Implementation of an Electronic Medication Management System in 41 Residential Care Homes in Hong Kong: Pre–Post Interventional Study
Bibliographic record
Abstract
Background: The medication management process in resident care homes for the elderly (RCHEs) is complex and can be labor intensive. In 2019, a nongovernment organization led by pharmacists with special interest in informatics developed the SafeMed Medication Management System (SMMS), which is a digital web-based system that integrates electronic medical profiles and medication profiles to revamp the traditional manual medication management process in RCHEs in Hong Kong. Objective: This study aimed to assess the effectiveness of the SMMS in improving RCHE staff's time efficiency and competencies in the medication management process and how this could potentially reduce human resource costs after its implementation. Methods: This was a pre-post interventional study conducted from September 2022 to August 2024. Time efficiency was evaluated using time-motion analysis. The time spent on each process-preparing the medication doses, checking the prepared doses, and administering the doses to residents-was evaluated in 10-minute blocks. The mean numbers of doses prepared, checked, and administered were calculated for each block. A three-way ANOVA was used to compare the doses before and after the system implementation. Staff competencies and perceived acceptance of the system were evaluated using a structured survey adapted from the technology acceptance model. An exploratory analysis was conducted to estimate the potential financial savings attributable to SMMS implementation using RCHE staff salary data obtained from publicly available governmental sources. Results from the time-motion analysis were used to estimate the cost per dose prepared by RCHE staff before and after the system implementation. Results: Forty-one RCHEs implemented the SMMS, serving a total of 3911 residents. The time-motion analysis (n=6 RCHEs) revealed that the mean (SD) number of doses significantly increased in 10-minute blocks after system implementation (medication preparation: 25, SD 14 to 49, SD 15 doses; medication checking: 21, SD 6 to 85, SD 33 doses; medication administration: 9, SD 1 to 16, SD 6 doses). The overall mean number of doses handled across all processes combined was significantly higher after implementation (18.9 vs 51.9 doses, P=.02). RCHE staff (n=392) reported significantly improved competencies in entering and accessing residents' records and preparing, checking, and administering medications after the system implementation (all P<.001). The estimated cost of managing one dose of medication dropped substantially from HKD 2.00 (US $0.25) before to HKD 0.74 (US $0.09) after system implementation. If fully implemented in all RCHEs across Hong Kong, the daily human resource cost associated with the medication management process could potentially be reduced from HKD 2,574,000 (US $330,000) to HKD 952,380 (US $122,100). Conclusions: The time-motion analysis and quantitative survey findings suggest that digital technology combined with automation can improve staff's time efficiency and competencies and promote human resource cost-saving in the medication management process. Future work should evaluate the long-term impact of this system on medication safety and its cost-effectiveness in RCHEs.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".