ABSTRACT
Background and Aims
Shifting patterns in the financing of plastic surgery are not fully understood. We analyze payer trends for office and outpatient plastic surgery visits from 2002 to 2022.
Methods
We investigated office‐based and hospital outpatient department plastic surgery visits in the Medical Expenditure Panel Survey, a national dataset of healthcare usage and expenditures. During the study period (2002–2022), we calculated the yearly median amount of money charged and paid per visit, the distribution of payer type expenditures, and the mean payment‐to‐charge ratio per visit. Dollar amounts were inflation‐adjusted to 2022 estimates.
Results
From 2002 to 2022, there were 5824 office‐based plastic surgery visits and 850 outpatient department plastic surgery visits, representing 71,597,980 and 10,589,575 total visits, respectively. For office‐based visits, the number of visits declined across the study period from 3,982,280 to 1,418,032 (−64.4%), the median (IQR) payment per visit increased from $119.06 ($188.49) to $263.48 ($721.35) (+121.3%), and the median (IQR) payment‐to‐charge ratio decreased from 0.71 (0.43) to 0.47 (0.45) (−33.8%). Payments were primarily private insurance (37.6%) and out‐of‐pocket (36.0%). For outpatient department visits, the median (IQR) payment‐to‐charge ratio also decreased significantly from 0.56 (0.44) to 0.35 (0.45) (−37.1%); median (IQR) payment per visit was $1587.93 ($1287.80) without significant changes over time. Most expenditures were from private insurance (58.5%), then self‐pay (17.8%), and Medicare (11.7%).
Conclusion
National trends in plastic surgery expenditures show decreasing payment‐to‐charge ratios for both office and outpatient department settings. However, the payer mix driving these trends differs by practice setting. Limitations of the study include survey‐based data, a small sample size, and the exploratory nature of the study with limited statistical analysis. Further research is warranted to uncover factors that may account for these findings and their implications.
1. Introduction
Healthcare spending in the United States has reached unprecedented levels, now estimated at $4 trillion per year and comprising over one‐sixth of the national GDP [1]. Despite these expenditures, health outcomes in the United States are comparable, or slightly worse, compared to its international counterparts [2]. Within this landscape, plastic surgery represents a significant and growing component of healthcare costs. The American Society of Plastic Surgeons (ASPS) estimates that $26 billion was spent on cosmetic plastic surgery alone in 2022, not including reconstructive procedures for which spending data remain limited [3]. Identifying avenues for cost reduction while maintaining quality and accessibility of care is important, both for plastic surgery and healthcare in general. To achieve this, it is important to understand the payer trends for plastic surgery to develop effective strategies to optimize healthcare expenditures.
Plastic surgery services are delivered across a diverse range of settings. A subset of these hospital procedures take place in hospital‐affiliated outpatient departments, where many patients who require a hospital operation without an overnight stay receive their care. Though this is not the most common place of service, hospital outpatient department visits nonetheless make up a significant proportion of plastic surgery visits and have distinct patient populations and procedures performed [4, 5]. ASPS estimates that 16% of cosmetic procedures and 57% of reconstructive procedures are performed in hospital‐associated settings [6]. Another significant portion of care in plastic surgery is delivered in an office setting, where an estimated 42% of cosmetic procedures and 19% of reconstructive procedures take place [6]. Despite these distributions, national analyses have historically focused on publicly available data such as Medicare, which excludes a large portion of plastic surgery activity occurring in privately insured or self‐pay environments [7, 8, 9, 10]. As a result, there is a lack of centralized and transparent financial data on utilization and expenditures for the broader population of plastic surgery patients, particularly in nonhospital settings. This presents a significant gap in our understanding of how payer trends and expenditures have evolved across practice environments.
In this study, we aim to address this gap by providing the first national, all‐payer analysis of plastic surgery visits using the Medical Expenditure Panel Survey (MEPS) database, focusing on two key practice settings: hospital outpatient departments and office‐based visits. MEPS is a nationally representative survey that captures healthcare utilization and spending from patients, providers, and payers, including both public and private insurers, offering a unique lens through which to examine plastic surgery trends beyond traditional Medicare data [11]. While MEPS has been employed in health economics research across other surgical specialties, it has not previously been used to study plastic surgery. Our analysis will characterize utilization and expenditure trends over the past two decades and assess the potential of MEPS as a resource for future plastic surgery research.
