multiplied ASA units (base, time, and modifier) by 15 so that 1 point was equal to 1 min (assuming 15-min time units). Thus, the average daily productivity per OR per day was 750 points, and this became the benchmark for an average workday’s productivity per OR full-time equivalent (FTE).

The chairperson of the department was advised by two committees to create valuations for activities, including call assignments, non-OR anesthesia (NORA), office-based anesthesia, obstetrical (OB), noncall late hour duties, and academic valuations. Details of the program can be found in the Appendix available at www.anesthesia-analgesia.org.

Baseline Period and Initial Implementation

We collected data regarding the current numbers of OR FTEs, anesthetizing locations, and ASA units billed from 2000 through the quarter immediately preceding the implementation of the model. (Table 3) These data were exclusive of pain management, critical care, OB, and OBA ASA units.

Table 3
Table 3:
Average Clinical Anesthesiologist Full-Time Equivalents, Average Number of Anesthetizing Locations, and Total ASA Unit Production per Quarter

The initial 3-mo data collection period for the new compensation model began on July 1, 2004. During this interval, salaries were frozen at the level of the preceding quarter. Clinical data were derived from the assignment database for intensive care unit, OB, pain, and call assignments and the anesthesia information management system for general OR work. A working group that reviewed submissions of all publication, educational, research, community service, and related activities assessed academic points. Professionalism points were calculated using a sliding scale based upon the results of resident evaluations of attending teaching and mentorship, and the results of evaluations of attendings by their peers. An acute upward adjustment in the points values assigned to overnight calls was implemented in the first month so as to fill the voluntary call schedule. At regular intervals, the faculty received reports of clinical point productivity during the initial 3 mo, both individually and for the department as a whole.

At the completion of the initial 3-mo data collection interval, a data analysis was performed to establish the following: 1) revalidation of the average OR day point value; 2) conversion of earned points into supplemental salary for each faculty member; 3) determination of the effect of the new model on individual faculty salaries; and 4) counseling of the department by disclosure of descriptive statistics of point generation and supplemental salary distribution. The process of budgeting and setting the quarterly dollar value of points is described in the Appendix available at www.anesthesia-analgesia.org.

Adjustments to the Compensation Model

There were three major adjustments to the supplemental compensation model:

  1. During the first month of the first quarter, it became clear that there was an under-valuation of overnight call assignments that led to difficulty filling the call schedule. The initial valuation had failed to fully account for the value of postcall day off within the call assignment credit.
  2. After 12 mo, the chairperson increased the value of all publication-based academic point values to promote academic productivity.
  3. The NORA system was changed from a fixed assignment-based scheme (e.g., 175 points for electroconvulsive therapy; 750 points for interventional radiology) to an ASA time unit-based sliding scale for all NORA activities on any particular day (Table 4).
  4. Table 4
    Table 4:
    Sliding Scale for Nonoperating Room Anesthesia (NORA) Productivity

The technical implementation and detailed point valuations for the model are detailed in the Appendix available at www.anesthesia-analgesia.org.

Data Analysis

The clinical productivity of the group was defined as ASA units per anesthetizing location according to the method described by Abouleish et al.7 Additionally, we calculated ASA units per OR FTE. For the productivity data analyses, only ASA units generated in the main ORs and NORA locations (excluding OB, intensive care unit, and pain) were considered. We defined three intervals: a preimplementation phase (January 2000 through June 2004; an implementation phase (July 2004 through June 2005); and a postimplementation phase (July 2005 through June 2007).

A linear regression model encompassing the entire study time was used to estimate the slope (rate of change) during the preimplementation period and during the postimplementation period for the parameters ASA units per anesthetizing location and ASA units per OR FTE. The estimated slopes for the period, their standard errors, 95% confidence intervals, and the P values for testing whether the slopes differed significantly from zero were calculated. The quarterly values for mean monthly ASA units, mean monthly ASA units per OR FTE, and mean monthly ASA units per anesthetizing location during the preimplementation period were compared with corresponding values during the postimplementation periods by Kruskal-Wallis tests.

