Today@Dal

Project title: Developing an e-prescription system using machine learning and blockchain to minimize medication errors.

We are recruiting participants to take part in an online survey to evaluate the proposed e-prescription system. The system aims to introduce a more robust and secure method to share the patient medication information with all parties (i.e., prescribers, pharmacists, and patients) in the e-prescription private network. Moreover, confirming the patient's identity and minimizing dispensing errors and prescribing errors. The system also provides a feature that generates alerts to avoid any conflict of prescribed medication with past medication history, prescription information, and current health condition. The study will help to enhance and improve the proposed e-prescription system by giving feedback about the implemented features. Thus, contribute to the research area of minimizing medication errors. To be eligible to participate, you must be 18 years and older and have experienced using e-prescription systems or paper prescriptions to pick up prescribed medication in the past year as a patient.

The participant will first read an introduction to the survey (informed consent) and consent to participate in the study. The survey will start with a question to choose in which group you will be participating. Then, general demographic questions. The participant will then answer a set of questions regarding the proposed e-prescription system we developed and the implemented features based on their participation group (i.e. patient, prescriber, or pharmacist). The survey will take from 20 to 30 minutes to complete. Participation is completely voluntary. All data will be treated confidentially and for research purposes only, and participants will not be asked for any identifiable personal information. However, the IP addresses will be stored in the Opinio server (i.e. a secure and private server provided by Dalhousie University). During the data collection process to ensure no multiple submissions. The IP addresses will be deleted after data collection is complete since they are not part of this study. Moreover, no identifiable information will be stored locally, and all participants will be assigned participation numbers (e.g. P1, P2, etc.) randomly to ensure the participants' anonymity. All the other data will be stored on a password-protected laptop and will only be accessible by the primary investigator. As mentioned, none of the data will contain any identifiable information about the participants.

If you are interested in participating, please click on this link: https://surveys.dal.ca/opinio/s?s=60538

If you know anyone interested in taking part in the study, please share this recruitment notice with them.