Composition Vendor Newgen KnowledgeWorks (P) Ltd. PLoS One plos plosone PLOS One 1932-6203 Public Library of Science San Francisco, CA USA 10.1371/journal.pone.0350512 PONE-D-25-53640 Research Article Medicine and health sciencesHealth careHealth care providersPhysicians People and placesPopulation groupingsProfessionsMedical personnelPhysicians Medicine and health sciencesComplementary and alternative medicineHerbal medicine Social sciencesEconomicsHealth economicsHealth insurance Medicine and health sciencesHealth careHealth economicsHealth insurance Medicine and health sciencesHealth carePatientsOutpatients Medicine and health sciencesHealth careMedical services Medicine and health sciencesEpidemiologyMedical risk factorsTraumatic injury risk factorsRoad traffic collisions Medicine and health sciencesPublic and occupational healthTraumatic injury risk factorsRoad traffic collisions Biology and life sciencesPsychologyBehavior Social sciencesPsychologyBehavior Engineering and technologyManagement engineeringRisk managementInsurance Physician behavior for “invisible” treatment; Korean herbal medicine doctor’s treatment covered by auto insurance Korean herbal medicine doctor’s treatment https://orcid.org/0000-0002-9616-1858 Lee Changwoo Conceptualization Data curation Formal analysis Investigation Methodology Resources Software Supervision Validation Visualization Writing – original draft Writing – review & editing * Visiting Scholar, Department of Economics, Boston University, Boston, Massachusetts, United States of America Mahesh Pasyodun Koralage Buddhika Editor Ministry of Health, Sri Lanka, SRI LANKA

The authors have declared that no competing interests exist.

 * E-mail: changwooda@gmail.com 362026 2026 21 6 e0350512 3102025 1452026 2026 Changwoo Lee This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Background

This study examines healthcare utilization patterns and provider behavior under auto insurance coverage in South Korea. We investigate whether the significant growth in insurance claims for Korean Herbal Medicine (KHM) is driven by clinical necessity or supply-side structural incentives, such as physician-induced demand.

 Methods

We analyze the utilization gap between Conventional Medicine (CM) and Korean Herbal Medicine (KHM) using cross-sectional secondary data from the Korea Health Panel (KHP). The decomposition method developed by Chernozhukov et al. (2013) allows for the decomposition of differences across the entire distribution of medical visits and length of stay (LOS), distinguishing between patient characteristics and provider-side factors.

 Results

The results indicate that the higher utilization of KHM services is primarily attributable to structural factors rather than patient endowments. In the upper deciles of outpatient visits, structural effects accounted for over 100% of the observed difference, suggesting that provider-side incentives are the dominant driver of high-utilization outliers. Similarly, prolonged hospitalization in the KHM sector was largely unexplained by patient characteristics and remained robust after controlling for patient demographics.

 Conclusions

The observed disparities suggest that structural incentives within the KHM sector may influence provider behavior and utilization patterns differently than in the CM sector. While these findings are consistent with the theoretical framework of supply-side inducements, further research incorporating direct clinical severity measures is needed to establish definitive causal links. These results show the need for policy interventions targeting reimbursement structures to enhance the efficiency of auto insurance healthcare delivery.

 The author(s) received no specific funding for this work. Data Availability All Korea Health Panel Survey files are available from their database at https://www.khp.re.kr:444/web/data/data.do. Introduction

The rapid increase in Korean herbal medicine claims in auto insurance is a significant issue that has emerged in recent years. Total medical use claims in auto insurance were about 1.9 billion dollars in 2019. Among the total claims, Korean herbal medicine claims are approximately 0.8 billion dollars, accounting for 43.2% in 2019 from 23% in 2015 [1]. The disproportionate growth is evident when comparing the increase in total medical use claims, which rose 1.4 times, to the increase in herbal medicine claims, which rose 2.7 times over the five-year period from 2015 to 2019.

The medical costs for a car accident victim are covered by the assailant’s compulsory auto insurance, not national health insurance. The auto insurance fee schedule for the providers follows the national health insurance fee schedule. However, the fee schedule for medical services not covered by national health insurance is not well defined in auto insurance. Therefore, suppliers have different incentives for treating patients covered by auto insurance compared to those covered by national health insurance.

However, as Fig 1 indicates, Korean Herbal Medicine (KHM) doctors expand the medical services covered by auto insurance more aggressively than usual medical doctors, even though both types of doctors may engage in opportunistic behavior in these services. This study explores the causes of the rapid increase in Korean herbal medicine use covered by auto insurance for an assaulter in a traffic accident. In particular, we revisit the theoretical background of induced demand and attempt to explain the differences in marginal psychic costs between the two types of doctors, considering the role of opportunism. KHM doctors have a more integrative approach to treatment than conventional medicine (CM) doctors, which may make them less vulnerable to reimbursement cuts. It is challenging for the reviewer to assess the treatment result and refuse reimbursement. Therefore, it may give them more incentives than usual medical doctors to increase their profits through overtreatment [2,3].