2. Methods
The MEPS—a national database of healthcare utilization based on surveys of patients and medical providers (physicians, hospitals, pharmacies)—was used to analyze office‐based and outpatient department plastic surgery visits from 2002 to 2022 [11]. Office‐based visits were defined as healthcare encounters taking place in a nonhospital‐associated setting, such as a group practice office, clinic, managed care plan center, or surgical center. Outpatient department visits were defined as those taking place in a hospital‐affiliated facility or unit, and do not include overnight, inpatient visits occurring in the hospital itself. Plastic surgery visits were identified using the variable DRSPLTY_M18, which indicates the primary specialty of the physician conducting the visits. The database is structured with a panel design incorporating five rounds of interviews for each household across two calendar years; outcomes were calculated as the average of all these panels, with person‐year as the primary sampling unit. For the initial exploratory data analysis, outcomes of interest included the number of visits per year, the median amount paid, median charge and median payment‐to‐charge ratio per visit. Survey weights, provided in the database as person‐weight (“PERWT”), were used to calculate the adjusted population values for the number of patients, visits, and dollar amounts for expenditures. For subgroup analyses, expenditures were also stratified by payer type (Medicaid, Medicare, private insurance, out‐of‐pocket, other). “Other payers” included TRICARE and VA programs, Workers' Compensation, state/local government programs, and other federal insurance.
Spearman's rank correlation tests were used to evaluate trends over time. Correlation coefficients (ρ) were approximated using the Fisher z transformation and reported with the p‐value from Spearman's rank correlation tests. To account for multiple comparisons, p‐values were adjusted using the Bonferroni correction. Kruskal–Wallis tests were used to compare median expenditures by payer type. Post hoc pairwise comparisons for Kruskal–Wallis testing were not included, as the analysis was intended to be limited to omnibus testing for this exploratory analysis. Median expenditures for each payer type were calculated as the median of all nonzero expenditures for that respective payer per year. Means were reported as the value and standard deviation (n [standard deviation (SD)]), and medians were reported as the value and interquartile range (n [interquartile range (IQR)]). All dollar amounts were inflation‐adjusted to 2022 estimates based on Consumer Price Index values from the US Bureau of Labor Statistics. Statistical significance level was evaluated at α = 0.05. Analysis was performed using R 4.3.0, with the “tidyverse” (version 2.0.0) and “MEPS” (version 1.4.0) packages specifically.
3. Results
3.1. Office‐Based Visits
From 2002 to 2022, there were 5824 office‐based plastic surgery visits for 1965 patients noted, representing 71,597,980 visits for 24,017,671 patients. Patients analyzed had an average age (SD) of 48.7 (22.3) and were majority female (1343/1965; 68.3%), white (1651/1965; 84.0%), and privately insured (1448/1965; 73.7%) (Table 1). Each year, there was an average (SD) of 3,409,428 (847,725) adjusted visits, with a median (IQR) payment of $131.02 ($38.61), median charge of $266.68 ($88.56), and a median (IQR) payment‐to‐charge ratio of 0.55 (0.14).
Table 1.
Demographic characteristics for office‐based plastic surgery visits, 2002–2022. Provided survey weights in the data were used to calculate adjusted national estimates for demographic variables.
