Freedom to Heal: Financial Well-Being and the Capabilities of Health Tourism

Health Tourism: Differentiating Medical and Wellness Services

Health tourism is one of the fastest-growing segments of global travel, encompassing two key areas: medical tourism, which centers on diagnosis, treatment, and recovery, and wellness tourism, which focuses on preventive care and holistic well-being. Medical tourism involves the cross-border movement of patients seeking curative or preventive interventions such as organ transplants, dental care, fertility treatments, and oncology services (Bristeir, 2016; Centers for Disease Control and Prevention [CDC], 2023; Pessot et al., 2021; Seow et al., 2022; UNWTO, 2018). In contrast, wellness tourism revolves around mind-body balance and self-care practices such as yoga retreats, alternative therapies, detox programs, and traditional medicine (Majeed & Kim, 2023; Ridderstaat et al., 2019).

While the distinction between medical and wellness tourism seems clear in theory, in practice, these categories often blur. Tourists frequently combine wellness services with medical procedures to support recovery, or as part of a preventive health strategy, complicating the segmentation of this market (Fetscherin & Stephano, 2016; Kemppainen et al., 2021; Voigth & Laing, 2013). This overlap has significant implications for classification, market research, consumer profiling, and policy-making, as it challenges assumptions about motivations, risk tolerance, and spending behavior across health tourism types (Dolnicar & Leisch, 2017; Mazanec & Dolnicar, 2022).

The literature documents the increasing convergence of wellness and medical services within health tourism. Scholars such as Fetscherin and Stephano (2016) and Kemppainen et al. (2021) note that tourists often navigate both sectors during a single journey. For example, a patient receiving dental surgery may recuperate at a wellness resort offering acupuncture, spa treatments, or nutritional counseling. Similarly, individuals pursuing stress relief through yoga retreats may undergo health screenings or cosmetic procedures during their stay. These overlapping motivations and services complicate traditional segmentation strategies, which typically rely on consumer characteristics such as age, income, and travel motivation (Dolnicar, 2008).

Attempts to categorize health tourists according to static typologies are increasingly being challenged by data-driven segmentation methods. Ridderstaat (2023) proposes a time-series approach to classify health tourism patterns into trend, cyclical, and irregular components. Trends reflect long-term shifts, such as increasing demand for wellness travel due to demographic aging or lifestyle changes. Cyclical segments reflect behaviors shaped by recurrent needs, for example, regular dental visits or fertility treatments, while irregular components include unpredictable events like emergency surgeries during travel. These analytical distinctions allow for more nuanced insights into how various health motivations interact and evolve over time (Beladi et al., 2019; Hunter-Jones, 2005).

The growing consensus is that rigid segmentation undermines the complexity of health-related travel. Instead, segmentation should reflect the capabilities people aim to enhance through their travel. This perspective invites a shift from market-driven typologies to capability-informed frameworks, recognizing that health tourists are not just consumers but are also agents pursuing valued outcomes.

Factors Driving Health Tourism and the Role of Financial Well-being

The expanding health tourism sector is driven by the demand for superior healthcare expertise, innovative treatments, and wellness experiences (E. Cohen, 2008; M. Cohen et al., 2017). The increasing demand is reshaping the global healthcare landscape (López-Barreda et al., 2019). A broad range of push and pull factors influence individuals’ decisions to engage in health tourism. Push factors originate in the home country and include long waiting times, high healthcare costs, lack of medical expertise, inadequate insurance coverage, and limited treatment options (C. M. Hall, 2011; Lunt & Carrera, 2010). Pull factors are destination-based and often include cost savings, availability of advanced technology, quality assurance, and reputation of health providers (Khan et al., 2016; UNWTO, 2018). Travel distance, familiarity with the destination, visa accessibility (Puczkó & Stackpole, 2021), and the reputations of physicians and hospitals (Heung et al., 2010; UNWTO, 2018) are also critical. Additionally, wellness-related motivations like stress reduction, indulgence, and physical or appearance improvements further entice travelers (M. Cohen et al., 2017; Hudson et al., 2017).

