Titre : Collecte de données

Collecte de données : Questions médicales fréquentes

Nom anglais: Data Collection

Descriptor UI: D003625

Tree Number: N06.850.520.308

Questions fréquentes et termes MeSH associés

Diagnostic 5

#1

Comment les données sont-elles collectées pour un diagnostic ?

Les données sont collectées via des questionnaires, examens cliniques et tests de laboratoire.
Diagnostic médical Collecte de données
#2

Quels outils sont utilisés pour la collecte de données diagnostiques ?

Des outils comme les dossiers médicaux électroniques et les systèmes d'information sont utilisés.
Dossiers médicaux Systèmes d'information
#3

Quelle est l'importance de la collecte de données pour le diagnostic ?

Elle permet d'établir un diagnostic précis et d'orienter le traitement approprié.
Diagnostic médical Prise de décision clinique
#4

Quelles données sont essentielles pour un diagnostic ?

Les antécédents médicaux, les symptômes et les résultats d'examens sont essentiels.
Antécédents médicaux Symptômes
#5

Comment la collecte de données influence-t-elle le diagnostic ?

Des données précises améliorent la qualité du diagnostic et réduisent les erreurs.
Qualité des soins Erreurs médicales

Symptômes 5

#1

Comment les symptômes sont-ils enregistrés lors de la collecte de données ?

Les symptômes sont enregistrés par auto-évaluation, entretiens et questionnaires standardisés.
Symptômes Questionnaires
#2

Pourquoi est-il crucial de collecter des données sur les symptômes ?

Cela aide à identifier les maladies et à évaluer la gravité des conditions médicales.
Évaluation clinique Maladies
#3

Quels types de symptômes sont souvent collectés ?

Les symptômes physiques, psychologiques et fonctionnels sont souvent collectés.
Symptômes Santé mentale
#4

Comment les symptômes influencent-ils la collecte de données ?

Les symptômes guident les tests nécessaires et orientent le diagnostic et le traitement.
Diagnostic médical Traitement
#5

Quelles méthodes améliorent la collecte de données sur les symptômes ?

L'utilisation de technologies comme les applications mobiles et les capteurs améliore la collecte.
Technologie de la santé Applications mobiles

Prévention 5

#1

Comment les données sont-elles utilisées pour la prévention des maladies ?

Elles aident à identifier les facteurs de risque et à développer des stratégies de prévention.
Prévention des maladies Facteurs de risque
#2

Quelles données sont essentielles pour les programmes de prévention ?

Les données épidémiologiques et les comportements de santé sont essentielles pour ces programmes.
Épidémiologie Comportements de santé
#3

Comment la collecte de données aide-t-elle à évaluer les programmes de prévention ?

Elle permet d'analyser l'impact des interventions et d'ajuster les stratégies en conséquence.
Évaluation des programmes Interventions de santé
#4

Quels outils facilitent la collecte de données pour la prévention ?

Les enquêtes, les registres de santé et les systèmes de surveillance facilitent cette collecte.
Registres de santé Systèmes de surveillance
#5

Pourquoi est-il crucial de collecter des données sur les comportements de santé ?

Cela permet de cibler les interventions et d'améliorer l'efficacité des programmes de prévention.
Interventions de santé Comportements de santé

Traitements 5

#1

Comment les données influencent-elles les choix de traitement ?

Les données cliniques et les résultats de recherche guident les décisions thérapeutiques.
Traitement médical Recherche clinique
#2

Quels types de données sont collectées pour évaluer les traitements ?

Les données sur l'efficacité, les effets secondaires et la satisfaction des patients sont collectées.
Efficacité des traitements Effets secondaires
#3

Comment la collecte de données aide-t-elle à personnaliser les traitements ?

Elle permet d'adapter les traitements en fonction des caractéristiques individuelles des patients.
Médecine personnalisée Caractéristiques des patients
#4

Quelles sont les méthodes de collecte de données sur les traitements ?

Les essais cliniques, les études observationnelles et les enquêtes sont des méthodes courantes.
Essais cliniques Études observationnelles
#5

Pourquoi est-il important de suivre les données de traitement ?

