Efficiency and Usability of a Near Field Communication-Enabled Tablet for Medication Administration
Bibliographic record
Abstract
BACKGROUND: Barcode-based technology coupled with the electronic medication administration record (e-MAR) reduces medication errors and potential adverse drug events (ADEs). However, many current barcode-enabled medication administration (BCMA) systems are difficult to maneuver and often require multiple barcode scans. We developed a prototype, next generation near field communication-enabled medication administration (NFCMA) system using a tablet. OBJECTIVE: We compared the efficiency and usability of the prototype NFCMA system with the traditional BCMA system. METHODS: We used a mixed-methods design using a randomized observational cross-over study, a survey, and one-on-one interviews to compare the prototype NFCMA system with a traditional BCMA system. The study took place at an academic medical simulation center. Twenty nurses with BCMA experience participated in two simulated patient medication administration scenarios: one using the BCMA system, and the other using the prototype NFCMA system. We collected overall scenario completion time and number of medication scanning attempts per scenario, and compared those using paired t tests. We also collected participant feedback on the prototype NFCMA system using the modified International Business Machines (IBM) Post-Study System Usability Questionnaire (PSSUQ) and a semistructured interview. We performed descriptive statistics on participant characteristics and responses to the IBM PSSUQ. Interview data was analyzed using content analysis with a qualitative description approach to review and categorize feedback from participants. RESULTS: Mean total time to complete the scenarios using the NFCMA and the BCMA systems was 202 seconds and 182 seconds, respectively (P=.09). Mean scan attempts with the NFCMA was 7.6 attempts compared with 6.5 attempts with the BCMA system (P=.12). In the usability survey, 95% (19/20) of participants agreed that the prototype NFCMA system was easy to use and easy to learn, with a pleasant interface. Participants expressed interest in using the NFCMA tablet in the hospital; suggestions focused on implementation issues, such as storage of the mobile devices and infection control methods. CONCLUSIONS: The NFCMA system had similar efficiency to the BCMA system in a simulated scenario. The prototype NFCMA system was well received by nurses and offers promise to improve nurse medication administration efficiency.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".