10.1371/journal.pone.0350512.g001Trends in Korean herbal medicine claims cost.

We also attempt to empirically identify this difference and demonstrate a structural difference between herbal medicine doctors and conventional medical doctors in Korea. We employ the empirical methods suggested by Chrenozhukov et al. [4] to investigate the hypothesis that KHM doctors are more likely to pursue opportunistic behavior by inducing demand. Among those using medical care covered by the assaulter’s auto insurance, those who utilize herbal medicine and those who use conventional medicine are divided. We implement the decomposition of the distribution of medical use between herbal medicine and conventional medicine. We explain the difference in the distribution of medical use among mildly injured patients covered by auto insurance, comparing herbal medicine and conventional medicine, using outpatient data from the 2017 Korea Health Panel (KHP). To analyze differences in the distribution of medical use covered by auto insurance between herbal and conventional medicine, we use the length of stay (LOS) of inpatients covered by the assailant’s auto insurance.

The result shows that the difference between herbal medicine and conventional medical use covered by auto insurance is largest at the upper quantile of the distribution and is mainly attributable to structural differences. This means that the conditional distribution of herbal medicine differs structurally from that of conventional medical use. It might imply that KHM doctors’ opportunistic behaviors are more common than those of CM doctors at the upper quantile of medical use distribution.

The rest of the paper is organized as follows. Section 2 presents a theoretical background on why herbal doctors’ opportunistic behaviors are strengthened more than those of physicians. Section 3 presents the empirical model, and Section 4 describes the data set. Section 5 presents the result of the decomposition and discusses my findings and the limitations of the study. Finally, Section 6 concludes the paper.

 Theoretical background Treatment behavior of physicians

The literature on physicians’ opportunistic behavior is mainly on physician-induced demand (PID) [5]. Physicians act as patients’ agents to improve patients’ health status and pursue their own income and work satisfaction, which are related to the quantity they can set and the prices they charge [6]. According to Santerre and Rexford [5], studies beginning with Newhouse [7], Evans [6], Farley [8], and Fuchs [9] find that physicians sometimes capitalize on their asymmetric information relative to patients by increasing the demand for their services. Therefore, the theory based on PID treats physicians as “rent-seekers” rather than profit-seekers [5]. We refer to McGuire [10] for the theoretical model related to PID. McGuire [10] suggests a physician’s utility maximization problem as follows.

Max U=U(Y, I) where Y=N(m1x1(i1)+m2x2(i2)), I=N(i1+i2) 

The physician selects the level of inducement, i, to maximize her utility which depends on her net income, Y and the total inducement, I. m is the margin for each service equal to the difference between the doctor’s fee and the service’s cost [10].

The utility maximization condition is as follows.

m1x1′=m2x2′=−UIUY

The marginal (dollar) return to inducement for each service must be equated to the marginal psychic cost (in dollar term) of inducement [10]. Thus the argument of this study holds only if UI<0, UII<0 and marginal disutility from the inducement of herbal doctors is smaller than marginal disutility of regular doctors, i.e., UIKHM doctor<UICM doctor.

 Medical services in the auto insurance market as in-kind subsidy

Jones [2] presents a theory on the rent-seeking behavior of the producers in the in-kind subsidy market. The in-kind subsidy market could emerge as the altruistic community is generated. According to Jones [2], altruism is difficult to manifest at an individual level. Still, self-interested producers in in-kind subsidy markets exert political pressure for altruistic policies so that altruism can emerge, and, based on this, they establish in-kind subsidy policies. Producers in the in-kind subsidy market participate in the political process of subsidy policymaking, leading to policies that provide subsidies in kind rather than cash, thereby further strengthening their rent-seeking behavior.

Applying Jones [2]’s argument to the current provision of medical services of Korean auto insurance, it can be said that auto insurance, which is compulsory insurance, is a system that provides in-kind medical services to victims of traffic accidents through the perpetrator’s auto insurance. Because traffic accident victims are recipients of medical services compensated by the auto insurance of the at-fault driver, these services are a kind of in-kind compensation. A compulsory auto insurance subscription is an expression of an altruistic policy to ensure traffic accident victims receive proper medical services by forcing traffic accident perpetrators to subscribe to auto insurance. It compensates for the health of traffic accident victims in a policy manner. Medical service providers have incentives to pursue rent as producers of in-kind subsidies.

