Understanding the barriers to adopting blockchain-based applications in healthcare: A risk perception perspective (Preprint)
Bibliographic record
Abstract
BACKGROUND Traditional healthcare systems face security challenges that prevent them from ensuring medical data security. To overcome these challenges, healthcare organizations are turning to blockchain-based systems. Thanks to blockchain's characteristics, these applications provide high security in managing medical data and transactions. However, despite this advantage, healthcare organizations remain reluctant to adopt blockchain-based applications. This reluctance could be explained by the risks that healthcare organizations perceive regarding these applications. OBJECTIVE Our research aims to study the adoption of blockchain-based healthcare applications in healthcare organizations by adopting a risk-based approach. Through perceived risk theory (PRT), we built a research model that relates certain perceived risks of blockchain-based healthcare applications to the intention to adopt these applications. METHODS The research model was tested by collecting data from 194 IT professionals from 46 healthcare organizations in Canada and Africa. We used partial least squares structural equation modeling (PLS-SEM) to evaluate the hypothesized relationships of the research model. RESULTS Our study identified six dimensions of risk that are likely to influence the overall perceived risk regarding blockchain-based healthcare applications and, therefore, their adoption in healthcare organizations. Our study also showed that the association between the general perception of risks regarding blockchain-based healthcare applications and the intention to adopt them is significant in healthcare organizations in Canada but not in healthcare organizations in Africa. Moreover, our results indicate that the availability of cloud-based blockchain solutions reduces healthcare organizations’ risk perception regarding the lack of resources needed to adopt and use blockchain-based healthcare applications. CONCLUSIONS Our findings contribute to the literature by extending perceived risk theory (PRT), originally developed in marketing, through the integration of risks likely to prevent the adoption of blockchain in healthcare sector. This makes PRT more suitable for examining factors that may hinder the adoption of blockchain-based healthcare applications. Future research that focuses on analyzing the barriers to blockchain adoption in the healthcare sector will be able to use the extended PRT as a theoretical lens to develop its theoretical research model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".