Analysis of salaries by faculty rank also focused on calendar year data from the pre- and postimplementation periods that were defined as 2003–2004, and 2006–2007, respectively. Salary data for each faculty rank were expressed as percentage of the mean salary for the rank in 2001, which was considered the baseline period. Salary data for Instructors and Assistant Professors were grouped (lower ranks), as were data for Associate and Full Professors (higher ranks), in a linear regression model encompassing the entire study period, used to test for significant differences between the ranks, between the periods, and for an interaction effect between rank and period. Linear regression was used to examine the association between ASA units per FTE and average faculty compensation over the entire period of investigation (2001–2007), including the implementation period. A two-tailed P < 0.05 was considered statistically significant.

RESULTS

Trends (Rates of Change) of Productivity Metrics in the Pre- and Postimplementation Periods

ASA units per quarter did not demonstrate any directional change in the preimplementation period, however, they moved in a positive direction in the postimplementation period coincident with an observed increase in OR volume (not reported). ASA units per OR FTE per quarter demonstrated positive directional change in both the pre- and postimplementation periods. ASA units per anesthetizing location per quarter demonstrated a negative directional change in the preimplementation period, and no statistically significant directional change in the post-implementation period (Table 5).

Table 5
Table 5:
Trends for ASA Units, ASA Units per OR FTE, and ASA Units per Anesthetizing Location in the Pre- and Postimplementation Periods

Comparison of Productivity Metrics in the Pre- and Postimplementation Periods

Monthly units were averaged over each quarter. The median value of these monthly average ASA units was 43,563 for the preimplementation quarters, and 49,594 for the postimplementation quarters, an increase of 14% (P = 0.0001). The median value of monthly ASA units per OR FTE (averaged over each quarter) increased by 31% in the postimplementation period compared with the preimplementation period (P < 0.0001). The median value of ASA units per anesthetizing location (averaged over each quarter) decreased by 10% in the postimplementation period compared with the preimplementation period (P = 0.046) (Table 6).

Table 6
Table 6:
Comparison of Average Monthly ASA Units, ASA Units per OR FTE, and ASA Units per Anesthetizing Location in the Pre- and Postimplementation Periods

Comparison of Mean Faculty Compensation Data in the Pre- and Postimplementation Periods

The mean compensation relative to 2001 baseline values is shown in Table 7 for Instructors and Assistant Professors and for Associate and Full Professors in the pre- and the postimplementation periods. The overall estimated mean compensation increased by 40% (95% CI: 29.0%–50.4%) There were statistically significant differences between the ranks (P < 0.001) and between the periods (P < 0.0001). The relative increase in mean compensation between the rank groupings (interaction term) was not statistically significant (P = 0.34). There was a very strong association (r2 = 0.96) between ASA units per FTE and average faculty compensation over the entire period of investigation (2001–2007).

Table 7
Table 7:
Mean Faculty Salary Ratios (Compared with 2001) by Rank Grouping in the Pre- and Postimplementation Periods

Academic Output in the Pre- and Postimplementation Periods

As an index of academic activity within the department, a National Library of Medicine Medline search was conducted to indicate the number of publications during the years 2000 through 2007. The search is graphed with a least-squares regression trendline in Figure 1. The trendline (slope = 0.015 per year) indicates a stable rate of publications by the faculty in the preimplementation, implementation, and the postimplementation periods.

Figure 1.
Figure 1.:
Results are based upon a National Library of Medicine Medline search for Department of Anesthesiology articles originating from Mount Sinai School of Medicine, New York, NY.

DISCUSSION

The implementation of a new physician compensation model in a university hospital anesthesiology department was associated with increased ASA units per anesthesiologist, although there was no increase in work productivity per OR anesthetizing location. Total compensation per faculty member increased across the spectrum of academic ranks. This change was also associated with relatively fewer OR FTE anesthesiologists per caseload and per anesthetizing location compared with the preceding years. These compensation effects are explained by the combination of a stable gross collection rate and an increasing workload that was undertaken by a stable number of OR FTEs. It is further supported by the strong association between ASA units per FTE and average annual compensation. The productivity model, which placed 70% of total compensation at risk, likely contributed to the willingness of the faculty to undertake an increased workload.

An important change that coincided with the implementation of the compensation model was the initiation of electronic point-of-care charge capture and submission as reported previously.8 This system established a linkage between physician compensation and accurate, complete, and compliant documentation. Therefore, the observed increase in supplemental compensation in this report may be partially related to improvement in documentation of services.

