The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study
Open accessAbstract
OBJECTIVES: To assess the impact of a closed-loop electronic prescribing, automated dispensing, barcode patient identification and electronic medication administration record (EMAR) system on prescribing and administration errors, confirmation of patient identity before administration, and staff time. DESIGN, SETTING AND PARTICIPANTS: Before-and-after study in a surgical ward of a teaching hospital, involving patients and staff of that ward. INTERVENTION: Closed-loop electronic prescribing, automated dispensing, barcode patient identification and EMAR system. MAIN OUTCOME MEASURES: Percentage of new medication orders with a prescribing error, percentage of doses with medication administration errors (MAEs) and percentage given without checking patient identity. Time spent prescribing and providing a ward pharmacy service. Nursing time on medication tasks. RESULTS: Prescribing errors were identified in 3.8% of 2450 medication orders pre-intervention and 2.0% of 2353 orders afterwards (p<0.001; chi(2) test). MAEs occurred in 7.0% of 1473 non-intravenous doses pre-intervention and 4.3% of 1139 afterwards (p = 0.005; chi(2) test). Patient identity was not checked for 82.6% of 1344 doses pre-intervention and 18.9% of 1291 afterwards (p<0.001; chi(2) test). Medical staff required 15 s to prescribe a regular inpatient drug pre-intervention and 39 s afterwards (p = 0.03; t test). Time spent providing a ward pharmacy service increased from 68 min to 98 min each weekday (p = 0.001; t test); 22% of drug charts were unavailable pre-intervention. Time per drug administration round decreased from 50 min to 40 min (p = 0.006; t test); nursing time on medication tasks outside of drug rounds increased from 21.1% to 28.7% (p = 0.006; chi(2) test). CONCLUSIONS: A closed-loop electronic prescribing, dispensing and barcode patient identification system reduced prescribing errors and MAEs, and increased confirmation of patient identity before administration. Time spent on medication-related tasks increased.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
To Err Is Human: Building a Safer Health System
(2000) · National Academies Press eBooks · doi:10.17226/9728 · Cited by 14,044
To Err is Human: Building a Safer Health System
Hendee, W. R. (2001) · Journal of Vascular and Interventional Radiology · doi:10.1016/s1051-0443(01)70072-3 · Cited by 10,103
Bates, D. W. (1998) · JAMA · doi:10.1001/jama.280.15.1311 · Cited by 2,107
To Err is Human: Building a Safer Health System in 1999.
Stefl, M. E. (2001) · PubMed · Cited by 1,738
The Impact of Computerized Physician Order Entry on Medication Error Prevention
Bates, D. W., Teich, J. M., Lee, J., Seger, D., Kuperman, G. J., Ma'Luf, N., Boyle, D., & Leape, L. (1999) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.1999.00660313 · Cited by 1,260
Poissant, L. (2005) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m1700 · Cited by 901
Using information technology to reduce rates of medication errors in hospitals
Bates, D. W. (2000) · BMJ · doi:10.1136/bmj.320.7237.788 · Cited by 483
Guided Medication Dosing for Inpatients With Renal Insufficiency
Chertow, G. M. (2001) · JAMA · doi:10.1001/jama.286.22.2839 · Cited by 447
Prescribing errors in hospital inpatients: their incidence and clinical significance
Dean, B. (2002) · Quality and Safety in Health Care · doi:10.1136/qhc.11.4.340 · Cited by 419
Dean, B., Barber, N., & Schachter, M. (2000) · Quality in Health Care · doi:10.1136/qhc.9.4.232 · Cited by 415
Self-citation
Cited by
Nuckols, T. K., Smith-Spangler, C., Morton, S. C., Asch, S. M., Patel, V. M., Anderson, L. J., Deichsel, E. L., & Shekelle, P. G. (2014) · Systematic Reviews · doi:10.1186/2046-4053-3-56 · Cited by 340
Ahtiainen, H. K., Kallio, M. M., Airaksinen, M., & Holmström, A.-R. (2020) · European Journal of Hospital Pharmacy · doi:10.1136/ejhpharm-2018-001791 · Cited by 78
Systematic review of computerized prescriber order entry and clinical decision support
Vélez-Díaz-Pallarés, M., Pérez-Menéndez-Conde, C., & Bermejo-Vicedo, T. (2018) · American Journal of Health-System Pharmacy · doi:10.2146/ajhp170870 · Cited by 50
Cresswell, K. M., & Sheikh, A. (2008) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2007.12.1180 · Cited by 38
Wang, Y.-C., Tsan, C.-Y., & Chen, M.-C. (2021) · JMIR Formative Research · doi:10.2196/24542 · Cited by 17
Metadata
- Authors
- Franklin, B. D., O'Grady, K., Donyai, P., Jacklin, A., & Barber, N.
- Venue
- Quality and Safety in Health Care
- Publisher
- BMJ
- Published
- 2007-08-01
- Volume
- 16
- Issue
- 4
- Pages
- 279-284
- ISSN
- 1475-3898 (ISSN-L)
- 1475-3901
- OpenAlex
- W2112043716
- Cited by
- 313
- References
- 26
- FWCI
- 29.22
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study. https://app.cochat.ai/papers/10.1136/qshc.2006.019497
- The paper
- Franklin, B. D., O'Grady, K., Donyai, P., Jacklin, A., & Barber, N. (2007). The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study. Quality and Safety in Health Care. https://doi.org/10.1136/qshc.2006.019497
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.