| Office‐based visits | Outpatient department visits | |||
|---|---|---|---|---|
| Sample | Adjusted | Sample | Adjusted | |
| Number of patients, n | 1965 | 24,017,671 | 537 | 6,578,524 |
| Number of visits, n | 5824 | 71,597,980 | 850 | 10,589,575 |
| Age, n (SD) | 48.7 (22.3) | 47 (22.3) | ||
| Sex, n (%) | ||||
| Male | 622 (31.7) | 7,610,332 | 195 (36.3) | 2,428,275 |
| Female | 1343 (68.3) | 16,407,337 | 342 (63.7) | 4,150,249 |
| Race, n (%) | ||||
| White | 1651 (84.0) | 21,655,602 | 420 (78.2) | 5,574,796 |
| Black | 206 (10.5) | 1,328,277 | 79 (14.7) | 616,266 |
| American Indian/Alaska Native | 8 (0.4) | 49,170 | 6 (1.1) | 17,701 |
| Asian/Native Hawaiian/Pacific Islander | 52 (2.6) | 481,291 | 17 (3.2) | 229,383 |
| Other | 48 (2.4) | 503,330 | 15 (2.8) | 140,378 |
| Ethnicity, n (%) | ||||
| Hispanic | 220 (11.2) | 1,468,744 | 61 (11.4) | 425,718 |
| Not Hispanic | 1,745 (88.8) | 22,548,927 | 476 (88.6) | 6,152,806 |
| Insurance status, n (%) | ||||
| Any private insurance | 1448 (73.7) | 19,096,453 | 370 (68.9) | 4,919,405 |
| Public insurance only | 439 (22.3) | 4,168,960 | 150 (27.9) | 1,472,755 |
| Uninsured | 78 (4.0) | 752,257 | 17 (3.2) | 186,363 |
The number of visits declined across the study period, from 3,982,280 to 1,418,032 (64.4% decrease; ρ [95% CI] = −0.70 [−0.87, −0.38], p‐value < 0.001) (Figure 1a). From 2002 to 2022, the median (IQR) payment increased from $119.06 ($188.49) to $263.48 ($721.35) (121.3% increase; ρ [95% CI] = 0.68 [0.36, 0.86], p‐value < 0.001) (Figure 1b) and the median charge increased from $160.34 ($298.45) to $527.50 ($3,114.37) (229.0% increase; ρ [95% CI] = 0.91 [0.80, 0.97], p‐value < 0.001) (Figure 1c). The median payment‐to‐charge ratio decreased 33.8%, from 0.71 (0.43) in 2002 to 0.47 (0.45) in 2022 (ρ [95% CI] = 0.77 (−0.90, −0.50), p‐value < 0.001) (Figure 1d).
Figure 1.
(a) Number of visits for office‐based plastic surgery visits. x‐axis indicates calendar year and y‐axis indicates number of visits. (b) Median amount paid for office‐based plastic surgery visits. x‐axis indicates calendar year and y‐axis indicates amount in US dollars. Median amount paid was calculated as the median for the sum of expenditures from all payer sources. (c) Median charge for office‐based plastic surgery visits. x‐axis indicates calendar year and y‐axis indicates amount in US dollars. (d) Median payment‐to‐charge ratio for office‐based plastic surgery visits. Payment‐to‐charge ratio was calculated as the ratio between median amount paid and total charge for the visit. x‐axis indicates calendar year and y‐axis indicates amount in US dollars.
From 2002 to 2022, the average (SD) annual expenditure was $2,387,791,187 ($1,133,134,552), and did not significantly change over time (ρ [95% CI] = 0.13 [−0.32, 0.53], p = 1.00). Private insurance payments made up 37.6% (mean [SD] of $896,973,194 [$535,826,989]) of total expenditures, followed by out‐of‐pocket payments (36.0%; mean [SD] of $859,960,635 [$459,358,665]), Medicare (15.0%; mean [SD] of $357,790,147 [$324,430,906]), other (9.8%; mean [SD] of $233,029,744 [$584,242,512]), and Medicaid (1.7%; mean [SD] of $39,975,850 [$32,230,588]). The greatest median (IQR) payment amount was associated with private insurers ($127.83 [$257.67]) and Medicare ($127.67 [$231.46]), then Medicaid ($95.37 [$144.87]), other ($90.34 [$166.55]), and self‐pay ($41.53 [$98.61]). The median (IQR) payment per visit increased significantly per year for self‐pay ($24.21 [$47.63] in 2002 to $57.21 [$129.25] in 2022; ρ [95% CI] = 0.82 [0.60, 0.92], p < 0.001). There were no other significant trends over time for median payment, adjusted expenditures, or proportion of expenditures by payer type (Table 2a).
Table 2.
Median payment and adjusted expenditures for plastic surgery visits from 2002 to 2022, by payer type for (a) office‐based visits and (b) outpatient department visits. Median amount paid was calculated as the median for the sum of expenditures from all payer sources. Provided survey weights in the database were used to extrapolate national estimates for expenditures. Other payers were defined as all payers other than Medicare, Medicaid, private insurance, and out‐of‐pocket spending, and include TRICARE, VA, Workers’ Compensation, and local government programs.