Problems Communicated by Faculty

Among the senior faculty, there was significant anxiety and concern regarding the adoption of a radical change that placed approximately 70% of compensation at risk after many years of experiencing only 6% of income at risk. The underlying concerns included fears of:

  1. Systemic errors in point credits related to improper programming, data entry, or business rules;
  2. Replacement of a simple system with a very complex model;
  3. The motivations of a new departmental chairperson with a commitment to control costs and to reward academic accomplishment (potentially at the expense of rewards for clinical activity);
  4. Distrust of a new system and a pessimistic viewpoint that compensation would decrease; and
  5. Unfair valuations across clinical, research, academic, and strategic productivity domains (e.g., inclusion of base and modifier ASA units in contrast to time units alone).

These concerns diminished over time, although there was no formal assessment of concerns.

At the point of implementation of the new model, senior faculty perceived less potential for income gain compared with the junior faculty. Seniority was rewarded previously with exemption from call assignments, guaranteed supplements, and protected nonclinical time (for some faculty). The experience of the senior faculty during the conversion to the new system is analogous to the erosion of the “social safety net” with the conversion from socialist to market economies in eastern European nations in the last decade.9 This effect was ameliorated somewhat by the new base pay structure arrangement wherein clinical experience and academic rank increased base pay for many senior faculty.

Inclusion of Base and Modifier Units in Compensation Model

Other compensation models devalue or eliminate the consideration of base and modifier ASA units.10 Groups may reach this decision so as not to “over-compensate” cardiac, other high-complexity procedures, or shorter surgical durations11 when the camaraderie of the practice would be adversely affected. In the current model, the value of the base and modifier units was maintained by departmental leadership to reward subspecialization and the additional skills that practitioners must acquire in becoming functional members of the subspecialty anesthesia teams. There is no consensus among the faculty on this point.

Review and Revision of the Compensation Model

Compensation systems require review and revision over time. There were several alterations in this system, mostly in the first 12 mo of implementation, including increases in valuation for various call categories, publications, and other academic activities. For this reason, the statistical testing excluded the implementation period. Bierstein and Venters described systems of anesthesia physician group income distribution in an article published in the ASA Newsletter.12 They emphasized the need to continually review and revise these systems over time.

Productivity Measurement

Abouleish et al. described three possible measures for productivity-based compensation models that are applicable to individuals (rather than groups): clinical days per year, clinical time units, and ASA units.13 A method emphasizing clinical days values all clinical sites regardless of ASA billing and is independent of nonanesthesiologist factors, but devalues specialty anesthesia care, longer workdays, and higher workload clinical sites. An ASA time-unit measure values time-billable work sites and higher concurrency and devalues nonbillable anesthesia care, personally performed cases, specialty anesthesia care, and shorter cases. Finally, an ASA unit system values specialty anesthesia care and high concurrency and devalues cases similar to those devalued by an ASA time unit system.

These different measures of individual faculty productivity do not necessarily result in consistent measures of productivity. For example, faculty clinical time may be expressed as either time available (e.g., days in the OR or nights on call) or productivity during time available. Feiner et al.10 compared these two measures of faculty clinical responsibility. They defined productivity during time available as billable hours; no adjustments were made for ASA units. They observed significant differences between these two measures of productivity both between faculty in different subspecialty groups as well as within subspecialty groups. In the current study, we used the method of Abouleish et al. to monitor our group productivity, which decreased somewhat. This reflected a mildly less productive OR environment due to factors not under the control of the anesthesia leadership (more anesthetizing locations to cover). (http://www.asahq.org/Newsletters/2007/12-07/Abouleish1207.html, Accessed June 12, 2008).

The Effect of the Compensation Model on Academic Productivity

A major aim of the new compensation model was to incentivize academic activities. There was a perception upon initial implementation of the model that all teaching and research activities were under-valued. During the implementation period, academic points were reevaluated to create parity in the financial rewards for clinical and academic work. For example, a single author paper in a major anesthesiology journal is currently valued at the equivalent of 53.3 average OR clinical days (Appendix available at anesthesia-analgesia.org). Over the entire period of investigation, we observed stable academic output.

