AI-Powered Buy-Now-Pay-Later Smart Contracts in Healthcare

Abstract

As healthcare systems face mounting pressure to modernise payment infrastructure, fintech innovations have emerged as potential tools to improve affordability and efficiency. However, the adoption of these technologies in clinical settings remains limited. This study investigated the perceptions and resistance patterns of healthcare professionals toward Buy-Now-Pay-Later technology and blockchain in healthcare finance, using Innovation Resistance Theory as the guiding framework. Survey data collected from medical practitioners (N = 366) were analysed to identify knowledge gaps, perceived risks, and tradition-related barriers that influence adoption intent. The findings reveal that while interest in financial innovation exists, resistance is driven by institutional conservatism, regulatory uncertainty, and limited familiarity with decentralised finance systems. This research contributes to the literature by offering a theory-based explanation for why even high-potential financial tools face behavioural and structural resistance in healthcare environments.

1. Introduction

Pricing models are approaches how organisations determine their prices of products or services; these models are based on factors such as costs, market demand, competition, and perceived value [1].

Pricing models in the healthcare sector play a crucial role in determining how medical services are billed and reimbursed. These models also influence the affordability/costs of care provided to patients. Over the years, different pricing models have evolved to balance cost efficiency and patient satisfaction. Among the various models, the most common ones are the Fee-for-Service (FFS), Capitation, Value-Based Pricing (VBP), Bundled Payments, and Subscription-Based models.

One of the most prevalent models in traditional healthcare is the Fee-for-Service (FFS) model. Under this system, healthcare providers are compensated for each individual service, test, or procedure. For example, a patient undergoing surgery would receive separate bills for the consultation, the procedure itself, the hospital stay, and post-operative care. This model ensures that providers are reimbursed to the full extent of the care given. Critics argue that it incentivises excessive service utilisation, resulting in inflated medical costs [2].

The Capitation model offers a fixed, prepaid amount to healthcare providers per patient over a specific period regardless of the actual services utilised. While capitation encourages preventive care, there is concern that it might lead to under-treatment as providers may attempt to minimise services to stay within the budget [3].

The Value-Based Pricing (VBP) model, also known as Pay-for-Performance (P4P), seeks to address inefficiencies in traditional healthcare pricing by linking provider reimbursement to patient health outcomes. Instead of paying for the quantity of services rendered, insurers reward healthcare providers for improving a patient`s health. A major challenge is the accurate measurement of a patient’s health [4].

Another approach, the Bundled Payments model, consolidates multiple services into a single payment for an entire episode of care. For instance, a hospital may charge a fixed price for a knee replacement procedure that includes pre-operative consultations, surgery, rehabilitation, and follow-up visits. By reducing administrative complexity and bundled payments, it should lead to higher efficiency. However, it can be difficult to implement this model across multiple healthcare providers who need to agree on cost-sharing arrangements [5].

With the rise of telemedicine, Subscription-Based models have gained popularity. This approach allows patients to pay a recurring fee (monthly or annually) for unlimited access to healthcare services. Telehealth companies offer subscription-based plans that grant members virtual consultations and preventive care. This model ensures predictable costs and enhanced accessibility, though it may not cover all medical procedures, requiring additional out-of-pocket expenses for specialised care [6]. Predominant pricing models differ from country to country; see

Table 1. Pricing models by selected countries.

Pricing Model	Predominant In
Fee-for-Service	U.S.A.
Capitation	UK
Value-Based Pricing	Sweden
Bundled Payments	The Netherlands
Subscription-Based	Singapore

Source: https://www.statista.com/markets/412/topic/454/health-system (accessed 1 May 2025) [7].

. The Fee-for-Service model remains the most common in the United States.

As the financial sector moves forward, Artificial Intelligence (AI) fintech models are likely to play a greater role in future [8]. AI in payment systems can be used for predictive analytics, fraud detection, and personalisation [9]. For example, the benefits of AI-driven Subscription models include personalisation. Patients pay for what they use, making healthcare more affordable. In addition, there is cost efficiency in the sense that providers reduce administrative costs by optimising pricing in real time. Hopefully, this way, patients stay motivated to follow healthier habits when rewarded with lower fees. AI could also flag high-risk patients, helping insurers and clinics adjust their prices accordingly. AI could also track treatment progress, ensuring that payments only trigger when health outcomes improve (e.g., improved blood sugar levels for diabetics). It could go that far that a patient pays nothing if a prescribed therapy does not show expected results. This is a model that the medical trade may disagree with.