| 2002 | 2022 | Percent change | Adjusted p‐value* | |
|---|---|---|---|---|
| a. Office‐based visits | ||||
| Medicaid | ||||
| Median payment, n (IQR) | 140.84 (67.57) | 110.03 (227.81) | −21.9 | 1.00 |
| Adjusted expenditures | 17,728,704 | 12,164,772 | −31.4 | 1.00 |
| Medicare | ||||
| Median payment, n (IQR) | 111.62 (156.32) | 199.04 (496.10) | 78.3 | 0.18 |
| Adjusted expenditures | 164,592,435 | 141,173,816 | −14.2 | 0.16 |
| Private insurance | ||||
| Median payment, n (IQR) | 89.74 (147.21) | 219.96 (654.66) | 145.1 | 1.00 |
| Adjusted expenditures | 643,300,661 | 747,803,881 | 16.2 | 1.00 |
| Out‐of‐pocket | ||||
| Median payment, n (IQR) | 24.21 (47.63) | 57.21 (129.25) | 136.3 | < 0.001 |
| Adjusted expenditures | 1,088,127,152 | 1,158,402,795 | 6.5 | 1.00 |
| Other | ||||
| Median payment, n (IQR) | 73.74 (126.11) | 0 (0) | −100.0 | 1.00 |
| Adjusted expenditures | 120,053,064 | 0 | −100.0 | 0.15 |
| Total | ||||
| Median payment, n (IQR) | 119.06 (188.49) | 263.48 (721.35) | 121.3 | 0.002 |
| Adjusted expenditures | 2,034,513,065 | 2,059,545,264 | 1.2 | 1.00 |
| b. Outpatient department visits | ||||
| Medicaid | ||||
| Median payment, n (IQR) | 545.79 (922.99) | 166.36 (883.24) | −69.5 | 1.00 |
| Adjusted expenditures | 11,632,211 | 74,147,220 | 537.4 | 0.02 |
| Medicare | ||||
| Median payment, n (IQR) | 1006.82 (516.80) | 220.52 (302.79) | −78.1 | 1.00 |
| Adjusted expenditures | 269,608,235 | 133,529,458 | −50.5 | 1.00 |
| Private insurance | ||||
| Median payment, n (IQR) | 1518.98 (1865.34) | 114.12 (1548.36) | −92.5 | 0.21 |
| Adjusted expenditures | 562,485,093 | 616,955,132 | 9.7 | 1.00 |
| Out‐of‐pocket | ||||
| Median payment, n (IQR) | 277.02 (552.26) | 147.30 (1360.01) | −46.8 | 1.00 |
| Adjusted expenditures | 103,015,271 | 871,317,992 | 745.8 | 0.44 |
| Other | ||||
| Median payment, n (IQR) | 234.93 (630.36) | 0.00 (0) | −100.0 | 1.00 |
| Adjusted expenditures | 136,886,198 | 0 | −100.0 | 0.58 |
| Total | ||||
| Median payment, n (IQR) | 1797.51 (2606.99) | 247.74 (1111.21) | −86.2 | 1.00 |
| Adjusted expenditures | 1,083,627,009 | 1,695,949,802 | 56.5 | 1.00 |
* Calculated with Spearman's rank correlation with Bonferroni correction.
3.2. Outpatient Department Visits
A total of 850 outpatient department plastic surgery visits corresponding to 537 patients were analyzed, which represents 10,589,575 visits and 6,578,524 patients, respectively, when adjusting for survey weights. Our patient sample had an average age (SD) of 47.0 (22.3) years, with the majority being female (342/537; 63.7%), white (420/537; 78.2%), non‐Hispanic (476/537; 88.6%), and covered by private insurance (370/537; 68.9%) (Table 1). On average (SD), there were an estimated 504,265 (210,923) outpatient plastic surgery visits per year with a median (IQR) total payment of $1,587.93 ($1,287.80), median charge of $5,420.21 ($5,055.12), and median payment‐to‐charge ratio of 0.36 (0.19) per visit.
The number of patient visits per year increased over 200% from 2002 to 2022 (379,222–1,153,454 adjusted visits), but this trend was not significant (Figure 2a). Median amount paid declined across the study period, but correlation analysis did not reveal any significant trends over time (Figure 2b). Similar decreases were observed for the median charge per visit, but were also not statistically significant (Figure 2c). Median (IQR) payment‐to‐charge decreased significantly from 2002 to 2022 from 0.56 (0.44) to 0.35 (0.45) (37.1% decrease; correlation [95% CI] = −0.57 (−0.81, −0.19), p = 0.007) (Figure 2d).
Figure 2.