Le suivi permet d'évaluer l'efficacité et d'ajuster les traitements en temps réel.
Suivi médical Efficacité des traitements

Complications 5

#1

Comment les données aident-elles à identifier les complications ?

Elles permettent de surveiller les effets indésirables et d'analyser les résultats cliniques.
Complications médicales Surveillance clinique
#2

Quelles données sont cruciales pour la gestion des complications ?

Les données sur les antécédents médicaux et les traitements antérieurs sont cruciales.
Antécédents médicaux Gestion des complications
#3

Comment la collecte de données influence-t-elle la prévention des complications ?

Elle permet d'identifier les patients à risque et d'adapter les soins pour prévenir les complications.
Prévention des complications Soins personnalisés
#4

Quels outils sont utilisés pour collecter des données sur les complications ?

Les bases de données cliniques et les systèmes de signalement des événements indésirables sont utilisés.
Bases de données cliniques Événements indésirables
#5

Pourquoi est-il important de suivre les complications ?

Le suivi permet d'améliorer la qualité des soins et de réduire les risques pour les patients.
Qualité des soins Risques pour les patients

Facteurs de risque 5

#1

Comment les données aident-elles à identifier les facteurs de risque ?

Elles permettent d'analyser les corrélations entre comportements et maladies.
Facteurs de risque Analyse des données
#2

Quels types de données sont collectées pour évaluer les facteurs de risque ?

Les données démographiques, comportementales et environnementales sont collectées.
Données démographiques Comportements de santé
#3

Pourquoi est-il crucial de comprendre les facteurs de risque ?

Cela aide à développer des stratégies de prévention ciblées et efficaces.
Prévention des maladies Stratégies de santé
#4

Comment les facteurs de risque sont-ils mesurés ?

Ils sont mesurés par des enquêtes, des tests de laboratoire et des évaluations cliniques.
Évaluations cliniques Tests de laboratoire
#5

Quels outils facilitent l'analyse des facteurs de risque ?

