Titre : Management par la qualité

Management par la qualité : Questions médicales fréquentes

Termes MeSH sélectionnés :

Problem-Based Learning
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dans un établissement de santé ?", "position": 1, "acceptedAnswer": { "@type": "Answer", "text": "Utiliser des indicateurs de performance et des audits internes pour mesurer la qualité." } }, { "@type": "Question", "name": "Quels outils sont utilisés pour le diagnostic qualité ?", "position": 2, "acceptedAnswer": { "@type": "Answer", "text": "Les outils incluent les diagrammes de Pareto, les cartes de contrôle et les enquêtes de satisfaction." } }, { "@type": "Question", "name": "Qu'est-ce qu'un audit qualité ?", "position": 3, "acceptedAnswer": { "@type": "Answer", "text": "C'est une évaluation systématique des processus pour identifier les améliorations nécessaires." } }, { "@type": "Question", "name": "Comment identifier les problèmes de qualité ?", "position": 4, "acceptedAnswer": { "@type": "Answer", "text": "Analyser les retours des patients et les données de performance pour détecter les anomalies." } }, { "@type": "Question", "name": "Quel rôle joue le feedback dans le diagnostic qualité ?", "position": 5, "acceptedAnswer": { "@type": "Answer", "text": "Le feedback des patients et du personnel aide à identifier les lacunes et à orienter les améliorations." } }, { "@type": "Question", "name": "Quels symptômes indiquent un manque de qualité ?", "position": 6, "acceptedAnswer": { "@type": "Answer", "text": "Des plaintes fréquentes des patients et des erreurs médicales peuvent signaler des problèmes." } }, { "@type": "Question", "name": "Comment mesurer la satisfaction des patients ?", "position": 7, "acceptedAnswer": { "@type": "Answer", "text": "Utiliser des enquêtes et des questionnaires pour recueillir des avis sur les services reçus." } }, { "@type": "Question", "name": "Quels indicateurs montrent une mauvaise qualité ?", "position": 8, "acceptedAnswer": { "@type": "Answer", "text": "Des taux élevés d'infections nosocomiales et de réadmissions peuvent indiquer des faiblesses." } }, { "@type": "Question", "name": "Comment les erreurs de communication affectent-elles la qualité ?", "position": 9, "acceptedAnswer": { "@type": "Answer", "text": "Elles peuvent entraîner des malentendus, des erreurs de traitement et une insatisfaction des patients." } }, { "@type": "Question", "name": "Quels sont les signes d'une culture de qualité faible ?", "position": 10, "acceptedAnswer": { "@type": "Answer", "text": "Un manque d'engagement du personnel et une résistance au changement sont des indicateurs clés." } }, { "@type": "Question", "name": "Comment prévenir les erreurs médicales ?", "position": 11, "acceptedAnswer": { "@type": "Answer", "text": "Mettre en place des protocoles de vérification et des formations sur la sécurité des patients." } }, { "@type": "Question", "name": "Quel rôle joue la communication dans la prévention ?", "position": 12, "acceptedAnswer": { "@type": "Answer", "text": "Une communication claire entre le personnel et les patients réduit les risques d'erreurs." } }, { "@type": "Question", "name": "Comment sensibiliser le personnel à la qualité ?", "position": 13, "acceptedAnswer": { "@type": "Answer", "text": "Organiser des ateliers et des formations sur l'importance de la qualité dans les soins." } }, { "@type": "Question", "name": "Quelles pratiques préventives sont essentielles ?", "position": 14, "acceptedAnswer": { "@type": "Answer", "text": "L'hygiène, la vérification des médicaments et la formation continue sont cruciales." } }, { "@type": "Question", "name": "Comment impliquer les patients dans la prévention ?", "position": 15, "acceptedAnswer": { "@type": "Answer", "text": "Encourager les patients à poser des questions et à exprimer leurs préoccupations sur les