RESUMO
BACKGROUND: The incidence of falls on inpatient oncology units indicated the need for quality improvement. This project aimed to reduce falls by implementing a fall reduction plan including the "Traffic Light" Fall Risk Assessment Tool (TL-FRAT). LOCAL PROBLEM: We retrospectively reviewed the oncology unit fall data from January 2013 to September 2014 and found that the average fall incidence was high. METHODS: The project used a program evaluation design, and the process was guided by Kotter's 8-step change model. INTERVENTIONS: We implemented the TL-FRAT to classify oncology inpatients at a high risk of falling in advance. RESULTS: The average fall incidence and falls with injury during the project were reduced. CONCLUSIONS: Adding the TL-FRAT to the fall protocol on the units effectively reduced the incidence of falls related to impaired mobility. The TL-FRAT can improve nurses' sensitivity to falls related to impaired mobility and, subsequently, guide corresponding fall prevention strategies.
Assuntos
Acidentes por Quedas/prevenção & controle , Acidentes por Quedas/estatística & dados numéricos , Pacientes Internados , Oncologia , Avaliação de Programas e Projetos de Saúde , Melhoria de Qualidade , Humanos , Incidência , Inovação Organizacional , Estudos Retrospectivos , Medição de Risco/métodos , Gestão da Segurança , Inquéritos e QuestionáriosRESUMO
The Gastric Cancer (Biomarkers) Knowledgebase (GCBKB) (http://biomarkers.bii.a-star.edu.sg/background/gastricCancerBiomarkersKb.php) is a curated and fully integrated knowledgebase that provides data relating to putative biomarkers that may be used in the diagnosis and prognosis of gastric cancer. It is freely available to all users. The data contained in the knowledgebase was derived from a large literature source and the putative biomarkers therein have been annotated with data from the public domain. The knowledgebase is maintained by a curation team who update the data from a defined source. As well as mining data from the literature, the knowledgebase will also be populated with unpublished experimental data from investigators working in the gastric cancer biomarker discovery field. Users can perform searches to identify potential markers defined by experiment type, tissue type and disease state. Search results may be saved, manipulated and retrieved at a later date. As far as the authors are aware this is the first open access database dedicated to the discovery and investigation of gastric cancer biomarkers.