ArticleFrontiers in pharmacology2026
Statistical process control within the Six Sigma DMAIC framework for monitoring medication dispensing errors in hospital pharmacies: a Saudi case study.
Article in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study employed the Six Sigma DMAIC methodology to monitor and analyze medication dispensing errors in hospital pharmacies in Saudi Arabia, with a focus on Mecca and Jeddah cities. Using aggregated data from the Ministry of Health (MOH) Statistical Yearbook 2024, a synthetic dataset was generated through a Conditional Tabular Generative Adversarial Network (CTGAN) to enable the Statistical Process Control (SPC) analysis. The p-chart application demonstrated how process stability can be evaluated within a synthetic healthcare dataset, while the calculated sigma level (2.49) represents the performance characteristics of the generated data rather than actual hospital pharmacy performance. Pareto analysis illustrated how dominant error categories can be prioritized within a synthetic dataset generated under predefined assumptions. Based on these findings, several improvement strategies were proposed, particularly targeting dispensing errors within pharmacy operations. However, due to the use of synthetic data, the improvement and control phases remain conceptual. The generated synthetic dataset enabled demonstration of Statistical Process Control (SPC) techniques within the Six Sigma DMAIC framework. The p-chart indicated statistical stability within the generated data, while Pareto analysis illustrated the prioritization of error categories under predefined synthetic distributions. Because all analyses were conducted using CTGAN-generated synthetic records, the findings should be interpreted as a methodological demonstration rather than as evidence of actual medication error performance in Saudi hospital pharmacies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.