We hypothesized that surgeon productivity is directly related to hospital operating margin, but significant variation in margin contribution exists between specialties.
Summary Background Data:
As the independent practitioner becomes an endangered species, it is critical to better understand the surgeon's importance to a hospital's bottom line. An appreciation of surgeon contribution to hospital profitability may prove useful in negotiations relating to full-time employment or other models.
Surgeons contribute significantly to hospital margin with certain specialties being more profitable than others. Payer mix, the penetration of managed care, and negotiated contracts as well as a number of other factors all have an impact on an individual hospital's margin. Surgeons should be fully cognizant of their significant influence in the marketplace.
Key Words: hospital margin, relative value units, operating room productivity, hospital financial data, surgeon productivity, surgeon contribution to hospital margin
The traditional mission of an academic medical center is a 3-tiered goal to engage in tertiary clinical care, education, and research. However, the decline of the fee-for-service reimbursement system, the encroachment of managed care, the cost-containment strategies of payers, and the malpractice crisis have all contributed to the increasing difficulty of sustaining and fulfilling this mission. In addition, in the past 2 years, both academic medical centers and physician practices have had to cope with the imposition of resident work hour limitations, forcing many hospitals and practices to hire costly nurse practitioners and physician assistants to perform the work lost because of the duty hour restrictions. 1 The cost to employ these healthcare providers is significantly greater than that expended for a resident, and it effectively takes 4 of these individuals to compensate for the loss of a single resident from a clinical service, often mandated by the shifts required in resident allocation to compensate for the lost work hours. These nonphysician providers work only one-half the number of hours as a resident and usually see only one-half as many patients.
Both hospitals and physician practices are suffering as reimbursement continues to fall, costs continue to climb, and the malpractice crisis looms large. Many academic medical centers are struggling financially as a result of the current environment, and if financial conditions do not improve, the traditional academic mission may be forced to change. 2 From a financial standpoint, options include further reductions in costs or even elimination of certain service lines found to be unprofitable. Although possible on a small scale, widespread adoption of this kind of policy would truly jeopardize the mission of academic medical centers across the country.
The resource-based relative value system (RBRVS) was instituted in the late 1980s as Medicare set out to contain increasing costs in the healthcare system. However, several academic institutions have demonstrated that the system reimburses too little such that hospitals and practices may be losing money by performing certain types of procedures. The RBRVS system was instituted with the goal of containing costs, but the calibration was not intended to reimburse below costs. One obvious explanation for this discrepancy is that physician effort has been underestimated and thus underpaid. In addition, because academic medical centers usually are in the position that they accept all patients as part of their mission and thus tend to have more complex, more highly variable, and more costly cases than community hospitals, they have higher financial risk that is inadequately compensated by the RBRVS. 3
Costs continue to increase and reimbursement remains inadequate. Although many academic medical centers continue to focus on cost containment at a macro level, there has been inadequate investigation into the relationship between hospital margin and the clinical practices. An unfortunate explanation for this is that most hospitals do not have adequate cost-accounting systems, and the information that is shared between medical centers and clinical practices is insufficient. Taheri and colleagues rigorously examined a trauma service line margin, finding that although losses were rare on fee-for-service patients, they were common on fixed-fee patients. 4 This group proposed that payers potentially could game the system by moving patients to hospitals in which they had more favorable contracts, using as an example their own medical center where a fixed-fee trauma patient, on average, would result in a $500 loss for the hospital and trauma service. Academic medical centers are vulnerable to this kind of strategy by payers because of their obligation to accept all patients.
Medical centers and practices across the country are putting considerable effort into cost containment, but as a result of the complexity of clinical care and the separation of hospital and practice operational data, little is known about what case mixes are profitable for practices, medical centers, or both. Because most hospitals and surgical services have only crude cost-accounting systems, neither hospitals nor practices have enough information to make appropriate and advantageous strategic decisions, giving payers a clear information advantage. As the financial situation becomes more precarious for both practices and medical centers, this information becomes increasingly important. Standard operational management from other industries demonstrates that optimizing a value chain in its entirety is always the best financial strategy for a whole system. Increasing the transparency and accuracy of the cost-accounting systems for both practices and hospitals, in addition to sharing more information, should lead us closer to this goal.
