Skip to main content
Surgery Open Science logoLink to Surgery Open Science
. 2022 Jun 20;10:36–42. doi: 10.1016/j.sopen.2022.06.002

Exploring the paradigm of robotic surgery and its contribution to the growth of surgical volume

Emily A Grimsley a,, Tara M Barry a, Haroon Janjua a, Emanuel Eguia b, Christopher DuCoin a, Paul C Kuo a
PMCID: PMC9307663  PMID: 35880190

Abstract

Background

Robotic surgery is an appealing option for both surgeons and patients. The question around the introduction of new surgical technology, such as robotics, with the potential link to increased procedure-specific volume has not been addressed. We hypothesize that hospital adoption of robotic technology increases the total volume of specific procedures as compared to nonrobotic hospitals.

Methods

The 2010–2020 Florida Agency for Health Care Administration Inpatient database was queried for open, laparoscopic, and robotic colectomy, lobectomy, gastric bypass, and antireflux procedures. International Classification of Diseases, 9th and 10th Revisions, codes were used. Difference in difference method was used to evaluate the impact of robotics on total procedure-specific volume of robotic hospitals versus nonrobotic hospitals before and after adopting robotic technology. Incident rate ratios from the difference in difference analysis determined the significance of adding robotics. Patient demographics were evaluated using χ2 test.

Results

A total of 291,826 procedures were performed at 217 hospitals, 151 with robotic capabilities. Robotic hospitals experienced a 37% increase in surgical volume due to robotic technology (incident rate ratio 1.37, P < .05), which was consistent for each surgery except antireflux procedures (incident rate ratio 0.95). Robotic procedures had significantly higher charges for medical/surgical supplies; however, the mean length of stay for robotic procedures was significantly shorter than that of laparoscopic and open cases.

Conclusion

Hospital adoption of robotic technology significantly increases surgical volume for select procedures. Hospitals should consider the benefits of introducing robotic technology which leads to higher volume and decreased length of stay, benefitting both hospital systems and patients.

Highlights

  • Robotic hospitals had a 37% increase in surgical volume.

  • Gastric bypass surgeries saw the largest increase in volume.

  • Robotic lobectomy, gastric bypass, and antireflux surgery cost less than open.

INTRODUCTION

The utilization of robotic surgery is growing; however, its impact on hospital systems and patient care is still being established. Research has that shown select robotic procedures (as compared to laparoscopic and/or open) carry a shorter length of stay (LOS) but higher cost [1]. The upfront cost of purchasing the robot and accoutrement can also not be ignored. One study suggested that the cost of adding a robot to a hospital was more than US$2.6 million [2].

Knowing that there is a high startup cost, hospital systems want to ensure a return on investment. As one could postulate that increasing surgical volume would increase revenue, our study sought to evaluate the effect of adding robotic technology to hospitals in terms of change in overall surgical volume. We hypothesized that the addition of robotic technology would increase hospital surgical volumes when studying select surgical procedures.

METHODS

This study was exempt from our institutional review board given that it was querying a deidentified database and did not contain HIPAA-protected information.

The 2010–2020 Florida Agency for Health Care Administration Inpatient database was queried for open, laparoscopic, and robotic colectomy, pulmonary lobectomy (lobectomy), gastric bypass, and antireflux surgeries [3]. These 4 procedures were chosen because they are very common operations performed in all 3 procedure types: open, laparoscopic (or thoracoscopic), and robotic. International Classification of Diseases, 9th and 10th Revisions (ICD9 and ICD10), codes were used to capture the 3 procedure types (open, laparoscopic, and robotic) using the primary procedure code. Open and laparoscopic procedures were coded based on their primary procedure code. The procedure was labeled "robotic" if a robotic qualifier appeared with the primary procedure code. A total of 257 procedure codes were used including the robotic qualifiers: 63 ICD9 codes (17 robotic qualifiers) and 192 ICD10 codes (234 robotic qualifiers; see Supplementary Material).

Patient demographics including sex, age, race, ethnicity, payer types, and Charlson comorbidity index (CCI) were studied. Stata software version 16 was used for all the data preparation steps and computing the descriptive statistics. R Studio was used to implement all the machine learning models using packages and libraries including "readstata13," "tableone," "MatchIT," "Matching," and "ICD." χ2 tests were performed to quantify the statistical significance among the 3 types of procedures and the descriptive categorical variables.

As our dataset did not include information on cost to hospital, we used the available data on charges to formulate a comparison between the different procedure types (open/laparoscopic/robotic). Analysis of similarities and differences between the procedure types (open/laparoscopic/robotic) for gross total charges, operating room charges, medical/surgical supply charges, anesthesia charges, and recovery room charges was completed with ANOVA with post hoc analysis; these analyses were risk-adjusted using CCI and separately by procedure type (open/laparoscopic/robotic). Data are presented as mean ± standard deviation.

Propensity score matching was used for a more comparative analysis between the 3 procedure types [4,5]. One-to-one matching was used to find the best and closest match for each robotic procedure in the open/laparoscopic groups based on age, sex, and CCI. One-to-one matching helped capture the closest match precisely based on defined factors and reduced effect of confounding. After propensity one-to-one matching, 32,144 robotic cases were compared with 32,144 open and 32,144 laparoscopic cases, which enabled us to perform a comparison between like cases.

For each hospital in each year, the total volume of each procedure type was calculated by adding the open, laparoscopic, and robotic cases together. Hospital classification as "robotic" or "nonrobotic" was done by flagging hospitals that performed at least 1 procedure (within our set procedure types) labeled with a robotic qualifier. We used a difference in difference (DID) method to test our hypothesis by analyzing the difference of total procedure volume in robotic hospitals pre- and postadoption of robotic technology and compared this to the difference in total procedure volume for nonrobotic hospitals across the same years using our propensity-matched cohorts. The DID is a causal analysis that uses longitudinal data from a treatment and a control group to estimate a causal effect of a specific intervention/treatment. The DID method is based on Poisson regression and is used to predict a response (a dependent variable in the form of "count data") that is impacted by 1 or more independent variables. DID was used to assess the relationship between total procedure volumes of robotic versus nonrobotic hospitals (the dependent variable) before and after adopting robotics (the independent variable). This DID analysis was performed for all procedures together and then individually for colectomy, lobectomy, gastric bypass, and antireflux. Incident rate ratios (IRRs) from the DID analysis determined the size of the effect adding robotics to a hospital had on surgical volume [[6], [7], [8]]. We regressed total procedure volume for robotic versus nonrobotic hospitals in addition to time when a hospital started performing robotic procedures. All data preparation methods and modeling codes used can be accessed electronically [9].