AI-driven pricing models must analyse various data to calculate subscription fees, namely the following:

• Usage-Based Pricing: Subscription costs adjust based on the frequency of doctor visits, telehealth consultations, or prescription refills. Example: A patient who uses telehealth once a month pays less than someone who consults weekly;
• Predictive Health Risk Assessment: AI evaluates a patient’s health history and lifestyle to predict future healthcare needs and adjust pricing accordingly. Example: A diabetic patient who actively manages his/her/their health may receive a lower subscription cost than one with unmanaged symptoms;
• Tiered and Dynamic Pricing: AI can segment patients into different pricing categories based on their engagement and risk profile. Example: A fitness-conscious user with a wearable health device showing high activity levels should pay less for a healthcare plan;
• Behaviour-Based Discounts: AI can track preventive care habits (e.g., regular exercises, diet tracking, medication adherence) and offer discounted subscription rates. Example: An app-linked smart scale or smartwatch can reward users who maintain a healthy weight or exercise regularly;
• Adaptive Payment Plans: AI can detect financial hardship patterns and offer flexible payment plans for patients struggling with payments. Example: If a patient’s income fluctuates (e.g., freelancer), AI can adjust monthly fees accordingly.

There are already real-world applications in the form of telehealth platforms such as Teladoc and Babylon Health. Devices like the Apple Watch and Fitbit can potentially integrate with insurers for dynamic premium adjustments. AI-powered diabetes management apps (e.g., Livongo) can tailor subscription fees based on a patient’s engagement. PayZen is a fintech company specialising in providing “Care Now, Pay Later” (CNPL) solutions to address the growing challenge of medical debt in the United States. By leveraging AI, PayZen offers patients personalised, interest-free payment plans for their healthcare expenses, aiming to make medical treatments more accessible and affordable [10] (FinMasters, 2023). This approach enhances the likelihood of repayment and increases acceptance rates among patients. In collaboration with Mastercard, PayZen introduced a Buy-Now-Pay-Later (BNPL) card specifically designed for medical expenses. This card, available in both physical and virtual forms, allows patients to manage their healthcare payments more flexibly [10].

The BNPL model is widely used in retailing to make purchases more accessible by allowing consumers to split payments into instalments. Lupșa-Tătaru et al. [11] in their article “Buy Now Pay Later—A Fad or a Reality?” concluded that it is not a fad but that there are overspending risks involved, especially for young consumers. BNPL might be a new concept in healthcare but in sectors like retailing, where it is most common, prices are often quoted in instalments per month instead of the full price.

All European countries struggle to finance healthcare [12]. In countries where healthcare is “free” (e.g., Canada, Portugal, Romania, Spain), the quality is generally poor. In addition, “free healthcare” does not mean that healthcare is completely without cost; most systems are funded through income taxes (e.g., Portugal) or social (national health) insurance (e.g., France, Germany, Romania, Switzerland). For example, in Germany, one has to pay 14.6% of a normal (below EUR 5500 per month) income for national health plus an additional 3.6% for compulsory care insurance (equally shared with employer), which is still not enough, so the government is forced to subsidise [13]. The BNPL model could be advisable for countries like France, Germany, and other European countries due to Europe’s economic challenges and healthcare accessibility issues. Europe’s “free” public healthcare system faces long waiting times due to resource constraints, leading many patients to seek private care. In addition, not all services are free, so patients have to co-finance, but high inflation and cost of living pressures have reduced their disposable incomes. Studies have shown that financial concerns prevent people from seeking timely medical care, leading to worse health outcomes and higher long-term costs [14]. Implementing BNPL in Europe’s “free” healthcare sector could be a win–win solution by improving patient access to care, reducing financial stress, and supporting private healthcare providers in securing payments.

Free healthcare implies that medical services are provided at no direct cost to the patient at the point of use, often funded entirely through taxation. In contrast, universal healthcare refers to a system in which all citizens have access to necessary health services, but this access may still involve co-payments, insurance premiums, or other out-of-pocket costs, depending on the country’s model.

A BNPL (Buy Now Pay Later) model could be realistically applied in non-urgent, elective, and out-of-pocket medical services, where patients often face significant cost barriers. For key areas, see Figure 1. These services are typically discretionary or not fully covered by insurance, making them more compatible with consumer financing models like BNPL.

