Influence of the revision of surgical fee schedule on surgeons’ productivity in Japan: A cohort analysis of 7602 surgical procedures in 2013

Abstract

The goal of this study is to evaluate the pure impact of the revision of surgical fee schedule on surgeons’ productivity. We collected data from the surgical procedures performed by the surgeons working in Teikyo University Hospital from 1 April through 30 September in 2013–2016. We employed non-radial and non-oriented Malmquist model. We defined the decision-making unit as a surgeon with the highest academic rank in surgery. Inputs were defined as (1) the number of doctors who assisted surgery and (2) the time of surgical operation. The output was defined as the surgical fee for each surgery. We focused on the revisions in 2014 and 2016. We first calculated each surgeon’s natural logarithms of the changes in productivity, technique and efficiency in 2013–2014, in 2014–2015 and in 2015–2016. Then, we subtracted the changes in 2014–2015 from the changes in 2013–2014 and in 2015–2016. We analyzed 62 surgeons who performed 7602 surgical procedures. The productivity changes were not significantly different from 0. Their efficiency change was significantly greater than 0, while their technical change was smaller than 0 in revision 2014. Their efficiency change was significantly smaller than 0, while their technical change was greater than 0 in revision 2016 (p < 0.05). This finding suggests that we could increase overall productivity through revision if we could increase both efficiency and technique.

Keywords

Malmquist index productivity surgical reimbursement system

Introduction

The Japanese healthcare system has had universal health insurance for more than half a century.¹ However, the sustainability of this healthcare system is in question because the Japanese government has had a huge fiscal debt. Despite an enormous effort to cut the deficit, our healthcare expenditure is increasing every year because of our rapidly aging population. One of the solutions for this problem is to improve the productivity of healthcare.

Most healthcare providers in Japan are reimbursed on a fee-for-service basis according to the fee schedule that set prices uniformly at the national level. The same fee schedule is enforced for all plans and almost all healthcare providers. The services covered and the fees set for physicians and hospitals have been uniform across nation since 1959.¹ This nation-wide uniform fee schedule has been revised every two years at Central Social Insurance Medical Council. Japan ranks third in the world in gross domestic product only after the United States and China² and is the largest economy in the world with the nation-wide controlled price system for healthcare which the United States and China do not have. The revision of its fee schedule has an enormous impact on healthcare productivity throughout Japan.

Among the various healthcare expenditures, 40.2% was spent in hospital care in 2013.³ Operating room productivity is an important concern in most hospitals today.⁴ The operating room productivity is considered to depend on surgeons’ productivity because they usually utilize the longest time portion of the operating room time.

Malmquist index (MI) represents total factor productivity change of a decision-making unit (DMU) between two time periods under dynamic situation and is an example of comparative statics analysis.^5,6 It is based on data envelopment analysis (DEA), which evaluates relative efficiency of DMUs against the efficient frontier under static conditions in a single period. By comparing DEA results between two time periods, MI can divide productivity change into two components, one measuring efficiency change (EC) and the other measuring technical change (TC).⁷ The MI models have been used to assess productivity change in a variety of sectors such as agriculture, airlines, banking, electric utilities, insurance companies, public sectors and healthcare.^6–9

We had previously reported a study that evaluated the productivity change of surgeons in Japan before and after the revision of the fee schedule.⁸ Subsequently, we also found that surgeons’ productivity, efficiency or technique might have naturally changed in one year even without the revision of fee schedule.⁹ In order to exclude the effects of natural productivity change, we need to isolate the effects of revision on surgeons’ productivity change from the natural change. The goal of this study is to evaluate the pure impact of the revision on surgeons’ productivity.

Methods

The Teikyo University Institutional Review Board approved our study.