Les logiciels d'analyse statistique et les bases de données épidémiologiques facilitent cette analyse.
Analyse statistique Bases de données épidémiologiques
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"https://questionsmedicales.fr/mesh/D003625#section-facteurs de risque" } ] }, { "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "Comment les données sont-elles collectées pour un diagnostic ?", "position": 1, "acceptedAnswer": { "@type": "Answer", "text": "Les données sont collectées via des questionnaires, examens cliniques et tests de laboratoire." } }, { "@type": "Question", "name": "Quels outils sont utilisés pour la collecte de données diagnostiques ?", "position": 2, "acceptedAnswer": { "@type": "Answer", "text": "Des outils comme les dossiers médicaux électroniques et les systèmes d'information sont utilisés." } }, { "@type": "Question", "name": "Quelle est l'importance de la collecte de données pour le diagnostic ?", "position": 3, "acceptedAnswer": { "@type": "Answer", "text": "Elle permet d'établir un diagnostic précis et d'orienter le traitement approprié." } }, { "@type": "Question", "name": "Quelles données sont essentielles pour un diagnostic ?", "position": 4, "acceptedAnswer": { "@type": "Answer", "text": "Les antécédents médicaux, les symptômes et les résultats d'examens sont essentiels." } }, { "@type": "Question", "name": "Comment la collecte de données influence-t-elle le diagnostic ?", "position": 5, "acceptedAnswer": { "@type": "Answer", "text": "Des données précises améliorent la qualité du diagnostic et réduisent les erreurs." } }, { "@type": "Question", "name": "Comment les symptômes sont-ils enregistrés lors de la collecte de données ?", "position": 6, "acceptedAnswer": { "@type": "Answer", "text": "Les symptômes sont enregistrés par auto-évaluation, entretiens et questionnaires standardisés." } }, { "@type": "Question", "name": "Pourquoi est-il crucial de collecter des données sur les symptômes ?", "position": 7, "acceptedAnswer": { "@type": "Answer", "text": "Cela aide à identifier les maladies et à évaluer la gravité des conditions médicales." } }, { "@type": "Question", "name": "Quels types de symptômes sont souvent collectés ?", "position": 8, "acceptedAnswer": { "@type": "Answer", "text": "Les symptômes physiques, psychologiques et fonctionnels sont souvent collectés." } }, { "@type": "Question", "name": "Comment les symptômes influencent-ils la collecte de données ?", "position": 9, "acceptedAnswer": { "@type": "Answer", "text": "Les symptômes guident les tests nécessaires et orientent le diagnostic et le traitement." } }, { "@type": "Question", "name": "Quelles méthodes améliorent la collecte de données sur les symptômes ?", "position": 10, "acceptedAnswer": { "@type": "Answer", "text": "L'utilisation de technologies comme les applications mobiles et les capteurs améliore la collecte." } }, { "@type": "Question", "name": "Comment les données sont-elles utilisées pour la prévention des maladies ?", "position": 11, "acceptedAnswer": { "@type": "Answer", "text": "Elles aident à identifier les facteurs de risque et à développer des stratégies de prévention." } }, { "@type": "Question", "name": "Quelles données sont essentielles pour les programmes de prévention ?", "position": 12, "acceptedAnswer": { "@type": "Answer", "text": "Les données épidémiologiques et les comportements de santé sont essentielles pour ces programmes." } }, { "@type": "Question", "name": "Comment la collecte de données aide-t-elle à évaluer les programmes de prévention ?", "position": 13, "acceptedAnswer": { "@type": "Answer", "text": "Elle permet d'analyser l'impact des interventions et d'ajuster les stratégies en conséquence." } }, { "@type": "Question", "name": "Quels outils facilitent la collecte de données pour la prévention ?", "position": 14, "acceptedAnswer": { "@type": "Answer", "text": "Les enquêtes, les registres de santé et les systèmes de surveillance facilitent cette collecte." } }, { "@type": "Question", "name": "Pourquoi est-il crucial de collecter des données sur les comportements de santé ?", "position": 