The reporting of errors resulting in dose deviations are well-studied. Less studied is the amount of inconsequential errors that will not harm the patient but could lead to inefficiency. This paper reports an institutional effort to quantify and reduce these less significant errors. Dosimetry items discovered during physicist plan/record and verify (R&V) check prior to treatment were recorded in a shared document and called Therapy Anomaly Gathering System (THANGS) and individual items were called a "thang." Items were categorized to 1 of 4 types: Treatment Plan, Plan Document, R&V, and Secondary MU. The aggregate numbers were presented to the dosimetry staff at regular staff meetings. It was emphasized to the staff that this was a Quality Improvement (QI) study and would not be used punitively. Thangs were tracked over a 4-year period. In Q1 of year 1 of the study, the average number of errors identified was 179/month. This was reduced to 114/month by Q4 of year 1 and 68/month by the end of year 4, a 62% reduction. The number of errors/plan in Q1 Year 1 was 1.25, and that was reduced by Q4 Year 4 to 0.4, a 68% reduction. The percentage of errors by type did not vary much over the 4 years. By far, R&V errors were the most common, and QI efforts were primarily aimed at them. We have developed a simple method to identify areas in dosimetric work that are vulnerable to minor errors and, through consistent reminders, drastically reduce them. This leads to a seamless throughput for a given plan ultimately resulting in improved physics, therapist, and most importantly patient satisfaction.
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http://dx.doi.org/10.1016/j.meddos.2023.10.003 | DOI Listing |
J Nurs Adm
December 2024
Author Affiliations: Assistant Professor (Dr Prothero) and Nurse (Sorhus and Huefner), College of Nursing, Brigham Young University, Provo, Utah.
Objective: This study explored nurse leaders' perspectives and experiences in supporting nurses following a serious medical error.
Background: Appropriate support is crucial for nurses following an error. Authentic leadership provides an environment of psychological safety and establishes a patient safety culture.
Proc Natl Acad Sci U S A
January 2025
Institute of Science and Technology Austria, AT-3400 Klosterneuburg, Austria.
Biophysical constraints limit the specificity with which transcription factors (TFs) can target regulatory DNA. While individual nontarget binding events may be low affinity, the sheer number of such interactions could present a challenge for gene regulation by degrading its precision or possibly leading to an erroneous induction state. Chromatin can prevent nontarget binding by rendering DNA physically inaccessible to TFs, at the cost of energy-consuming remodeling orchestrated by pioneer factors (PFs).
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Section on Perception, Cognition, Action, Laboratory of Sensorimotor Research, National Eye Institute, NIH, Bethesda, MD 20892.
To what extent does concept formation require language? Here, we exploit color to address this question and ask whether macaque monkeys have color concepts evident as categories. Macaques have similar cone photoreceptors and central visual circuits to humans, yet they lack language. Whether Old World monkeys such as macaques have consensus color categories is unresolved, but if they do, then language cannot be required.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Psychiatry, Yongin Severance Hospital, Yongin, Republic of Korea.
Background: The COVID-19 pandemic has accelerated the digitalization of modern society, extending digital transformation to daily life and psychological evaluation and treatment. However, the development of competencies and literacy in handling digital technology has not kept pace, resulting in a significant disparity among individuals. Existing measurements of digital literacy were developed before widespread information and communications technology device adoption, mainly focusing on one's perceptions of their proficiency and the utility of device operation.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
Transfer learning aims to integrate useful information from multi-source datasets to improve the learning performance of target data. This can be effectively applied in genomics when we learn the gene associations in a target tissue, and data from other tissues can be integrated. However, heavy-tail distribution and outliers are common in genomics data, which poses challenges to the effectiveness of current transfer learning approaches.
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