Data collection

Teikyo University Hospital is located in metropolitan Tokyo, Japan, serving a population of ∼1,000,000. It has 1152 beds and has a surgical volume of approximately 9000 cases annually. It has 13 surgical specialty departments. We selected the surgeons who had been working in Teikyo University Hospital since 1 April 2013 through 30 September 2016. We collected data from all the surgical procedures performed by them in the main operating rooms of Teikyo University Hospital from 1 April through 30 September in 2013–2016, which added up to 24 months in total. We extracted the necessary information from surgical records in the Teikyo University Hospital electronic medical record system.

Exclusion criteria for surgery were as follows. First, surgical procedures performed under local anesthesia by surgeons were excluded. Oral and dermatologic surgical procedures were excluded because most of their cases were minor surgeries performed under local anesthesia without anesthesiologists’ involvement, and those under general anesthesia do not represent the activity of their surgeons. Second, the surgical procedures were excluded if the patients died within one month after surgery to maintain a constant quality outcome of surgery. Third, the surgical procedures which were not reimbursed under the surgical payment system in 2013–2016 were excluded. Fourth, the surgical procedures were excluded if their records were incomplete for any reason.

All the surgeons analyzed were employees of Teikyo University and were salaried according to their ranks and experiences. The hospital charges surgeons’ surgical fees to Health Insurance Claims Review & Reimbursement Services, and the reimbursement becomes the revenue of the hospital.^10–12 It pays to surgeons their salary from this revenue. The surgeons analyzed in this study belong to one of the following 11 surgical specialty departments: cardiovascular surgery, emergency surgery, general surgery, neurosurgery, obstetrics & gynecology, orthopedics, otolaryngology, plastic surgery, thoracic surgery, ophthalmology and urology.

We also collected the data of surgeons’ personal characteristics. Most surgeons analyzed in this study publish their years of graduation from medical schools in directories and/or websites.^13,14 Surgeons’ experience was defined as the number of years since medical school graduation through 2016. Those who do not obtain medical license upon graduation from medical school are rare. Surgical volume was defined as the number of surgical cases that a surgeon performed during the six-month study period for four years which totals to 24 months. This information was extracted from the Teikyo University Hospital electronic medical record system. The information on gender and academic rank was extracted from the website of Teikyo University Hospital, where it makes this information publicly available for patients’ convenience.¹⁴

Analysis framework

We employed non-radial and non-oriented Malmquist model under the constant returns-to-scale assumptions, which was particularly relevant because of its ability to employ multiple inputs and outputs simultaneously.¹⁵ MI is defined as the product of EC and TC terms. The EC term relates to the degree to which a DMU improves or worsens its efficiency, while the TC term reflects the change in the efficient frontiers between the two time periods. If productivity change of a DMU is compared between Period 1 and Period 2, they are mathematically defined as follows,^4,8

\begin{matrix} EC = {\begin{matrix} Efficiency of a DMU in Period 2 withrespect to Period 2 frontier \end{matrix}} \\ / {\begin{matrix} Efficiency of a DMU in Period 1  with respect to Period 1 frontier \end{matrix}} \\ TC = [{\begin{matrix} Efficiency of a DMU in Period 1 withrespect to Period 1 frontier \end{matrix}} \\ / {\begin{matrix} Efficiency of a DMU in Period 1  with respect to Period 2 frontier \end{matrix}} \\ \times {\begin{matrix} Efficiency of a DMU in Period 2 withrespect to Period 1 frontier \end{matrix}} \\ {/ {\begin{matrix} Efficiency of a DMU in Period 2 withrespect to Period 2 frontier \end{matrix}}]}^{1 / 2} \\ MI=EC TC. \end{matrix}

In this analysis, we focused on the surgeons’ activities and their clinical decisions. We defined the DMU as a surgeon with the highest academic rank that scrubbed in the surgery. All the inputs and outputs are under the control of a DMU. Inputs were defined as (1) the number of medical doctors who assisted surgery (assistants) and (2) the time of surgical operation from skin incision to skin closure (surgical time). The output was defined as the surgical fee for each surgery. It is classified as K000–K915 in the Japanese surgical fee schedule and is called “K codes.” Each surgical procedure is assigned to one of the K codes which correspond with surgical fees. The fee is identical regardless of who (a senior surgeon or a surgical trainee) performs surgery as long as they have medical licensure, how many assistants they use, or how long it takes to complete surgery.^10–12 Other fees for blood transfusion, medications, special insurance medical materials and anesthesia were excluded. The monetary values of surgical fees were originally expressed in the Japanese yen and were converted to U.S. dollars at $1 = 100 yen to facilitate understanding by international readers.

