# Acceptance of Smart Contracts in Patients Receiving Primary Care: Exploratory Study

## TL;DR

This study explores how patients' perceptions of security, risk, and trust influence their acceptance of blockchain-based smart contracts in primary care.

## Contribution

The study identifies key factors influencing patient adoption of smart contracts in primary care, including perceived security and trust in providers.

## Key findings

- Perceived security had the strongest positive impact on smart contract adoption (β=.50; P<.001).
- Trust in healthcare providers significantly influenced adoption (β=.43; P<.001).
- Male patients and those in clinic-based settings showed lower adoption intentions.

## Abstract

Health care has seen several new disruptive technologies. One such innovation is the introduction of blockchain smart contracts. These smart contracts are activated automatically once preprogrammed conditions are met. Smart contracts have improved patient outcomes, the efficiency of care delivery, and reduced costs. Despite their benefits, patients have had limited interactions with smart contracts in primary care; therefore, they may not trust blockchain-based smart contracts and may perceive them as risky or have concerns about their security.

This study aimed to evaluate how patients’ perceptions of smart contracts affect their adoption in primary care. Specifically, we investigated the impact of patients’ perceptions of smart contract security, risk, and trust in their health care providers.

This study used an experimental survey design to evaluate acceptance of smart contracts. Patients were randomly assigned to 1 of 2 research scenarios proposing either the positive use of blockchain smart contracts or the loss of benefits if a patient opted out. We collected data from a total of 387 participants. The Likert survey used 3 items to measure 5 constructs in the conceptual model. The 5 hypotheses were that gain-loss-framed messaging, perceived security, and trust would have a positive impact on the adoption of smart contracts, whereas perceived risk and the clinical setting would have a negative impact on patients’ intention to adopt smart contracts. The conceptual model was tested using structural equation modeling, and the model fit indices suggested a good fit.

Most of the hypotheses were supported, except for the gain-loss-framing effect. As hypothesized, perceived security had the strongest positive influence on the intention to use smart contracts (β=.50; P<.001). Trust in health care providers also showed a significant positive relationship (β=.43; P<.001), while perceived risk had a smaller but still significant negative impact (β=–.071; P.048). Patients in clinic-based settings had a lower intention to use smart contracts than patients in telehealth settings, and male patients had a lower intention to use smart contracts than female patients.

The results of this study have implications for health care providers who intend to adopt smart contracts early, that is, early majority or late adopters. To facilitate their implementation, providers should highlight the security benefits of smart contracts and leverage patient trust. Providers should customize smart contract implementation strategies based on patient demographics such as age, health status, and gender. By understanding these factors, health care organizations can more effectively promote the adoption of smart contracts and realize the potential benefits of this disruptive technology in primary care.