The question remains which specialties produce high margins on the professional fee side and which produce high margins on the hospital side. Although few academic medical centers likely would consider managing risk by dropping entire surgical service lines, this information would allow hospitals and practices to see where better-negotiated reimbursement contracts need to be created, where further cost cutting might be most helpful, and where resources should be allocated. At a minimum, those service lines with high margins for both the hospital and the practice should be strongly supported, whereas those with negative margins for both should be more closely monitored. In certain situations, depending on the employment model, this creates a need for transfer of dollars between the institution and individual practices. This becomes particularly important for those services providing a high margin to the hospital where the professional fees generated do not adequately support the individual practitioners. Unfortunately, this situation is becoming all too common among surgical specialties where hospital reimbursement has remained strong and the Medicare payment to the practitioner has continued to be cut on a yearly basis. It is well known that other third party payers tend to follow Medicare's lead when it comes to physician reimbursement, thus creating the crunch on the professional fee side that the majority of surgeons now face. It is a sad commentary when the costs to a cardiac surgical practice exceed the payment received for performing a coronary artery bypass, a situation that currently exists in a number of regions in this country. Cardiac surgery, as well as a number of other surgical specialties, has provided a particularly vulnerable target to professional fee reductions despite the best efforts of the specialty societies to resist these changes. The dichotomy that exists between hospital reimbursement and professional fees has to be addressed, in many situations, by a funds flow model, and surgeons should be aware of their leverage when entering into negotiations regarding these types of models.
All operative cases performed at the Hospital of the University of Pennsylvania in fiscal year 2004 (FY04) were recorded in the operating room case log database. Data were available for surgical specialties, including neurosurgery, cardiac surgery, gynecologic surgery, otorhinolaryngology, endocrine and oncologic surgery, transplant surgery, urologic surgery, vascular surgery, plastic surgery, thoracic surgery, orthopedic surgery, trauma surgery/surgical critical care, oral maxillofacial surgery, gastrointestinal surgery, and colorectal surgery. Pediatric surgery and ophthalmology cases are performed outside of the Hospital of the University of Pennsylvania and thus are not included. Urology clinic
procedures were included in the study, because they use the operating room case log database. However, plastic surgery and oral and maxillofacial surgery clinic procedures, performed outside of the operating room, are not included in this study. These cases represent a small minority of cases for those specialties, however.
Operative case length (patient in room to patient out of room) and scheduled Current Procedural Terminology (CPT) codes were obtained from the case log database for each case performed in FY04. Using the April 2004 Revision file (www.cms.hss.gov/providers/pufdownload/rvudown.asp ), the resource-based relative value scale (RBRVS) scale was used to translate each scheduled CPT into total relative value units (RVU). 5 Operative statistics were available on cases in the operative log, but not from the hospital billing system. To verify that the scheduled case RVU and billed RVUs were correlated, total RVUs calculated from scheduled CPT versus billed CPT for all cases in FY04 were compared by linear regression, with R 2 = 0.70 and P = 0.0000, so scheduled CPTs were used in all further calculations. Therefore, neither postprocedure coding optimization nor secondary procedure codes were captured. However, because the RVUs corresponding to billed CPT codes correlated closely with those corresponding to the scheduled primary procedure CPT codes, this method is not only valid, but provides advantages, including the ability to link RVUs to operating room data, the fact that no differences in postprocedure optimization strategies by specialties interfere with the analysis, and the fact that many payers put much or all weight on the primary procedure for reimbursement.
Total operating room (OR) time was calculated for each specialty by summing the OR case length for every procedure performed in FY04. Annual cumulative RVU was calculated for each specialty by adding the total RVUs performed by each surgeon. RVU/OR HR was calculated for each specialty by dividing the total cumulative RVUs by the total OR time used by each individual surgeon and specialty.
The hospital finance department calculated hospital margin for fiscal years FY03 and FY04 for each clinical division and department based on primary physician specialty. Margin, as used here, is defined as what remains after subtracting both direct and indirect costs from the operating revenue collected by specialty. Revenue used in calculations included all payments collected for each patient, including payments for preadmission testing, operative services, and postoperative care. No attempt was made to divide the hospital diagnosis-related group (DRG) payment between services, but instead the entire DRG payment and the entire cost of the patient stay in the hospital was allocated to the primary service. Costs are assigned to each patient using standard costing methods for healthcare organizations. Nursing costs are assigned using the average cost per day and the length of stay of each patient on each specialty specific patient floor. Operating room costs are allocated to patients based on the individual case time multiplied by the average cost per OR minute. High-cost devices and implantables are not included in the OR time-based averages, but instead specifically assigned to each patient. Ancillary testing is allocated using hospital department-specific costs per RVU multiplied by the actual RVUs each department provided. All of these costs are summed to come up with direct patient care costs. Indirect costs are assigned to patients using standard stepdown method of cost accounting used for Medicare cost reporting to the Center for Medicare and Medicaid Services (CMS). Indirect costs include, most significantly, overhead of operations and administration, interns and residents, building and equipment depreciation, housekeeping, dietary, and other space-related costs.
