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Enregistrement W2045978941 · doi:10.1097/01.asw.0000459889.36550.d7

The ABCs of ICD

2015· editorial· en· W2045978941 sur OpenAlex

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

aboutLe titre ou le résumé porte un signal canadien du lexique géographique.
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Notice bibliographique

RevueAdvances in Skin & Wound Care · 2015
Typeeditorial
Langueen
DomaineHealth Professions
ThématiqueMedical Coding and Health Information
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineEpidemiologyPublic healthDiseasePandemicSmallpoxNosologyPlague (disease)Medical emergencyInfectious disease (medical specialty)PsychiatryCoronavirus disease 2019 (COVID-19)Pathology

Résumé

récupéré en direct d'OpenAlex

FigureThe International Statistical Classification of Diseases and Related Health Problems (ICD) is steeped in history, international cooperation, and improvement over the last 150 years, with the most recent revision known as ICD-10 to be implemented by October 1, 2015. The concept of ICD is rooted in the theory of Nosology (the systematic classification of diseases). Nosologic classification began in antiquity, resulting out of the need for nurses, physicians, epidemiologists, and public health entities to classify and make sense of cause of death and morbidity; the parallel use of these data can be traced to the 15th century in Italy, as a result of the “great pandemics of plague.”1 During the Crimean War (1853–1856), Florence Nightingale (1820–1910) applied scientific rigor to explain her observations—“more soldiers died of infection and disease transmission than battlefield wounds—for every 1 soldier that died of his wounds, 7 died of disease.”1 In her attempts to classify morbidity and mortality, she used evidence-based techniques, such as epidemiology, surveillance, and prevention through infection control (hand washing, infection control, and infectious waste management). She also collaborated with William Farr, MD (1807–1883)—a pioneer of epidemiology and statistics. This relationship facilitated her participation in the 1860 International Statistical Congress, where she advocated for “the first model for the systematic collection of hospital data using a uniform classification of diseases and operations that was to form the basis of the ICD code used today.”1–3 In 1891, Jacques Bertillon introduced an alphanumeric method of disease classification,1 which incorporated disease by anatomical site and cause of death. With subsequent revisions, his list became known as “The First Revision (ICD-1).”1 The ICD-1 was principally used in European countries and translated from French to English, Spanish, and German. In 1897, the American Public Health Association (APHA) recommended adoption of the Bertillon classification by all registrars of vital statistics in the United States, Canada, and Mexico. Coincidental to the seventh revision, the World Health Care Organization (WHO) established the “WHO Center for Classification of Diseases.” In 1948, the WHO coordinated and organized “international study groups” and adopted the concept of the ICD and expanded morbidity coding in 1949. In 1977, the WHO published the ninth revision, known as ICD-9, consisting of 3 volumes containing diagnostic and procedure codes.1,5 Although we think of ICD-10 as new, the development of ICD-10 began in 1993 and was released in 1993 in Europe and several other countries, but was not adopted in the United States until October 2014 (implementation date October 1, 2015). The ICD-10 is intended to provide more specificity about disease conditions and healthcare interventions than previous versions. To that end, the alphanumeric designation numeric codes for billing and statistical analysis jump from 14,000 in ICD-9 to 69,000 in ICD-10. Moreover, the number of hospital inpatient procedure codes will escalate from 3800 to 72,000.5 The enhanced specificity and increased number of diagnostic codes with ICD-10 allow a more robust capture of the context of the clinical note (medical record), including the traditional subjective, objective, assessment plan (SOAP). The clinical data in the record are matched with an ICD diagnostic code or codes. With ICD-10, there are now lateralization and pinpointing of the diagnostic disease entities, such as diabetes, diabetic foot ulcer, and to the region of the body and/or anatomic site (See Clinical Management Extra, page 84). The “forced change to ICD-10” is like most significant changes in healthcare; 2 camps emerge—for and against! According to the Centers for Medicare & Medicaid Services (CMS), the American Health Information Management Association, and other proponents of implementing ICD-10, the system may improve patient outcomes through the use of robust health information technology applications in research, population health, payment, and healthcare economics. All of these attributes are in line with the managed care philosophy of the Affordable Care Act, signed into law by President Obama in March 2010. Those against ICD-10, including the American Medical Association (AMA), support a delay in its implementation by citing cost and burdensome health information technology systems, especially coming on the heels of meeting the basic meaningful use of electronic medical records mandated by CMS. According to a study by the AMA, the cost per physician practice to implement ICD-10 includes training, vendor and software upgrades, testing, and payment disruption. These costs could be as much as $8 million for large physician practices and approximately $225,000 for smaller practices.6 The move is expected to be “much more disruptive for physicians” than previous mandates. Other detractors include the Texas Medical Association; according to Dr Joseph Schneider,7 “ICD-10 is already 25 years old,” and “it predates modern health technology.” There are even suggestions that “ICD-11” should be developed with advances in modern health information technology in mind.7 To top off the controversy, Congress may have the last word on whether ICD is postponed beyond the new implementation date of October 1, 2015.FigureRichard “Sal” Salcido, MD, EdD

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,005
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesIntégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,064
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,067
Tête enseignante GPT0,486
Écart entre enseignants0,418 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle