Objective: To evaluate the attitudes of the parties (i.e. patients, prescribers, and pharmacists) involved in the ePrescription systems toward the new features and measure the potential benefits of introducing the use of blockchain and machine learning (ML) to strengthen the in-place methods for safely prescribing and dispensing medication.

Methods: The survey contains questions about the features introduced in the proposed approach of using blockcahin and ML in the ePrescription system to evaluate the security, privacy, reliability, and availability of the ePrescription information in the system.

The study population is comprised of 284 respondents in the patient group, 39 respondents in the pharmacist group, and 27 respondents in the prescriber group, all of whom met the inclusion criteria. The response rate was 80% (226/284) in the patient group, 87% (34/39) in the pharmacist group, and 96% (26/27) in the prescriber group. We removed the responses of participants who did not complete the surevy questions.

Conclusion: Our survey showed that a vast majority of respondents in all groups had positive attitudes towards using blockchain and ML algorithms to safely prescribe medications. However, a need for minor improvements regarding the proposed features was identified, and a post-implementation user study is needed to evaluate the proposed ePrescription system in depth.

The survey was a web-based questionnaire composed using the Dalhousie University's Opinio survey system in January 2021. The system enables the users to compose a survey question and collect the data in CSV files, and it provides a link to be distributed for participation.

The patients' group questionnaire link was distributed using the university emailing list and Amazon Mechanical Turk.

For the prescribers and pharmacists groups, we disturbed their questionnaire links using emailing the prescribers' offices and pharmacies, the physicians and pharmacists associations, posting in the prescribers' and pharmacists' LinkedIn groups in Canada, the US, and the UK.

Each group was presented with a different questionnaire related to their role in the proposed ePrescription system. Each group’s questionnaire consisted of two sections. The first section evaluated the current ePrescription system in general and its related security and information availability features. The second section evaluated the proposed ePrescription system’s new features from their perspective in relation to their role in the system. However, in the second section of the patient group’s questionnaire we did not provide any questions on the alert generation feature using ML. That is because the role of the patients in the proposed system will not involve encountering the ML feature directly.

We removed all the responses in which the participants did not meet the requirements for the study to be included. The following are other exclusion criteria:

- we removed the responses of participants in which they did not complete the whole survey to the end.

- we removed the responses of participants who did not answer the check questions (i.e. for the auto survey complete bots) correctly.

- we removed any responses which had missing values in any of the questions.

All the questions used a 7-point Likert scale in which the participants responded on a scale from 1 - strongly agree to 7 - strongly disagree. Also, a final, open-ended question was asked so the participants could provide any suggestions and improvements on the proposed ePrescription framework.

We reversed the negative questions in the questionnaire during our final data processing. The original negative questions are kept and the reversed questions are marked with Q#R at the end.

Also, we include the demographic questions pdf file

Each group survey data are stored in a separate file titled with the name of the group.