RESULTS

A total of 291,826 surgical cases of our selected types were performed at 217 hospitals within the database: 139,796 open, 119,886 laparoscopic, and 32,144 robotic cases. Of these 217 hospitals, 151 had robotic capabilities. Our analysis was performed on a propensity-matched cohort to the robotic cases such that we had a total N of 96,432 with 32,144 each of robotic, laparoscopic, and open cases. Most patients were female (57%), white (84%), non-Hispanic or Latino (83%), and ages 51–70 (45%). Overall, 64% of procedures were elective; 9%, urgent; and 28%, emergent. Most patients (51%) fell into the severe CCI (CCI 3; all P < .001; Table 1).

Table 1.

Patient characteristics

Open
Lap
Robotic
Total
n = 139,796
n = 119,886
n = 32,144
n = 291,826
n % n % n % n %
Patient sex
Female 74,446 53% 73,028 61% 19,714 61% 167,188 57%
Male 65,350 47% 46,858 39% 12,430 39% 124,638 43%



Age range
≤ 30 3663 3% 7217 6% 835 3% 11,715 4%
31–50 18,490 13% 27,126 23% 5505 17% 51,121 18%
51–70 62,278 45% 53,534 45% 15,470 48% 131,282 45%
71–90 53,035 38% 31,203 26% 10,231 32% 94,469 32%
90 + 2330 2% 806 1% 103 0% 3239 1%



Race
White 118,253 85% 100,116 84% 27,389 85% 245,758 84%
Black 14,044 10% 12,541 10% 2596 8% 29,181 10%
Asian 942 1% 707 1% 256 1% 1905 1%
American Indian 162 0% 154 0% 48 0% 364 0%
Hawaiian Pacific Islander 55 0% 46 0% 14 0% 115 0%
Other 6340 5% 6322 5% 1841 6% 14,503 5%



Ethnicity
Hispanic or Latino 20,029 14% 22,790 19% 6866 21% 49,685 17%
Non-Hispanic or Latino 119,767 86% 97,096 81% 25,278 79% 242,141 83%



Admission source
Non–health care facility point of origin 104,816 75% 86,513 72% 22,900 71% 214,229 73%
Clinic or physician's office 26,650 19% 30,184 25% 8910 28% 65,744 23%
Transfer from a hospital 1846 1% 1070 1% 173 1% 3089 1%
Transfer from a skilled nursing facility (SNF) or intermediate care facility (ICF) 858 1% 184 0% 21 0% 1063 0%
Transfer from another health care facility 393 0% 154 0% 22 0% 569 0%
Other transfers 5233 4% 1781 1% 118 0% 7132 2%



Admission priority
Emergency 58,905 42% 19,800 17% 1894 6% 80,599 28%
Urgent 13,684 10% 9165 8% 2680 8% 25,529 9%
Elective 67,207 48% 90,921 76% 27,570 86% 185,698 64%



Payer types
Commercial health insurance 37,943 27% 44,346 37% 11,720 36% 94,009 32%
Medicaid 9409 7% 8652 7% 1317 4% 19,378 7%
Medicare 81,898 59% 56,756 47% 17,515 54% 156,169 54%
Other govt 3112 2% 3786 3% 854 3% 7752 3%
Self-pay 4764 3% 4646 4% 462 1% 9872 3%
All others 2670 2% 1700 1% 276 1% 4646 2%



Charlson comorbidity index
Low 38,905 28% 45,334 38% 9583 30% 93,822 32%
Moderate 19,859 14% 24,372 20% 4939 15% 49,170 17%
Severe 81,032 58% 50,180 42% 17,622 55% 148,834 51%



Discharge status
Discharged to home or self-care 67,143 48% 93,671 78% 24,134 75% 184,948 63%
Discharged or transferred to a skilled nursing facility 17,545 13% 4970 4% 899 3% 23,414 8%
Discharged or transferred to a short-term care facility 1301 1% 423 0% 83 0% 1807 1%
Discharged or transferred to home under care of home health care organization 38,613 28% 17,817 15% 6433 20% 62,863 22%
Expired 5701 4% 920 1% 185 1% 6806 2%
Discharged or transferred to a long-term care facility 9493 7% 2085 2% 410 1% 11,988 4%

All P values < .001 within category by χ2.

Altogether, robotic hospitals had a 37% increase in procedure volume (IRR 1.37, P < .0001). This significant increase held true for all procedure types except antireflux procedures where there was no such significant increase in surgical volume at robotic hospitals (IRR 0.959, P = .079). The largest increase in volume due to robotics (73% increase) was seen in gastric bypass surgeries (IRR 1.735, P < .0001), which were performed at 82 robotic hospitals and 110 nonrobotic hospitals (Table 2).

Table 2.

Difference in difference analysis and incident rate ratios

n (# hospitals) IRR 95% CI P value
Colectomy
Robotic hospitals 133 1.38 1.36–1.40 <.0001
Nonrobotic hospitals 78



Lobectomy
Robotic hospitals 124 1.11 1.07–1.14 <.0001
Nonrobotic hospitals 46



Gastric bypass
Robotic hospitals 82 1.73 1.67–1.79 <.0001
Nonrobotic hospitals 110



Antireflux
Robotic hospitals 141 0.95 0.91–1.00 NS
Nonrobotic hospitals 50



All procedures combined
Robotic hospitals 151 1.37 1.36–1.39 <.0001
Nonrobotic hospitals 66

IRRs shown for the overall difference in difference analysis and for each procedure. CI, confidence interval; NS, not significant.