Table 2. BNPL in retail vs. healthcare: comparison.

Healthcare BNPL	Retail BNPL	Category
To finance medical services and treatments	To finance consumer goods (e.g., electronics, clothing)	Purpose
Can vary: often non-urgent	Typically low urgency (discretionary purchases)	Urgency
Higher scrutiny due to ethical and health-related concerns	Light-to-moderate, varies by jurisdiction	Regulatory Oversight
Moderate to high (risk of over-indebtedness and delayed care)	High (consumer debt accumulation, impulse buying)	Risk of Overuse
May require stricter checks depending on provider	Often minimal or soft credit checks	Credit Assessment
High (patient vulnerability, informed consent, medical ethics)	Low to moderate	Ethical Considerations
Potential denial of future care, increased financial burden	Late fees, credit score impact	Default Consequences
Can vary: from short-term to extended plans	Fixed short-term instalments (usually every 4–6 weeks or monthly)	Repayment Structure
Need for treatment despite upfront cost barriers	Desire to buy now, manage cash flow	Customer Motivation

shows the difference between BNPL models in retailing and healthcare.

How AI could enhance BNPL in healthcare:

• Personalised Payment Plans: AI customises repayment schedules based on income, medical history, and financial behaviour;
• Real-Time Credit Scoring: AI analyses non-traditional financial data (e.g., spending habits, employment stability), for instance BNPL approvals;
• Predictive Risk Assessment: AI detects potential overdue payments and proactively suggests adjustments (e.g., lower monthly payments);
• Automated Fraud Detection: AI identifies high-risk transactions and prevents fraudulent BNPL applications.

Khezr et al. [15] outlined how blockchain technology can revolutionise healthcare. One could imagine an integration with cryptocurrencies and smart contracts. AI could automate subscription pricing using blockchain-based smart contracts including wearable BNPL plans (AI analyses health data from smartwatches and fitness trackers to adjust payment options). Blockchain-powered BNPL will be secure and be decentralised medical financing with automatic payment tracking. By integrating these technologies, BNPL platforms can offer secure, transparent, and automated payment solutions while reducing fraud and operational costs. A potential addition could be the combination with the medical Internet of Things (IoT) as outlined by Sharma et al. [16], which will not be further explored in the context of our research. The features of crypto and smart-contract BNPL are as follows:

• Decentralised Payments: Patients can use stablecoins or healthcare-specific tokens to pay for medical services over time;
• Smart Contract Automation: Self-executing contracts ensure automatic payments, refunds, and compliance based on pre-decentralised financing rules;
• Transparent and Fraud-Proof Transactions: Blockchain records every BNPL transaction, reducing disputes and ensuring trust;
• Lower Processing Fees: Eliminates intermediaries like banks, reducing transaction costs for both patients and providers.

However, there are three major challenges:

• Cryptocurrency Volatility: Using stablecoins (e.g., EUR Tether, EURS) instead of volatile cryptocurrencies could be the answer. Even more secure would be a Fiat cryptocurrency as outlined by Salhout and Bechter [17].
• Regulatory Uncertainty: Boustani and Elisabetta [18]. rightly asked, “How does a decentralized type of governance handle compliance and regulatory requirements? What happens if the majority of token owners in a decentralized voting system choose to abstain from voting for compliance upgrades or financial anti-money laundering requirements?” The answers can only lie in well-defined regulations regarding security and compliance. In other words, regulators must catch up and define clear rules. The deregulated community has to bite the bullet;
• Adoption Barriers: One may encourage practitioners and other medical services providers to accept crypto payments but nobody can be forced. This is the real bottleneck of a successful implementation. Our field research looked deeper into these barriers.
• The gap in existing literature on BNPL in healthcare is the lack of research on the intersection of consumer finance innovation (FinTech) and healthcare access, particularly within the context of decentralised finance, tokenisation, and AI-driven pricing models. While BNPL is well-studied in retail and increasingly in fintech contexts, its application in healthcare remains underexplored, especially regarding the perceptions and acceptance among medical professionals.