We added all the inputs and outputs of the surgical procedures for each DMU during these study periods and computed his/her MI, EC and TC using DEA-Solver-Pro Software (Saitech, Inc., Tokyo, Japan).⁵ All the surgeons in the sample were given an MI, EC and TC for each.¹⁶ In order to more easily interpret these results, we took the natural logarithms of the MI, EC and TC, which allows us to interpret them as percent changes.¹⁷ The natural logarithm of MI > 0 indicates progress in productivity of the DMU from Period 1 to 2, while that of MI = 0 and MI < 0, respectively, indicate the status quo and deterioration in the productivity. Similarly, a natural logarithm for EC and TC measure of greater than 0 implies that there is efficiency progress and frontier technology progress, respectively. The natural logarithm of MI equals the sum of natural logarithm of TC and that of EC.¹⁷

We focused on the revision of fee schedule that was implemented on 1 April 2014 (revision 2014) and 1 April 2016 (revision 2016).^10–12 We first calculated the natural logarithm of MIs, TCs and ECs between 2013 and 2014 (2013–2014), between 2014 and 2015 (2014–2015) and between 2015 and 2016 (2015–2016). There were revisions of fee schedule in 2013–2014 (revision 2014) and in 2015-16 (revision 2016), while there was no revision in 2014–2015. Therefore, the change in productivity, technique and efficiency in 2014–2015 can serve as a control to isolate the effects of revision. We computed each surgeon’s natural logarithms of MIs, ECs and TCs for three intervals. Then, we subtracted the changes in 2014–2015 from the changes in 2013–2014 and in 2015–2016 to exclude the effects of natural changes, and obtained the pure effects of revisions.

Statistical analysis

We used Excel Statistics 2008 Software (SSRI Co., Ltd., Tokyo, Japan) for our statistical analysis. We compared the natural logarithms of MIs, ECs and TCs of all surgeons and those of each surgical specialty against 0 using the Student’s t-tests. We also compared the difference in natural logarithms of MI, EC and TC between 2013–2014 and 2014–2015 and between 2015–2016 and 2014–2015 against 0 using the Student’s t-tests. A p-value < 0.05 was considered statistically significant.¹⁸

Results

We analyzed 7602 surgical procedures performed by 62 surgeons (DMUs) during the study period.

The characteristics of surgeons are shown in Table 1. They performed 123 surgical procedures on average for 24-month study period. Their average length of experience was 23.3 years, although the data were available for only 51 surgeons.

Table 1.

Characteristics of surgeons in the cohort.

Surgical volume (cases/24 months/surgeon) (n = 62)		122.6 ± 78.9	(19–400)
Emergency surgery (cases/24 months/surgeon) (n = 62)		7.6 ± 11.1	(0–52)
Gender (n = 62)	Female/male	3/59	(5/95)
Experience (years) (n = 51)		23.3 ± 5.1	(10–38)
Academic rank (n = 62)	Full professors		18 (29)
	Associate professors		17 (27)
	Other		27 (44)

Note: Data are presented as mean ± SD (range) or absolute values (%).

The characteristics of surgery are shown in Table 2. The average number of medical doctors who assisted surgery was two per case. The mean surgical time was 147 min per case, and the mean surgical fee per surgery was $3919.

Table 2.

Characteristics of surgery.