Suppose Jones [2]’s theory is combined with the theory in Section 2.1. In that case, the marginal psychic cost of induced demand for medical providers, directly related to the burden of patients’ medical expenses, may be reduced. In a system in which all victims’ medical costs are covered by insurance, the moral-psychic costs of opportunistic behavior by medical providers can be alleviated.

 Change in marginal psychic cost of herbal medicine providers

The marginal psychic costs of KHM doctors can be lower than those of CM doctors. The lower marginal psychic costs of KHM doctors may be attributable to transaction costs arising from opportunism. KHM providers have more opportunities in the auto insurance treatment contract than CM doctors due to the lack of information on the safety and effectiveness of herbal medicine, its ingredients, and its origin [11]. Herbal medicine has been developed as a more integrative treatment, based on Chinese meridian theory and using acupuncture, moxibustion, and traditionally recognized herbal medicines, rather than on a scientific assessment of safety and effectiveness. In addition, due to the collective political pressure of KHM doctors, they have been included in health insurance benefits since 2013.

KHM doctors are likely to exploit the legal status of herbal medicine, which allows it to be used without safety and effectiveness evaluation, unlike CM services. Therefore, the incentive for KHM doctors to capitalize on their advantages in the insurance review could appear stronger than that of medical doctors. Since in-kind subsidies for traffic accident victims can reduce marginal psychic costs for KHM doctors, their opportunistic behavior could be more substantial than that of CM doctors.

The advantages mentioned above in the insurance review of herbal medicine services motivate the KHM doctor, as an in-kind subsidy producer, to pursue inducing demand more than the CM doctor. Applying Jones’s theory [2] to these phenomena, car insurance holders who act as taxpayers or voters in his theory are not interested in finding information on the treatment effects of KHM services because the treatment is for the accident victims, not for them. Therefore, when insurance holders’ indifference to the treatment effect of herbal medicine and the advantages of insurance review for herbal medical services are combined, KHM doctors’ opportunistic behavior in inducing demand could be achieved more quickly than that of CM doctors. In addition, when auto insurance subscribers are indifferent to political pressure to expand KHM’s insurance benefits, KHM doctors can create favorable conditions for their rent-seeking.

 Methods Decomposition

This study employs an empirical method proposed by Chrenozhukov et al. [4] to test the hypothesis that herbal doctors may engage in rent-seeking behavior more than medical doctors. The Oaxaca-Blinder (1971) method attempts to decompose the difference between the oriental medicine service and the medical service. The decomposition technique can explain the difference between using herbal medicine and using regular medical services for two reasons. It can be explained by structural differences in medical use arising from differences in the coefficient estimates and from observable characteristics. The structural difference refers to the difference between the conditional probability of using herbal medicine services and that of using regular medical services. This study assumes that the structural difference in medical use between herbal and regular medicine corresponds to a difference in induced demand, i.e., in opportunistic behavior.

While the traditional Oaxaca-Blinder decomposition is limited to explaining differences in the mean of the dependent variable, this study employs the functional decomposition method developed by Chernozhukov et al. [4]. This approach is superior for our analysis because physician-induced demand in the Korean herbal medicine sector often manifests as extreme outliers in the upper deciles of utilization rather than uniform increases across the population. By estimating conditional and unconditional quantile effects, this method allows us to decompose the gap across the entire distribution of medical visits and costs. This enables us to distinguish whether the observed utilization gap is driven by patient characteristics (Endowment Effect) or by provider-side behavioral and incentive structures (Structural Effect) at various levels of treatment intensity.

The decomposition derives from the fact that the marginal distribution of an outcome(Y), in our case, medical use, is equal to the integral of its conditional distribution over the distribution of covariates(X) [12].

FYKHM(y)= ∫FYKHM|XKHM(y|x)dFXKHM(x)

Counterfactual distributions can be constructed by integrating the conditional distribution in herbal medicine over the distribution of covariates from regular medicine.

 FYKHMCM(y)=∫FYKHM|XKHM(y|x)dFXCM(x)

Implementation of the decomposition requires estimators of the conditional distribution of medical uses and the marginal distributions of its determinants. Distributional regression is used to obtain the estimators. The difference in the distribution of medical use between herbal medicine and regular medicine can then be decomposed as follows:

FYKHM(y)−FYCM(y)=[F^YKHM(y)−F^YKHMCM(y)]+[F^YKHMCM(y)−F^YCM(y)]+ε^

The first term on the right-hand side is the estimated difference in the distribution of medical use attributable to differences in the distributions of its determinants. The second term is the difference due to the structural shifts in the relationship of medical use to its determinants, i.e., the difference in the distribution of medical use conditional on a given distribution of determinants.

We present decompositions of differences in quantiles. These are obtained using the fact that the quantile function is the inverse of the cumulative distribution.