As healthcare systems worldwide explore digital transformation in financing models, innovations such as Buy-Now-Pay-Later (BNPL), blockchain, and AI-powered payment solutions offer new pathways for improving affordability and operational efficiency. However, the healthcare sector often exhibits resistance to adopting financial technologies that are already common in consumer markets. To better understand this phenomenon, this study adopted Innovation Resistance Theory (IRT) [19] as a guiding framework. IRT highlights how individuals and institutions may resist innovations due to perceived risks, lack of familiarity, traditional values, or doubts about usefulness. Barriers are especially relevant in professional healthcare settings, where financial and ethical stakes are high. By applying IRT to the intersection of healthcare and fintech, this study sought to explain not only patterns of adoption but also the psychological and contextual barriers that inhibit technological integration in medical practice.

The research objectives of our research were thus:

• To assess healthcare professionals’ perceptions and levels of awareness regarding blockchain and BNPL technologies as emerging tools for medical payment innovation;
• To identify key psychological and structural barriers to the adoption of these technologies, using Innovation Resistance Theory (IRT) to examine constructs such as knowledge, risk, usage, and tradition-related resistance;
• To explore the extent to which trust, openness to innovation, and perceived regulatory uncertainty influence willingness to pilot or adopt blockchain-based BNPL solutions in healthcare practice;
• To investigate whether individual lifestyle characteristics and attitudes toward related technologies (e.g., AI) moderate acceptance, serving as indicators of digital readiness rather than core components of the conceptual framework.

2. Materials and Methods

Table 3. Mapping of survey items to Innovation Resistance Theory (IRT) constructs.

IRT Construct	Survey Item
Knowledge Barrier	I have had prior exposure to cryptocurrencies like Bitcoin
Knowledge Barrier	I have heard about blockchain technology
Value Barrier	Artificial Intelligence can enhance healthcare
Knowledge Barrier	I believe that most healthcare practitioners lack awareness of Artificial Intelligence
Knowledge Barrier	I believe that most healthcare practitioners lack awareness of cryptocurrencies
Risk Barrier	I do not trust these new technologies like Artificial Intelligence and blockchains
Tradition/Image Barrier	I am too old for all these new technologies like Artificial Intelligence in healthcare
Risk Barrier	I worry about the regulatory uncertainty surrounding cryptocurrencies in healthcare
Knowledge Barrier (inverse)	I am interested in learning more about how cryptocurrencies could work in healthcare
Knowledge Barrier	I am familiar with healthcare financing systems in other countries
Value Barrier/Tradition Barrier	The current “free” healthcare should be replaced by a private healthcare system
Usage Barrier/Value Barrier	I would accept payments in monthly instalments as it is common practice in retailing
Value Barrier	I would be willing to pilot a small-scale crypto-based payment system in my practice/hospital
Value Barrier/Social Value	Individuals who actively maintain a healthy lifestyle. should be granted easier access and better fees for high-quality medical care and services

shows the mapping of each of our survey items to the corresponding Innovation Resistance Theory (IRT) construct. IRT includes six main types of barriers: Usage, Value, Risk, Tradition, Image, and Knowledge.

During the first quarter of 2025, 366 quantitative surveys were obtained from European practitioners and medical service providers. The aim of our field research was the evaluation of perceptions and state of knowledge regarding cryptocurrencies, blockchain, and smart contracts, see Appendix A. In total, 366 responses were analysed. Out of the 366 persons, 203 (55.5%) were medical doctors. The remaining ones were other healthcare specialists such as administrators. The demographics of the sample are listed in

Table 4. Age.

Percent	Frequency	Age
22.7	83	Below 30 years old
33.3	122	30–40 years old
27.6	101	40–50 years old
16.4	60	Above 50 years old
100.0	366	Total

and

Table 5. Employment sector.

Percent	Frequency	Sector
45.4	166	Government
28.4	104	Private Sector
26.2	96	Both
100.0	366	Total

.

We also asked some lifestyle questions about how they relax and how they keep their knowledge updated; see

Table 6. Relaxation Activity.

Percent	Frequency	Relaxation
22.7	83	Reading a book, classic radio, traditional relaxation
54.9	201	Streaming Netflix, social media, modern entertainment
100.0	366	Total

and

Table 7. Keeping Updated Activity.

Percent	Frequency	Knowledge Update
83.6	306	Online medical journals, webinars, and social media
12.3	45	Printed journals, books, and conferences
100.0	366	Total

. By asking these questions, we explored whether a “modern” or tech-oriented lifestyle correlates with greater acceptance of digital innovations in healthcare. Venkatesh et al. [20] found that technology adaptation correlates with tech-savviness and a more modern lifestyle.