				Time/case	Fee/case
Specialty	DMUs	Cases	Assistants/case	(min)	(U.S. dollars)
Cardiovascular surgery	3	709 (81)	2.44	270	10281
Emergency surgery	5	539 (135)	1.91	150	3571
General surgery	12	1338 (82)	2.09	206	4257
Neurosurgery	1	46 (9)	1.02	120	4399
Obstetrics & gynecology	7	1358 (121)	2.08	100	2906
Ophthalmology	2	288 (0)	1.24	34	1129
Orthopedics	15	1690 (25)	2.30	113	2919
Otolaryngology	7	722 (7)	1.42	125	2598
Plastic surgery	3	166 (1)	1.55	101	1763
Thoracic surgery	2	274 (1)	1.74	104	6499
Urology	5	472 (7)	1.71	168	3236
All surgical procedures	62	7602 (469)	2.00	145	3919

Note: The numbers in parentheses in cases are the numbers of emergency surgery defined by the fee schedule. Assistants/case, Time/case and Fee/case are expressed in mean.

The natural logarithms of MIs (percent change of productivity), ECs (percent change in efficiency) and TCs (percent change in technique) are shown in Table 3. The productivity changes in three intervals were not significantly different from 0. Their EC in 2013–2014 was significantly greater than 0 (p = 0.0000), while their TC in 2013–2014 was significantly smaller than 0 (p = 0.0000). Their EC in 2015–2016 was significantly smaller than 0 (p = 0.0012), while their TC in 2015–2016 was significantly greater than 0 (p = 0.0000). Their TC in 2014–2015 was significantly smaller than 0 (p = 0.0034).

Table 3.

Percent changes of productivity, efficiency and technique.

	2013–2014	2014–2015	2015–2016
Productivity change (%)	−1.6 ± 3.7	+1.6 ± 3.6	−0.9 ± 3.6
Efficiency change (%)	+17.8 ± 3.7a	+2.1 ± 3.6	−11.9 ± 3.8b
Technical change (%)	−19.3 ± 0.3b	−0.5 ± 0.2	+11.0 ± 0.9a

Note: The values are expressed as mean ± SE.

^aThe value is significantly greater than 0 (p < 0.05).

^bThe value is significantly smaller than 0 (p < 0.05).

The pure effects of revisions are shown in Table 4. The productivity changes in two revisions were not significantly different from 0. Their EC in revision 2014 was significantly greater than 0 (p = 0.0020), while their TC in revision 2014 was significantly smaller than 0 (p = 0.0000). Their EC in revision, 2016 was significantly smaller than 0 (p = 0.0078), while their TC in revision 2016 was significantly greater than 0 (p = 0.0000).

Table 4.

Effects of revision on percent changes of productivity, efficiency and technique.

	Revision 2014	Revision 2016
Productivity change (%)	−3.2 ± 5.2	−2.5 ± 5.5
Efficiency change (%)	+15.7 ± 5.2a	−14.0 ± 5.6^b
Technical change (%)	−18.8 ± 0.4^b	+11.5 ± 1.0^a

Note: The values are expressed as mean ± SE.

^aThe value is significantly greater than 0 (p < 0.05).

^bThe value is significantly smaller than 0 (p < 0.05).

Discussion

We demonstrated that the revision of surgical fee schedule has various effects on the change in productivity, efficiency and technique; the revision 2014 increased efficiency and decreased techniques while the revision 2016 had the opposite effects. There was no significant change in productivity, efficiency and technique in 2014–2015, when there was no revision of fee schedule. These revisions had no effects on total factor productivity. This finding suggests that we could increase total factor productivity through revision of fee schedule if we could increase both efficiency and technique.

The reason for the changes above is difficult to identify from the present study. However, in the second period (with no change in the fee schedule), the changes in productivity, efficiency and technique are less prominent compared to other periods. This fact suggests that there may be a causality in the association between fee schedule and the dependent variables. EC represents the relative position related to its efficiency frontier, while TC represents the shift in the frontier. Revision 2014 shifts the frontier negatively and relative positions of individual surgeons improved. This result was similar to our previous study.⁸ The changes in 2014–2015 were also similar to our previous study except its TC.⁹ Revision 2016 shifts the frontier positively and relative positions of individual surgeons worsened.