(a) Number of visits for outpatient department plastic surgery visits. x‐axis indicates calendar year and y‐axis indicates number of visits. (b) Median amount paid for outpatient department plastic surgery visits. x‐axis indicates calendar year and y‐axis indicates amount in US dollars. Median amount paid was calculated as the median for the sum of expenditures from all payer sources. (c) Median charge for outpatient department plastic surgery visits. x‐axis indicates calendar year and y‐axis indicates amount in US dollars. (d) Median payment‐to‐charge ratio for outpatient department plastic surgery visits. Payment‐to‐charge ratio was calculated as the ratio between median amount paid and total charge for the visit. x‐axis indicates calendar year and y‐axis indicates amount in US dollars.
The average (SD) yearly adjusted total expenditure was $2,118,134,949 ($1,713,694,343), which did not change significantly over time (correlation [95% CI] = 0.13 (−0.32, 0.53), p = 1.00). Of this amount, the majority of mean (SD) expenditures were from private insurance ($1,238,354,206 [$1,464,790,838]; 58.5%), followed by self‐pay ($377,288,381 [$388,480,291]; 17.8%), Medicare ($246,045,320 [$196,711,903]; 11.6%), other payers ($192,757,326 [$247,889,368]; 9.1%), and Medicaid ($63,689,717 [$68,454,340]; 3.0%). Private insurers were also associated with the highest median (IQR) payment ($1218.64 [$4,362.61]), then Medicare ($676.38 [$2,019.03]), other ($562.15 [$2,714.26]), out‐of‐pocket ($260.14 [$1,369.79]), and Medicaid ($221.89 [$865.44]). The median payment, total adjusted expenditure, and proportion of expenditures by payer type did not change significantly over time, except for an increase in adjusted total expenditures for Medicaid payers (correlation [95% CI] = 0.57 [0.18, 0.80], p = 0.02) (Table 2b).
4. Discussion
4.1. Office‐Based Visits
This study offers the first comprehensive, all‐payer analysis of national trends in office‐based plastic surgery using the MEPS database over a 20‐year period. Our findings reveal a marked decline in the number of office‐based plastic surgery visits, falling by 64.4% between 2002 and 2022. This decline began well before the COVID‐19 pandemic and contrasts with estimates from the ASPS, which report increasing numbers of cosmetic procedures over a similar period [6, 12]. A potential explanation for this discrepancy is that procedural counts reported by ASPS may include multiple procedures per visit, or procedures performed in settings not captured by MEPS [3]. The MEPS database reports its data in terms of number of visits, and would not differentiate between a visit during which several procedures were performed versus a visit where only one was performed. Moreover, the MEPS methodology, while robust for national inference, relies on patient‐reported data, which is susceptible to underreporting of visit frequency that may be underestimating true procedure volumes reported in the literature. Furthermore, differences in what is defined as “medical care” or procedures based on varying sources introduces further nuance into the definition of procedure volume, and may contribute to discrepancies in estimates in volume of healthcare delivery across different platforms.
Despite the decrease in visit volume, we observed substantial increases in the financial metrics associated with each visit. Median payments rose by 121%, and median charges increased by 229%, indicating that the cost burden per encounter has increased dramatically. Notably, the polymerase chain reaction (PCR) declined from 0.71 to 0.47, suggesting that provider charges have outpaced reimbursement increases. This decline in PCR reflects a broader national trend seen across medical and surgical specialties, where escalating charges are not matched by commensurate increases in payer reimbursement [8, 9, 10, 13]. This has important implications for provider sustainability, particularly for practices that rely heavily on third‐party payers. Out of pocket (OOP) payments and private insurance accounted for the majority of expenditures in the office‐based setting, together representing almost 75% of total spending. The high proportion of OOP payments is particularly noteworthy, as it likely reflects the prevalence of elective and cosmetic procedures in this setting, which are often not covered by insurance. This aligns with the growing trend of consumer‐driven, aesthetic procedures performed in office environments [14, 15, 16]. Moreover, the rise in median OOP payments over time, more than doubling from $24.21 to $57.21, suggests an increasing financial burden on patients, particularly those pursuing cosmetic care.
Total annual expenditures for office‐based plastic surgery remained relatively stable over the study period despite rising per‐visit costs. This suggests that inflation in per‐visit expenses, not increased utilization, is the primary driver of expenditure. The drivers of these rising costs are likely multifactorial. Potential contributors include increasing practice overhead, such as rent, salaries, equipment, and administrative burdens, as well as the adoption of newer technologies and minimally invasive techniques that carry higher costs [17, 18, 19]. These trends raise important questions of whether increased spending per visit correlates with improved patient outcomes remains unclear and warrants further investigation.
