Medication Management Service for Old Age Homes in Hong Kong Using Information Technology, Automation Technology, and the Internet of Things: Pre-Post Interventional Study
Bibliographic record
Abstract
BACKGROUND: Innovation in technology and automation has been increasingly used to improve conventional medication management processes. In Hong Kong, the current practices of medication management in old age homes (OAHs) are time consuming, labor intensive, and error prone. To address this problem, we initiated an integrated medication management service combining information technology, automation technology, and the Internet of Things in a cluster network of OAHs. OBJECTIVE: This pilot study aimed to evaluate the impact of the medication management program on (1) medication management efficiency, (2) medication safety, and (3) drug wastage in OAHs. We compared the time efficiency and the reductions in medication errors and medication wastage in OAHs before and at least 2 weeks after the implementation of the program. METHODS: From November 2019 to February 2020, we recruited 2 OAHs (serving 178 residents) in Hong Kong into the prospective, pre-post interventional study. The interventional program consisted of electronic medication profiles, automated packaging, and electronic records of medication administration. Using 3-way analysis of variance, we compared the number of doses prepared and checked in 10-minute blocks before and after implementation. We received anonymous reports of medication errors from OAH staff and analyzed the results with the Fisher exact test. We also calculated the quantity and cost of wasted medications from drug disposal reports. RESULTS: The number of doses prepared and checked in 10-minute blocks significantly increased postimplementation (pre: 41.3, SD 31.8; post: 70.6, SD 22.8; P<.001). There was also a significant reduction in medication errors (pre: 10/9504 doses, 0.1%; post: 0/5731 doses; P=.02). The total costs of wasted medications during January 2020 in OAH 1 (77 residents) and OAH 2 (101 residents) were HK $2566.03 (US $328.98) and HK $5249.48 (US $673.01), respectively. CONCLUSIONS: Our pilot study suggested that an innovative medication management program with information technology, automation technology, and Internet of Things components improved the time efficiency of medication preparation and medication safety for OAHs. It is a promising solution to address the current limitations in medication management in OAHs in Hong Kong.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".