Hospital charges were reviewed as well among our propensity-matched cohort of 32,144 of each procedure type. Overall, the mean total charge was $122,141 for open surgery, $90,178 for laparoscopic, and $125,998 for robotic. On the whole, charges for robotic surgeries were statistically significantly higher than charges for open or laparoscopic surgeries except for total charges for robotic lobectomy ($119,301), gastric bypass ($112,411), and antireflux surgery ($121,383), which were statistically significantly less than open lobectomy ($122,283; P = .041), gastric bypass ($135,094; P < .0001), and antireflux surgery ($133,372; P < .0001), respectively. These findings held true when risk-adjusted for CCI except for CCI 3 patients where robotic cost was not statistically significantly less than open (Table 3a, Table 3b, Table 3c). Across all procedure types, total charges for robotic surgery were higher than those for laparoscopic, which remained true when risk-adjusted for CCI (Table 3a, Table 3b, Table 3c). When the data were separated out by procedure type (open/laparoscopic/robotic) and then within those data risk-adjusted by CCI, CCI 3 patients had higher total charges than CCI 1 patients except in open lobectomy cases (Table 3d, Table 3e, Table 3f).

Table 3a.

Charges risk-adjusted by procedure type for Charlson comorbidity index 1

Open Laparoscopic Robotic ANOVA
Colectomy Lap vs open Rob vs open Rob vs lap
Total charges 94,655.3 ± 88,353 76,514.33 ± 55,136.63 127,846.5 ± 84,364.81 <.001 <.001 <.001
OR 30,595.23 ± 26,390.61 26,763.32 ± 20,082.41 60,259.28 ± 49,747.44 <.001 <.001 <.001
Med/surg supply 12,218.95 ± 11,752.53 12,978.54 ± 9165.513 19,799.81 ± 14,281.85 .016 <.001 <.001
Anesthesia 7468.17 ± 7103.78 6776.745 ± 6044.73 12,045.52 ± 10,739.19 .001 <.001 <.001
PACU 3538.424 ± 3065.859 3264.919 ± 2581.371 4356.761 ± 5260.719 .005 <.001 <.001
Lobectomy Lap vs open Rob vs open Rob vs lap
Total charges 115,739.1 ± 133,786.3 80,050.61 ± 58,726.24 96,493.48 ± 58,177.91 <.001 <.001 <.001
OR 30,173.05 ± 23,166.78 26,626.55 ± 19,925.29 33,472.99 ± 29,094.21 .003 .008 <.001
Med/surg supply 15,858.9 ± 15,926.35 13,191.21 ± 12,295.53 20,595.75 ± 15,991.59 <.001 <.001 <.001
Anesthesia 7777.866 ± 7176.86 5929.962 ± 5322.601 6348.524 ± 5751.171 <.001 <.001 .244
PACU 2654.527 ± 3067.164 2926.067 ± 2911.612 3424.7 ± 3134.339 .105 <.001 <.001
Gast bypass Lap vs open Rob vs open Rob vs lap
Total charges 124,560.6 ± 136,137 63,949.3 ± 33,410.63 105,120 ± 52,473.49 <.001 <.001 <.001
OR 39,071.56 ± 39,179.89 23,545.11 ± 17,324.75 52,867.78 ± 36,980.87 <.001 <.001 <.001
Med/surg supply 15,554.51 ± 13,620.4 17,150.05 ± 13,086.07 22,820.6 ± 15,008.82 .002 <.001 <.001
Anesthesia 8571.887 ± 8306.492 5509.143 ± 5646.901 8318.006 ± 8544.746 <.001 .576 <.001
PACU 3872.312 ± 3327.858 3899.316 ± 3318.818 3180.95 ± 2497.934 .963 <.001 <.001
Antireflux Lap vs open Rob vs open Rob vs lap
Total charges 119,073.9 ± 155,409.1 67,875.53 ± 66,148.05 105,970.4 ± 76,132.32 <.001 <.001 <.001
OR 31,627.63 ± 31,163.36 26,518.71 ± 19,863.28 52,371.57 ± 35,721.58 <.001 <.001 <.001
Med/surg supply 11,720.7 ± 17,223.87 11,681.67 ± 11,154.39 14,376.24 ± 11,737.29 .995 <.001 <.001
Anesthesia 8365.69 ± 10,183.23 6724.682 ± 6113.568 9421.881 ± 9044.877 <.001 <.001 <.001
PACU 3586.774 ± 3222.567 3299.32 ± 2690.251 4014.039 ± 4009.335 .012 <.001 <.001

Table 3b.