On the other hand, push factors compel individuals to seek healthcare abroad due to unmet needs at home. These include insufficient insurance coverage, rising pharmaceutical costs, financial barriers to essential healthcare services (Emanuel, 2020; Issenberg, 2016), and the lack of specific treatments in their home country (Lunt & Carrera, 2010). Quality of life issues, such as chronic pain, reduced self-esteem, and physical limitations, also drive individuals to seek health tourism (Bristeir, 2016). Long wait times, lack of expertise, and restrictive regulations further push individuals to pursue healthcare options abroad (C. M. Hall, 2011; Hanefeld & Smith, 2015; Romanova et al., 2016).

One of the most significant influences is financial well-being, which plays a dual role: it shapes the capacity to engage in health tourism and reflects the consequences of such engagement. Financial well-being encompasses the subjective and objective dimensions of an individual’s financial condition, including their ability to meet current obligations, feel secure about their financial future, and afford discretionary health spending (Brüggen et al., 2017; Riitsalu et al., 2024). The literature shows that financial factors frequently determine the type of service sought, such as urgent treatments in medical tourism versus luxury or discretionary spending in wellness tourism (Ridderstaat, 2021, 2022).

However, research also indicates that financial outcomes of health tourism are highly variable. While some travelers achieve substantial cost savings, for example, paying 10%–30% of U.S. procedure costs abroad (Hopkins et al., 2010), others may experience financial strain due to travel, accommodation, or complications (Johnston et al., 2010; Lunt et al., 2011). As Lunt et al. (2011) warn, anticipated savings often exclude indirect costs, raising questions about the long-term financial sustainability of such decisions.

Integrating Theory and Hypotheses: The Capability Approach and the Expanded Utility Function

To comprehensively evaluate the multidimensional dynamics of health tourism, this study draws on Sen’s Capability Approach (Sen, 1992, 1999). The Capability Approach is a normative framework that shifts the focus from utility or material wealth to the real freedoms individuals have to achieve valuable life outcomes. In this context, health is understood not only as a functioning (being healthy) but also as a capability (the freedom to pursue and maintain health). Health tourism, therefore, can be viewed as a deliberate strategy to expand one’s capability set in response to constraints within local healthcare systems.

Functionings, defined by Sen (1999, p. 75), are “the various things a person may value doing or being,” while capabilities are the substantive freedoms to achieve them. In health tourism, individuals may seek both medical interventions and wellness experiences to restore or enhance these valued states. This process involves agency, that is, the capacity to make informed, value-aligned decisions about one’s health, underscoring that individuals are not passive recipients of healthcare but active participants shaping their well-being (Robeyns, 2005; Sen, 1999). Agency emphasizes that individuals make choices aligned with their values, highlighting the role of personal freedom in well-being. For example, while travel is generally a capability, individuals can tailor it to align with specific personal goals (Robeyns, 2017). Health-related travel enables the functioning of health improvement, expanding one’s capability set, and influencing conversion factors—conditions that transform resources into valued outcomes. Such travel meets immediate health needs and supports other valued functionings, like participation in work, leisure, or social activities.

The capability approach emphasizes personal choice and active agency, recognizing that individuals exercise freedom according to their unique motivations and preferences (Sen, 1999). This focus on agency underscores the importance of empowering people to pursue capabilities they find meaningful, showing how personal choices contribute to well-being (Robeyns, 2005). Health tourism illustrates this dynamic: with adequate financial resources, individuals can access healthcare services that may be unavailable or costly in their home countries. This opportunity not only improves their physical well-being—a core functioning—but also enhances their ability to achieve other life goals. The link between financial resources and health tourism demonstrates that financial well-being extends beyond income; it provides individuals with the freedom to lead healthier, more fulfilling lives, reinforcing their agency to shape their future.

A core insight of the Capability Approach is that access to resources does not guarantee well-being; what matters is an individual’s ability to convert those resources into valuable functionings, which depends on conversion factors. Two individuals with identical income levels may experience different well-being based on their ability to access healthcare, education, or social support. These include personal (e.g., age, gender, physical condition), social (e.g., cultural support, family networks), and environmental (e.g., policy access, infrastructure) variables (Robeyns, 2003, 2005).