In the revision of fee schedule implemented on 1 April 2014, emergency surgery was more highly reimbursed than in 2013. For example, emergency surgery performed out of regular hospital hours is reimbursed 1.8 times of regular hour surgery and that performed in the late nights and holidays is reimbursed 2.6 times in 2014, while they were reimbursed 1.4 times and 1.8 times, respectively, in 2013.^10,11 On the other hand, the reimbursement for thoracoscopic partial resection for lung malignant tumor was reduced by 9.5% in 2014, and the reimbursement for Caesarean section was also reduced by 9.1% in 2014.^10,11 In the revision of fee schedule implemented on 1 April 2016, there was not much change in the surgical fee schedule; most changes were focused on division of functions among healthcare facilities and on nursing care. However, there were some changes in surgical fee schedule. For example, the reimbursement for emergency Caesarean section was increased by 10.2% in 2016.^11,12 It is difficult to know exactly how these minor changes reduces efficiency while improving techniques.

The design of our study has an advantage because it involves a cohort analysis. We followed up 62 identical surgeons for 24 months. Surgeons’ individual difference was eliminated by subtracting the control period (2014–2015). Case-mix difference among different study periods did not affect our results because each surgeon performed only 5–10 types of surgical procedures during the study periods. Thus, we have succeeded in isolating the pure effects of revisions of surgical fee schedules.

Our results of revisions 2014 and 2016 suggested that there was a possible trade-off between EC and TC. However, there is neither theoretical nor clinical ground for the trade-off between EC and TC.^5,16 It is theoretically and clinically possible to increase both efficiency and technique simultaneously. Therefore, we can increase total factor productivity through the revision of fee schedule if we could increase both efficiency and technique. However, our data obtained in the present study were insufficient to make an evidence-based simulation for an appropriate fee schedule.

There might be other possible sources of changes in productivity, efficiency and technique. For example, operating room nursing practices might have changed over time from 2013 through 2016. This change might have influenced surgeons’ changes in productivity, efficiency and technique. The other possibility is that the total number of surgical trainees in the hospital might have increased from 2013 through 2016. This might have increased the number of assistants scrubbed in each surgery, thus affecting the productivity, efficiency and technique. However, these speculations were inconclusive because the necessary data were unavailable for the analysis.

There are some limitations in our study. First, this is a study conducted in a single large teaching hospital in Tokyo, Japan. Our surgeons may not represent all the surgeons in Japan. However, there is an advantage to studying surgeons’ productivity in a single hospital. Since one of the significant resource inputs is ancillary services such as operating room nursing practices and availability of support personnel, all these factors are held constant at least in the same period. Comparing surgeons in different hospitals can be misleading if some ancillary services are more efficient than others. By comparing surgeons in the same institution, they all face the same systemic advantages and disadvantages.¹⁹ Second, we simply considered the number of assistants without taking their experience into account. It is obvious that a full surgeon of lesser rank or a newly appointed senior resident is not equivalent to junior surgical trainees in assisting surgery. An unequal distribution of residents with different experience is also likely. On services like cardiovascular, thoracic and neurosurgery, more senior trainees need to be present, and it unlikely that a junior trainee would get to do anything technically, or perhaps even scrub.²⁰ However, the detailed data were unavailable.

In conclusion, the revision of surgical fee schedule has various effects on the change in productivity, efficiency and technique. This finding suggests that we could increase total factor productivity through revision of fee schedule if we could increase both efficiency and technique.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Research Grant from Pfizer Health Research Foundation and Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number: 17K09247.

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Influence of the revision of surgical fee schedule on surgeons’ productivity in Japan: A cohort analysis of 7602 surgical procedures in 2013–2016

Abstract

Keywords

Introduction

Methods

Data collection

Analysis framework

Statistical analysis

Results

Discussion

Footnotes

Declaration of conflicting interests

Funding

References