The potential of cryptocurrencies and blockchains in financing healthcare is enormous, but the implementation depends on the people involved, especially on their perceptions; see Figure 2. In our research approach, we followed Innovation Resistance Theory (IRT) [19].

This study aimed to evaluate the perceptions, awareness, and acceptance of emerging blockchain-based financial technologies, specifically cryptocurrencies, smart contracts, and BNPL) models, among medical professionals. The objective was to understand the readiness and potential resistance within the healthcare sector to adopting decentralised payment systems. While blockchain and fintech innovations are increasingly discussed in healthcare finance (e.g., patient billing automation, transparent records, and decentralised insurance), the existing research has primarily focused on technical feasibility and institutional deployment. There is a gap in empirical studies assessing healthcare professionals’ attitudes toward these technologies. Few studies have explored how physicians and administrators perceive tokenised payment systems or BNPL solutions in clinical contexts. Addressing this gap is critical for evaluating the feasibility and ethical implications of integrating such technologies in patient care.

The survey items can be interpreted through the lens of Innovation Resistance Theory (IRT), which explains why individuals may resist adopting modern technologies despite potential benefits. Questions expressing distrust, age-related hesitation, or concern over regulatory uncertainty align with functional barriers (e.g., usage and risk) and psychological barriers (e.g., tradition and image) outlined in IRT. Items reflecting a lack of familiarity or perceived practitioner unawareness correspond to the knowledge barrier, a key precursor to resistance. Conversely, questions indicating curiosity, willingness to pilot new systems, or support for behaviour-based incentives demonstrate potential pathways to overcoming resistance through compatibility and perceived value. Overall, the responses can help identify which types of resistance dominate and what strategies may facilitate the acceptance of disruptive health technologies like AI, blockchain, and cryptocurrency-based payments.

3. Results

We analysed the data by both descriptive analyses as well as multivariate methods. Significant group differences were also extracted.

3.1. Descriptive Statistics

For all questions, we used a 0 (totally disagree)-to-10 (totally agree) Likert scale.

Most participants agreed that AI will have an impact on healthcare (mean 7.6); see

Table 8. Descriptive statistics: survey items.

Std. Deviation	Mean	N
2.21	7.60	366	AI Healthcare
2.62	6.69	366	Telehealth
3.16	5.55	366	Crypto doubts
3.38	4.81	366	Learning
3.22	4.58	366	Rewarded
2.96	4.36	366	Replaced
2.94	3.77	366	Instalments
3.04	3.39	366	Pilot
3.43	3.14	366	Know crypto

. The knowledge about cryptocurrencies was low and the perceived uncertainty of cryptocurrencies was high. The willingness to accept instalment payments or to run a crypto test was also more on the negative side. The observed low willingness among European healthcare professionals to accept cryptocurrencies can be explained by examining the region’s regulatory landscape and professional environment. Europe’s healthcare sector is characterised by stringent regulations and a cautious approach to adopting new financial technologies. The introduction of the Markets in Crypto-Assets (MiCA) regulation aims to provide a harmonised legal framework for crypto-assets across the EU [21]. While MiCA seeks to enhance legal clarity, it also imposes significant compliance requirements that may deter healthcare institutions from integrating cryptocurrencies into their operations. This regulatory complexity contributes to hesitancy in adopting such technologies within the healthcare sector. Incorporating frameworks such as the European MiCA regulation [22] is crucial to ensure legal clarity and consumer protection in the tokenisation of healthcare financing. Ethical risks, particularly the potential for patient over-indebtedness, must be carefully assessed to prevent exploitation or undue financial stress. A responsible approach requires balancing innovation with safeguards that prioritise health equity, informed consent, and financial literacy.

Additionally, the European healthcare industry often exhibits institutional scepticism toward emerging technologies like blockchain and cryptocurrencies. Concerns about data privacy, financial stability, and the speculative nature of crypto-assets further exacerbate this cautious stance. These factors collectively influence the low acceptance rates of cryptocurrencies among healthcare professionals in Europe. Therefore, the reluctance to embrace cryptocurrencies in European healthcare settings is not merely a reflection of individual attitudes but is deeply rooted in the broader regulatory and professional context of the region [23].

3.2. Group Differences

We conducted a t-test to evaluate whether medical doctors working in the private sector have different views than the ones working in the public sector. The null hypothesis that there was no difference at the 95% confidence level could be rejected in two cases, namely, being replaced by AI and telehealth; see

Table 9. Group differences by employment sector.