15, "acceptedAnswer": { "@type": "Answer", "text": "Cela permet de cibler les interventions et d'améliorer l'efficacité des programmes de prévention." } }, { "@type": "Question", "name": "Comment les données influencent-elles les choix de traitement ?", "position": 16, "acceptedAnswer": { "@type": "Answer", "text": "Les données cliniques et les résultats de recherche guident les décisions thérapeutiques." } }, { "@type": "Question", "name": "Quels types de données sont collectées pour évaluer les traitements ?", "position": 17, "acceptedAnswer": { "@type": "Answer", "text": "Les données sur l'efficacité, les effets secondaires et la satisfaction des patients sont collectées." } }, { "@type": "Question", "name": "Comment la collecte de données aide-t-elle à personnaliser les traitements ?", "position": 18, "acceptedAnswer": { "@type": "Answer", "text": "Elle permet d'adapter les traitements en fonction des caractéristiques individuelles des patients." } }, { "@type": "Question", "name": "Quelles sont les méthodes de collecte de données sur les traitements ?", "position": 19, "acceptedAnswer": { "@type": "Answer", "text": "Les essais cliniques, les études observationnelles et les enquêtes sont des méthodes courantes." } }, { "@type": "Question", "name": "Pourquoi est-il important de suivre les données de traitement ?", "position": 20, "acceptedAnswer": { "@type": "Answer", "text": "Le suivi permet d'évaluer l'efficacité et d'ajuster les traitements en temps réel." } }, { "@type": "Question", "name": "Comment les données aident-elles à identifier les complications ?", "position": 21, "acceptedAnswer": { "@type": "Answer", "text": "Elles permettent de surveiller les effets indésirables et d'analyser les résultats cliniques." } }, { "@type": "Question", "name": "Quelles données sont cruciales pour la gestion des complications ?", "position": 22, "acceptedAnswer": { "@type": "Answer", "text": "Les données sur les antécédents médicaux et les traitements antérieurs sont cruciales." } }, { "@type": "Question", "name": "Comment la collecte de données influence-t-elle la prévention des complications ?", "position": 23, "acceptedAnswer": { "@type": "Answer", "text": "Elle permet d'identifier les patients à risque et d'adapter les soins pour prévenir les complications." } }, { "@type": "Question", "name": "Quels outils sont utilisés pour collecter des données sur les complications ?", "position": 24, "acceptedAnswer": { "@type": "Answer", "text": "Les bases de données cliniques et les systèmes de signalement des événements indésirables sont utilisés." } }, { "@type": "Question", "name": "Pourquoi est-il important de suivre les complications ?", "position": 25, "acceptedAnswer": { "@type": "Answer", "text": "Le suivi permet d'améliorer la qualité des soins et de réduire les risques pour les patients." } }, { "@type": "Question", "name": "Comment les données aident-elles à identifier les facteurs de risque ?", "position": 26, "acceptedAnswer": { "@type": "Answer", "text": "Elles permettent d'analyser les corrélations entre comportements et maladies." } }, { "@type": "Question", "name": "Quels types de données sont collectées pour évaluer les facteurs de risque ?", "position": 27, "acceptedAnswer": { "@type": "Answer", "text": "Les données démographiques, comportementales et environnementales sont collectées." } }, { "@type": "Question", "name": "Pourquoi est-il crucial de comprendre les facteurs de risque ?", "position": 28, "acceptedAnswer": { "@type": "Answer", "text": "Cela aide à développer des stratégies de prévention ciblées et efficaces." } }, { "@type": "Question", "name": "Comment les facteurs de risque sont-ils mesurés ?", "position": 29, "acceptedAnswer": { "@type": "Answer", "text": "Ils sont mesurés par des enquêtes, des tests de laboratoire et des évaluations cliniques." } }, { "@type": "Question", "name": "Quels outils facilitent l'analyse des facteurs de risque ?", "position": 30, "acceptedAnswer": { "@type": "Answer", "text": "Les logiciels d'analyse statistique et les bases de données épidémiologiques facilitent cette analyse." } } ] } ] }
Dr Olivier Menir

