A framework to lower the risk of medication prescribing and dispensing errors: A usability study of an NFC-based mobile application
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Wrong medication and wrong dosage are major risks in the pharmaceutical industry, as many medication errors occur when dispensing medication. The dispensing process in its current form is limited in verifying the patient's identity before dispensing the medication. Furthermore, this process does not offer a robust method for providing accurate medication intake instructions. Therefore, we have developed a framework to accurately and securely overcome issues associated with transferring patient credentials and prescription information. The long-term goal of this research is to develop a framework to mitigate medication dispensing errors. One of the framework components is the mobile application that uses near-field communication (NFC) to transfer information. Therefore, in this paper, we designed a user study to assess the proposed NFC-based mobile application in terms of usefulness and ease of use compared with the traditional method of picking up a prescribed medication. METHODS: We conducted a usability study with 21 participants to perform four tasks to simulate the process of picking up a prescribed medication using the proposed NFC application method and the traditional method of picking up medication. Then, we asked the participants to complete two post-questionnaires after using each method to evaluate the participants' experience of the process. Next, we asked the participants to complete an additional questionnaire about the usefulness of the NFC application method. Finally, we conducted semi-structured interviews with the participants to get more evidence to back up the questionnaire answers. RESULTS: Our findings show that 91% of the participants believe using the NFC application method will improve patient safety during the medication pickup process. Nearly 97% of participants found the NFC application method easy to use. Our findings show that the participants scored lower when using the NFC application method compared with the traditional method when trying to identify the wrong medication after dispensing. In addition, 90% of the participants successfully identified the wrong medication when using the NFC application method, compared to only 38% when using the traditional method. Finally, the results show that the participants preferred using the NFC application method in terms of information availability, security, and privacy. CONCLUSIONS: The study findings show that the proposed NFC application for managing patients' prescriptions and picking up medication might improve patient safety. The results show that the participants believe the NFC application will mitigate medication dispensing errors, at least from their end. The participants believe the application will provide a fast and accurate method of verifying dispensed medication from the patient end. Moreover, the application will help the patient to track their current prescription, which also helps them remember the medication intake instructions. Finally, the study indicates that the application will provide a secure, private, and accurate method to help verify the patient's identity, thus minimizing medication errors during the medication dispensing process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it