Among these, financial well-being is particularly influential in health tourism, functioning both as a resource and a conversion factor (Kim & Adu-Ampong, 2024; Storchi & Johnson, 2016). Financial well-being affects not only the decision to participate in health tourism but also the type and quality of care sought. As prior research shows, individuals with adequate financial resources can access specialized or higher-quality care abroad, while those with limited means face restrictions, even when their health needs are urgent (Johnston et al., 2010; Lunt & Carrera, 2010; Riitsalu et al., 2024).

To operationalize these insights, this study introduces an expanded utility function:

Where:

This function allows for a capability-informed segmentation of health tourism by incorporating personal and contextual variation into a unified framework. Importantly, it enables assessment of how different tourism behaviors, prolonged (trend-driven), recurrent (cyclical), and incidental (irregular), interact with financial well-being over time (Beladi et al., 2019; Ridderstaat, 2023).

From this model two interrelated sets of hypotheses emerge, reflecting the bidirectional relationship between financial well-being and health tourism behavior. The model highlights the recursive relationship between health tourism and financial well-being: financial resources shape access, and access (and its consequences) in turn shape financial status. This duality forms the basis of this study’s hypotheses (H₁–H₁₂) linking types of expenditure (prolonged, recurrent, incidental) with changes in financial well-being, both positive and negative. This bidirectional framing of hypotheses reflects the Capability Approach’s emphasis on dynamic interaction between resources and outcomes, showing that financial well-being is both a condition for and a consequence of capability expansion through health tourism.

Conceptual Overview and Data

This study examines the complex relationship between financial well-being and health tourism expenditure. Figure 1 presents a conceptual framework illustrating two primary effects: Wealth and Demand. The Wealth Effect captures how health tourism impacts an individual’s financial well-being and immediate social networks. Health tourists may benefit financially from lower-cost healthcare services abroad, potentially improving their financial position upon returning home. However, unexpected costs due to complications or unsatisfactory outcomes can adversely affect their financial status and that of their close associates. Understanding these effects is essential to fully grasp the relationship between health tourism and financial well-being.

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Figure 1.

Conceptual Framework of the Connection Between Financial Well-Being and Health Tourism.

The Demand Effect explores how financial circumstances influence individuals’ decisions to pursue health tourism and allocate resources for health-related travel. It underscores how financial conditions affect the likelihood of becoming health tourists. For example, individuals who face high dental treatment costs at home may seek affordable options abroad due to financial constraints in domestic healthcare. Thus, financial status shapes the propensity for health tourism and impacts resource allocation. Understanding the Demand Effect is crucial to comprehending the complex interactions between financial factors and health tourism behavior.

Analyzing the relationships in Figure 1 requires data on household perceptions of wealth and health tourism expenditures. Financial well-being data were obtained from the University of Michigan’s monthly Surveys of Consumers (University of Michigan, 2023), which conduct monthly telephone interviews with over 600 U.S. households (excluding Alaska and Hawaii). The survey includes 50 questions on personal finances, business conditions, and buying attitudes, allowing respondents to share opinions and reasoning (University of Michigan, 2015). For this study, one of the questions used was: “Would you say that you (and your family living there) are better off or worse off financially than you were a year ago?” This question was chosen for its relevance to financial well-being. Although monthly financial well-being data are collected, this research used the University of Michigan’s annual summaries of this question.

Brüggen et al. (2017) and Porter and Garman (1993) show that demographic factors significantly influence financial well-being. While the University of Michigan segments data by gender, age, region, education, and income, analyzing all these variables would require extensive analyses. Therefore, only gender and age were included in this study to reduce complexity, as suggested by Cleveland et al. (2011), Moschis (2012), and Stewart and McDermott (2004).

Annual health tourism expenditure data from 1999 to 2023 was obtained from the U.S. Bureau of Economic Analysis (BEA), a division of the U.S. Department of Commerce that measures economic growth, national income, and international trade. Trend, cycle, and irregular components were extracted from the health tourism spending data, using a decomposition technique detailed in the next section.

Methodology

This study applied a logarithmic transformation to the variables to reduce data variability, address outliers (Wooldridge, 2016), and standardize units of analysis. The Christiano and Fitzgerald bandpass filter technique (Christiano & Fitzgerald, 2003) was used to separate the trend, cycle, and irregular components of health tourism spending data. Finally, variables were standardized by subtracting each variable’s mean and dividing by the standard deviation to ensure measurement consistency.