soins." } }, { "@type": "Question", "name": "Comment améliorer les processus de traitement ?", "position": 16, "acceptedAnswer": { "@type": "Answer", "text": "Implémenter des protocoles standardisés et former le personnel aux meilleures pratiques." } }, { "@type": "Question", "name": "Quel est l'impact de la formation sur la qualité ?", "position": 17, "acceptedAnswer": { "@type": "Answer", "text": "Une formation continue améliore les compétences et réduit les erreurs, augmentant ainsi la qualité." } }, { "@type": "Question", "name": "Comment impliquer le personnel dans le management qualité ?", "position": 18, "acceptedAnswer": { "@type": "Answer", "text": "Encourager la participation à des comités qualité et à des sessions de feedback régulières." } }, { "@type": "Question", "name": "Quels traitements peuvent être standardisés ?", "position": 19, "acceptedAnswer": { "@type": "Answer", "text": "Les traitements pour des pathologies courantes peuvent être standardisés pour garantir la qualité." } }, { "@type": "Question", "name": "Comment évaluer l'efficacité des traitements ?", "position": 20, "acceptedAnswer": { "@type": "Answer", "text": "Analyser les résultats cliniques et les retours des patients pour ajuster les pratiques." } }, { "@type": "Question", "name": "Quelles complications peuvent résulter d'une mauvaise qualité ?", "position": 21, "acceptedAnswer": { "@type": "Answer", "text": "Des complications comme les infections, les erreurs de médication et les réadmissions peuvent survenir." } }, { "@type": "Question", "name": "Comment évaluer les complications liées à la qualité ?", "position": 22, "acceptedAnswer": { "@type": "Answer", "text": "Analyser les données des incidents et les retours des patients pour identifier les tendances." } }, { "@type": "Question", "name": "Quel est l'impact des complications sur les patients ?", "position": 23, "acceptedAnswer": { "@type": "Answer", "text": "Les complications peuvent prolonger le séjour hospitalier et affecter la satisfaction des patients." } }, { "@type": "Question", "name": "Comment réduire les complications dans les soins ?", "position": 24, "acceptedAnswer": { "@type": "Answer", "text": "Mettre en œuvre des pratiques basées sur des preuves et des protocoles de soins standardisés." } }, { "@type": "Question", "name": "Quelles sont les conséquences économiques des complications ?", "position": 25, "acceptedAnswer": { "@type": "Answer", "text": "Les complications entraînent des coûts supplémentaires pour les soins et des pertes de revenus." } }, { "@type": "Question", "name": "Quels facteurs de risque affectent la qualité des soins ?", "position": 26, "acceptedAnswer": { "@type": "Answer", "text": "Le manque de formation, la surcharge de travail et la communication insuffisante sont des facteurs clés." } }, { "@type": "Question", "name": "Comment la culture organisationnelle influence-t-elle la qualité ?", "position": 27, "acceptedAnswer": { "@type": "Answer", "text": "Une culture qui valorise la qualité et l'amélioration continue favorise de meilleurs résultats." } }, { "@type": "Question", "name": "Quel est l'impact du leadership sur la qualité ?", "position": 28, "acceptedAnswer": { "@type": "Answer", "text": "Un leadership engagé et visionnaire est essentiel pour promouvoir une culture de qualité." } }, { "@type": "Question", "name": "Comment les ressources humaines influencent-elles la qualité ?", "position": 29, "acceptedAnswer": { "@type": "Answer", "text": "Des équipes bien formées et motivées sont cruciales pour maintenir des standards de qualité élevés." } }, { "@type": "Question", "name": "Quels sont les risques liés à la technologie dans les soins ?", "position": 30, "acceptedAnswer": { "@type": "Answer", "text": "Les erreurs de saisie et les défaillances technologiques peuvent compromettre la qualité des soins." } } ] } ] }