4.2. Outpatient Department Visits
In contrast to office‐based care, hospital‐affiliated outpatient department plastic surgery visits demonstrated a different set of financial dynamics over the same period. While visit volume more than doubled from 379,222 in 2002 to 1,153,454 in 2022, this increase was not statistically significant. Nonetheless, the magnitude of change suggests that more plastic surgery care may be shifting toward hospital‐affiliated outpatient departments, possibly due to regulatory, safety, or logistical considerations. For example, more complex, or reconstructive procedures that require coordination with multidisciplinary teams or specialized resources may be increasingly concentrated in these settings [20].
One of the most striking findings in this cohort was the sharp decline in both median payments and charges. Median payment per visit declined almost 90%, and the overall median charge decreased by over 75%. Despite these large reductions in unit payments, the total adjusted annual expenditure for outpatient plastic surgery remained relatively stable, averaging approximately $2.1 billion. This stability suggests that increasing visit volume may have offset declining per‐visit payments and raises the possibility that hospitals are adapting to lower reimbursements through increased procedural throughput or changes in case mix. As in the office setting, we observed a significant decline in the payment‐to‐charge ratio, from 0.56 to 0.35. This decline was statistically significant and indicates a deepening gap between charges and reimbursements. Prior research has suggested that reimbursement rates from Medicare and Medicaid have not kept pace with inflation or the rising costs of surgical care [7, 13, 21, 22, 23, 24]. Our findings support this and may also reflect hospital pricing strategies that respond to declining public payer reimbursements by increasing healthcare charges.
Private insurance remained the largest source of payment in the outpatient department setting, contributing over half of total expenditures. The proportion of self‐pay visits was higher than what has previously been reported, likely reflecting the continued performance of cosmetic procedures in these settings [25]. Medicaid accounted for the smallest share of expenditures but was the only payer group to show a statistically significant increase in total spending over time. This may be consistent with, though cannot be directly attributed to, the Affordable Care Act‐related Medicaid expansion or the broader availability of reconstructive procedures for Medicaid beneficiaries in hospital settings [26].
Overall, the trends observed in outpatient department visits suggest a complex interplay between increasing utilization, declining per‐visit reimbursements, and changing payer dynamics. The downward pressure on reimbursements may incentivize hospitals to adjust pricing or expand service volume, but this may not be sustainable in the long term. Future research should explore whether rising expenditures are linked to improved access or outcomes, particularly among underserved populations.
4.3. Comparing Office‐Based and Outpatient Settings
Although office‐based and outpatient department visits represent fundamentally different practice settings, with distinct regulatory, operational, and clinical environments, our analysis reveals several overlapping trends. Both settings demonstrate increasing per‐visit costs, decreasing PCRs, and relatively stable total expenditures. This convergence suggests that plastic surgery, regardless of setting, is experiencing broader economic forces: reimbursement stagnation, cost inflation, and increasing patient demand for elective procedures.
However, the payer distributions highlight key differences. Office‐based care is predominantly financed through out‐of‐pocket and private insurance, consistent with the high proportion of cosmetic procedures. Outpatient department visits, while also dominated by private insurance, show a greater presence of public payers such as Medicare and Medicaid, which may reflect a larger proportion of reconstructive surgeries. These differences underscore the importance of considering care settings when designing cost‐containment strategies or reimbursement reform. Policymakers can help develop targeted interventions based on the specific economic realities of office versus outpatient plastic surgery care. For example, in the outpatient setting, policy efforts might focus on aligning Medicare reimbursement with procedure complexity or incentivizing efficiency through bundled payments. In contrast, strategies in office‐based care may center on price transparency, patient education, and regulation of out‐of‐pocket costs for elective services. Both settings would benefit from research exploring how rising costs affect patient access, practice sustainability, and long‐term outcomes.
4.4. Study Strengths/Limitations
The MEPS database represents one of the few nationally representative, all‐payer resources capable of capturing healthcare utilization and spending across diverse care settings. Its strength lies in the inclusion of both public and private payers, as well as patient‐reported financial data. However, the use of MEPS data comes with several important limitations that must be acknowledged.
