USE OF STAY S.A.F.E. STRATEGY DURING MEDICATION ADMINISTRATION IN REDUCING ERRORS

Healthcare related medical errors are the third leading cause of death in the United States. Interruptions and distractions can lead to an increased risk of nurses making errors in healthcare, particularly during medication administration. Student nurses should receive education during their prelicensure period on the management of interruptions especially before being given the responsibility of performing high risk tasks such as medication administration. Using a novel interruption management strategy called Stay S.A.F.E., nursing students were interrupted during a simulated medication administration. Students were evaluated on the time spent on the task and if errors were committed. Lastly, perceived workload was measured using the NASA Task Load Index (TLX) tool

Health Technology Scenarios and Implications for Spectrum

Supporting Informatio

Artificial Intelligence for Hospital Health Care:Application Cases and Answers to Challenges in European Hospitals

The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent research and management question. Especially for hospitals, the expectations regarding improved efficiency and effectiveness by the introduction of novel AI applications are huge. However, experiences with real-life AI use cases are still scarce. As a first step towards structuring and comparing such experiences, this paper is presenting a comparative approach from nine European hospitals and eleven different use cases with possible application areas and benefits of hospital AI technologies. This is structured as a current review and opinion article from a diverse range of researchers and health care professionals. This contributes to important improvement options also for pandemic crises challenges, e.g., the current COVID-19 situation. The expected advantages as well as challenges regarding data protection, privacy, or human acceptance are reported. Altogether, the diversity of application cases is a core characteristic of AI applications in hospitals, and this requires a specific approach for successful implementation in the health care sector. This can include specialized solutions for hospitals regarding human-computer interaction, data management, and communication in AI implementation projects

Investigating the prevalence and causes of prescribing errors in general practice : the PRACtICe Study

Report prepared by the University of Nottingham, University of Reading and University of Hertfordshir

Content Analysis Of Applied Learning From High FIdelity Patient Simulation Orientation to Critical Care

The Institute of Medicine (IOM) reported that over 98,000 deaths occur in hospitals from medical errors in the United States. In a follow-up IOM report, it was noted that nurses have a direct impact on patient morbidity and mortality and are often the last line of defense for patient safety. The challenge for nurse educators in hospitals is to ensure that as newly licensed nurses enter the workforce, orientation outcomes reflect acquisition of knowledge and skills, which are applied in practice. When newly licensed registered nurses are hired into critical care units, this puts them in a position where they have to learn basic competencies as well as the specialized practice of critical care. One teaching strategy adopted in acute care hospitals is use of high fidelity patient simulation as a way to address the competency gap of these nurses and improve patient safety and outcomes. However, little is known about the practice application of the skills and knowledge used by nurses who complete such orientation. This qualitative exploratory study analyzed newly licensed nurses\u27 description of knowledge and skills used in critical care practice following critical care orientation using high fidelity patient simulation. Data collection consisted of individual, semi-structured, guided interviews based on the Nursing Education Simulation Framework. A sample of 8 registered nurses participated in the interview and completion of a demographic questionnaire. Content analysis was performed using Krippendorf technique. The 8 themes that emerged are consistent with previous research studies that point to the steep learning curve faced by newly licensed nurses in critical care. Implications for nursing practice include expanding high fidelity simulation to specialty practice, developing interdisciplinary orientation and to proactively address the continued experience of culture shock

UWOMJ Volume 81, Issue 1, Spring 2012

Schulich School of Medicine & Dentistryhttps://ir.lib.uwo.ca/uwomj/1013/thumbnail.jp