Charges risk-adjusted by procedure type for Charlson comorbidity index 2

OPEN LAPAROSCOPIC ROBOTIC ANOVA
Colectomy Lap vs open Rob vs open Rob vs lap
Total charges 116,726.8 ± 127,949.1 87,195 ± 66,374.52 145,269.6 ± 98,417.39 <.001 <.001 <.001
OR 31,195.01 ± 25,793.25 28,158.21 ± 21,154.61 64,582.55 ± 52,536.78 .054 <.001 <.001
Med/surg supply 13,476.14 ± 13,700.66 13,628.27 ± 9897.232 21,573.38 ± 15,857.79 .949 <.001 <.001
Anesthesia 7779.025 ± 7234.903 7176.901 ± 6338.175 13,075.71 ± 10,580.75 .118 <.001 <.001
PACU 3918.997 ± 3791.265 3500.623 ± 2883.185 4487.421 ± 3658.178 .003 <.001 <.001
Lobectomy Lap vs open Rob vs open Rob vs lap
Total charges 119,043.7 ± 129,993.5 90,642.78 ± 73,291.32 106,638.6 ± 69,672.1 <.001 .025 .001
OR 29,376.23 ± 29,251.75 25,076.32 ± 17,000.02 36,076.22 ± 30,924.79 .001 <.001 <.001
Med/surg supply 15,335.22 ± 15,560.47 14,126.52 ± 14,577.15 21,001.52 ± 16,114 .213 <.001 <.001
Anesthesia 7150.308 ± 6690.582 5756.216 ± 4830.622 6677.189 ± 5969.829 <.001 .248 .002
PACU 2870.329 ± 3370.97 3160.028 ± 2922.892 3513.992 ± 3293.657 .125 <.001 .047
Gast bypass Lap vs open Rob vs open Rob vs lap
Total charges 136,978.3 ± 148,591.1 66,958.55 ± 37,563.16 110,261.6 ± 61,864.17 <.001 <.001 <.001
OR 36,904.11 ± 36,789.47 23,987.67 ± 18,813.92 53,697.37 ± 36,038.01 <.001 <.001 <.001
Med/surg supply 16,553.82 ± 14,974.09 17,957.27 ± 12,676.55 22,903 ± 14,285.01 .01 <.001 <.001
Anesthesia 8658.287 ± 9076.686 5984.756 ± 4918.212 9728.854 ± 10,212.45 <.001 .001 <.001
PACU 4206.803 ± 4235.797 3894.175 ± 3470.711 3388.174 ± 2517.72 .022 <.001 <.001
Antireflux Lap vs open Rob vs open Rob vs lap
Total charges 129,643 ± 152,427.1 82,237.19 ± 89,221.29 130,900.1 ± 109,153.7 <.001 .97 <.001
OR 31,440.65 ± 33,740.25 29,438.11 ± 25,948.48 57,494.21 ± 42,963.13 .402 <.001 <.001
Med/surg supply 11,015.4 ± 12,687.59 12,355.68 ± 11,048.87 14,679.51 ± 11,805.91 .03 <.001 <.001
Anesthesia 8294.456 ± 9168.844 7304.118 ± 6700.476 11,167.05 ± 10,608.35 .035 <.001 <.001
PACU 3691.341 ± 3833.096 3572.346 ± 3083.741 4456.894 ± 4070.033 .749 <.001 <.001

Table 3c.

Charges risk-adjusted by procedure type for Charlson comorbidity index 3

Open Laparoscopic Robotic ANOVA
Colectomy Lap vs open Rob vs open Rob vs lap
Total charges 121,332.9 ± 150,154.1 99,170.39 ± 86,102.06 154,037.1 ± 115,172.1 <.001 <.001 <.001
OR 32,168.89 ± 30,385.56 29,735.09 ± 24,490.73 64,474.09 ± 51,866 .003 <.001 <.001
Med/surg supply 13,387.41 ± 12,195.15 13,702.23 ± 10,430.27 20,701.21 ± 16,771.37 .467 <.001 <.001
Anesthesia 7881.825 ± 7796.974 7569.164 ± 6904.681 13,488.04 ± 11,299.49 .181 <.001 <.001
PACU 3781.377 ± 3457.973 3661.935 ± 3397.939 4605.33 ± 4165.976 .236 <.001 <.001
Lobectomy Lap vs open Rob vs open Rob vs lap
Total charges 123,053.9 ± 117,855.8 108,845 ± 88,317.8 122,476.1 ± 82,267.99 <.001 .903 <.001
OR 33,472.23 ± 29,066.97 32,825.86 ± 24,277.44 43,288.45 ± 38,464.6 .299 <.001 <.001
Med/surg supply 17,030.9 ± 15,469.1 18,102.37 ± 16,023.67 23,987.32 ± 17,297.48 <.001 <.001 <.001
Anesthesia 8278.606 ± 7268.962 7062.693 ± 6300.246 7755.483 ± 6707.74 <.001 <.001 <.001
PACU 2967.321 ± 3219.008 2991.406 ± 3001.685 3661.673 ± 3760.589 .863 <.001 <.001
Gast bypass Lap vs open Rob vs open Rob vs lap
Total charges 139,395.7 ± 156,939.1 84,956.56 ± 70,259.85 130,049.5 ± 109,754 <.001 .088 <.001
OR 29,586.87 ± 26,458.96 27,623.76 ± 23,316.61 54,290.94 ± 39,256.42 .109 <.001 <.001
Med/surg supply 15,473.54 ± 15,031.02 20,590.08 ± 14,436.88 24,583.38 ± 17,193.01 <.001 <.001 <.001
Anesthesia 7573.191 ± 6981.913 6912.194 ± 6105.275 11,702.16 ± 12,566.22 .049 <.001 <.001
PACU 4024.848 ± 3690.569 3612.774 ± 3056.891 3667.342 ± 2694.047 .001 .005 .909
Antireflux Lap vs open Rob vs open Rob vs lap
Total charges 172,160 ± 162,225.3 100,524.2 ± 104,854.5 157,878.2 ± 139,023.8 <.001 .108 <.001
OR 34,281.75 ± 29,831.23 29,685.62 ± 21,350.46 60,532.18 ± 42,258.57 .015 <.001 <.001
Med/surg supply 13,558.15 ± 17,008.62 13,181.86 ± 10,980.11 17,156.97 ± 14,556.07 .871 <.001 <.001
Anesthesia 9445.393 ± 10,327.4 7606.396 ± 6524.486 12,616.67 ± 12,692.79 .001 <.001 <.001
PACU 3813.73 ± 3510.809 3478.583 ± 2976.502 4793.128 ± 4549.322 .187 <.001 <.001

For Table 3a, Table 3b, Table 3c: charges (mean ± standard deviation), risk-adjusted per procedure type, after separating by CCI. Post-Hoc ANOVA with pairwise comparison of the means, P values presented with significant values in italics. PACU, recovery room charges; Gast bypass, gastric bypass; Lap, laparoscopic.

Table 3d.

Charges risk-adjusted by Charlson comorbidity index for open procedures

CCI 1 CCI 2 CCI 3 ANOVA
Colectomy CCI 2 vs CCI 1 CCI 3 vs CCI 1 CCI 3 vs CCI 2
Total charges 94,655.3 ± 88,353 116,726.8 ± 127,949.1 121,332.9 ± 150,154.1 <.001 <.001 .462
OR 30,595.23 ± 26,390.61 31,195.01 ± 25,793.25 32,168.89 ± 30,385.56 .78 .025 .5
Med/surg supply 12,218.95 ± 11,752.53 13,476.14 ± 13,700.66 13,387.41 ± 12,195.15 .003 <.001 .97
Anesthesia 7468.17 ± 7103.78 7779.025 ± 7234.903 7881.825 ± 7796.974 .383 .026 .894
PACU 3538.424 ± 3065.859 3918.997 ± 3791.265 3781.377 ± 3457.973 .001 .002 .374
Lobectomy CCI 2 vs CCI 1 CCI 3 vs CCI 1 CCI 3 vs CCI 2
Total charges 115,739.1 ± 133,786.3 119,043.7 ± 129,993.5 123,053.9 ± 117,855.8 .843 .189 .648
OR 30,173.05 ± 23,166.78 29,376.23 ± 29,251.75 33,472.23 ± 29,066.97 .841 .003 <.001
Med/surg supply 15,858.9 ± 15,926.35 15,335.22 ± 15,560.47 17,030.9 ± 15,469.1 .775 .079 .01
Anesthesia 7777.866 ± 7176.86 7150.308 ± 6690.582 8278.606 ± 7268.962 .186 .118 <.001
PACU 2654.527 ± 3067.164 2870.329 ± 3370.97 2967.321 ± 3219.008 .366 .015 .703
Gast bypass CCI 2 vs CCI 1 CCI 3 vs CCI 1 CCI 3 vs CCI 2
Total charges 124,560.6 ± 136,137 136,978.3 ± 148,591.1 139,395.7 ± 156,939.1 .069 .008 .871
OR 39,071.56 ± 39,179.89 36,904.11 ± 36,789.47 29,586.87 ± 26,458.96 .183 <.001 <.001
Med/surg supply 15,554.51 ± 13,620.4 16,553.82 ± 14,974.09 15,473.54 ± 15,031.02 .164 .985 .059
Anesthesia 8571.887 ± 8306.492 8658.287 ± 9076.686 7573.191 ± 6981.913 .954 <.001 <.001
PACU 3872.312 ± 3327.858 4206.803 ± 4235.797 4024.848 ± 3690.569 .046 .437 .292
Antireflux CCI 2 vs CCI 1 CCI 3 vs CCI 1 CCI 3 vs CCI 2
Total charges 119,073.9 ± 155,409.1 129,643 ± 152,427.1 172,160 ± 162,225.3 .183 <.001 <.001
OR 31,627.63 ± 31,163.36 31,440.65 ± 33,740.25 34,281.75 ± 29,831.23 .987 .091 .126
Med/surg supply 11,720.7 ± 17,223.87 11,015.4 ± 12,687.59 13,558.15 ± 17,008.62 .491 .013 .002
Anesthesia 8365.69 ± 10,183.23 8294.456 ± 9168.844 9445.393 ± 10,327.4 .981 .019 .034
PACU 3586.774 ± 3222.567 3691.341 ± 3833.096 3813.73 ± 3510.809 .709 .23 .724

Table 3e.

Charges risk-adjusted by Charlson comorbidity index for laparoscopic procedures

CCI 1 CCI 2 CCI 3 ANOVA
Colectomy CCI 2 vs CCI 1 CCI 3 vs CCI 1 CCI 3 vs CCI 2
Total charges 76,514.33 ± 55,136.63 87,195 ± 66,374.52 99,170.39 ± 86,102.06 <.001 <.001 <.001
OR 26,763.32 ± 20,082.41 28,158.21 ± 21,154.61 29,735.09 ± 24,490.73 .106 <.001 .045
Med/surg supply 12,978.54 ± 9165.513 13,628.27 ± 9897.232 13,702.23 ± 10,430.27 .081 .002 .965
Anesthesia 6776.745 ± 6044.73 7176.901 ± 6338.175 7569.164 ± 6904.681 .111 <.001 .1
PACU 3264.919 ± 2581.371 3500.623 ± 2883.185 3661.935 ± 3397.939 .031 <.001 .169
Lobectomy CCI 2 vs CCI 1 CCI 3 vs CCI 1 CCI 3 vs CCI 2
Total charges 80,050.61 ± 58,726.24 90,642.78 ± 73,291.32 108,845 ± 88,317.8 .007 <.001 <.001
OR 26,626.55 ± 19,925.29 25,076.32 ± 17,000.02 32,825.86 ± 24,277.44 .239 <.001 <.001
Med/surg supply 13,191.21 ± 12,295.53 14,126.52 ± 14,577.15 18,102.37 ± 16,023.67 .311 <.001 <.001
Anesthesia 5929.962 ± 5322.601 5756.216 ± 4830.622 7062.693 ± 6300.246 .768 <.001 <.001
PACU 2926.067 ± 2911.612 3160.028 ± 2922.892 2991.406 ± 3001.685 .138 .749 .169
Gast bypass CCI 2 vs CCI 1 CCI 3 vs CCI 1 CCI 3 vs CCI 2
Total charges 63,949.3 ± 33,410.63 66,958.55 ± 37,563.16 84,956.56 ± 70,259.85 .088 <.001 <.001
OR 23,545.11 ± 17,324.75 23,987.67 ± 18,813.92 27,623.76 ± 23,316.61 .741 <.001 <.001
Med/surg supply 17,150.05 ± 13,086.07 17,957.27 ± 12,676.55 20,590.08 ± 14,436.88 .12 <.001 <.001
Anesthesia 5509.143 ± 5646.901 5984.756 ± 4918.212 6912.194 ± 6105.275 .015 <.001 <.001
PACU 3899.316 ± 3318.818 3894.175 ± 3470.711 3612.774 ± 3056.891 .999 .031 .045
Antireflux CCI 2 vs CCI 1 CCI 3 vs CCI 1 CCI 3 vs CCI 2
Total charges 67,875.53 ± 66,148.05 82,237.19 ± 89,221.29 100,524.2 ± 104,854.5 <.001 <.001 <.001
OR 26,518.71 ± 19,863.28 29,438.11 ± 25,948.48 29,685.62 ± 21,350.46 .001 .003 .972
Med/surg supply 11,681.67 ± 11,154.39 12,355.68 ± 11,048.87 13,181.86 ± 10,980.11 .241 .006 .293
Anesthesia 6724.682 ± 6113.568 7304.118 ± 6700.476 7606.396 ± 6524.486 .041 .004 .603
PACU 3299.32 ± 2690.251 3572.346 ± 3083.741 3478.583 ± 2976.502 .029 .322 .785

Table 3f.