QYKHM(τ)−QYCM(τ)=[Q^YKHM(τ)−Q^YKHMCM(τ)]+[Q^YKHMCM(τ)−Q^YCM(τ)]+ν^ Data

This study is a cross-sectional secondary data analysis utilizing the Korea Health Panel (KHP) data from 2017. KHP, designed to sample from the 2015 Population and Housing Census data to ensure national representativeness, includes data on medical use as well as demographic and socioeconomic information at the individual level. The KHP also contains data on medical use covered by auto insurance, which is the main interest of this study. However, the KHP does not include information on medical expenses incurred for medical use covered by auto insurance, as such detailed items are investigated only when medical use is covered by health insurance. Only data on medical utilization covered by auto insurance are selected and investigated separately, distinguishing between outpatient and inpatient use. For the outpatient covered by auto insurance, 187 observations out of 301,540 are observed, and for the inpatient covered by auto insurance, 95 observations out of 3521 are observed in the 2017 KHP.

 Variables

The number of individual outpatient visits is used as a dependent variable for outpatient utilization. The number of outpatient visits variable is created by identifying individuals who paid medical expenses through auto insurance and summing the number of outpatient cases for which those expenses were paid for each individual. The length of stay (LOS) is a dependent variable for inpatient utilization.

Fig 2 shows the number of outpatient visits (A) and the length of stay (B) for each quantile of people who utilize medical care through auto insurance. The left one shows the number of outpatient visits, and the right one shows the length of hospital stay. There is little difference in the number of outpatient visits between herbal medicine and conventional medical utilization in the low quantiles. However, in the 5th decile, the number of visits to herbal medicine appears higher than that for conventional medical utilization. In addition, the length of hospital stays in herbal medicine appears to be higher than that in conventional medicine in the high quantiles.

10.1371/journal.pone.0350512.g002Distribution of the number of outpatient visits and the length of stay.

Table 1 presents descriptive statistics for the dependent variable and the covariates used in the outpatient visit decomposition. Using auto insurance claims, outpatients visited about eight times on average during 2017. In addition, socioeconomic variables such as male dummy, married dummy, college dummy indicating the respondent have college degree, disability dummy indicating whether the respondent was classified as having a disability by the government; categorical income dummies, age; and labor income level are included as covariates in the decomposition analysis.

10.1371/journal.pone.0350512.t001Descriptive statistics of variables for outpatient visit decomposition.
Variable(Obs = 187) Mean Std. Dev.
Number of Outpatient Visits 7.963 14.581
Male .433 .497
Married .668 .472
Age 48.914 18.649
College .374 .485
Disability .096 .296
Income Quintile 1 .07 .255
Income Quintile 2 .182 .387
Income Quintile 3 .251 .435
Income Quintile 4 .278 .449
Income Quintile 5 .219 .415
Labor income 1565.171 1969.209

Note: For dummy variables, the Mean represents the proportion of the sample.

Table 2 presents the descriptive statistics for the dependent variable and covariates in the LOS decomposition. According to auto insurance claims, the average length of stay was approximately 11 days in 2017. Socioeconomic factors, as in the outpatient analysis, are covariates in the decomposition analysis.

10.1371/journal.pone.0350512.t002Descriptive statistics of variables for LOS decomposition.
Variable(Obs = 95) Mean Std. Dev.
LOS(Length of Stay) 11.084 9.251
Male .442 .499
Age 48.211 18.425
Married .642 .482
College .316 .467
Labor income 1435.768 1705.468
Disability .105 .309
Income Quintile 1 .095 .294
Income Quintile 2 .274 .448
Income Quintile 3 .179 .385
Income Quintile 4 .253 .437
Income Quintile 5 .2 .402
Results

Table 3 shows the results of decomposing the difference in quantiles of outpatient use between herbal medicine and regular medicine into the difference attributable to the determinants and the difference attributable to the conditional probability. These results are derived from the distribution regression using a linear probability model. In the overall quantile, the total difference in the number of visits between herbal and regular medicine has a similar pattern, with the difference attributable to structural differences. The direction of the structural difference appears to account for the overall difference across all quantiles.

10.1371/journal.pone.0350512.t003Decomposition of differences in quantiles of outpatient visit number b/w Korean herbal medicine and conventional medicine.
Quantile Differences b/w the observable distribution Difference in covariates Differences in conditional distribution
0.1 0.349 −0.182 0.531
0.2 1.000 −0.318 1.318
0.3 1.911 −0.089 2.000
0.4 2.802 −0.127 2.929
0.5 4.050 −0.661 4.711
0.6 4.877 −1.837 6.714
0.7 6.697 −2.263 8.960
0.8 9.064 −0.238 9.302
0.9 7.186 −1.122 8.309

The decomposition results for outpatient visits indicate that structural factors (conditional distribution) are the primary drivers of the utilization gap, particularly in the higher deciles. Specifically, the ratio of the structural effect to the total observed difference shows that structural factors account for 133.8% (8.96 of 6.697 visits), 102.6% (9.302 of 9.064 visits), and 115.6% (8.309 of 7.186 visits) of the differences in the 7th, 8th, and 9th deciles, respectively. These values exceeding 100% signify that the structural gap is wider than the total observed difference, being partially offset by negative endowment effects (patient characteristics) in those quantiles.