Contenu validé par Dr Olivier Menir

Expert en Médecine, Optimisation des Parcours de Soins et Révision Médicale

Validation scientifique effectuée le 14/06/2026

Contenu vérifié selon les dernières recommandations médicales

En savoir plus sur Dr Menir

Sous-catégories

74 au total
└─

Jeux de données comme sujet

Datasets as Topic D066264 - N06.850.520.308.056
└─

Groupes de discussion

Focus Groups D017144 - N06.850.520.308.112
└─

Évaluation gériatrique

Geriatric Assessment D015577 - N06.850.520.308.225
└─

Entretiens comme sujet

Interviews as Topic D007407 - N06.850.520.308.420
└─

Sondage par lots appliqué à l'assurance qualité

Lot Quality Assurance Sampling D055810 - N06.850.520.308.461
└─

Narration

Narration D033262 - N06.850.520.308.502
└─

Résultats négatifs

Negative Results D000076723 - N06.850.520.308.544
└─

Évaluation de l'état nutritionnel

Nutrition Assessment D015596 - N06.850.520.308.585
└─

Données préliminaires

Preliminary Data D000074286 - N06.850.520.308.763
└─

Documents

Records D011996 - N06.850.520.308.940
└─

Enregistrements

Registries D012042 - N06.850.520.308.970
└─

Enquêtes sanitaires sur l'approvisionnement en eau

Sanitary Surveys, Water Supply D000082463 - N06.850.520.308.975
└─

Enquêtes et questionnaires

Surveys and Questionnaires D011795 - N06.850.520.308.980
└─

Registre civil

Vital Statistics D014798 - N06.850.520.308.985
└─└─

Certificats de naissance

Birth Certificates D001719 - N06.850.520.308.940.250
└─└─

Certificats de décès

Death Certificates D003644 - N06.850.520.308.940.350
└─└─

Dossiers dentaires

Dental Records D003792 - N06.850.520.308.940.375
└─└─

Archives administratives hospitalières

Hospital Records D006753 - N06.850.520.308.940.425
└─└─

Dossiers médicaux

Medical Records D008499 - N06.850.520.308.940.968
└─└─

Dossiers de soins infirmiers

Nursing Records D009737 - N06.850.520.308.940.984
└─└─

Programme SEER

SEER Program D018426 - N06.850.520.308.970.725
└─└─

Enquêtes sur la prévalence de la contraception

Contraceptive Prevalence Surveys D000069216 - N06.850.520.308.980.313
└─└─

Enquêtes sur les soins de santé

Health Care Surveys D019538 - N06.850.520.308.980.344
└─└─

Enquêtes de santé

Health Surveys D006306 - N06.850.520.308.980.438
└─└─

Enquêtes nutritionnelles

Nutrition Surveys D009749 - N06.850.520.308.980.469
└─└─

Questionnaire de santé du patient

Patient Health Questionnaire D000073222 - N06.850.520.308.980.485
└─└─

Autorapport

Self Report D057566 - N06.850.520.308.980.500
└─└─

Espérance de vie

Life Expectancy D008017 - N06.850.520.308.985.450
└─└─

Morbidité

Morbidity D009017 - N06.850.520.308.985.525
└─└─

Mortalité

Mortality D009026 - N06.850.520.308.985.550
└─└─

Taux de grossesse

Pregnancy Rate D018873 - N06.850.520.308.985.775
└─└─└─

Codage clinique

Clinical Coding D059019 - N06.850.520.308.940.968.249
└─└─└─

Couplage des dossiers médicaux

Medical Record Linkage D008498 - N06.850.520.308.940.968.500
└─└─└─

Dossiers médicaux basés sur les problèmes

Medical Records, Problem-Oriented D008501 - N06.850.520.308.940.968.550
└─└─└─

Systèmes informatisés de dossiers médicaux

Medical Records Systems, Computerized D016347 - N06.850.520.308.940.968.625
└─└─└─

Comptes-rendus de sortie des patients

Patient Discharge Summaries D063886 - N06.850.520.308.940.968.750
└─└─└─

Indices de gravité des traumatismes

Trauma Severity Indices D015599 - N06.850.520.308.940.968.875
└─└─└─

Mesures des résultats rapportés par les patients

Patient Reported Outcome Measures D000071066 - N06.850.520.308.980.344.500
└─└─└─

Système de surveillance des facteurs de risques comportementaux

Behavioral Risk Factor Surveillance System D040343 - N06.850.520.308.980.438.149
└─└─└─

Indicateurs d'état de santé

Health Status Indicators D006305 - N06.850.520.308.980.438.475
└─└─└─

Enquêtes sur le régime alimentaire

Diet Surveys D004034 - N06.850.520.308.980.469.350
└─└─└─

Espérance de vie corrigée de l'incapacité

Disability-Adjusted Life Years D000087509 - N06.850.520.308.985.450.250
└─└─└─

Espérance de vie en bonne santé

Healthy Life Expectancy D000087508 - N06.850.520.308.985.450.500
└─└─└─

Tables de survie

Life Tables D015993 - N06.850.520.308.985.450.750
└─└─└─

Années de vie ajustées sur la qualité

Quality-Adjusted Life Years D019057 - N06.850.520.308.985.450.875
└─└─└─

Taux de reproduction de base

Basic Reproduction Number D050936 - N06.850.520.308.985.525.080
└─└─└─

Incidence

Incidence D015994 - N06.850.520.308.985.525.375
└─└─└─

Prévalence

Prevalence D015995 - N06.850.520.308.985.525.750
└─└─└─

Cause de décès

Cause of Death D002423 - N06.850.520.308.985.550.250
└─└─└─

Mortalité de l'enfant

Child Mortality D046688 - N06.850.520.308.985.550.287
└─└─└─

Issue fatale

Fatal Outcome D017809 - N06.850.520.308.985.550.325
└─└─└─

Mortalité foetale

Fetal Mortality D046689 - N06.850.520.308.985.550.362
└─└─└─

Mortalité hospitalière

Hospital Mortality D017052 - N06.850.520.308.985.550.400
└─└─└─

Mortalité infantile

Infant Mortality D007226 - N06.850.520.308.985.550.475
└─└─└─

Mortalité maternelle

Maternal Mortality D008428 - N06.850.520.308.985.550.500
└─└─└─

Mortalité prématurée

Mortality, Premature D061213 - N06.850.520.308.985.550.550
└─└─└─

Taux de survie

Survival Rate D015996 - N06.850.520.308.985.550.900
└─└─└─

Taux de natalité

Birth Rate D001723 - N06.850.520.308.985.775.500
└─└─└─└─

Dossiers médicaux électroniques

Electronic Health Records D057286 - N06.850.520.308.940.968.625.250
└─└─└─└─

Cartes à puce de santé

Health Smart Cards D000069098 - N06.850.520.308.940.968.625.500
└─└─└─└─

Test d'impact des symptômes sino-nasaux

Sino-Nasal Outcome Test D000079602 - N06.850.520.308.980.344.500.500
└─└─└─└─

Troubles propices aux soins ambulatoires

Ambulatory Care Sensitive Conditions D000090004 - N06.850.520.308.980.438.475.012
└─└─└─└─

Indicateurs de maladies chroniques

Chronic Disease Indicators D000079662 - N06.850.520.308.980.438.475.023
└─└─└─└─

Coûts indirects de la maladie

Cost of Illness D017281 - N06.850.520.308.980.438.475.046
└─└─└─└─

Charge mondiale de morbidité

Global Burden of Disease D000071219 - N06.850.520.308.980.438.475.091
└─└─└─└─

Acuité des besoins du patient

Patient Acuity D062072 - N06.850.520.308.980.438.475.364
└─└─└─└─

Profil d'impact de la maladie

Sickness Impact Profile D018434 - N06.850.520.308.980.438.475.730
└─└─└─└─

Mortalité périnatale

Perinatal Mortality D054238 - N06.850.520.308.985.550.475.500
└─└─└─└─└─

Indice de gravité de la maladie

Severity of Illness Index D012720 - N06.850.520.308.980.438.475.364.500
└─└─└─└─└─└─

Indice APACHE

APACHE D018806 - N06.850.520.308.980.438.475.364.500.250
└─└─└─└─└─└─

Score d'alerte précoce

Early Warning Score D000080162 - N06.850.520.308.980.438.475.364.500.375
└─└─└─└─└─└─

Indice de performance de Karnofsky

Karnofsky Performance Status D017567 - N06.850.520.308.980.438.475.364.500.500
└─└─└─└─└─└─

Scores de dysfonction d'organes

Organ Dysfunction Scores D063305 - N06.850.520.308.980.438.475.364.500.625
└─└─└─└─└─└─

Indice de gravité simplifié

Simplified Acute Physiology Score D000072878 - N06.850.520.308.980.438.475.364.500.750

Auteurs principaux

Anchi Cheng

2 publications dans cette catégorie

Affiliations :
  • New York Structural Biology Center, New York, NY, USA.
Publications dans "Collecte de données" :

M Jason de la Cruz

2 publications dans cette catégorie

Affiliations :
  • Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA 20147, USA; Structural Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA. Electronic address: delacrm1@mskcc.org.
Publications dans "Collecte de données" :

Erin D Maughan

2 publications dans cette catégorie

Affiliations :
  • Director of Research, NASN, Silver Spring, MD.
Publications dans "Collecte de données" :

Florian Keusch

2 publications dans cette catégorie

Affiliations :
  • Professor of Social Data Science and Methodology, Department of Sociology, School of Social Sciences, University of Mannheim, Mannheim, Germany.
Publications dans "Collecte de données" :

Janet Wittes

1 publication dans cette catégorie

Affiliations :
  • Wittes LLC, Washington, DC, USA. Janet@Wittesllc.com.
Publications dans "Collecte de données" :