Charges risk-adjusted by Charlson comorbidity index for robotic procedures

CCI 1 CCI 2 CCI 3 ANOVA
Colectomy CCI 2 vs CCI 1 CCI 3 vs CCI 1 CCI 3 vs CCI 2
Total charges 127,846.5 ± 84,364.81 145,269.6 ± 98,417.39 154,037.1 ± 115,172.1 <.001 <.001 .017
OR 60,259.28 ± 49,747.44 64,582.55 ± 52,536.78 64,474.09 ± 51,866 .024 <.001 .997
Med/surg supply 19,799.81 ± 14,281.85 21,573.38 ± 15,857.79 20,701.21 ± 16,771.37 .001 .02 .179
Anesthesia 12,045.52 ± 10,739.19 13,075.71 ± 10,580.75 13,488.04 ± 11,299.49 .01 <.001 .453
PACU 4356.761 ± 5260.719 4487.421 ± 3658.178 4605.33 ± 4165.976 .647 .028 .684
Lobectomy CCI 2 vs CCI 1 CCI 3 vs CCI 1 CCI 3 vs CCI 2
Total charges 96,493.48 ± 58,177.91 106,638.6 ± 69,672.1 122,476.1 ± 82,267.99 .022 <.001 <.001
OR 33,472.99 ± 29,094.21 36,076.22 ± 30,924.79 43,288.45 ± 38,464.6 .312 <.001 <.001
Med/surg supply 20,595.75 ± 15,991.59 21,001.52 ± 16,114 23,987.32 ± 17,297.48 .874 <.001 <.001
Anesthesia 6348.524 ± 5751.171 6677.189 ± 5969.829 7755.483 ± 6707.74 .551 <.001 <.001
PACU 3424.7 ± 3134.339 3513.992 ± 3293.657 3661.673 ± 3760.589 .868 .116 .54
Gast bypass CCI 2 vs CCI 1 CCI 3 vs CCI 1 CCI 3 vs CCI 2
Total charges 105,120 ± 52,473.49 110,261.6 ± 61,864.17 130,049.5 ± 109,754 .051 <.001 <.001
OR 52,867.78 ± 36,980.87 53,697.37 ± 36,038.01 54,290.94 ± 39,256.42 .746 .516 .9
Med/surg supply 22,820.6 ± 15,008.82 22,903 ± 14,285.01 24,583.38 ± 17,193.01 .983 .003 .007
Anesthesia 8318.006 ± 8544.746 9728.854 ± 10,212.45 11,702.16 ± 12,566.22 <.001 <.001 <.001
PACU 3180.95 ± 2497.934 3388.174 ± 2517.72 3667.342 ± 2694.047 .021 <.001 .008
Antireflux CCI 2 vs CCI 1 CCI 3 vs CCI 1 CCI 3 vs CCI 2
Total charges 105,970.4 ± 76,132.32 130,900.1 ± 109,153.7 157,878.2 ± 139,023.8 <.001 <.001 <.001
OR 52,371.57 ± 35,721.58 57,494.21 ± 42,963.13 60,532.18 ± 42,258.57 .001 <.001 .25
Med/surg supply 14,376.24 ± 11,737.29 14,679.51 ± 11,805.91 17,156.97 ± 14,556.07 .793 <.001 <.001
Anesthesia 9421.881 ± 9044.877 11,167.05 ± 10,608.35 12,616.67 ± 12,692.79 <.001 <.001 .011
PACU 4014.039 ± 4009.335 4456.894 ± 4070.033 4793.128 ± 4549.322 .013 <.001 .222

For Table 3d, Table 3e, Table 3f: charges (mean ± standard deviation), risk-adjusted by CCI, after separating by procedure type (open, laparoscopic, or robotic). Post-hoc ANOVA with pairwise comparison of the means, P values presented with significant values in italics.

LOS was statistically significantly shorter for robotic surgery when compared to open and laparoscopic (P < .0001) except when comparing robotic versus laparoscopic gastric bypass (2.56 vs 2.47 days; P = .788) and antireflux surgery (3.86 vs 3.55 days; P = .104), where there was no statistically significant difference. When risk-adjusted for CCI, this held true except for CCI 1 and CCI 2 colectomy patients, and CCI 1 lobectomy patients where robotic versus laparoscopic LOS was not statistically different (Table 4a). Table 4b displays the LOS risk-adjusted by procedure type; in general, the more severe CCI (2 or 3), the longer the length of stay regardless of procedure type (open/laparoscopic/robotic).

Table 4a.

Length of stay risk-adjusted per Charlson comorbidity index

Open Lap Robotic ANOVA
Colectomy Lap vs open Rob vs open Rob vs lap
CCI 1 6.66 ± 5.38 4.67 ± 4.02 4.47 ± 3.51 <.001 <.001 .114
CCI 2 8.17 ± 8.45 5.47 ± 4.72 5.30 ± 5.03 <.001 <.001 .754
CCI 3 8.67 ± 11.47 6.51 ± 5.97 5.98 ± 5.73 <.001 <.001 .003



Lobectomy
CCI 1 6.89 ± 7.03 4.07 ± 3.70 3.66 ± 3.51 <.001 <.001 .107
CCI 2 8.44 ± 7.89 5.58 ± 6.06 4.34 ± 3.95 <.001 <.001 <.001
CCI 3 7.73 ± 7.28 5.83 ± 5.57 4.78 ± 4.98 <.001 <.001 <.001



Antireflux
CCI 1 7.15 ± 8.70 2.83 ± 4.86 3.02 ± 3.89 <.001 <.001 .553
CCI 2 8.57 ± 9.05 3.79 ± 5.27 4.32 ± 5.34 <.001 <.001 .192
CCI 3 12.03 ± 10.31 5.63 ± 7.21 5.95 ± 7.02 <.001 <.001 .763



Gast bypass
CCI 1 7.54 ± 10.61 2.06 ± 1.97 2.12 ± 1.98 <.001 <.001 .907
CCI 2 8.78 ± 11.40 2.27 ± 2.05 2.32 ± 2.44 <.001 <.001 .96
CCI 3 11.16 ± 12.17 3.47 ± 4.87 3.79 ± 6.62 <.001 <.001 .678

Length of stay (mean ± standard deviation), risk-adjusted per CCI, comparing laparoscopic (Lap) versus open, robotic (Rob) versus open, and Rob versus Lap. Post-hoc ANOVA with pairwise comparison of the means, P values presented with significant values in italics.