Using the bilateral theoretical framework, this study identified relationships between financial well-being and health tourism spending through regression Equations 1 to 5. Equations 1 to 3 examine how financial well-being influences three aspects of health tourism spending, while Equations 4 and 5 assess the effects of the trend, cycle, and irregular components of health tourism spending on worsened and improved financial well-being. For instance, Equation 1 addresses the impact of financial well-being on the trend component of health tourism spending, while Equation 4 examines the influence of health tourism components on worsened financial well-being. The regression equations exclude an intercept, following standard practice for analyses with standardized variables, where the intercept is inherently nullified (Gujarati & Porter, 2009). Equations 1 to 5 are estimated in aggregate and controlled for age and gender.

where

X = Health tourism spending;

Y = Worsened financial wellness;

Z = Improved financial wellness;

T = Trend component;

C = Cycle component;

I = Irregular component;

D = Dummy variable (representing the effects of the COVID-19 pandemic);

T = Time component;

m, n, o, p, q = Lags;

α, γ, δ, ϑ, π = Coefficients;

Ε = White noise error term.

To assess data stationarity, the Augmented Dickey-Fuller (ADF) test (Dickey & Fuller, 1979) and the Phillips-Perron test (Phillips & Perron, 1988) were used. A structural break unit root test was also applied to account for disruptions like the COVID-19 pandemic and the 2008–2010 financial crisis. The ADF test identified the break date by minimizing the Dickey-Fuller t-statistic. The structural break test included the additive outlier (A.O.) model for sudden changes and the innovative outlier (I.O.) model for gradual shifts (Perron, 1989; Vogelsang, 1993).

Revisiting Equations 1 to 5, some independent variables may correlate with error terms, causing endogeneity issues that lead to biased and inconsistent elasticity estimates (Gujarati, 2015; Wooldridge, 2016). The ordinary least squares (OLS) estimator cannot correct this bias (Kennedy, 2008), so this study used the Limited Information Maximum Likelihood (LIML) estimator, an instrumental variable method. LIML relies on two main groups of instruments: variables correlated with the endogenous variables (Gujarati, 2015) and lagged versions of these variables, as supported by R. Hall (1988), Murray (2006), and Yogo (2004). This approach, endorsed by Hayashi (2000), Poi (2006), and Stock et al. (2002), addresses omitted variable bias effectively in small samples. Stock and Yogo (2005) suggest that LIML offers more robust testing than two-stage least squares.

Data Characteristics

Table 1 provides an overview of the study variables, including financial well-being indicators by gender and age. Standardization resulted in variables with a mean of zero and a standard deviation of one, ranging from -2.9 to 2.3. These standardized variables will be used in the data analysis phase.

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Table 1.

Data Description.

Note. Statistics are standardized.

Stationarity testing

Table 2 shows the results from unit root tests on the variables. All variables are stationary at their original levels or first differences. Thus, these variables can be used in their native form or first differences for regression analysis. The Vogelsang (1993) unit root test identified abrupt and gradual shifts in data means. Given these findings, the study uses the data in its original form for analysis to accurately depict relationships among the variables, ensuring robust results.

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Table 2.

Unit Root Testing (Standard and Breakpoint Unit Toot Tests).

Note. The symbols ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

LIML Results and Discussion

Table 3 presents the LIML analysis results. The left side displays regression outcomes for Equations 1 to 3, adjusted for gender and age, while the right side includes visual representations clarifying the relationships between financial well-being and health tourism spending. With standardized variables, the coefficients show that a one standard deviation increase in the independent variable leads to an x standard deviation change in the dependent variable, holding all else constant.

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Table 3.

Elasticity Effects of Financial Wellbeing on Health Tourism Spending.

Note. The table is by gender and age group; P = prolonged, R = recurring, I = incidental; L = lag; + = positive impact, - = negative effect. Across all models, the χ² statistics were statistically significant. The Harvey LM test results showed no evidence of autocorrelation, while the Engle LM ARCH tests confirmed the absence of heteroskedasticity; The symbols ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

Health tourism spending effects on worsened/improved financial well-being (Models 4 and 5)

General effects

The results showed that health tourism spending (prolonged, recurrent, or incidental) had no effect on financial conditions (Table 4, Equations 4 and 5). This suggests that health tourism, regardless of type or frequency, is financially sustainable and manageable within individuals’ budgets.

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Table 4.

Elasticity Effects of Health Tourism Spending on Financial Wellbeing.

Note. The table is by gender and age group; P = prolonged, R = recurring, I = incidental; L = lag; + = positive impact, - = negative effect. Across all models, the χ² statistics were statistically significant. The Harvey LM test results showed no evidence of autocorrelation, while the Engle LM ARCH tests confirmed the absence of heteroskedasticity. The symbols ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

Effects by gender

Gender analysis showed that a one standard deviation increase in recurring health tourism spending worsened females’ financial positions (ϑ = -3.243*), likely due to prioritizing regular wellness or preventive care, which can strain finances if it consumes a large budget portion. Prolonged and incidental health tourism spending had no significant effect on females’ financial well-being. For males, however, prolonged and incidental health tourism spending worsened financial well-being (ϑ = 1.481* and 1.707**), likely due to significant out-of-pocket costs for travel, accommodations, and treatments. Additionally, prolonged health tourism spending negatively impacted financial improvement for both genders (ϑ = -1.804** and ϑ = -2.043**), as high costs reduced available funds for savings or investment. Recurring and incidental health tourism spending did not affect improving financial conditions for either gender, suggesting these types of health tourism are financially sustainable.

Effects by age

For individuals aged 18–34, increased prolonged health tourism spending worsened financial conditions (ϑ = 2.527*), as younger people often have lower savings, making them more vulnerable to high costs associated with extended stays, treatments, and travel. For those aged 35–44, incidental health tourism spending did not relieve financial strain (ϑ = 1.984*), as sudden, unplanned expenses can add to financial stress. Health tourism spending (prolonged, recurring, or incidental) did not affect financial well-being for those aged 45–97, likely due to greater financial stability from savings, investments, or higher incomes, making them less susceptible to its financial impacts.

For individuals aged 35–44, both prolonged and incidental health tourism spending negatively impacted financial well-being, as this age group often manages significant expenses like mortgages, childcare, and education, making unplanned or extended health tourism an added burden. Similarly, for those aged 45–54, prolonged health tourism weakened financial improvement by reducing disposable income and limiting financial growth.

For individuals aged 55–64, recurring and incidental health tourism spending positively impacted financial well-being, as financial stability allows them to budget for these manageable expenses. In contrast, for those aged 65–97, increased prolonged health tourism spending eroded financial well-being, as high costs from extended stays and treatments can deplete reserves, especially with a fixed retirement income.

Model Statistics

Subsequent model evaluations confirmed the significance of all selected independent variables, as indicated by the statistically significant F statistic. The models were adequately identified, with the Kleibergen-Paap rk L.M. statistic rejecting under-identification and suggesting relevant instruments. The non-significant Hansen J statistic confirmed that the models were not over-identified, validating instrument appropriateness. Additionally, the endogeneity test supported the exogeneity of variables, indicating no omitted variable bias.

Hypotheses Validation

Table 5 illustrates the validation results of hypotheses across the overall sample, gender, and age groups, highlighting significant variations in health tourism spending effects on financial well-being (and vice versa). In the overall sample, only hypotheses H₂, H₃, and H₅ were validated, indicating a limited link between financial well-being and health tourism spending. Gender analysis reveals that more hypotheses were validated for males than females, suggesting that males may experience a stronger relationship between financial well-being and health tourism expenditures.

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Table 5.

Summary of Hypotheses Validation Across Overall Sample, Gender, and Age Groups.

	Validation
		Gender		Age (In Years)
Hypotheses	Overall	Female	Male	18-34	35–44	45–54	55–64	65–97
H1 - H6: Financial well-being → health tourism spending
H1 : Worsening financial well-being impacts prolonged health tourism expenditures.							✓-	✓-
H2 : Improving financial well-being impacts prolonged health tourism expenditures.	✓+	✓+	✓+	✓+	✓+	✓+		✓-
H3 : Worsening financial well-being impacts recurrent health tourism spending.	✓+	✓+	✓+	✓+	✓+	✓+	✓+
H4 : Improving financial well-being impacts recurrent health tourism spending.						✓+	✓+	✓-
H5 : Worsening financial well-being impacts incidental health tourism spending.	✓-							✓-
H6 : Improving financial well-being impacts incidental health tourism spending.				✓+
H7 - H12: Health tourism spending → financial well-being
H7 : Prolonged health tourism spending causes a decline in financial well-being.			✓+	✓-
H8 : Prolonged health tourism spending causes an improvement in financial well-being.		✓-	✓-		✓-	✓-		✓-
H9 : Recurrent health tourism spending causes a decline in financial well-being.
H10 : Recurrent health tourism spending causes an improvement in financial well-being.							✓+
H11 : Incidental health tourism spending causes a decline in financial well-being.			✓-		✓+
H12 : Incidental health tourism spending causes an improvement in financial well-being.					✓-		✓+

Note. Validation of 12 hypotheses on the bidirectional relationship between financial well-being and health tourism spending; ✓ = validated, empty space = rejected; + positive effect, - = negative effect.

Age groups 35–44, 55–64, and 65–97 showed the highest number of validated hypotheses, indicating slightly stronger connections between financial well-being and health tourism spending in these segments compared to those aged 18–34 and 45–54. These validation differences highlight the importance of demographic-specific analysis, as aggregated data may obscure subgroup effects, emphasizing the need for targeted analyses to capture nuanced behaviors and preferences.

Summary and Findings

This study provides valuable insights into the complex relationship between financial well-being and health tourism expenditure in the United States. It examines how financial status influences health tourism behavior across different demographic groups. Recent research has reported a significant rise in health tourism, particularly for elective procedures (Smith, 2022). The findings of this study highlight that financial well-being plays a crucial role in health tourism decisions, although its impact varies across age groups. Younger and middle-aged adults (18–54 years) are more likely to increase their spending on extended health tourism services when their financial situation improves. They often perceive these services as essential to their overall well-being. In contrast, older adults (65–97 years) show less willingness to increase spending on such services, even when their financial situation improves. This suggests that their healthcare priorities or other limitations may influence their decisions. These findings contribute to a more comprehensive understanding of how financial health shapes health tourism expenditure patterns across different age groups in the United States.

This variation highlights the importance of considering age-specific motivations when analyzing health tourism expenditure. The observed differences align with previous research by Tomoaia-Cotisel et al. (2013) and Ridderstaat (2022), which emphasize the role of contextual factors and targeted demographic analyses in identifying patterns that may be obscured in aggregate data. By adopting an age-specific approach, researchers and industry professionals can uncover valuable insights that may otherwise remain hidden when relying solely on combined data. For example, younger individuals may prioritize preventive health services or wellness-oriented experiences, whereas older adults may focus more on medical treatments or specialized care. These differences in spending patterns and motivations have important implications for the development and marketing of health tourism products and services. Furthermore, this targeted approach to analyzing health tourism expenditure can reveal subtle variations in spending behaviors across different age groups, offering a more refined understanding of consumer preferences.

These variations may be influenced by factors such as disposable income, health status, lifestyle preferences, and cultural attitudes towards healthcare. By identifying these age-specific trends, stakeholders in the health tourism industry can tailor their offerings and marketing strategies to address the needs and expectations of different age segments. This study also implies that understanding age-related factors in health tourism spending can have broader implications for healthcare policy and resource allocation. By recognizing the diverse needs and spending patterns of different age groups, policymakers and healthcare providers can develop more effective strategies to address the evolving demands of health tourism across their lifespan.

The study also revealed a complex relationship between financial status and health tourism expenditure across age groups. Most individuals increase their recurring health tourism expenditure during periods of financial difficulty, indicating a prioritization of essential health services. This suggests that health tourism is perceived as a crucial investment in well-being rather than a discretionary expense. However, older adults deviate from this trend, implying that their health tourism decisions are influenced by factors beyond financial considerations such as health status, mobility limitations, or travel preferences. This divergence presents an opportunity to further investigate the unique needs and behaviors of older adults in the context of health tourism, potentially leading to more tailored services and marketing strategies.