Mean	N	Work	Question
4.80	104	private	Replaced
4.30	166	public
6.00	104	private	Telehealth
6.70	166	public

. Medical experts working in the private sector (e.g., doctors having their own practice) are more afraid of being replaced by AI than their publicly employed colleagues. These, however, see a brighter future for telehealth than their private counterparts.

3.3. Major Factors and Correlations

A factor analysis extracted three major dimensions, which, in total can explain more than 60% of variance; see

Table 10. Total variance explained.

% of Variance	Rotation Sums of Squared Loadings
27.49	Factor 1
20.23	Factor 2
13.51	Factor 3

. In factor analysis, the number of factors is commonly determined using the eigenvalue-greater-than-one rule (Kaiser criterion), which retains only those factors whose eigenvalues exceed 1, indicating that they explain more variance than a single original variable. In our case, the Kaiser criterion resulted in three factors.

Factor (Component, Dimension) 1 showed four variables with high (>6) loadings; see

Table 11. Rotated component matrix.

Component
3	2	1
0.065	0.145	0.813	Learning
−0.225	−0.083	0.736	Know crypto
0.175	0.160	0.696	Instalments
0.198	0.222	0.652	Pilot
0.196	0.100	0.526	Crypto doubts
0.027	0.901	0.151	Telehealth
−0.020	0.861	0.184	AI healthcare
0.728	0.222	0.081	Rewarded
0.722	−0.325	0.149	Replaced

. Factor 1 could be described as “open to new ideas” because it included the willingness to learn more about cryptocurrencies, the willingness to run a pilot test with crypto payments, accepting instalments (BNPL), and existing knowledge about cryptocurrencies. These four attributes correlated significantly with each other; see

Table 12. Correlations.

Pilot	Instalments	Know Crypto	Learning
0.581 *	0.451 *	0.484 *	1	Pearson Correlation	Learning
270 0.329 *	366 0.283 *	366 1	366 0.484 *	N Pearson Correlation	Know Crypto
270 0.580 *	366 1	366 0.283 *	366 0.484 *	N Pearson Correlation	Instalments
270 1	366 0.580 *	366 0.329 *	366 0.581 *	N Pearson Correlation	Pilot

* Correlation was significant at the 0.01 level (2-tailed).

. This finding was in line with [20] Venkatesh et al. (2012), stating that openness to new ideas correlates with technology acceptance.

Extraction method: Principal Component Analysis. Rotation method: Varimax with Kaiser normalisation.

3.4. Predicting

We conducted a regression analysis to predict who may be willing to run a pilot test with cryptocurrency as payment. Before conducting the regression analysis, diagnostic tests were performed to verify that key assumptions were met. Multicollinearity was assessed using the Variance Inflation Factor (VIF) with all predictors returning VIF values below 5, indicating no serious multicollinearity concerns (see

Table 13. Variance Inflation Factor.

		Collinearity Statistics
Model	Tolerance	VIF
(Constant)
Learning	0.784	1.275
Crypto_doubts	0.819	1.222
Rewarded	0.929	1.076
Replaced	0.884	1.132
Telehealth	0.867	1.154

).

To examine the assumption of normality, the Shapiro–Wilk test was applied to the regression residuals. The results (W = 0.976, p = 0.067) suggest that the residuals were approximately normally distributed, satisfying the assumption for parametric testing; see

Table 14. Shapiro–Wilk test.

		Shapiro-Wilk
	Statistic	df	Sig.
Standardised Residual	0.991	270	0.105

.

The strongest influencing parameter was the willingness to learn more about cryptocurrencies. Unsurprisingly, doubts about cryptocurrencies (legality etc.) had a negative impact.

Table 15. Coefficients—dependent variable: pilot-test cryptocurrency.

Standardised Coefficients Beta	Unstandardised Coefficients B	Model
	−0.717	(Constant)
0.532	0.462	Learning
−0.094	−0.090	Crypto doubts
0.097	0.090	Rewarded
0.123	0.127	Replaced
0.200	0.219	Telehealth

shows the betas of the model. With an R square of 0.4, the fit of the model was good; see

Table 16. Model fit pilot.

Adjusted R Square	R Square	R
0.39	0.40	0.63

No issues regarding heteroscedasticity or residuals were found; see Figure 3, Figure 4 and Figure 5.