Table 4b.

Length of stay risk-adjusted per procedure type

CCI 1 CCI 2 CCI 3 ANOVA
Colectomy CCI 2 vs CCI 1 CCI 3 vs CCI 1 CCI 3 vs CCI 2
Open 6.66 ± 5.38 8.17 ± 8.45 8.67 ± 11.47 <.001 <.001 .182
Laparoscopic 4.67 ± 4.02 5.47 ± 4.72 6.51 ± 5.97 <.001 <.001 <.001
Robotic 4.47 ± 3.51 5.30 ± 5.03 5.98 ± 5.73 <.001 <.001 <.001



Lobectomy
Open 6.89 ± 7.03 8.44 ± 7.89 7.73 ± 7.28 <.001 .003 .026
Laparoscopic 4.07 ± 3.70 5.58 ± 6.06 5.83 ± 5.57 <.001 <.001 .306
Robotic 3.66 ± 3.51 4.34 ± 3.95 4.78 ± 4.98 .009 <.001 .043



Antireflux
Open 7.15 ± 8.70 8.57 ± 9.05 12.03 ± 10.31 <.001 <.001 <.001
Laparoscopic 2.83 ± 4.86 3.79 ± 5.27 5.63 ± 7.21 <.001 <.001 <.001
Robotic 3.02 ± 3.89 4.32 ± 5.34 5.95 ± 7.02 <.001 <.001 <.001



Gast bypass
Open 7.54 ± 10.61 8.78 ± 11.40 11.16 ± 12.17 .012 <.001 <.001
Laparoscopic 2.06 ± 1.97 2.27 ± 2.05 3.47 ± 4.87 .061 <.001 <.001
Robotic 2.12 ± 1.98 2.32 ± 2.44 3.79 ± 6.62 .184 <.001 <.001

Length of stay (mean ± standard deviation), risk-adjusted per procedure type—open, laparoscopic, and robotic—comparing CCI for each patient in the cohort. Post hoc ANOVA with pairwise comparison of the means, P values presented with significant values in italics.

DISCUSSION

This study establishes that robotic surgery increases surgical volume, decreases LOS, and, for select procedures studied, has lower total charges, which may have great benefit for both hospitals and patients. We used propensity matching for comparison of the robotic, laparoscopic, and open procedures to minimize unaccounted for variance in the cohorts [4,5] and a DID analysis to determine what amount of the change in surgical volume can be attributed to the addition of robotic technology. The DID analysis has been used in similar studies to establish a causal relationship between a dependent and independent variable in 2 continuous groups of data that our otherwise similar, ie, propensity-matched cohorts, over time [8,[10], [11], [12]].

One may postulate that the decreased total charges for robotic surgery compared with open surgery (and laparoscopic compared to open) can be attributed to the significant decrease in LOS for robotic and laparoscopic surgeries compared with open operations.

Decreased LOS following robotic surgery has been proven. Several colorectal surgeries have identified decreased LOS with robotic colectomy versus laparoscopic [[13], [14], [15]]. One study demonstrated equivalent overall cost between robotic and laparoscopic colectomy [15], leading to the conclusion that robotic surgery is more valuable to hospitals and patients than previously thought.

In the thoracic surgery arena, it has been shown that although robotic procedure cost was higher, there was no statistically significant difference in overall cost to patients due to lower postoperative costs [16]. Two studies even documented a profit margin with robotic lobectomy [17,18]. Although we did not examine cost, our study demonstrates significantly lower charges for robotic lobectomy versus open but still significantly higher charges for robotic versus laparoscopic. Interestingly, the LOS for robotic lobectomy was statistically significantly less when compared with open and laparoscopic lobectomy, pointing to the fact that decreasing LOS alone does not result in decreased overall charges to patient. Based on our risk-adjusted analysis, there is a strong element to patient severity of illness/comorbidities that contributes to LOS across procedure types. That said, one must also consider that a decreased LOS could mean less complications, risk of hospital-acquired infection, and faster recovery; these should all be further studied.

Our study does show decreased LOS across 4 major surgical procedures and adds the next step of identifying an overall increase in surgical volume in 3 out of 4 procedures. It will be interesting to see the long-term effects of adding robotic technology. The increase in volume may be short-lived because the prevalence of disease is likely not increasing and other hospitals will adopt robotic technology as time goes on. This phenomenon demonstrates 2 of Porter's 3 competitive strategies: cost leadership and differentiation.

Robotic technology does come with a high upfront cost to the hospital (and theoretically explains the relative lack of robotic surgery at ambulatory surgery centers). There is also a higher charge per procedure for robotics, although how each hospital establishes this cost/charge is unknown. Do they add in a base-charge for use of the robot to recuperate cost to purchase said robot? And if they do, for how long will they do that given the fixed startup cost? The answer to these questions is unknown and likely hospital-specific, but these are prudent questions as we move forward in a robot-centric surgical world.

Retrospective database review is an inherent limitation as one's conclusions are limited to the data provided. The data set we queried did not include hospital information that may also have an impact on surgical volumes, such as expansion of surgical space (adding operating rooms), personnel, marketing, and quality measures. The data set did not include information about surgeon training or information about length of surgery and postoperative complications. In the future, a prospective collection of surgical volume data with more hospital-specific data may be warranted to further evaluate the effects of adding robotics, as being able to control for other factors would help narrow the focus and increase the power of the study. Additionally, our data set was limited to the state of Florida, and 84% of the patients in the set were white; this reduces generalizability to the rest of the United States and warrants a larger exploration into similar data in different parts of the country.