First, as a survey‐based dataset, MEPS is subject to recall and reporting bias. Household respondents may underreport healthcare utilization, particularly for routine office visits. Indeed, prior studies comparing MEPS to Medicare claims have found that office‐based visits are underreported by as much as 19% [27]. Additionally, MEPS lacks procedural detail as CPT and diagnosis codes are not available, preventing differentiation between cosmetic and reconstructive procedures or assessment of case complexity.
Second, the sample sizes used for extrapolation, 5824 office‐based visits and 850 outpatient department visits, are relatively small and susceptible to sampling error, even with survey weighting. This introduces uncertainty into national estimates, particularly for subgroup analyses by payer type. Though we elected to analyze median in order to account for increased variability in the data, we observed large swings in median payments and charges observed in the outpatient department visits in particular, likely due to sample size limitations. Furthermore, MEPS does not capture care delivered in ambulatory surgery centers, which are an increasingly important setting of plastic surgery in recent years and may represent a growing share of outpatient procedures not reflected in this analysis [28, 29].
Furthermore, as an exploratory study, our statistical analyses were limited to correlations and trend analyses over time as well as descriptive statistics. We introduce some preliminary subgroup analyses based on variables like practice setting and payer type, but our statistical evaluations may not be adequately powered for these subgroup studies, and thus our conclusions of statistical significance may not be robust. Future studies using the MEPS database can be conducted using more focused research questions and extensive statistical testing.
Despite these limitations, MEPS remains a valuable tool for understanding macro‐level trends in healthcare utilization and spending. It provides a foundation for future studies aimed at refining our understanding of economic trends in plastic surgery, particularly when linked with more granular datasets or clinical registries. Continued investment in national, all‐payer databases with richer procedural detail will be essential for guiding reimbursement policy, resource allocation, and equity‐driven interventions in surgical care.
5. Conclusion
Our study provides one of the first national, all‐payer estimates of utilization and expenditures for both office‐based and hospital outpatient department plastic surgery. We demonstrate patterns of rising costs as well as decreasing reimbursements, driven by different payer mixes for the two practice settings surveyed. However, our analysis is limited by its retrospective, survey‐based nature and relatively small sample size. There is a paucity of existing data to compare these to, but this present study can serve as an early reference point for further analyses. Efforts should be made to understand the drivers of this cost inflation in plastic surgery, and initiatives to control cost while maintaining patient outcomes are important.
Author Contributions
Jason Zhang: conceptualization, investigation, writing – original draft, methodology, validation, visualization, writing – review and editing, software, formal analysis, data curation, project administration, supervision. Anitesh Bajaj: conceptualization, investigation, methodology, writing – review and editing, validation. Shivani A. Shah: conceptualization, investigation, methodology, validation, writing – review and editing. Kristian Nenchev: writing – original draft, writing – review and editing, validation. Timothy Burke: validation, writing – review and editing. Matthew D. Ramsey: conceptualization, writing – original draft, methodology, validation, writing – review and editing. Hannah Soltani: validation, writing – review and editing. Michael A. Howard: validation, writing – review and editing. Chad M. Teven: conceptualization, investigation, methodology, validation, writing – review and editing.
Funding
The authors have nothing to report.
Ethics Statement
This study is exempt from IRB review as it uses public, de‐identified survey data.
Conflicts of Interest
The authors declare no conflicts of interest.
Author Statement
All authors have read and approved the final version of the manuscript. Dr. Chad Teven had full access to all of the data in this study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis.
Transparency Statement
The manuscript guarantor, Dr. Chad Teven, affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.
Acknowledgments
We thank the co‐authors for the contributions to this study. Jason Zhang, Michael A. Howard, and Chad M. Teven participated in study design, data analysis, interpretation, and preparation of the manuscript. Anitesh Bajaj, Shivani A. Shah, Kristian Nenchev, and Timothy Burke assisted in data analysis and manuscript writing. Matthew D. Ramsey and Hannah Soltani helped with revisions and review of the manuscript. We also acknowledge the Agency for Healthcare Research and Quality for their work in creating and making available the Medical Expenditure Panel Survey. AI was not used in any way or form during the process of preparing this manuscript.
Data Availability Statement
The data that support the findings of this study are openly available in the Medical Expenditure Panel Survey at https://meps.ahrq.gov/mepsweb/.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are openly available in the Medical Expenditure Panel Survey at https://meps.ahrq.gov/mepsweb/.