The sizable structural difference in the high quantile of outpatient visits may indicate that herbal medicine is more likely to be provided structurally than conventional medical services. These significant structural differences suggest that reimbursement incentives and provider-side factors play a more prominent role in KHM than in CM. This result also shows that the marginal utility of KHM doctors in inducing demand may be greater than that of CM doctors, in line with the theoretical background. While these findings are consistent with the theoretical framework of physician-induced demand, they also show how the uniqueness of KHM’s fee review may create structural environments in which utilization patterns deviate significantly from those driven solely by patient need.

If outpatients in traffic accidents generally get mildly injured, the outpatients in the higher quantile of visits are structurally more likely to use herbal medicine than conventional medicine. Therefore, this can explain the rapid increase in the cost of herbal medicine in recent years.

Fig 3, which visualizes Table 1, shows the difference in the quantile function of outpatient visits between herbal and conventional medicine. In the lower quantile, there is little difference in the number of outpatient visits between herbal and conventional medicine. However, as the quantile of outpatient use increases, the difference grows, peaking at the 8th quantile. The structural difference in outpatient visits between herbal medicine and conventional medicine, the main focus of this study, increases at the quantile with high numbers of outpatient visits. The difference in outpatient utilization between herbal and conventional medicine, attributable to the determinants, shows a sharp decrease at the high quantile.

10.1371/journal.pone.0350512.g003Decomposition of differences in quantiles of outpatient visits between herbal medicine and conventional medicine.

Table 4 shows the results of decomposing the difference in quantiles of the length of hospital stay between herbal medicine and conventional medicine into the difference attributable to the determinants and the difference attributable to the conditional probability. These results are derived from the distribution regression using a linear probability model. In the overall quantile, the difference in LOS between herbal and conventional medicine attributable to determinants follows an opposite pattern, whereas the difference attributable to structural differences is positive. Above the first deciles, the directions of the effects are opposite, and they offset each other’s effects. Changes in the relationship of hospital stay to the conditional distribution account for seven-day differences (442%) of a 1.5-day increase in the 7th decile, two days (300%) of a 1-day decrease in the 8th decile, and 8.842 days(186%) of a 4.75-day increase in the 9th decile. The difference in the LOS quantiles between herbal and conventional medicine is positive only at the 7th and 8th quantiles. Across the entire distribution of Length of Stay (LOS), the endowment effect (determinants) consistently shows negative values, suggesting that based on patient characteristics alone, KHM patients would be expected to have shorter stays. However, the structural effect (provider-side factors) is primarily positive and dominant. This indicates that the observed prolonged hospitalization in the KHM sector is not driven by patient health needs but is largely attributable to structural incentives and provider behavior within the herbal medicine framework.

10.1371/journal.pone.0350512.t004Decomposition of differences in quantiles of LOS b/w Korean herbal medicine and conventional medicine.
Quantile Differences b/w the observable distribution Difference in covariates Differences in conditional distribution
0.1 −1.152 −1.932 0.780
0.2 −1.047 −2.988 1.941
0.3 −0.500 −3.500 3.000
0.4 −0.801 −4.416 3.614
0.5 −1.794 −5.680 3.886
0.6 −3.312 −7.898 4.586
0.7 1.583 −5.419 7.002
0.8 4.750 −4.093 8.843
0.9 −3.011 −1.534 −1.477

The result shows that the LOS in herbal medicine hospitals is structurally more prolonged than in regular hospitals in the most quantile of LOS. Hospitalization is usually highly severe, so it may show that patients with severe traffic accidents do not use herbal medicine often. It may also indicate that patients who are severely injured in traffic accidents and are hospitalized through herbal medicine will likely stay longer in KHM institutions than in CM hospitals.

This structural difference may suggest that the induced demand for inpatient use in herbal medicine occurs more frequently than in conventional medicine. In other words, the opportunistic behavior of herbal doctors may occur more frequently than that of regular medical doctors, even in inpatient settings. A positive structural difference in the overall quantile of LOS between herbal and conventional medical services may indicate that KHM doctors are structurally more likely to provide medical services than CM doctors.