.

We also wanted to predict who may accept instalment payments. In this case, the strongest predictor was the willingness to run a pilot test with cryptocurrency payments; see

Table 17. Coefficients—dependent variable: instalments.

Standardised Coefficients Beta	Unstandardised Coefficients B	Model
0.455	0.457	Pilot

. The R square was 0.368; see

Table 18. Model fit instalments.

Adjusted R Square	R Square	Model R
0.363	0.368	0.606

.

3.5. Grouping

We conducted a cluster analysis to group respondents. Two distinct clusters were identified. Looking at

Table 19. Relaxation.

Streaming Netflix, Social Media, Modern Entertainment		Reading a Book, Classic Radio, Traditional Relaxation
Percent	Frequency	Percent	Frequency
9.9%	14	100.0%	64	1	Cluster
90.1%	128	0.0%	0	2
100.0%	142	100.0%	64	Combined

, we can see that Cluster 1 represented a more traditional lifestyle. A similar picture was found when looking at how respondents kept their knowledge updated; see

Table 20. Keeping updated.

Printed Journals, Books, and Conferences		Online Medical Journals, Webinars, and Social Media
Frequency	Percent	Frequency	Percent
41	100.0%	90	41.3%	Cluster	1
0	0.0%	128	58.7%		2
41	100.0%	218	100.0%		Combined

. Cluster 2 was more into streaming than Cluster 1 members, who updated their knowledge by reading printed journals; see Table 20.

However, the centroids (of the willingness to run a pilot test) were not significantly different; see

Table 21. Centroids.

Pilot
Std. Deviation	Mean		Cluster
3.05112	3.0986	1
3.01104	3.7266	2
3.04281	3.3963	Combined

.

The authors hypothesised that a modern lifestyle leads to a higher probability of running a pilot test. A chi-square test showed no relationship. Therefore, we conducted a cluster analysis but without a different result. This contradicts the current research that states that there is a link between lifestyle and the early adoption of a product/service [24,25].

4. Discussion and Implementation

Our findings are in line with previous research conducted by [26] Govindarajan et al. in the sense that there are many barriers to fully accepting modern technologies such as cryptocurrencies in healthcare. AI is accepted as a force that will shape the years to come [27,28]. However, there are concerns about the legality of cryptocurrencies [29,30].

The observed reluctance among healthcare professionals to adopt cryptocurrency-based payment systems aligns with prior research on institutional resistance to financial innovation in highly regulated sectors. Studies have shown that healthcare organisations are particularly cautious in adopting unregulated technologies due to perceived risks around compliance, accountability, and data integrity [31,32]. Furthermore, regulatory uncertainty, such as in evolving guidelines under the EU’s MiCA framework, contributes to an environment where novel financial tools are met with scepticism [33]. This resistance can also be understood through the lens of Innovation Resistance Theory, which highlights how risk and tradition barriers inhibit the uptake of technologies that diverge from established institutional practices [19]. Additionally, empirical findings by [25] McElheran et al. indicate that even in technologically advanced industries, AI and fintech adoption is often delayed by organisational inertia and legal ambiguity. Regulatory clarity and perceived legitimacy are essential for innovation acceptance in healthcare.

To fully integrate AI-powered BNPL with crypto payments and smart contracts, the system must include on-chain and off-chain components to ensure seamless financing, automation, and security. Below is a detailed breakdown of how it would work from a technical perspective.

System architecture and components:

• AI-Powered Credit and Risk Assessment uses machine learning algorithms to analyse;
• Patient’s financial history (bank accounts, crypto wallets, spending patterns);
• On-chain activity (decentralised financing transactions, crypto staking, wallet balances);
• Healthcare history (frequency of doctor visits, medical insurance claims);
• The AI generation of a personalised BNPL plan with terms tailored to the risk profile.

Crypto staking is a process where cryptocurrency holders lock up a certain number of their digital assets in a specific blockchain network to support its operations such as validating transactions or securing the network. In return for staking their crypto, participants earn rewards (usually in the form of additional tokens or coins). This process is typically associated with Proof of Stake (PoS) and its variants, which are consensus mechanisms used by some cryptocurrencies.