Conclusions

By using propensity matching and difference-in-difference method to control for changes over time, we found that hospitals that adopt robotic technology increase their overall surgical volume by 37%. Robotic surgeries had decreased LOS but higher charges than their laparoscopic or open counterparts. Our study is limited by inability to control for all other factors, and a prospective trial or larger database review should be performed to reduce bias and increase generalizability of our findings.

The following are the supplementary data related to this article.

Supplementary Material

ICD9 and 10 codes used for our data search

mmc1.docx (36.5KB, docx)

Author Contribution

  • Dr. Emily Grimsley: methodology, data curation, writing – original draft, writing – review & editing

  • Dr. Tara Barry: conceptualization, investigation, methodology, data curation, software

  • Dr. Haroon Janjua: data curation, formal analysis, methodology, software

  • Dr. Emanuel Eguia: conceptualization, investigation, methodology, supervision

  • Dr. Christopher DuCoin: supervision, writing – review & editing

  • Dr. Paul Kuo: conceptualization, investigation, methodology, supervision, validation, writing – review & editing.

Conflict of Interest

The authors have no related conflicts of interest to declare.

Funding Source

This study was not funded.

Ethics Approval

This study was exempt from the Institutional Review Board given that it was a query of a deidentified database.

Footnotes

Presented at The American Surgical Congress, Orlando, FL, February 1–3, 2022, as a Quickshot oral presentation.

References

  • 1.Barry T.M., Janjua H., DuCoin C., Eguia E., Kuo P.C. Does adoption of new technology increase surgical volume? The robotic inguinal hernia repair model. J Robot Surg. 2021 doi: 10.1007/s11701-021-01304-6. Published online ahead of print. [DOI] [PubMed] [Google Scholar]
  • 2.Ho C., Tsakonas E., Tran K., Cimon K., Severn M., Mierzwinski-Urban M., et al. Canadian Agency for Drugs and Technologies in Health; Ottawa (ON): 2011. Robot-assisted surgery compared with open surgery and laparoscopic surgery: clinical effectiveness and economic analysis. [PubMed] [Google Scholar]
  • 3.2020. Agency for Health Care Administration SoF. Inpatient date file 2010–2020. [Google Scholar]
  • 4.Austin P.C., Stuart E.A. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med. 2015;34:3661–3679. doi: 10.1002/sim.6607. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Rosenbaum P.R. Model-based direct adjustment. J Am Stat Assoc. 1987;82:387–394. [Google Scholar]
  • 6.Angrist J.D., Pischke J.-S. Princeton University Press; 2008. Mostly harmless econometrics. [Google Scholar]
  • 7.Gertler P.J., Martinez S., Premand P., Rawlings L.B., Vermeersch C.M. World Bank Publications; 2016. Impact evaluation in practice. [Google Scholar]
  • 8.Lechner M. Now Hanover; MA: 2011. The estimation of causal effects by difference-in-difference methods. [Google Scholar]
  • 9.OneToMap analytics, inpatient data preparation and modeling code. https://githubcom/onetomapanalytics/Inpatient_Surgeries_DID
  • 10.Cataife G., Pagano M.B. Difference in difference: simple tool, accurate results, causal effects. Transfusion. 2017;57:1113–1114. doi: 10.1111/trf.14063. [DOI] [PubMed] [Google Scholar]
  • 11.Eguia E., Cobb A.N., Kothari A.N., Molefe A., Afshar M., Aranha G.V., et al. Impact of the Affordable Care Act (ACA) Medicaid expansion on cancer admissions and surgeries. Ann Surg. 2018;268:584. doi: 10.1097/SLA.0000000000002952. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Rogers M.P., Janjua H., Eguia E., Lozonschi L., Toloza E.M., Kuo P.C. Adopting robotic thoracic surgery impacts hospital overall lung resection case volume. Am J Surg. 2022;223:571–575. doi: 10.1016/j.amjsurg.2021.11.016. [DOI] [PubMed] [Google Scholar]
  • 13.Clarke E.M., Rahme J., Larach T., Rajkomar A., Jain A., Hiscock R., et al. Robotic versus laparoscopic right hemicolectomy: a retrospective cohort study of the Binational Colorectal Cancer Database. J Robot Surg. 2021 doi: 10.1007/s11701-021-01319-z. [DOI] [PubMed] [Google Scholar]
  • 14.Palomba G., Dinuzzi V.P., Capuano M., Anoldo P., Milone M., De Palma G.D., et al. Robotic versus laparoscopic colorectal surgery in elderly patients in terms of recovery time: a monocentric experience. J Robot Surg. 2021:1–7. doi: 10.1007/s11701-021-01332-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Vasudevan V., Reusche R., Wallace H., Kaza S. Clinical outcomes and cost-benefit analysis comparing laparoscopic and robotic colorectal surgeries. Surg Endosc. 2016;30:5490–5493. doi: 10.1007/s00464-016-4910-1. [DOI] [PubMed] [Google Scholar]
  • 16.Kneuertz P.J., Singer E., D’Souza D.M., Abdel-Rasoul M., Moffatt-Bruce S.D., Merritt R.E. Hospital cost and clinical effectiveness of robotic-assisted versus video-assisted thoracoscopic and open lobectomy: a propensity score-weighted comparison. J Thorac Cardiovasc Surg. 2019;157 doi: 10.1016/j.jtcvs.2018.12.101. 2018–26.e2. [DOI] [PubMed] [Google Scholar]
  • 17.Musgrove K.A., Hayanga J.A., Holmes S.D., Leung A., Abbas G. Robotic versus video-assisted thoracoscopic surgery pulmonary segmentectomy: a cost analysis. Innovations (Phila) 2018;13:338–343. doi: 10.1097/IMI.0000000000000557. [DOI] [PubMed] [Google Scholar]
  • 18.Novellis P., Bottoni E., Voulaz E., Cariboni U., Testori A., Bertolaccini L., et al. Robotic surgery, video-assisted thoracic surgery, and open surgery for early stage lung cancer: comparison of costs and outcomes at a single institute. J Thorac Dis. 2018;10:790–798. doi: 10.21037/jtd.2018.01.123. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material

ICD9 and 10 codes used for our data search

mmc1.docx (36.5KB, docx)

Articles from Surgery Open Science are provided here courtesy of Elsevier

RESOURCES