Fig 4, which visualizes Table 4, shows the difference in the quantile function of the LOS between herbal and conventional medicine. There is little difference in the LOS between herbal and conventional medicine in the lower quantile. As the quantile of LOS increases, the difference jumps to positive but tends to be negative at the last quantile. However, the structural difference in the LOS between herbal and conventional medicine, which is the main interest of this study, is increasing at the most quantile.

10.1371/journal.pone.0350512.g004Decomposition of differences in quantiles of LOS between herbal medicine and regular medicine. Conclusions

This study investigates the causes of over- and underutilized medical use when a traffic accident victim uses medical care covered by the assailant’s car insurance through theoretical background and empirical analysis. Jones’s theory [2] on strengthening producers’ incentives for opportunistic behavior in the in-kind subsidy market confirms that healthcare providers may induce opportunistic behavior toward the traffic accident victim covered by car insurance. Reviewing herbal medical services is complex, and rejection at the review stage is less likely. It is due to uncertainty in treatment fees and recognition criteria, an inadequate system for determining treatment fees, severe information asymmetry, and supervisors’ blind spots. The KHM doctors, taking advantage of this, are likely to induce demand for their services, which may be covered by the assailant’s auto insurance.

Using the method suggested by Chrenozhukov et al. [4], we examine the hypothesis that KHM doctors’ opportunistic behavior is more prevalent than that of CM doctors. The results show a significant structural difference between herbal and conventional medicine in the high quantile of medical utilization. This result indicates that medical services are provided more structurally in herbal medicine than in conventional medicine when a patient seeks the same type of service. However, as this is an observational study using secondary data, these results should be interpreted as identifying associations rather than definitive causal links. Future research incorporating clinical severity measures and direct provider-level data is required to further distinguish between clinical necessity and supply-side inducements.

While the overall KHP is large, the subset of auto-insurance claimants is smaller, which is why the Chernozhukov method is so valuable for extracting meaning from this specific group. Since this study lacks data on the number of inpatient observations covered by auto insurance, the results indicating that KHM doctors may engage in more opportunistic activities through induced demand than medical doctors warrant further investigation in future research. However, the empirical results of this paper suggest that KHM doctors may be influenced by supply-side incentives, as outpatient and inpatient results are consistent.

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5 Nov 2025

PONE-D-25-53640Physician behavior for “invisible” treatment; Korean herbal medicine doctor's treatment covered by auto insurancePLOS ONE

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 10.1371/journal.pone.0350512.r002 Author response to Decision Letter 1 Submission Version 1

6 Jan 2026

Response to Editor

Comment 1 (Editor):

“The current version of the abstract is less informative. You are requested to revise it (especially the methods and results sections) before the review process can be further continued.”

Response:

We appreciate the editor’s helpful guidance. In response, we have substantially revised the abstract to provide more precise and more detailed information on the study design, data source, empirical method, and main findings. Specifically, we now summarize the dataset used, the decomposition method applied, and the key quantitative results highlighting the structural differences in medical utilization between herbal and conventional medicine.

The updated abstract is as follows:

Background

This study examines the sharp increase in the use of Korean herbal medicine covered by auto insurance following traffic accidents, focusing on whether this trend reflects the opportunistic behavior of Korean herbal medicine doctors.

Methods

We extend the theoretical framework of physician-induced demand by incorporating differences in marginal costs between medical and herbal doctors. Using the Korea Health Panel data from 2017, we apply a recently developed decomposition method to compare utilization patterns between conventional and herbal medicine providers treating the same types of injuries.

Results

The analysis identifies significant structural differences in treatment behavior between herbal and medical doctors. At higher quantiles of medical utilization, herbal medicine shows disproportionately greater service provision, consistent with more substantial opportunistic incentives. Changes in the relationship between outpatient visits and structural differences account for 8.96 visit differences (133%) of 6.697 visits, a 9.302 visit increase (102%) of 9.064 visits, and 8.309 visit differences (115%) of 7.186 visits in the 7th, 8th, and 9th deciles, respectively. Changes in the relationship of hospital stay to the structural differences account for seven-day differences (442%) of a 1.5-day increase in the 7th decile, two days (300%) of a 1-day decrease in the 8th decile, and 8.842 days (186%) of a 4.75-day increase in the 9th decile. These results remain robust after controlling for patient characteristics and injury severity.

Conclusions

Findings suggest that herbal medicine services under auto insurance are more structurally driven by provider behavior than patient need, implying a need for closer scrutiny of reimbursement and review procedures to mitigate opportunistic practice.