In FY04, neurosurgery was the most profitable specialty overall for the hospital. To maintain some level of confidentiality for the University of Pennsylvania Health System that kindly provided the type of financial information rarely provided to physician practices, we devised a relative hospital margin (RHM) that is calculated by dividing true hospital margin by a constant to normalize all specialty hospital margins such that the margin for neurosurgery was 1 million margin units (mu). We have chosen not to use the dollar sign because we are referring to a relative margin, and not dollars of margin and thus use the term margin unit. The comparison between specialties and the magnitudes of each value remain valid, although we are not referring directly to dollars of hospital margin. Relative hospital margin per RVU was calculated by dividing total annual relative margin by total annual RVU for each specialty. Relative hospital margin per case was calculated by dividing total annual relative margin by total number of cases performed by each clinical service in FY04. Relative hospital margin per OR HR was calculated by dividing total annual relative margin by the sum of all cases performed by each specialty in FY04. JMP software (JMP IN 4; SAS Institute, Cary, NC) was used to perform linear regressions for RHM and annual divisional RVU, average RVU per case, and average RVU per OR HR.
For all calculations, average institutional quantities reflect mean values with all individual specialties weighted equally. For all figures containing quadrants, boundary lines represent mean values for both axes.
Fifteen surgical services at our institution performed 21,050 cases in FY04. The average number of cases per service was 1403 (standard deviation [SD] = 772) with a range from 509 (oral maxillofacial surgery) to 3161 (urology). As shown in Table 1. the total annual RVUs for all operative cases was 461,671 with a mean 30,778 per clinical service (SD = 16,263). Cumulative annual RVUs per service ranged from 9849 (oral maxillofacial surgery) to 67,220 (neurosurgery). The mean number of RVUs per case for the institution was 24.49 (SD = 12.88) with a range of 9.04 (urology) to 57.12 (transplant).
The total cumulative operating room time (time in room to time out of room) used in FY04 was 64,905 hours. The OR time used by each clinical service ranged from 1489 hours (oral maxillofacial surgery) to 7451 hours (cardiac surgery) with a mean of 4327 hours (SD = 2054). Average case length (total cumulative operating room time divided by total number of cases) ranged from 1.71 hours (urology) to 6.15 hours (cardiac surgery) with a mean of 3.32 hours (SD = 1.14). The mean number of RVUs per OR HR for the institution was 7.10 (SD = 1.56) and ranged from 5.23 (otorhinolaryngology) to 10.97 (transplant).
Relative hospital margin (RHM) expressed as margin units (mu) in FY04 ranged from a loss of 13,772 mu (plastic surgery) to a gain of 1,000,000 mu (neurosurgery) with overall institutional RHM of 5,718,569 mu. The mean RHM per service was 381,238 mu (SD = 325,270). As shown in Table 2. the mean RHM per RVU for the entire institution was 12.64 mu (SD = 9.76), with services ranging from a loss of 0.57 mu per RVU (plastic surgery) to a gain of 34.55 mu per RVU (thoracic surgery). The mean RHM per OR HR was 94.54 mu/h (SD = 81.56) with a range of (3.83) mu/h (plastic surgery) to a gain of 275.74 mu/h (transplant surgery) (parentheses around a number indicates a loss). On a per-case basis, the mean institutional RHM per case was 361.82 mu (SD = 388.42) ranging from (10.50 mu) per case (plastic surgery) to a gain of 1435.18 mu per case (transplant surgery).
Hospital margin includes revenue only from cash actually received and finalized financial data lags behind operative data by at least several months, and perhaps up to 6 months. Thus, when we initially looked at the operative data for FY04, the FY03 hospital finance data were used rather than the FY04 data now available. It was proposed at that time that there should be strong correlation between relative hospital margin per service on a year-to-year ongoing basis. This is demonstrated in Figure 1. which shows an R 2 of 0.88 and P < 0.001.