Other Compensation Models and Literature Review

Revenue-based compensation increases productivity in industrial studies; however, these work environments are highly routine, involve little cognitive challenge, and measure productivity as units produced.14 In an academic medical system, revenue-based compensation rewards high revenue-producing faculty members and may enhance the institution’s bottom line.4 The disadvantages of this type of compensation plan include the distraction of faculty from other important academic missions such as teaching, research, and service and the risk of creating incentives for compliance-related violations.

An important issue to consider when comparing anesthesiology with other medical specialties and nonmedical industries is that there is an obligation to provide service without regard to efficiency. For example, a hospital may require that an anesthesia team cover an endoscopy suite that is sparsely scheduled. There are also hospital-induced pressures to cover multiple simultaneous locations at peak periods that create inefficient staffing models.

Miller wrote, “Only productivity-based incentives actually achieve the goals and aspirations of academic anesthesia departments and medical centers and enhance the anesthesia faculty’s relationship with other specialties….”15 Productivity-based incentives may decrease salary expenses, increase individual faculty clinical productivity and efficiency, and increase billing effectiveness when faculty compensation is dependent on the completeness and accuracy of the anesthetic record.10,16 Abouleish et al. also reviewed productivity-based incentives for academic departments.17

In an electronic survey, Abouleish et al. categorized compensation plans used by 88 academic departments.16 Twenty-nine percent of the programs had no system for performance-based compensation (i.e., financial compensation did not vary with clinical productivity). Thirty percent of the programs used a late/call system, 20% used a shift system (compensation based upon all shifts worked), 11% used a charge system, 6% used a time system, and 3% used another method of compensation. While 69% of practices did not vary compensation based upon clinical activity during regular hours, most departments varied payments on the basis of late or call rooms worked.

Andreae and Freed described the effect of changing their pediatric academic faculty practice compensation system to a performance-based model.18 A base salary of 70% of the benchmark value was established for all faculty members. Clinical activity was measured and a dollar per work relative value units (wRVU) was rewarded for clinical productivity that was in excess of those required to cover the base salary. The dollars per wRVU were independent of years of service or additional training. During initial assessment, more than half of the faculty had productivity that was below the 25th percentile of the Medical Group Management Association standard for clinical productivity. After 1 yr, 89% of the faculty increased their clinical productivity with a 20% increase in clinical productivity during the first year and a 15% increase during the second year. These increases in wRVU billed were a result of both increasing visit volume and improving office visit billing codes, with resultant net positive impact on the operating margins of the pediatric centers. Two-thirds of the faculty were satisfied with the new compensation model, while the remaining third of the faculty preferred to be compensated independent of their clinical productivity.

The Department of Internal Medicine at Vanderbilt University instituted a performance-based compensation program, which closely linked the four faculty appointment tracks.19 After determination of benchmark salaries and wRVU, a dollar figure per wRVU was calculated for each subspecialty, which was used for all faculty regardless of tenure, rank, or academic track. Additional wRVU credits were given to faculty with important administrative responsibilities and extramural research grant support. With its implementation, there was a 40% increase in the compound annual growth rate for clinical work and a 170% increase in federal funding for research; mean wRVUs per clinician significantly increased. Faculty satisfaction improved with the implementation of this plan.

Consistent with these reports from nonanesthesiology departments, the results of the current investigation suggest that anesthesiologists do have some control over their productivity. For example, they can make themselves available to work longer hours, to volunteer for more call shifts, etc.

The current report is a retrospective analysis of a faculty compensation plan that is limited by various factors. This model was designed to address the particular concerns and problems at a single university medical center and may not be generalizable to other institutions. The notable confounding factor was the concurrent implementation of an electronic billing voucher system. Additionally, we did not analyze variations in case mix, OR efficiency, and anesthesia care team mix, although we did not observe important changes in these metrics over the course of the period of investigation.

In conclusion, implementing a productivity-based faculty compensation model in an academic department was associated with increased compensation with relatively fewer faculty. ASA units per month and ASA units per OR FTE increased, and these metrics are the most likely drivers of the increased compensation. This occurred despite a stable trend in clinical productivity as measured by ASA units per anesthetizing location. Academic and educational output was stable.

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