The DOI matches its Crossref record.
Welcome
Open accessAbstract
WELCOMEThe Patient Safety Forum (PSF) 2019 is aimed primarily at healthcare professionals and stakeholders with expertise in quality care, and is intended to promote research and education in improving healthcare services and patient safety.National and international professionals will share their inspirational expertise to reach optimal healthcare quality and patient safety.A total of 112 abstracts will be presented as poster or oral presentations and will embrace 7 different themes that are central to patient safety.These themes include the role of innovation and technology in improving patient safety, safe patient journey, learning from other industries, medication safety, environmental safety, infection prevention, and integrating patient safety in medical education; all incorporated in one event.All thanks to the abstract review committee team and the organizers for their boundless cooperation and harmonized teamwork.The 9 th annual PSF meeting always provides an exceptional opportunity for inter-professional collaboration and networking.We look forward to a delightful forum! on August 20,
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Metadata
- Venue
- Welcome
- Published
- 2019-04
- Pages
- iii-iii
- OpenAlex
- W4246318821
- Semantic Scholar
- 75328b478a5fb74376f8b107a1aacf27135fc4c6
- Cited by
- 0
- References
- 0
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Patient Safety and Medication Errors
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Healthcare Quality and Management
- Social Sciences › Education › Innovations in Medical Education
- Keywords
- patient safety, quality of care, drug safety, infection control, environmental safety, interprofessional collaboration, medical education
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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.
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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.