Theoretical Implications

The theoretical contribution of this study lies in its application of Sen’s Capability Approach to the health tourism context. By framing financial well-being as a conversion factor, this study examines how economic capability influences an individual’s ability to access healthcare services, ultimately improving health outcomes. This framework provides a comprehensive perspective on the ways financial well-being shapes health tourism behavior. It extends beyond mere affordability to explore how resources interact with healthcare access, mobility, and information availability. Additionally, the study highlights how financial well-being can help overcome geographical and systemic barriers to healthcare, enabling individuals to seek optimal health services across borders.

Furthermore, this research underscores the potential of financial well-being to overcome geographical and systemic barriers to healthcare access. This insight is especially relevant given the global health disparities and the rising prevalence of medical tourism. By demonstrating how financial resources help individuals navigate local healthcare limitations, this study deepens the understanding of health-seeking behaviors in an increasingly interconnected world. This perspective not only enhances the theoretical discourse on health tourism but also offers practical implications for policymakers and healthcare providers. Addressing healthcare equity and accessibility on a global scale requires strategies that consider the financial capability of individuals seeking cross-border medical care.

Practical Implications

According to the study’s findings, health tourism providers can markedly enhance their effectiveness by implementing targeted, data-driven strategies. Central to this transformation is the application of data analytics to personalize digital marketing and service offerings. By analyzing key data, such as age, financial well-being, health objectives, and travel preferences, providers can tailor content across platforms such as social media, email campaigns, and websites, ensuring relevance and resonance with diverse consumer segments.

Demographic targeting is a critical component in the strategic planning of health tourism services. Younger adults, aged 18–34, are frequently attracted to wellness-oriented and preventive care programs, particularly when integrated with leisure activities. In contrast, middle-aged adults, aged 35–54, often prioritize stress management and chronic disease prevention, with a preference for family-friendly options. Older adults, aged 65–97, tend to emphasize the importance of specialized treatments, comfort, and accessibility, often valuing virtual previews of facilities and seamless service delivery.

Financial well-being is also a significant factor. For individuals aged 18–64, expenditure on recurring services often persists even during financial constraints, rendering affordable subscription-based packages and flexible payment plans highly appealing. Conversely, for those over 65, increased financial stability does not necessarily correlate with higher spending, thus convenience and essential services should be prioritized over luxury offerings. To enhance their appeal, providers should offer a range of service tiers, including both economical and premium packages, to accommodate varying financial capacities. This inclusivity supports a broader client base while enhancing satisfaction and loyalty.

Collaborative partnerships with airlines, hotels, and tour operators can augment the value of bundled offerings by integrating medical treatment with accommodation and leisure. For example, a younger customer might appreciate a package that includes yoga retreats, spa treatments, and annual check-ups. In contrast, an older client may prefer a comprehensive medical evaluation paired with private transport and personalized care. Furthermore, the utilization of technology is imperative. Telemedicine, mobile applications, and AI-driven recommendation engines can assist in tailoring and managing experiences based on user profiles. Virtual reality tours can also address accessibility challenges, particularly for senior clients who may be hesitant to travel for preliminary consultations.

Ultimately, by combining insightful data collection with personalized digital marketing, dynamic service design, and technology-enabled delivery, health tourism providers can effectively address global consumers’ complex and evolving needs, transforming health journeys into seamless, personalized experiences.

Limitations

This study provides valuable insights but has certain limitations. The use of annual data restricts the ability to analyze short-term variations in health tourism expenditures, such as seasonal or cyclical trends. Future research could address this gap by utilizing monthly or quarterly data, offering a more detailed view of spending patterns.

Additionally, this study primarily examines age and gender as key segmentation factors. However, incorporating variables such as education, income, and regional differences could enhance the understanding of health tourism spending behaviors. Expanding the analysis to include these factors would provide a more nuanced perspective on how financial well-being influences health tourism decisions.

Future Research

Future research could examine health tourism behavior in other countries to provide a comparative perspective on how financial well-being influences health tourism decisions across different healthcare systems and economic environments. Additionally, studying the long-term effects of health tourism on domestic healthcare systems and public policy would offer valuable insights into how these expenditures impact healthcare access and delivery both locally and internationally. Furthermore, researchers should explore the broader, long-term impact of health tourism on local healthcare infrastructure, assessing its implications for resource allocation, service availability, and policy development.