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Abstract

China required regions to expand outpatient benefit package since 2021, but whether it may induce moral hazard among non-chronic disease patients remains undetermined. We aimed to clarify effects of expanding outpatient benefit package on health-seeking behaviors, medical expenses and reimbursement of people without chronic disease. This study employed segmented regression of interrupted time series, utilized 5 765 419 non-chronic disease patients’ medical services records from January 2021 to December 2023 in Guangzhou, to analyze influence of expanding outpatient benefit package on non-chronic disease patients. (1) For health-seeking behaviors: monthly medical visits (β₁ + β₃=0.0069, P = .001) and the monthly number of outpatient visits (β₁ + β₃=0.0066, P = .001) exhibited a significant increasing long-term trend. This corresponds to significant annual increases of 0.08 per person in both medical and outpatient visits. (2) For medical expenses: the monthly outpatient medical expenses showed a significant upward trend (β₁ + β₃ = 6.9308, P = .002), which was equivalent to a significant increase of 83.17 yuan in outpatient medical expenses per person per year. (3) For expenditures of basic medical insurance funds: expenditure of basic medical insurance funds on outpatient medical services were significantly higher than pre-intervention levels by 35 yuan (β₃ = 2.9169, P = .036), with a sustained significant growth rate of 52.51 yuan per person per year (β₁ + β₃ = 4.3762, P = .004). Monthly outpatient out-of-pocket expenses exhibited a significant instantaneous decrease of 135.27 yuan (P < .001), followed by a significant upward trend (β₁ + β₃ = 2.5562, P = .038), corresponding to an annual increase of 30.67 yuan per person. Within well-designed payment models, expanding outpatient benefit package among patients without chronic disease could enable them to get a more comprehensive and prompt treatment while total medical expenses and expenditure of the basic medical insurance funds are stable totally.

Keywords

expanding outpatient benefit package health-seeking behaviors medical expenses expenditure of the basic medical insurance funds non-chronic disease patients interrupted time series analysis

Introduction

Outpatient services, as the cornerstone of modern healthcare systems, play a critical role in early disease screening, primary diagnosis and treatment, chronic disease management and patient education, which is essential for optimizing medical resource allocation and reducing societal disease burdens.^1,2 In China, over 90% of all medical services are delivered through outpatient care (China Health Statistics Yearbook (2016-2023) shows annual outpatient visits accounted for 93.60% of the total medical visits on average). Against the backdrop of socioeconomic progress, demographic aging, transformation of spectrum of disease and medical technology innovations, the demand for outpatient services among China’s insured populations has become increasingly prominent compared to inpatient care in recent years. According to China Health Statistics Yearbook, from 2015 to 2020, outpatients’ per visit medical cost grew at an annual average rate of 6.79%, surpassing the 5.14% annual growth rate of inpatients’ per admission medical costs during the same period. However, owing to limited pooled funds and management capacity, the Chinese social health insurance system (SHI) only reimbursed the hospitalization expenses while outpatient expenses were predominantly settled through out-of-pocket payments or individual medical savings accounts in the early years. Along with advancement of China’s health care system reform, several regions attempted to make some compensation for outpatient costs of some chronic conditions like diabetes and hypertension. But it still couldn’t change the fact that the reimbursement for outpatient expenses was relatively low,³ especially compared with inpatient care, which may discourage people from utilizing outpatient services, even inducing unjustified hospital admissions.^4,5 To deal with these problems, the Chinese government issued the Guidelines on Establishing a Mutual-Aid Outpatient Guarantee Mechanism for Urban Employee Basic Medical Insurance in April 2021, requiring regions to set up a pooling mechanism for costs of general outpatient services formally so as to further relieve medical financial burdens. Subsequently, all regions set out to expand their own benefit package for outpatient services one after another in response to national requirements.

Theoretically, expanding outpatient benefit package could help to alleviate patients’ economic burden, take unmet medical needs of outpatient services and mitigate previously prevalent unjustified hospitalization demands stemming from inadequate compensation for outpatient services.^6,7 Meanwhile, it enables patients to receive timely treatment that prevents minor illnesses from developing into serious conditions.^8
-10 As a result, patients’ health outcomes improve while their medical expenses are reduced, with savings realized in the expenditure of the basic medical insurance funds,^11,12 which aligns fundamentally with principles of cost-effectiveness.¹³ These hypotheses have been substantiated by ample evidence in prior researches. For example, Du et al¹⁴ and Liu et al¹⁵ found expanding outpatient benefit package for diabetes outpatient care will make patients have a more rational health-seeking behaviors, a better health status while total medical expenses and expenditure of the basic medical insurance funds decrease at the same time. Similar conclusions also emerge in researches examining the impacts of polishing up outpatient coverage on hypertensive and neoplastic disease cohorts (He et al⁴; Jiang et al¹⁶).Nevertheless, some scholars have raised objections to this position. On the one hand, the current evidence for outpatient benefit package enhancements centers overwhelmingly on chronic disease populations, leaving influence on acute or episodic care patients largely unstudied. Considering the significant disparities in utilization patterns of outpatient services between these 2 cohorts,^17,18 whether existing findings are generalizable to people without chronic conditions remains uncertain. On the other hand, at present the financing mechanism for outpatient pooled funds of Urban Employee Basic Medical Insurance—which reallocates premiums from individual medical savings accounts—may incentivize moral hazard in non-chronic disease populations through excessive service utilization,¹⁹ ultimately bringing about inefficient resource use and unnecessary claims on the basic medical insurance funds. Official statistics from China Medical Security Statistical Yearbook,2024 show that there were 2.18 billion outpatient visits obtaining the claims in 2023, representing a 23.86% year-over-year increase that notedly exceeded the growth rate of 2.15% in 2022. Does the blowout growth in outpatient utilization involve unreasonable or inappropriate service use? No scholarly consensus has been established on this matter to date.

Given the unresolved contention, this paper employs China’s unified medical insurance claims database—with complete health service utilization records for all enrollees—to examine the following questions via an interrupted time-series regression analysis: (1) Will expanding outpatient benefit package affect non-chronic disease populations’ health-seeking behaviors and medical expenses? (2) Does expanding outpatient benefit package for non-chronic disease populations lead to accelerated growth of expenditure of the basic medical insurance funds and induce moral hazard?

Methods

Changes in Policy and Data Overview of the Sample Region

Changes in Policy of the Sample Region

Prior to December 2022, the outpatient benefit package for Urban Employee Basic Medical Insurance in Guangzhou, Guangdong Province, China was structured as follows: all eligible outpatient expenses incurred by the insured were reimbursable through the basic medical insurance funds according to a unified standard. No deductible applied for outpatient visits. For outpatient services delivered at community health service centers, pooled funds covered 80% of expenses, otherwise 55%. The reimbursement cap line for outpatient services was RMB300 per month.

After December 2022, Guangzhou optimized its outpatient benefits on the basis of the original system aforementioned. No modifications was made to the deductible. For outpatient services delivered at community health service centers, pooled funds cover 80% of expenses, otherwise 65%. But for retirees, the coverage percentage above mentioned increased by 5%. The reimbursement cap line for outpatient services now is RMB7200 per year and for retirees is RMB10 100 per year.

Details of Sample Data

Since 2021, there were no major policy change in Urban Employee Basic Medical Insurance for inpatient services in Guangzhou. And for outpatient services, the only reform was improving its outpatient benefits as mentioned before. To ensure the accuracy of results, this study chose to make use of detailed information concerning enrollees’ healthcare services from 2021 to 2023. As a retrospective study, the insured are divided into chronic disease cohorts and non-chronic disease cohorts by Guangzhou Healthcare Security Administration according to an official document which listed chronic disease with their identification criteria at the beginning of each year.

Therefore, we adopted a fixed cohort design. Specifically, we first identified all unique individuals who were registered in the chronic disease database in any year from 2021 to 2023, obtaining a union set of 1 070 173 chronic disease patients. This set of patients was then entirely excluded from all individuals who had outpatient visits during the 3-year period.

Finally, a total of 5 765 419 non-chronic disease patients were fully included in this study, which could avoid retrospective bias due to the use of survey data or sampling data and help improve the accuracy of the study results. The detailed data included all the medical visits records of both outpatient services and inpatient services utilized by non-chronic population within 3 years, whose amount were 59 767 971 records, including 58 605 421 outpatient records and 1 162 550 inpatient records. The claims database contained individual information (beneficiary ID, gender, age), medical institution information (name, level, type of institution), medical visit information (admission date and discharge date, outpatient service or inpatient service, disease diagnosis and disease code) and expenses information (total medical expenses, outpatient expenses, inpatient expenses, out-of-pocket expenses, expenditure of the basic medical insurance funds; Supplemental Additional File 1).

Study Model

Interrupted Time Series Analysis(ITSA)

Interrupted time series analysis (ITSA) has been proven to be the most effective quasi-natural experiment design for evaluating policy intervention effects.^20,21 In this study, the breakpoint was set on December 1st, 2022 when improving outpatient benefit was officially executed in Guangzhou as an intervention point of the policy.

Variable Selection

The dependent variables (Y_i) in this study included non-chronic disease patients’ health-seeking behaviors, medical expenses, expenditure of the basic medical insurance funds for them, which spanned 36 months from January 2021 to December 2023. See Table 1 for specific indicators of variables and their descriptions.

Table 1.

Specific Indicators of Variables and Their Interpretation.

Variables			Definition
Dependent variables	Health-seeking behaviors	Monthly number of medical visits (Y₁)	Total number of medical visits of a non-chronic disease patient per month, which is equal to his or her monthly number of outpatient visits plus monthly number of inpatient visits
		Monthly number of outpatient visits (Y₂)	Total number of outpatient visits of a non-chronic disease patient per month
		Monthly number of hospitalizations (Y₃)	Total number of inpatient visits of a non-chronic disease patient per month
		Monthly average length of a hospital stay (Y₄)	Average hospital days per patient per month among non-chronic disease patients
	Medical expenses	Monthly medical expenses (Y₅)	Total amount of medical expenses incurred by a non-chronic disease patient per month, which is equal to his or her monthly outpatient expenses plus monthly inpatient expenses
		Monthly outpatient expenses (Y₆)	Total amount of outpatient expenses incurred by a non-chronic disease patient per month
		Monthly inpatient expenses (Y₇)	Total amount of inpatient expenses incurred by a non-chronic disease patient per month
	Expenditure of the basic medical insurance funds	Monthly expenditure of the basic medical insurance funds on medical services (Y₈)	Total expenses paid by the basic medical insurance funds in the medical expenses incurred by a non-chronic disease patient per month
		Monthly expenditure of the basic medical insurance funds on outpatient services (Y₉)	Total expenses paid by the basic medical insurance funds in the outpatient expenses incurred by a non-chronic disease patient per month
		Monthly expenditure of the basic medical insurance funds on inpatient services (Y₁₀)	Total expenses paid by the basic medical insurance funds in the inpatient expenses incurred by a non-chronic disease patient per month
		Monthly out-of-pocket expenses (Y₁₁)	Expenses that are covered by the basic medical insurance but fall within the deductible and above the reimbursement cap per month; and medical expenses that fall above the deductible and below reimbursement cap but need to be borne by a non-chronic disease patient per month
		Monthly outpatient out-of-pocket expenses (Y₁₂)	Expenses in outpatient services that are covered by the basic medical insurance but fall within the deductible and above the reimbursement cap per month; and outpatient expenses that fall above the deductible and below reimbursement cap but need to be borne by a non-chronic disease patient per month
		Monthly inpatient out-of-pocket expenses (Y₁₃)	Expenses in inpatient services that are covered by the basic medical insurance but fall within the deductible and above the reimbursement cap per month; and inpatient expenses that fall above the deductible and below reimbursement cap but need to be borne by a non-chronic disease patient per month
Independent variables	Time		The study period encompassed 23 months pre-implementation (January 2021–November 2022) and 13 months post-implementation (December 2022-December 2023) of the policy

Statistical Analysis

We employed segmented regression of interrupted time series to analyze policy impacts on 13 dependent variables metrics. The intervention occurred in December 2022, which coincided with the lifting of “Zero-COVID” policies and a subsequent surge in COVID-19 infections. To address these concurrent events, we included the natural logarithm of the monthly count of confirmed COVID-19 cases as a covariate. Furthermore, to account for seasonal patterns in healthcare utilization, particularly the increase in respiratory illnesses in winter, we controlled for seasonal effects using third-order Fourier terms in the models, which provided the best fit and were thus selected for the primary analysis. The model was set as follows:

\begin{array}{l} Y_{t} = β_{0} + β_{1} T_{t} + β_{2} X_{1 t} + β_{3} X_{1 t} T_{1 t} + γ \ln (C_{t} + 1) \\ + \sum_{n = 1}^{3} (a_{n} \cos \frac{2 n π t}{L} + b_{n} \sin \frac{2 n π t}{L}) + ε_{t} \end{array}

Here, Y_t represents 13 dependent variables at time t; Tt is a continuous variable counting the number of months at time t from the start of study, assigned values 0 to 35 for the period January 2021 to December 2023. X_1t is an indicator for time t occurring before or after improving outpatient benefit, which was implemented at month 23 in the series (December 2022); X_1tT_1t is a continuous variable counting the number of months at time t after the reform (0 before the reform and 1―13 after the reform).

For parameters, β₀ estimates the baseline level of the outcome at time zero; β₁ estimates the baseline trend of the outcome before the reform; β₂ estimates the level change in the outcome immediately after the reform; β₃ assesses the post-intervention trend slope change. Thus, β₁ + β₃ can be used to evaluate the long-term trend of change following policy implementation. $γ$ is the coefficient for the control variable, capturing the impact of the pandemic on healthcare utilization. $ε_{t}$ is an error term at time t.

All analyses and figures were produced using Stata 17. The Durbin–Watson (DW) test was applied to assess autocorrelation, as its absence is an assumption in interrupted time series analysis. A DW value close to 2 suggests no autocorrelation. Since autocorrelation was detected in some cost components, we employed the Newey–West estimator to fit the regression models, which provides consistent standard errors in the presence of autocorrelation and heteroskedasticity. We used the SQUIRE writing guide when drafting this article, and the SQUIRE checklist (Supplemental Additional File 2) to demonstrate adherence to the SQUIRE reporting guideline.

Results

Health-Seeking Behaviors

The regression results indicated that (Tables 2 and 3 and Figure 1) the monthly number of medical visits (β₁ = −.0004, P = .641), the monthly number of outpatient visits (β₁ = −.0005, P = .515), the monthly number of hospitalizations (β₁ = −.0005, P = .003), and the monthly average length of a hospital stay (β₁ = −.0197, P = .562) all exhibited a declining trend prior to the policy intervention. However, a transient increase occurred at the point of policy intervention. Specifically, the monthly number of medical visits increased significantly by 0.0982 (P < .001), the monthly number of outpatient visits increased significantly by 0.1014 (P < .001) and the monthly average length of stay per capita increased significantly by 0.7919 days (P = .039). The monthly number of hospitalizations per capita also increased (0.003, P = .430), but this change was not statistically significant.

Table 2.

Parameter Estimate (P value) in Segmented Regression Model.

Variables		Intercept: β₀		Time: β₁		Intervention: β₂ (change in level)		Time after Intervention: β₃ (change in slope)		Long-term reform slope:
		Intercept: β₀		Time: β₁		Intervention: β₂ (change in level)		Time after Intervention: β₃ (change in slope)		β₁ + β₃
		Coefficient	P-value	Coefficient	P-value	Coefficient	P-value	Coefficient	P-value	Coefficient	P-value
Health-seeking behaviors	Monthly number of medical visits (Y₁)	1.6359	<.001^***	−.0004	.641	.0982	<.001^***	.0072	.001^**	.0069	.001^**
	Monthly number of outpatient visits (Y₂)	1.6224	<.001^***	−.0005	.515	.1014	<.001^***	.0071	.001^**	.0066	.001^**
	Monthly number of hospitalizations (Y₃)	1.0531	<.001^***	−.0005	.003^**	.0030	.430	.0012	.002^**	.0007	.08
	Monthly average length of a hospital stay (Y₄)	5.8529	<.001^***	−.0197	.562	.7919	.039^*	−.0454	.549	−.0652	.172
Medical expenses	Monthly medical expenses (Y₅)	895.6143	<.001^***	4.6265	.026^*	−17.9679	.784	1.0352	.856	5.6618	.344
	Monthly outpatient expenses (Y₆)	450.8347	<.001^***	4.0928	<.001^***	29.1526	.112	2.8381	.174	6.9308	.002^**
	Monthly inpatient expenses (Y₇)	15250.3700	<.001^***	−68.1639	<.001^***	−385.2476	.478	24.0544	.624	−44.1095	.401
Expenditure of the basic medical insurance funds	Monthly expenditure of the basic medical insurance funds on medical services (Y₈)	432.9533	<.001^***	1.8467	.138	144.8301	.001^**	1.2500	.299	3.0967	.413
	Monthly expenditure of the basic medical insurance funds on outpatient services (Y₉)	150.6008	<.001^***	1.4592	<.001^***	164.3495	<.001^***	2.9169	.036^**	4.3762	.004^**
	Monthly expenditure of the basic medical insurance funds on inpatient services (Y₁₀)	9620.247	<.001^***	−41.7685	<.001^***	136.8689	.717	−.8869	.329	−42.6554	.243
	Monthly out-of-pocket expenses (Y₁₁)	440.7042	<.001^***	2.8420	.001^**	−170.5311	<.001^***	−.6308	.743	2.2111	.228
	Monthly outpatient out-of-pocket expenses (Y₁₂)	300.1242	<.001^***	2.6277	<.001^***	−135.1267	<.001^***	−.0714	.954	2.5562	.038^*
	Monthly inpatient out-of-pocket expenses (Y₁₃)	4895.774	<.001^***	−19.5738	.002^**	−837.0642	<.001 ^***	6.6647	.642	−12.9091	.397

Note. All monetary values are in Chinese yuan (¥). The model controls for seasonal effects and the number of COVID-19 cases. The complete set of control variables and seasonal coefficients are reported in Supplemental Additional File 5. Bold entries represent that the variables of concern are significant.

Significance levels: ^*P < .05, ^**P < .01, ^***P < .001.

Table 3.

Annual Changes in Key Estimated Values of the Interrupted Time Series Analysis (ITSA) Model.

Variables	Annual net effect after policy intervention	Annual trend change after policy intervention
Health-seeking behaviors	Number of medical visits	0.09^**	0.08^**
	Number of outpatient visits	0.09^**	0.08^**
	Number of hospitalizations	0.01^**	0.01
	Average length of a hospital stay	−0.54	−0.78
Medical expenses	Medical expenses	12.42	67.94
	Outpatient expenses	34.06	83.17^**
	Inpatient expenses	288.65	−529.31
Expenditure of the basic medical insurance funds	Expenditure of the basic medical insurance funds on medical services	15.00	37.16
	Expenditure of the basic medical insurance funds on outpatient services	35.00^*	52.51^**
	Expenditure of the basic medical insurance funds on inpatient services	−10.64	−511.86
	Out-of-pocket expenses	−7.57	26.53
	Outpatient out-of-pocket expenses	−0.86	30.67^*
	Inpatient out-of-pocket expenses	79.98	−154.91

Note. Significance levels: ^*P < .05, ^**P < .01. All monetary values are in Chinese yuan (¥).

Figure 1.

(a) Monthly number of medical visits, (b) monthly number of outpatient visits, (c) monthly number of hospitalizations, and (d) monthly average length of a hospital stay.

After the policy intervention, the changes in the utilization frequency of medical services (β₃ = .00724, P = .001), the changes in the frequency of outpatient visits (β₃ = .00724, P = .001), and the changes in the frequency of hospitalization (β₃ = .0012, P = .002) all showed a significant upward trend compared with those before the policy intervention, while there was no significant change in the changes of hospitalization days (β₃ = −.0454, P = .065).

Regarding the long-term trend (β₁ + β₃), both the monthly number of medical visits (β₁ + β₃=0.0069, P = .001) and the monthly number of outpatient visits (β₁ + β₃=0.0066, P = .001) exhibited a significant increasing long-term trend. This corresponds to a significant increase of 0.08 in the annual number of medical visits per person and a significant increase of 0.08 in the annual number of outpatient visits per person. In contrast, the long-term trends for the monthly number of hospitalizations (β₁ + β₃=0.0001, P = .08) and the monthly average length of a hospital stay (β₁ + β₃=-0.0652, P = .172) were not statistically significant.

Figure 1a to d ITS analysis of changes in health-seeking behaviors among non-chronic disease patients

Medical Expenses

As shown in Tables 2 and 3 and Figure 2, prior to the policy intervention, both the monthly medical expenses (β₁ = 4.6265, P = .026) and the monthly outpatient medical expenses (β₁ = 4.0928, P < .001) showed a significant upward trend, while the monthly inpatient medical expenses showed a significant downward trend (β₁ = −68.1639, P < .001). At the time point of the policy intervention, the instantaneous changes in the monthly medical expenses (β₂ = −17.9679, P = .784), the monthly outpatient medical expenses (β₂ = 29.1526, P = .112), and the monthly inpatient medical expenses (β₂ = −385.2676, P = .478) were not statistically significant.

Figure 2.

(a) Monthly medical expenses, (b) monthly outpatient expenses, and (c) monthly inpatient expenses.

After the policy intervention, the monthly outpatient medical expenses showed a significant upward trend (β₁ + β₃ = 6.9308, P = .002), which was equivalent to a significant increase of 83.17 yuan in the outpatient medical expenses per person per year. In contrast, the monthly inpatient medical expenses showed a downward trend (β₁ + β₃ = −44.1095, P = .401), and the monthly medical expenses also showed an upward trend (β₁ + β₃ = 5.6618, P = .344), but neither of these 2 changes was statistically significant.

Figure 2a to c ITS analysis of changes in medical expenses among non-chronic disease patients

Expenditure of the Basic Medical Insurance Funds

As shown in Tables 2 and 3 and Figure 3, prior to the policy intervention, monthly expenditure of the basic medical insurance funds on medical services exhibited a non-significant upward trend (β₂ = 1.8467, P = .138). The monthly expenditure of the basic medical insurance funds on outpatient medical services increased significantly (β₂ = 1.4592, P < .001), while monthly expenditure of the basic medical insurance funds on inpatient medical services decreased significantly (β₂ = −41.7685, P < .001).

Figure 3.

(a) Monthly expenditure of the basic medical insurance funds on medical services, (b) monthly expenditure of the basic medical insurance funds on outpatient services, and (c) monthly expenditure of the basic medical insurance funds on inpatient services.

At the intervention point, there was a significant instantaneous increase in the monthly expenditure of the basic medical insurance funds on medical services, with an increase of 144.8301 yuan (P = .001). The monthly expenditure of the basic medical insurance funds on outpatient medical services also increased significantly and instantaneously by 164.3459 yuan (P < .001). Although the monthly expenditure of the basic medical insurance funds on inpatient medical services increased by 136.8689 yuan, this change was not statistically significant (P = .717).

Following the intervention, annual expenditure of the basic medical insurance funds on outpatient medical services were significantly higher than pre-intervention levels by 35 yuan (β₃ = 2.9169, P = .036), with a sustained significant growth rate of 52.51 yuan per person per year (β₁ + β₁₃ = 4.3762, P = .004). In contrast, monthly expenditure of the basic medical insurance funds on inpatient services showed a decreasing trend (β₁ + β₁₃ = −42.6554, P = .243), while the monthly expenditure of the basic medical insurance funds on medical services exhibited an upward trend (β₁ + β₁₃ = 3.0967, P = .413), neither trend was statistically significant.

Figure 3a to c ITS analysis of changes in expenditure of the basic medical insurance fund among non-chronic disease patients

The results in Tables 2 and 3 and Figure 4 show that before the policy intervention, both the monthly out-of-pocket expenses (β₁ = 2.8420, P = .001) and the monthly outpatient out-of-pocket expenses (β₁ = 2.6277, P < .001) showed a significant upward trend, while the monthly inpatient out-of-pocket expenses showed a significant downward trend (β₁ = −19.5738, P = .002).

Figure 4.

(a) Monthly out-of-pocket expenses, (b) monthly outpatient out-of-pocket expenses, and (c) monthly inpatient out-of-pocket expenses.

At the time of policy intervention, the monthly out-of-pocket expenses of patients decreased significantly instantaneously, with a decrease of 170.5311 yuan (P < .001); the monthly outpatient out-of-pocket expenses also decreased significantly instantaneously by 135.267 yuan (P < .001); the monthly inpatient out-of-pocket expenses also decreased significantly instantaneously by 837.0642 yuan (P < .001).

After the policy intervention, the monthly outpatient out-of-pocket expenses showed a significant upward trend (β₁ + β₁₃ = 2.5562, P = .038), that is, an increase of 30.67 yuan per person per year. In contrast, the monthly inpatient out-of-pocket expenses showed a downward trend (β₁ + β₁₃ = −12.901, P = .397). Although the monthly out-of-pocket expenses showed an upward trend in the long-term (β₁ + β₁₃ = 2.2111, P = .228), neither of them reached the level of statistical significance.

Figure 4a to c ITS analysis of changes in out-of-pocket expenses among non-chronic disease patient

Sensitivity Analysis

The study excluded the period from December 2022 to March 2023 (the peak of the pandemic) and re-evaluated the impact of policy interventions. The results show that the overall results remain robust. The results after the sensitivity analysis are presented in the Supplemental Documents (Additional File 3).

Discussions

The empirical results of this study indicated that expanding outpatient benefit package can facilitate non-chronic disease patients to make more use of medical services. Previous researches showed that insufficient security for outpatient expenses may result in pent-up demand for outpatient care among chronic disease patients,²² whereas optimizing benefit package motivates proactive utilization of ambulatory services.²³ Our findings provide robust evidence that non-chronic disease cohorts exhibit same behavioral responses. But different from former conclusions on chronic disease population—where improved outpatient access reduces hospital utilization^24,25 —our study yielded a somewhat counterintuitive finding that a reduction in outpatient out-of-pocket costs was associated with a higher hospitalization frequency for non-chronic disease patients, coupled with a decrease in their average length of stay. This phenomenon could be attributed to the increased outpatient volume, which potentially enables medical institutions to engage in “subthreshold admission” (a term used in China for unnecessary hospitalization), a financially motivated practice to secure higher income (Supplemental Additional File 4).

Generally speaking, increased healthcare utilization will bring about rising medical costs. However, results of this study revealed that through non-chronic disease patients added their monthly number of medical visits, there was no significant change in their monthly total medical expenses. As mentioned earlier, expanding the outpatient benefit package could encourage patients to actively utilize outpatient services, thereby facilitating early disease detection and intervention, which in turn helps prevent disease progression and save subsequent medical expenditure. Because of this, patient’s health status improves and the rapid growth of medical expenditure are restrained. What’s more, there are literatures demonstrating that when China’s annual practical outpatient reimbursement rate falls within the 42.27% to 64.11% range, expanding outpatient benefit package could make patients have a more rational health-seeking behaviors, a better health condition and a more reasonable medical expenses.^14,26 Before December 2022, annual outpatient reimbursement rate of non-chronic disease cohorts in Guangzhou (32.56%) already neared this optimal interval. After policy implementation, it rose significantly to 61.14%, fully satisfying the evidence-based reimbursement rate threshold. It also further explains the absence of notable expenditure escalation following benefit improvements for outpatient.

From the results of this study, although expanding outpatient benefit package for non-chronic disease patients meant the medical insurance department would pay more in outpatient expenses, it did not cause a remarkable growth in total basic medical insurance fund expenditures. This stability results not only from the lower expenditure of the basic medical insurance funds for inpatient expenses due to patients’ better health outcomes,^15,27,28 but also, significantly, from payment reforms in recent years in Guangzhou.^29,30 Presently, Guangzhou employs caption payment for outpatient care while Diagnosis-Intervention Packet (DIP) payment for hospitalization. Under such circumstances, the transfer of medical expenses between outpatient services and inpatient services may be largely eliminated. To receive a stable even higher payment, medical institutions have to simultaneously cut down unnecessary expenditure for both outpatient services and inpatient services covered by the basic medical insurance scheme.^31,32 As a result, patient’s financial burden of medical care is decline sharply. In addition, this study found expanding outpatient benefit package could induce moral hazard among non-chronic disease patients to some extent (See Supplemental Additional File 5). Specifically, it manifested as a concentrated surge in outpatient visits approaching the end of the insurance fiscal year (The settlement year of China’s basic medical insurance usually follows a natural year cycle). What it comes about mainly on account of the current financing mechanism for outpatient pooled funds, which relies on reductions in individual medical savings account contributions.^33,34 This policy design makes non-chronic disease patients foster an improper perception that their personal benefits are injured.³⁵ Therefore, they deliberately pursue outpatient services that may be non-essential, especially near the end of the year, to maximize utilization of the insurance pool funds.

Conclusions

In conclusion, within well-designed payment models, expanding outpatient benefit package among patients without chronic conditions could enable them to get a more comprehensive and prompt treatment while total medical expenses and expenditure of the basic medical insurance funds are stable totally.

The positive findings on expanding outpatient benefit package for non-chronic disease patients in this paper are extremely important to China and other countries particularly those with comparatively deficient medical resources. The analysis for influence of improving outpatient benefit on non-chronic disease patients’ health-seeking behaviors, medical expenses and reimbursement not only supplement existing studies’ conclusions, but also inspire policy makers whose country implements a social health insurance system that it’s feasible to expand outpatient benefit package for non-chronic disease patients.

This study also has some limitations. First, it could not evaluate the effects of the policy expansion on patients’ health outcomes due to the absence of health status data in the insurance claims dataset. While we examine changes in healthcare utilization and financial protection, which are key intermediate steps toward improved health, the absence of direct health outcome measures (eg, clinical indicators, functional status, or quality of life) constitutes a significant omission. This limits our ability to assess whether the increased access and reduced financial barriers ultimately translated into better health, which is the paramount goal of the policy. Future research combining claims data with clinical records or survey-based health measures is essential to address this critical question.

Second, this study was conducted in Guangzhou, a high-income urban area in eastern China where advanced payment reforms (eg, DIP and capitation) have been implemented. Although these payment models are increasingly being adopted across China in the inpatient and outpatient setting, the economic development and health system maturity of the study setting may limit the direct generalizability of the results to rural or less-developed regions within China, or to other countries with different health insurance structures and reform trajectories.

Third, since it verifies the incidence of moral hazard attributable to the financing mechanism for outpatient pooled funds of Urban Employee Basic Medical Insurance, but how to provide strong protection against moral hazard still needs further research.

Supplemental Material

sj-docx-1-inq-10.1177_00469580261441100 – Supplemental material for Expanding Outpatient Benefit Package Without Increasing Total Spending: Evidence From Outpatient Benefit Reform for Non-Chronic Disease Patients

Supplemental material, sj-docx-1-inq-10.1177_00469580261441100 for Expanding Outpatient Benefit Package Without Increasing Total Spending: Evidence From Outpatient Benefit Reform for Non-Chronic Disease Patients by Kefan Shi, Yuhao Wang, Tianyu Jing, Guojuan Ma, Wei Xu and Wenwen Du in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

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sj-pdf-2-inq-10.1177_00469580261441100 – Supplemental material for Expanding Outpatient Benefit Package Without Increasing Total Spending: Evidence From Outpatient Benefit Reform for Non-Chronic Disease Patients

Supplemental material, sj-pdf-2-inq-10.1177_00469580261441100 for Expanding Outpatient Benefit Package Without Increasing Total Spending: Evidence From Outpatient Benefit Reform for Non-Chronic Disease Patients by Kefan Shi, Yuhao Wang, Tianyu Jing, Guojuan Ma, Wei Xu and Wenwen Du in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

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sj-xlsx-3-inq-10.1177_00469580261441100 – Supplemental material for Expanding Outpatient Benefit Package Without Increasing Total Spending: Evidence From Outpatient Benefit Reform for Non-Chronic Disease Patients

Supplemental material, sj-xlsx-3-inq-10.1177_00469580261441100 for Expanding Outpatient Benefit Package Without Increasing Total Spending: Evidence From Outpatient Benefit Reform for Non-Chronic Disease Patients by Kefan Shi, Yuhao Wang, Tianyu Jing, Guojuan Ma, Wei Xu and Wenwen Du in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

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sj-xlsx-4-inq-10.1177_00469580261441100 – Supplemental material for Expanding Outpatient Benefit Package Without Increasing Total Spending: Evidence From Outpatient Benefit Reform for Non-Chronic Disease Patients

Supplemental material, sj-xlsx-4-inq-10.1177_00469580261441100 for Expanding Outpatient Benefit Package Without Increasing Total Spending: Evidence From Outpatient Benefit Reform for Non-Chronic Disease Patients by Kefan Shi, Yuhao Wang, Tianyu Jing, Guojuan Ma, Wei Xu and Wenwen Du in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

Supplemental Material

sj-xlsx-5-inq-10.1177_00469580261441100 – Supplemental material for Expanding Outpatient Benefit Package Without Increasing Total Spending: Evidence From Outpatient Benefit Reform for Non-Chronic Disease Patients

Supplemental material, sj-xlsx-5-inq-10.1177_00469580261441100 for Expanding Outpatient Benefit Package Without Increasing Total Spending: Evidence From Outpatient Benefit Reform for Non-Chronic Disease Patients by Kefan Shi, Yuhao Wang, Tianyu Jing, Guojuan Ma, Wei Xu and Wenwen Du in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

Footnotes

Acknowledgements

The authors are grateful to the National Natural Science Foundation of China (Grant No. 72074220) and the Guangzhou Healthcare Security Administration for supporting the research. And then sincerely thanks to the whole research group in China Pharmaceutical University for collecting and organizing data.

ORCID iD

Kefan Shi

Ethical Considerations

All methods in our study were carried out in accordance with the Declaration of Helsinki. This retrospective study utilized fully de-identified administrative claims data from the Guangzhou Healthcare Security Administration. As the research involved no direct contact with human participants and all data were anonymized prior to analysis, it was exempt from requiring formal ethical review by the institutional review board of China Pharmaceutical University. The study protocol and data use were formally approved by the Guangzhou Healthcare Security Administration on July 10, 2024. Access to and use of the data were governed by a strict data use agreement between China Pharmaceutical University and the data provider, which ensured compliance with data protection regulations and ethical standards for secondary data analysis.

Consent to Participate

The requirement for individual informed consent was waived for this study due to the anonymized nature of the data.

Author Contributions

K.S. and Y.W. performed statistical analysis of data together and made major contributions to writing the manuscript. K.S. and Y.W. contributed equally to the work, and therefore they share first authorship. T.J. and G.M. were responsible for the quality control of the study and assisted in writing the manuscript. W.X. and W.D. conceived the research ideas, collected and organized data regarding medical visits of sample population, provided funding and analytical framework, and offered critical supervision on the first draft and revision. W.X. and W.D. contributed equally to the work, and therefore sharing corresponding authorship. All authors read and approved the final manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support for the study was provided by the general program of National Natural Science Foundation of China: Study on the influence of medical insurance management and payment policy of medical treatment beyond pooling regions on recurrent population’s health-seeking behavior, health outcome and medical expenditure (Grant No. 72074220).

Declaration of Conflicting Interests

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

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding authors on reasonable request.*

Supplemental Material

Supplemental material for this article is available online.

References

Winpenny

Miani

Pitchforth

, et al. Outpatient services and primary care: scoping review, substudies and international comparisons. Health Serv Deliv Res. 2016;4(15):1-290. doi:10.3310/hsdr04150

Shi

The impact of primary care: a focused review. Scientifica. 2012;2012:1-22. doi:10.6064/2012/432892

Wei

Xun

Paying for outpatient care in rural China: Cost escalation under China’s New Co-operative Medical Scheme. Health Policy Plan. 2015;30(2):187-196. doi:10.1093/heapol/czt111

Miao

Zhang

Yang

Effects of expanding outpatient benefit package on the rationality of medical service utilisation of patients with hypertension: a quasi-experimental trial in rural China. BMJ Open. 2019;9(5):e025254. doi:10.1136/bmjopen-2018-025254

Liu

Vortherms

Hong

China’s health reform update. Annu Rev Public Health. 2017;38(1):431-448. doi:10.1146/annurev-publhealth-031816-044247

Jian

Fang

[An empirical analysis on the substitution effect of outpatient services on inpatient services]. Beijing Da Xue Xue Bao. 2015;47(3):459-463. doi:10.3969/j.issn.1671-167X.2015.03.017

Zhou

Gao

Xue

Yang

Yan

. Effects of rural mutual health care on outpatient service utilization in Chinese village medical institutions: evidence from panel data. Health Econ. 2009;18(Suppl 2):S129-S136. doi: 10.1002/hec.1519

Pourat

Chen

Davis

AC.

Timely outpatient follow-up is associated with fewer hospital readmissions among patients with behavioral health conditions. J Am Board Fam Med. 2019;32(3):353-361. doi:10.3122/jabfm.2019.03.180244

Balasubramanian

Malhotra

Can timely outpatient visits reduce readmissions and mortality among heart failure patients?

J Gen Intern Med. 2024;39(13):2478-2486. doi:10.1007/s11606-024-08755-1

10.

NCHS. Health, United States, 2016: With Chartbook on Long-Term Trends in Health. National Center for Health Statistics (US; 2017.

11.

Iezzi

Lippi Bruni

Ugolini

The role of GP’s compensation schemes in diabetes care: evidence from panel data. J Health Econ. 2014;34:104-120. doi:10.1016/j.jhealeco.2014.01.002

12.

Lonnemann

Duttlinger

Hohmann

Hickstein

Reichel

Timely referral to outpatient nephrology care slows progression and reduces treatment costs of chronic kidney diseases. Kidney Int Rep. 2017;2:142-151. doi:10.1016/j.ekir.2016.09.062

13.

Wouterse

van Baal

Versteegh

Brouwer

The value of health in a cost-effectiveness analysis: theory versus practice. Pharmacoeconomics. 2023;41(6):607-617. doi:10.1007/s40273-023-01265-8

14.

Liu

Effects of decreasing the out-of-pocket expenses for outpatient care on health-seeking behaviors, health outcomes and medical expenses of people with diabetes: evidence from China. Int J Equity Health. 2022;21(1):162. doi:10.1186/s12939-022-01775-5

15.

Liu

Chen

Zhang

Jian

WY.

Expanding outpatient benefits package can reduce diabetes-related avoidable hospitalizations. Front Public Health. 2023;11:964789. doi:10.3389/fpubh.2023.964789

16.

Jiang

Zhang

Bingbing

Does outpatient mutual-aid reduce oncology patients' utilization of inpatient services?-An empirical study of outpatient mutual-aid policy in Wuhan, China. Int J Health Plann Manage. 2025;40(1):174-193. doi:10.1002/hpm.3858

17.

Andersen

Newman

. Societal and individual determinants of medical care utilization in the United States. Milbank Q. 2005;83(4). doi: 10.1111/j.1468-0009.2005.00428.x

18.

van den Bussche

Schön

Kolonko

, et al. Patterns of ambulatory medical care utilization in elderly patients with special reference to chronic diseases and multimorbidity--results from a claims data based observational study in Germany. BMC Geriatr. 2011;11:Article 54. doi: 10.1186/1471-2318-11-54

19.

Liu

The efficient moral hazard effect of health insurance: Evidence from the consolidation of urban and rural resident health insurance in China. Soc Sci Med. 2023;324:115884. doi:10.1016/j.socscimed.2023.115884

20.

Bernal

Cummins

Gasparrini

Interrupted time series regression for the evaluation of public health interventions: a tutorial. Int J Epidemiol. 2017;46(1):348-355. doi:10.1093/ije/dyw098. Erratum in: Int J Epidemiol. 2020;49(4):1414. doi: 10.1093/ije/dyaa118.

21.

Wagner

Soumerai

Zhang

Ross-Degnan

Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 2002;27(4):299-309. doi:10.1046/j.1365-2710.2002.00430.x

22.

Miao

Zhang

Sandeep

Improving the performance of social health insurance system through increasing outpatient expenditure reimbursement ratio: a quasi-experimental evaluation study from rural China. Int J Equity Health. 2018;17(1):89. doi:10.1186/s12939-018-0799-8

23.

Zhang

The impacts of health insurance on health care utilization among the older people in China. Soc Sci Med. 2013;85:59-65.

24.

Schlenger

Mulvaney-Day

Williams

, et al. PTSD and use of outpatient general medical services among veterans of the Vietnam war. Psychiatr Serv. 2016;67(5):543-550. doi:10.1176/appi.ps.201400576

25.

Korsa

Tesfaye

Sud

Krass

Castelino

RL.

Risk factor-based screening for early detection of chronic kidney disease in primary care settings: a systematic review. Kidney Med. 2025;7(4):100979.

26.

Effects of establishing a financing scheme for outpatient care on inpatient services: empirical evidence from a quasi-experiment in China. Eur J Health Econ. 2022;23(1):7-22. doi:10.1007/s10198-021-01340-x

27.

Shen

Yeoh

The association between an increased reimbursement cap for chronic disease coverage and healthcare utilization in China: an interrupted time series study. Health Policy Plan. 2020;35(8):1029-1038. doi:10.1093/heapol/czaa087

28.

Wang

Impact of an outpatient coverage scheme on health-care utilization among middle-aged and older adults: evidence from the outpatient mutual-aid security policy in China. J Aging Soc Policy. 2026;38:295-314. doi:10.1080/08959420.2025.2482300

29.

Wang

Yan

Zhang

Qian

Ying

Impact of the diagnosis-intervention packet payment reform on provider behavior in China: a controlled interrupted time series study. Int J Health Policy Manag. 2024;13:8463. doi:10.34172/ijhpm.8463

30.

Tang

Zhang

Chen

Yan

Qian

Ying

Variations in the impact of the new case-based payment reform on medical costs, length of stay, and quality across different hospitals in China: an interrupted time series analysis. BMC Health Serv Res. 2023;23(1):568. doi:10.1186/s12913-023-09553-x

31.

Jia

Meng

Scott

Yuan

Zhang

Payment methods for healthcare providers working in outpatient healthcare settings. Cochrane Database Syst Rev. 2021;1(1):CD011865. doi:10.1002/14651858.CD011865.pub2

32.

Sun

Zhang

Wagner

Ross-Degnan

Hogerzeil

HV.

Impacts of a new insurance benefit with capitated provider payment on healthcare utilization, expenditure and quality of medication prescribing in China. Trop Med Int Health. 2016;21(2):263-274. doi:10.1111/tmi.12636

33.

Zhang

Yuen

PP.

Medical savings account balance and outpatient utilization: Evidence from Guangzhou, China. Soc Sci Med. 2016;151:1-10. doi:10.1016/j.socscimed.2015.12.035

34.

Huang

Lei

How does undervaluation in medical savings accounts (MSAs) affect healthcare utilization? Evidence from administrative data in China. J Health Econ. 2024;98:102946. doi:10.1016/j.jhealeco.2024.102946

35.

Wouters

Cylus

Yang

Thomson

McKee

Medical savings accounts: assessing their impact on efficiency, equity and financial protection in health care. Health Econ Policy Law. 2016;11(3):321-335. doi:10.1017/S1744133116000025

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