Submitted filename: Rebuttal letter_LEE.docx

 10.1371/journal.pone.0350512.r003 Decision Letter 1 Mahesh Pasyodun Koralage Buddhika Academic Editor 2026 Pasyodun Koralage Buddhika Mahesh This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 1

24 Feb 2026

PONE-D-25-53640R1Physician behavior for “invisible” treatment; Korean herbal medicine doctor's treatment covered by auto insurancePLOS One

Dear Dr. Lee,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Pasyodun Koralage Buddhika Mahesh

Academic Editor

PLOS One

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Dear Dr. Lee,

Please note that my comments have been attached separately. I wish you all the best.

Thank you

Reviewer #2: This manuscript addresses an important and relevant topic concerning healthcare utilization and provider behaviour under auto insurance coverage. The use of nationally representative Korea Health Panel data enhances the external validity and generalizability of the findings. The study is supported by a strong theoretical foundation based on physician-induced demand and health economics principles. Applying a decomposition method to examine structural differences in utilisation patterns is methodologically appropriate and provides useful insight into potential differences between herbal and conventional medicine providers. The results are clearly presented using tables and figures, and the findings contribute to policy discussions regarding reimbursement systems and provider incentives.

However, several deficiencies need to be addressed:

• Absence of a separate Methods section: The manuscript does not include a clearly defined Methods section. Instead, elements of study design, empirical methods, and data description are dispersed across the Introduction, theoretical background, and separate sections such as “Decomposition” and “Data.” This structure reduces clarity and makes it difficult for readers to fully understand and replicate the study methodology.

• Study design not explicitly stated: The manuscript does not clearly identify the study as an observational cross-sectional secondary data analysis, which is essential for transparency and appropriate interpretation.

• Incomplete description of participants: The total sample size, inclusion and exclusion criteria, and the number of participants in each comparison group are not clearly reported, limiting assessment of representativeness and potential selection bias.

• Insufficient discussion of bias and confounding: Potential sources of bias, including residual confounding, selection bias, and limitations inherent to secondary data analysis, are not adequately addressed.

• Unclear variable definitions: The exposure and outcome variables are not explicitly defined in sufficient detail, which affects reproducibility and interpretability.

• Overinterpretation of findings: Some conclusions attribute structural differences directly to opportunistic behaviour without fully acknowledging the limitations of causal inference in observational studies.

• Limited discussion of study limitations: The limitations section does not comprehensively address key methodological constraints, including lack of clinical severity measures and possible unmeasured confounding.

• Inconsistent terminology: Multiple terms such as “normal doctors,” “usual doctors,” “ordinary doctors,” and “conventional doctors” are used interchangeably, which may create confusion and reduce clarity.

• Use of first-person narrative: The manuscript uses first-person expressions such as “I refer to” and “I present,” which are inconsistent with standard scientific writing conventions and reduce the objectivity of the manuscript.

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Reviewer #1: No

Reviewer #2: Yes:I.O.K.K.Nanayakkara

**********

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NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications.

Submitted filename: PLOS one reviewers comments.pdf

 10.1371/journal.pone.0350512.r004 Author response to Decision Letter 2 Submission Version 2

14 Apr 2026

[Response to Reviewer #1]

Major Comment 1: Justification for the Chernozhukov et al. (2013) method.

Response: We thank the reviewer for this insightful suggestion. We have revised the manuscript (Page 8) to clearly articulate the advantages of the Chernozhukov et al. (2013) method over the traditional Oaxaca-Blinder approach. Specifically, we now emphasize that while the traditional method only captures differences in the mean, the functional decomposition method allows us to analyze the entire distribution (quantiles). This is crucial for our hypothesis, as opportunistic provider behavior—represented here as a structural effect—is more likely to be prevalent in the higher deciles of healthcare utilization. We have also clarified the roles of endowment effects and structural effects to make the methodology more accessible to a broader audience.

Major Comment 2: Clarifying “Structural Effects.”

Response: We have added a brief explanation in the Methods section. We now clarify that Endowment Effects refer to differences in patient characteristics (e.g., age, education, income), while Structural Effects represent the 'unexplained' gap—which, in this study, we attribute to the differing incentive structures and behaviors of Korean Herbal Medicine (KHM) vs. Conventional Medicine (CM) providers.

Major Comment 3: Clarifying “Opportunistic behavior of the physician.”

Response: We now frame the structural differences as being indicative of supply-side incentives rather than definitive proof of opportunistic behavior. We have also explicitly acknowledged the limitations of causal inference in our observational study design.

Major Comment 4: Clarifying “Injury severity.”

Response: The variable named ‘disability’ is not a reflection of the severity of injury. We don’t have any information on the severity of injury in the data. We added an explanation of the ‘disability’ definition in the Variable section. We have also removed the injury severity from the manuscript to avoid confusion.