Sources (10000 au total)

Deep-KEDI: Deep learning-based zigzag generative adversarial network for encryption and decryption of medical images.

Medical imaging techniques have improved to the point where security has become a basic requirement for all applications to ensure data security and data transmission over the internet. However, clini... In this research, a novel deep learning-based key generation network (Deep-KEDI) is designed to produce the secure key used for decrypting and encrypting medical images.... Initially, medical images are pre-processed by adding the speckle noise using discrete ripplet transform before encryption and are removed after decryption for more security. In the Deep-KEDI model, t... The proposed ZZ-GAN is used for secure encryption by generating three different zigzag patterns (vertical, horizontal, diagonal) of encrypted images with its key. The zigzag cipher uses an XOR operati... According to the experiments, the Deep-KEDI model generates secret keys with an information entropy of 7.45 that is particularly suitable for securing medical images....

Cognitive load theory in workplace-based learning from the viewpoint of nursing students: application of a path analysis.

The present study aimed to test the relationship between the components of the Cognitive Load Theory (CLT) including memory, intrinsic and extraneous cognitive load in workplace-based learning in a cl... This study was conducted at Shahid Sadoughi University of Medical Sciences in 2021-2023. The participants were 151 nursing students who studied their apprenticeship courses in the teaching hospitals. ... In this study, the goodness of fit of the model based on the cognitive load theory was reported (GIF = 0.99, CFI = 0.99, RMSEA = 0.03). The results of regression analysis showed that the scores of dec... The present results showed that the CLT in workplace-based learning has a goodness of fit with the components of memory, intrinsic cognitive load, extraneous cognitive load, and clinical decision-maki...

Diagnostic accuracy of CT-based radiomics and deep learning for predicting lymph node metastasis in esophageal cancer.

Esophageal cancer remains a global challenge due to late diagnoses and limited treatments. Lymph node metastasis (LNM) is crucial for prognosis, yet traditional diagnostics fall short. Integrating rad... A systematic review and meta-analysis were conducted by searching PubMed, Scopus, Web of Science, and Embase up to October 1, 2023. The focus was on studies developing CT-based radiomics and/or DL mod... Twelve studies were reviewed, and seven were included in the meta-analysis, most showing excellent methodological quality. Training sets revealed a pooled AUC of 87 % (95 % CI: 78 %-90 %), and interna... Integrating CT-based radiomics and DL improves LNM detection in esophageal cancer. Including clinical data could enhance model performance. Future research should focus on multicenter studies with ind...

Patient stratification based on the risk of severe illness in emergency departments through collaborative machine learning models.

Emergency department (ED) overcrowding presents a global challenge that inhibits prompt care for critically ill patients. Traditional 5-level triage system that heavily rely on the judgment of the tri... This retrospective study was conducted at a tertiary teaching hospital. Data were collected from January 2015 to October 2022. Demographic and clinical information were collected at triage. The study ... The study analyzed 668,602 ED visits from 2015 to 2022. Among them, 278,724 visits from 2015 to 2018 were used for model training and validation, while 320,201 visits from 2019 to 2022 were for testin... The traditional 5-level triage system often falls short, leading to under-triage of critical patients. Our models include a score-based differentiation within a triage level to offer advanced risk str...

Machine-learning-based models assist the prediction of pulmonary embolism in autoimmune diseases: A retrospective, multicenter study.

Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely interv... In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariable log... In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respectively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, wi... ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE.... Chictr.org.cn: ChiCTR2200059599....

Deep learning-based pathway-centric approach to characterize recurrent hepatocellular carcinoma after liver transplantation.

Liver transplantation (LT) is offered as a cure for Hepatocellular carcinoma (HCC), however 15-20% develop recurrence post-transplant which tends to be aggressive. In this study, we examined the trans... We analyzed the transcriptomic profiles of primary and recurrent tumor samples from 7 pairs of patients who underwent LT. Following differential gene expression analysis, we performed pathway enrichme... The PI3K/Akt signaling pathway and cytokine-mediated signaling pathway were particularly activated in HCC recurrence. The recurrent tumors exhibited upregulation of an immune-escape related gene, CD27... Our deep learning approach identified PI3K/Akt signaling as potentially regulating cytokine-mediated functions and the expression of immune escape genes, leading to alterations in the pattern of immun...

The effect of modified team-based learning method on the knowledge and skills of medical emergency personnel: a clinical trial.

Trauma is one of the most important issues and problems considered in most countries in today's modern and industrial society. Since pre-hospital care is the first component of a trauma care system, i... The present study was a two-group clinical before/after study in which 96 technicians were selected using a stratified random sampling method. The sample members were randomly divided into an interven... The results of the repeated measures analysis of variance showed a significant difference between the intervention and control groups in learning skills for dealing with trauma patients (... The results indicate that training based on the modified team-based learning method is effective for the management of trauma patients by medical emergency personnel and improves the readiness of pers...

Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images.

Nottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology but has a high inter-assessor variability with many tumours being classified as intermediate... A total of 11,955,755 tiles from 1169 whole slide images of preoperative biopsies from 896 patients diagnosed with breast cancer in Stockholm, Sweden, were included. DeepGrade, a deep convolutional ne... Based on preoperative biopsy images, the DeepGrade model predicted resected tumour cases of clinical grades NHG1 and NHG3 with an AUC of 0.908 (95% CI: 0.88; 0.93). Furthermore, out of the 432 resecte... DeepGrade provided prediction of tumour grades NHG1 and NHG3 on the resection specimen using only the biopsy specimen. The results demonstrate that the DeepGrade model can provide decision support to ...