Medical Errors: Next Steps
Notice bibliographique
Résumé
Two decades of advances in genomics, information technology, and precision medicine hold the promise for better care and improved survival for patients with chronic disorders. Patients expect that the health-care system, especially in countries with a market economy, will continue to offer solutions and cures to many illnesses. Yet, concerns over morbidity and mortality from unsafe health-care practices continue to linger and erode patient confidence. The Institute of Medicine of the U.S. National Academies sounded the alarm on patient safety in a report published 17 years ago and called for an examination of health care practices (Institute of Medicine, 2000). Since then several epidemiological studies have been conducted to determine the extent and causes of, and interventions for, adverse medical events and patient safety (Jha et al., 2013; Kemp, Santana, Southern, McCormack, & Quan, 2016; O'Hagan, MacKinnon, Persaud, & Etchegary, 2009). A survey from Australia, Canada, Germany, the Netherlands, New Zealand, the United Kingdom, and the United States estimated 12 to 20 percent adverse events, with disability more common than mortality, and a higher disability-adjusted life year (DALY) in developing countries (O'Hagan et al., 2009). The authors estimate that seven types of adverse events considered in this study constitute the 20th leading cause of morbidity and mortality for the world's population. In the United States, medical errors and adverse effects (Grober & Bohenen, 2005; Makary & Daniel, 2016) continue to be at the center of controversy and are the subject of continued news media headlines. One in seven U.S. Medicare1 patients experiences a medical error (Agency for Healthcare Research and Quality, 2014). Prescription drugs are reported for nearly 100,000 hospitalizations each year. Many in-patient health institutions (hospitals) are increasingly employing physician hospitalists to care for the admissions. Transitions in care, from one physician to another, or to a hospitalist, can lead to preventable harm related to medications (Graham, Scudder, & Stokowski, 2015; Velo & Minuz, 2009). Many countries and international organizations such as the World Health Organization and the World Medical Association (53rd World Medical Association General Assembly, 2002) have published guidelines to improve patient safety. Most countries with a market economy have established registries for reporting medication adverse events. New tools such as health forecasting (Soyiri & Reidpath, 2013) can assist with better epidemiological data collection and research. Medical errors should be recognized as a standalone diagnostic code in the International Statistical Classification of Diseases and Related Health Problems (ICD) to facilitate the collection of global information on morbidity and mortality and DALY estimates. The global health-care enterprise is diverse, and one of the 20 largest industries financed by governments and private entities. It is estimated that by 2022, health expenditures for the developing (about 33 percent of the share) and market economy countries (about 67 percent) will exceed 12 trillion USD. The world population is growing and aging. The number of health-care professionals is not in step with future population needs. Health-care facilities are expected to help improve health outcomes. Preventable errors are costly, especially in human suffering, and should be addressed expediently. Many health-care providers have already adopted patient safety safeguards and standards. Many safety practices are simple and should be adopted as soon as possible. Increasing the number of providers, reducing working hours and fatigue, improving communications, providing systems for blame-free reporting, and engaging patients in understanding the health safety culture should be priorities.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,029 | 0,007 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».