Major Comment 5: Regarding Table 1 and 2

Response: We explicitly define "LOS" (Length of Stay), "Income Quintiles," and rename "outpatient visit no" to "Number of Outpatient Visits." We added an explanation of the ‘disability’ definition in the Variable section. ‘Disability’ dummy indicates whether the respondent is classified as having a disability by the government.

Major Comment 6: Regarding the observation number of the data.

Response: We have added details on how many observations are used for this study in the data section. For the outpatient covered by auto insurance, 187 observations out of 301,540 are observed, and for the inpatient covered by auto insurance, 95 observations out of 3521 are observed in the 2017 KHP.

Major Comment 7: Certain conditions in the theoretical background.

Response: We have added a statement that our empirical results indicate the marginal utility of KHM doctors in inducing demand may exceed that of CM doctors, consistent with the theoretical background in the manuscript.

Major Comment 8 & 9: Reporting the ratio of conditional to observable outcomes.

Response: We have revised the reporting of our results on Pages 13 and 15 to enhance clarity. We now explicitly state that we are reporting the ratio of the structural (conditional) effect to the total observed difference. We have also rephrased the results to clearly reflect that positive structural values signify provider-side drivers of utilization, while negative endowment values indicate that patient characteristics do not explain the observed gap. This clarification ensures that the significance of these values is immediately accessible to the reader.

Minor Comments: Use of language, Labelling figures, tables and equations,

Response: We consistently use Conventional Medicine (CM) doctors and Korean Herbal Medicine (KHM) doctors. We also changed the labeling accordingly. We have switched entirely to an impersonal voice (e.g., "This study finds...") or to a consistent "We" if the journal allows it. We have converted all citations to Vancouver Style as required by PLOS ONE.

[Response to Reviewer #2]

Comment 1: Absence of a separate Methods section.

Response: We sincerely apologize for the lack of clarity. Following your suggestion, we have created a dedicated 'Methods' section which consolidates empirical methods, data and variables previously dispersed throughout the manuscript.

Comment 2: Study design not explicitly stated.

Response: We have now explicitly identified the study as a cross-sectional secondary data analysis in both the Abstract and the new Methods section.

Comment 3: Incomplete description of participants.

Response: We have added details on how many observations are used for this study in the data section. For the outpatient covered by auto insurance, 187 observations out of 301,540 are observed, and for the inpatient covered by auto insurance, 95 observations out of 3521 are observed in the 2017 KHP.

Comment 4: Insufficient discussion of bias and confounding.

Response: We have expanded the Limitations section to include a discussion on the lack of clinical severity measures and the potential for unmeasured confounding.

Comment 5: Unclear variable definitions.

Response: We have added a variable section and explained the variables used in the study.

Comment 6: Overinterpretation of findings.

Response: We have carefully revised the Results and Conclusions sections to soften our claims. We now frame the findings as "indicative of structural incentives" rather than "direct proof of opportunistic behavior," acknowledging the constraints of observational research.

Comment 7: Limited discussion of study limitations.

Response: We have expanded the Limitations section to include a discussion on the lack of clinical severity measures and the potential for unmeasured confounding.

Comment 8: Inconsistent terminology (Normal, Usual, etc.).

Response: We sincerely apologize for the confusion. We have standardized all terms to "Conventional Medicine (CM) doctors" and “Korean Herbal Medicine (KHM) doctors” throughout the revised manuscript.

Comment 9: Use of first-person narrative

Response: We have switched entirely to an impersonal voice (e.g., "This study finds...") or a consistent "We" if the journal allows it.

Submitted filename: Response to Reviewers_2ndRevision.docx

 10.1371/journal.pone.0350512.r005 Decision Letter 2 Mahesh Pasyodun Koralage Buddhika Academic Editor 2026 Pasyodun Koralage Buddhika Mahesh This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 2

15 May 2026

Physician behavior for “invisible” treatment; Korean herbal medicine doctor's treatment covered by auto insurance

PONE-D-25-53640R2

Dear Dr. Lee,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Pasyodun Koralage Buddhika Mahesh

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Dear Dr. Lee,

Thank you very much for your impressive work and for considering my suggestions in the revised version of your manuscript.

All the very best to you and your colleagues!

Reviewer #2: The authors have carefully revised the manuscript and addressed all reviewer comments appropriately. I have no additional suggestions and recommend acceptance.

**********

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If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes:Herath Mudiyanselage Chathurika Dulmini Herath

Reviewer #2: Yes:I.O.K.K.Nanayakkara

**********

 10.1371/journal.pone.0350512.r006 Acceptance letter Mahesh Pasyodun Koralage Buddhika Academic Editor 2026 Pasyodun Koralage Buddhika Mahesh This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PONE-D-25-53640R2

PLOS One

Dear Dr. Lee,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS One. Congratulations! Your manuscript is now being handed over to our production team.

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PLOS One