Evidence map›Paper›PMID 39996214›Full record

ReviewCureus2025

Effectiveness of Pharmacy Automation Systems Versus Traditional Systems in Hospital Settings: A Systematic Review.

Enaam M Shbaily, Ibrahim M Dighriri, Norah S Alotaibi, Razan M Alqahtani, Ali M Mushawwal, Abdulrahman G Mohammed, Ghada S Barwaished, Maher M Almalki, Milaf Alshammari, Shahad B Alharbi and 4 more

Abstract readReview
In one paragraph

Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Enaam M ShbailyDepartment of Pharmacy, Armed Forces Hospital Jazan, Jazan, SAU.
Ibrahim M DighririDepartment of Pharmacy, King Abdulaziz Specialist Hospital, Taif, SAU.
Norah S AlotaibiCollege of Pharmacy, Taif University, Taif, SAU.
Razan M AlqahtaniCollege of Pharmacy, Princess Nourah bint Abdulrahman University, Riyadh, SAU.
Ali M MushawwalDepartment of Pharmacy, Al Nahdi Medical Company, Jazan, SAU.
Abdulrahman G MohammedDepartment of Pharmacy, General Directorate for Prison Health in Medical Service-Ministry of Interior (MOI), Jazan, SAU.
Ghada S BarwaishedCollege of Pharmacy, King Saud University, Riyadh, SAU.
Maher M AlmalkiDepartment of Pharmacy, Al Nahdi Medical Company, Makkah, SAU.
Milaf AlshammariCollege of Pharmacy, University of Hafr Albatin, Hafr Albatin, SAU.
Shahad B AlharbiCollege of Pharmacy, Qassim University, Qassim, SAU.
Saad M AlmalkiCollege of Pharmacy, Taif University, Taif, SAU.
Hanaa A AlatawiDepartment of Pharmacy, University of Tabuk, Tabuk, SAU.
Shamael A AlsharifCollege of Pharmacy, Umm Al-Qura University, Makkah, SAU.
Mohammed AlmuraytDepartment of Pharmacy, Armed Forces Hospital, Abha, SAU.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medication errors (MEs) in hospital settings remain a significant global healthcare challenge, resulting in adverse patient outcomes, increased healthcare costs, and reduced operational efficiency. Traditional pharmacy systems (TPS) are particularly vulnerable to human error, inefficient inventory management, and workflow bottlenecks. While pharmacy automation systems (PAS) have emerged as a potential solution, there is a notable gap in the literature regarding comprehensive comparative studies between PAS and TPS across multiple outcomes and settings. This systematic review addresses this gap by evaluating the comparative effectiveness of PAS versus TPS in hospital settings, focusing on MEs, operational efficiency, cost-effectiveness, and patient outcomes. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, searching PubMed and Cochrane Library databases for studies published between 2010 and June 2024 that directly compared PAS with TPS in hospital settings. The review examined various PAS technologies, including centralized pharmacy robots, automated dispensing cabinets (ADCs), and hybrid systems incorporating centralized and decentralized technologies. Of 1,085 studies initially identified, 32 met the inclusion criteria for comprehensive analysis. The overall mean effect size was 0.505 (95% confidence interval (CI): 0.487 to 0.523), indicating a moderately positive effect of PAS implementation. Key findings demonstrated that PAS significantly reduced MEs, particularly in automated dispensing systems (ADS) and computerized physician order entry (CPOE) systems. While initial implementation costs were substantial, long-term operational costs were significantly lower due to reduced labor requirements and medication wastage. Workflow efficiency improvements enabled pharmacists to dedicate more time to clinical activities. Patient outcomes improved through enhanced medication safety and reduced adverse drug events. This review provides robust evidence supporting PAS implementation in hospitals. It demonstrates that despite significant initial investment requirements, the long-term benefits in error reduction, operational efficiency, and patient safety justify implementation. Future research should focus on detailed cost-benefit analyses across various hospital settings and assessments of staff satisfaction to optimize implementation strategies.

Indexed as

hospital pharmacymedication errorsoperational efficiencypatient safetypharmacy automationsystematic review

Identifiers

PMID39996214
PMCPMC11847633

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.