A smart contract is created to manage the following:

• Payment schedule (monthly or customised);
• Crypto collateral requirements (if needed);
• Automated penalties and rewards for early/late payments;
• Instant fund distribution to healthcare providers;
• Deployment on a public (e.g., Ethereum) or private (Hyperledger) blockchain;
• Crypto payment processing and stablecoin integration.

Healthcare BNPL could be tokenised into so-called NFTs (non-fungible tokens). Nunes et al. [34] explored the potential applications of NFTs in healthcare, highlighting innovative solutions and challenges associated with integrating blockchain technology in the sector. As shown by [35] Zhang et al., NFTs can effectively hedge against traditional financial assets when used as long positions. NFTs can hedge because these healthcare tokens are somewhat uncorrelated with mainstream markets. This diversification effect will reduce overall portfolio risk during traditional market downturns, assuming that the NFT market behaves independently or inversely.

Patients could tokenise BNPL agreements as NFTs for trading on decentralised financing lending platforms and investors could fund BNPL pools in return for yield from patient repayments. Healthcare providers could then use BNPL-backed assets as collateral. Figure 6 illustrates a decentralised BNPL model in healthcare, where patients receive treatment from providers while entering a smart-contract-based repayment agreement. The smart contract manages the flow of funds between patients, providers, BNPL platforms, and investors with the option to tokenize the debt as NFTs for trade or collateral. This system leverages blockchain technology to automate, secure, and potentially decentralize healthcare financing.

The integration of tokenised BNPL agreements with decentralised financing lending pools can revolutionise healthcare financing by transforming patient debt into tradeable assets. This model allows investors to fund medical loans, hospitals to gain liquidity, and patients to access care without upfront costs. Tokenisation converts BNPL agreements into digital assets (NFTs or security tokens) recorded on a blockchain. These assets represent a patient’s outstanding medical balance and can be traded or used as collateral in decentralised finance lending markets. To implement tokenised BNPL for healthcare, it needs smart contracts that automate: loan issuance, payment tracking, default handling, and tokenisation. Tokenisation converts BNPL agreements into digital assets recorded on a blockchain. However, tokenising BNPL agreements in healthcare could introduce systemic financial risks like those seen in the 2008 US mortgage crisis. Just like mortgage-backed securities bundled subprime loans, tokenised BNPL agreements could be packaged and traded as financial assets. If healthcare BNPL debts are issued to high-risk patients who struggle to repay, these tokenised assets could become overvalued and unstable. A lack of regulation in tokenised debt markets could lead to predatory lending, where patients are offered BNPL without proper risk assessment. If speculative trading increases, healthcare debt could be treated like a financial commodity rather than a human necessity. However, there is a long way to go before this risk factor may come into play. A token economy in healthcare would involve the utilisation of blockchain-based tokens such as NFTs representing patient debt. These tokens would be tradeable, allowing investors to fund patient care by purchasing them in exchange for yield from future repayments. Sustainability would require transparent governance, regulatory compliance, and robust smart contracts to ensure trust. Additionally, patient data privacy must be protected through secure and anonymised token design. Long-term viability depends on the future adoption.

There were limitations in our study. We surveyed medical professionals but not health insurance providers. These may have a more forward-looking perspective; nor did we include government officials. Like any other technology, blockchains (or distributed server systems) have to prove that they become mainstream and are manageable. The acceptance and tradability of BNPL NFTs has not been evaluated.

5. Conclusions

BNPL is an additional payment method and not a substitute for conventional methods. However, integrating AI-powered BNPL with cryptocurrencies and smart contracts could revolutionise medical financing, making healthcare more accessible, transparent, and automated. As blockchain adoption grows, decentralised BNPL financing solutions could provide patients with flexible, low-cost, and secure payment options worldwide to the extent of a global market for bundled BNPL tokens. Overall medical costs will be reduced because we will, at the same token, be financially incentivised to actively manage our own health (preventing chronical diseases) and foster care habits (e.g., regular exercise, diet tracking) through AI. However, the stumbling block against changing the system constitutes the people involved such as politicians and medical professionals. To incentivise politicians and medical practitioners to accept BNPL tokens in a decentralised financial environment, it will need a strategy that aligns with their interests, i.e., economic benefits, risk reduction, regulatory alignment, and patient-centred incentives such as positioning BNPL tokens as cost-reduction tools for national healthcare, collaboration with the European Central Bank to issue regulated stablecoin-backed BNPL tokens to avoid volatility concerns, and using pilot programmes in high-profile hospitals.