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Patient-safety

سلامة المريض

1314   0   16   5.0 ( 1 )
 Publication date 2018
  fields Nursing
and research's language is العربية
 Created by Mikhael Moussa




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Patient safety is a modern but not new concept in global health care systems where reports and analyzes indicate that medical errors lead to adverse events. While the issue of safety in any health institution is a criterion in itself and a right of the patient, the importance of avoiding adverse patient events was not known until 1990, when astonishing numbers of statistical reports of multiple countries showed that patient morbidity and mortality had occurred Due to medical and nursing errors worldwide, where these statistics made their way to the public through the most famous statistical report prepared by the Institute of Medicine (IOM) and published in 1990, "To err is human, Kohn, Corrigan & Donaldson, 1990,". The main recommendations of the Medical Institute were to emphasize the need to adopt standards of practice and performance focused more on safety (patient safety).

References used
- Basic nursing book - Potter Perry - chapter 28 - safety & basic human needs - page 713 to 730
- DR. Rebecca A. Patronis Jones, DNSc, RN, CNAA, BCChancellor and Professor West Suburban College of Nursing Oak Park, IL \ Nursing Leadership & Management (Theories ,Processes & Practice).
- ROBERTA L. CARROLL EDITOR \ Risk management handbook for Health Care Organizations \ student edition 2009.
- ISO 31000:2009, Risk management, A practical guide for SMEs (medium-sized enterprises), (2015): 13.
- Hughes .R, Patient Safety and Quality: An Evidence-Based Handbook for Nurses, No 08-0043, 2008.
- Pulakos .E & SHRM (Society for Human Resource Management) Foundation, Performance Management, Effective Practice guidelines. A roadmap for developing, implementing and evaluating performance management systems, USA, (2004).
- http://www.ncbi.nlm.nih.gov/books/NBK2651/1
- World Health Organization (WHO). 10 facts on Patient safety. (2014). >www.who.int/features/factfiles/patient_safety/en/<
- JONA \ Volume 38, Number 7/8, pp 341-348 \ Copyright B 2008 Wolters Kluwer Health | Lippincott Williams & Wilkins \ the journal of nursing administration \ Regina Fink, PhD, RN, FAAN, AOCN , Mary Krugman, PhD ,RN, FAAN , Kathy Casey, MS, RN , Colleen Goode, PhD, RN, FAAN \ Research Tittle: The Graduate Nurse Experience: Qualitative Residency Program Outcomes \ 2008.

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The main objective of this study was to determine the level of consumer awareness toward food safety in Damascus, the biggest governorate in Syria. The data were obtained from a survey of 196 consumers in Damascus, during the year 2012. A special index was developed through out set of questions. ANOVA was used to recognize the significant different means of awareness index matching levels of each influent factor, and the study revealed that there were significant differences among index means for categories of: age, gender, education, marital status, income, sector of work, place of living and place of shopping. According to the Probit model analysis, older persons had higher probability of having awareness toward food safety by 18.8% and 16.8% for the second and third (older) category of age. Women were more aware than men by 9.2%. High education increases the probability. Likewise, The average level of income had the highest probability of having awareness by about 34.9% comparing with low and high income groups. The study recommended to create a special informative campaigns targeting especially groups with low levels of awareness, throughout the most favorite ways for consumers, which were T.V. and internet.
Fine-tuning pretrained models for automatically summarizing doctor-patient conversation transcripts presents many challenges: limited training data, significant domain shift, long and noisy transcripts, and high target summary variability. In this pa per, we explore the feasibility of using pretrained transformer models for automatically summarizing doctor-patient conversations directly from transcripts. We show that fluent and adequate summaries can be generated with limited training data by fine-tuning BART on a specially constructed dataset. The resulting models greatly surpass the performance of an average human annotator and the quality of previous published work for the task. We evaluate multiple methods for handling long conversations, comparing them to the obvious baseline of truncating the conversation to fit the pretrained model length limit. We introduce a multistage approach that tackles the task by learning two fine-tuned models: one for summarizing conversation chunks into partial summaries, followed by one for rewriting the collection of partial summaries into a complete summary. Using a carefully chosen fine-tuning dataset, this method is shown to be effective at handling longer conversations, improving the quality of generated summaries. We conduct both an automatic evaluation (through ROUGE and two concept-based metrics focusing on medical findings) and a human evaluation (through qualitative examples from literature, assessing hallucination, generalization, fluency, and general quality of the generated summaries).
The study included 480 patients , 260 males and 220 females who have 920 teeth to beextracted. The ages of patients were ranging from 20 to 40 years. In each group the teeth were divided according to its location , Mandible, Maxillary, anterior o r posterior. The extractions were symmetric .Socketol was used in one side while was not used in the other side.. Patients were recalled after 24 hours 36 hours,48 hours and 72 hours . Dry socket was determined by the symptoms and signs.
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We focus on dialog models in the context of clinical studies where the goal is to help gather, in addition to the close information collected based on a questionnaire, serendipitous information that is medically relevant. To promote user engagement a nd address this dual goal (collecting both a predefined set of data points and more informal information about the state of the patients), we introduce an ensemble model made of three bots: a task-based, a follow-up and a social bot. We introduce a generic method for developing follow-up bots. We compare different ensemble configurations and we show that the combination of the three bots (i) provides a better basis for collecting information than just the information seeking bot and (ii) collects information in a more user-friendly, more efficient manner that an ensemble model combining the information seeking and the social bot.
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