Thomas Fleming

1 publication dans cette catégorie

Affiliations :
  • University of Washington, Seattle, WA, USA.
Publications dans "Collecte de données" :

David DeMets

1 publication dans cette catégorie

Affiliations :
  • University of Wisconsin-Madison, Madison, WI, USA.
Publications dans "Collecte de données" :

Susan Ellenberg

1 publication dans cette catégorie

Affiliations :
  • University of Pennsylvania, Philadelphia, PA, USA.
Publications dans "Collecte de données" :

Hertzel Gerstein

1 publication dans cette catégorie

Affiliations :
  • Population Health Research Institute and Department of Medicine, McMaster University and Hamilton Health Sciences, Hamilton, Canada.
Publications dans "Collecte de données" :

Marc Pfeffer

1 publication dans cette catégorie

Affiliations :
  • Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA.
Publications dans "Collecte de données" :

Frank Rockhold

1 publication dans cette catégorie

Affiliations :
  • Duke University Medical Center and The Duke Clinical Research Institute, Durham, NC, USA.
Publications dans "Collecte de données" :

Salim Yusuf

1 publication dans cette catégorie

Affiliations :
  • Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Canada.
Publications dans "Collecte de données" :

Charles Hennekens

1 publication dans cette catégorie

Affiliations :
  • Florida Atlantic University, Boca Raton, FL, USA.
Publications dans "Collecte de données" :

Bonnie B Benetato

1 publication dans cette catégorie

Affiliations :
  • About the Authors The authors are faculty at East Carolina University College of Nursing, Greenville, North Carolina. Bonnie B. Benetato, PhD, MBA, FNP-BC, is a clinical associate professor. Jan Tillman, DNP, RN, FNP-BC, is a clinical associate professor. Robin Webb Corbett, PhD, FNP-C, RNC, is an associate professor. Alexis Hodges, DNP, RN, FNP-C, is a clinical assistant professor. For more information, contact Dr. Benetato at bonnie.benetato@gmail.com.
Publications dans "Collecte de données" :

Jan Tillman

1 publication dans cette catégorie

Publications dans "Collecte de données" :

Robin Webb Corbett

1 publication dans cette catégorie

Publications dans "Collecte de données" :

Alexis Hodges

1 publication dans cette catégorie

Publications dans "Collecte de données" :

Alexander J Towbin

1 publication dans cette catégorie

Affiliations :
  • Department of Radiology, Cincinnati Children's Hospital, Cincinnati, Ohio; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio. Electronic address: alexander.towbin@cchmc.org.
Publications dans "Collecte de données" :

Tristan Bepler

1 publication dans cette catégorie

Affiliations :
  • New York Structural Biology Center, New York, NY, USA.
Publications dans "Collecte de données" :

Andrew J Borst

1 publication dans cette catégorie

Affiliations :
  • University of Washington, Institute for Protein Design, Seattle, WA, USA.
Publications dans "Collecte de données" :

Sources (10000 au total)

Best Practices and Recommendations for Research Using Virtual Real-Time Data Collection: Protocol for Virtual Data Collection Studies.

14 May 2024
DOI: 10.2196/53790 PMID: 38743477

The COVID-19 pandemic and the subsequent need for social distancing required the immediate pivoting of research modalities. Research that had previously been conducted in person had to pivot to remote... This paper aims to review the remote or virtual research protocols that have been used in the past 10 years, gather existing best practices, and propose recommendations for continuing to use virtual r... Articles (n=22) published from 2013 to June 2023 were reviewed and analyzed to understand how researchers conducted virtual research that implemented real-time protocols. "Real-time" was defined as da... Findings from studies conducted both before and during the COVID-19 pandemic suggest that many types of data can be collected virtually in real time. Results and best practice recommendations from the... Our findings suggest that virtual real-time visits are doable across a range of participant populations and can answer a range of research questions. Recommended best practices for virtual real-time d... DERR1-10.2196/53790....

Humans COVID-19 Pandemics Data Collection SARS-CoV-2 +5 autres

A Systematic Review of Data Collection by National Joint Replacement Registries: What Opportunities Exist for Enhanced Data Collection and Analysis?

01 Oct 2023
DOI: 10.2106/JBJS.RVW.23.00062 PMID: 37956205

National joint replacement registries assist surgeons and hospitals with guiding decision making and quality of care. The data points collected are essential to interpret and analyze data and to under... All national registries were identified through Internet and publication search and contacted to invite participation. Data collection forms for both primary and revision THA and TKA were requested. D... The study group for primary and revision THA consisted of 28 national registries, with 26 agreeing to participate. The study group for primary TKA consisted of 27 national registries, with 24 agreeing... Substantial data are recorded by all registries, although each one is different. Data solicited lack many patient factors, surgeon variables, case complexity, and surgical techniques. Separate revisio... Level II, therapeutic study. See Instructions for Authors for a complete description of levels of evidence....

Humans Retrospective Studies Arthroplasty, Replacement, Hip Arthroplasty, Replacement, Knee Registries +1 autres

Researchers' experience in data collection with families of femicide victims.

2024
DOI: 10.1590/0034-7167-2023-0119 PMID: 39319963

to describe researchers' experience in collecting data from families of femicide victims.... this descriptive, qualitative study took the form of an experience report and was conducted in Manaus, Amazonas, Brazil. It involved documentary consultation, training researchers, scheduling and cond... the descriptions and photographs of the crime scene were both distressing and impactful for the researchers. The mementos of the victims (including clothing, objects, and childhood photos) shown by th... the experience encompassed both theoretical and methodological aspects that were planned and executed in data collection, fostering the development of skills and sensitivity towards the cases. Beyond ...

Humans Brazil Qualitative Research Female Research Personnel +6 autres

Informatics assessment of COVID-19 data collection: an analysis of UK Biobank questionnaire data.

31 Oct 2024
DOI: 10.1186/s12911-024-02743-5 PMID: 39482694

There have been many efforts to expand existing data collection initiatives to include COVID-19 related data. One program that expanded is UK Biobank, a large-scale research and biomedical data collec... A cohort of COVID-19 infected individuals was defined from the UK Biobank population using viral testing, diagnosis, and self-reported data. Changes over time, from March 2020 to October 2021, in tota... There were 62 042 distinct participants who had COVID-19, with 49 120 identified through diagnosis, 30 553 identified through viral testing and 934 identified through self-reporting, with many identif... To develop a robust cohort of COVID-19 participants from the UK Biobank population, multiple types of data were needed. The differences based on time and exposures show the important of comprehensive ...

Humans COVID-19 United Kingdom Surveys and Questionnaires Biological Specimen Banks +5 autres

Autoimmune hepatitis: Brighton Collaboration case definition and guidelines for data collection, analysis, and presentation of immunisation safety data.

07 Mar 2024
DOI: 10.1016/j.vaccine.2024.01.021 PMID: 38368225

This report introduces a Brighton Collaboration (BC) case definition for autoimmune hepatitis (AIH), which has been classified as a priority adverse event of special interest (AESI), as there were pos...

Humans Hepatitis, Autoimmune COVID-19 Vaccines Vaccination Immunization +1 autres

Data collection on rare bone and mineral conditions in Europe: The landscape of registries and databases.

Dec 2023
DOI: 10.1016/j.ejmg.2023.104868 PMID: 38832910

knowledge on the natural history of rare diseases is necessary to improve outcomes. Disease registries may play a key role in covering these unmet needs in the rare bone and mineral community.... to map existing bone and mineral conditions registries in Europe and their characteristics.... online survey about the use of registries/databases and their characteristics. This survey was disseminated among members of the European Reference Network on Rare Bone Diseases (ERN BOND) and non-ERN... sixty-three responses from health care providers (HCPs) and 10 responses from patient groups (PGs) were collected. The response rate for ERN BOND members was 55%. Of 63 HCPs, 37 declared using a regis... There is a need for developing a common high-quality platform for registering rare bone and mineral conditions....

Humans Registries Europe Rare Diseases Databases, Factual +3 autres