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Enregistrement W4242036144 · doi:10.5858/134.5.663

Electronic Pathology Reporting: Digitizing the College of American Pathologists Cancer Checklists

2010· letter· en· W4242036144 sur OpenAlexaboutno aff
Monica E. de Baca, John F. Madden, Mary Kennedy

Notice bibliographique

RevueArchives of Pathology & Laboratory Medicine · 2010
Typeletter
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueBiomedical Text Mining and Ontologies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésChecklistAccreditationMedicineCancerCommissionFamily medicineHealth careMedical physicsMEDLINEMedical educationPathologyPsychologyInternal medicineBusinessPolitical science

Résumé

récupéré en direct d'OpenAlex

The recent editorial1 by Mahul B. Amin, MD, reports on the College of American Pathologists (CAP) Cancer Committee's latest release of the CAP Cancer Protocols and Checklists, and offers perspective on the history and importance of standardized, structured pathology reporting for effective cancer care. The CAP cancer checklists (CCs) are recognized as the gold standard for pathology reporting of cancer cases. Developed by the CAP Cancer Committee in collaboration with pathology, surgery, oncology, and radiation therapy experts, the synoptic checklist format ensures consistent reporting of scientifically validated elements and enables the medical community to retrieve, share, compare, and research clinical data for improved patient care. Use of the CAP cancer checklists has become widespread throughout the United States. The American College of Surgeons Commission on Cancer requires the use of the essential data elements in the cancer checklists for accreditation. In addition, they are widely used in Canada (eg, Cancer Care Ontario [CCO]) and have become well known in many other countries.Historically, the CAP cancer checklists had been paper based. As health information technology advanced, it became evident that an electronic version of the checklists was needed. Several years ago, CAP began to offer a SNOMED CT (Systematized Nomenclature of Medicine–Clinical Terms)–encoded checklist version in a database format to software developers. With increased complexity of reporting, dynamic changes in the checklist content, and the need to support a broad range of rapidly evolving health information platforms, CAP formed the Pathology Electronic Reporting Taskforce (PERT) in 2005 with funding support from the Centers for Disease Control and Prevention (CDC). It is composed of CAP member experts in cancer and information technology, and also currently includes representatives from the North American Association of Central Cancer Registries, the American Joint Commission on Cancer (AJCC) and its Collaborative Staging initiative, the US Department of Health & Human Services Office of the Assistant Secretary for Planning and Evaluation, the CDC, the Canadian Partnership Against Cancer, CCO, CAP staff, and other specialists. PERT's mission is to advance the implementation of the CAP cancer checklists by using health information technology. This goal is one of the mandates of the PERT's parent department within the CAP, the Diagnostic Intelligence and Health Information Technology (DIHIT), which aims to improve patient care and extend the role of the pathologist by developing standards and electronic tools for pathology practice. By integrating the CC content with other relevant electronic reporting standards for public health data collection and clinical care (including SNOMED CT, LOINC, caBIG, HL7, and others), PERT aims to make the cancer committee's work accessible to an ever-wider audience, and to facilitate transmission and storage of CC-compliant patient reports.In January 2009, the PERT-developed electronic cancer checklists (eCCs) were first released in an eXtensible Markup Language (XML) format. This release format parses the paper-based checklists into datasets suitable for incorporation into software products and can be used to standardize the electronic collection and transmission of CC data. XML was chosen for its universal acceptance, ease of use, and its ability to facilitate the sharing of structured data across disparate systems, ranging from laboratory information systems and cancer registry systems to comprehensive electronic health records systems and future personal health records. In addition to patient care, we anticipate its increased use in public health surveillance, research, tissue banking, and quality improvement.An update in this format in December 2009 encoded the October 2009 CAP Cancer Protocols and Checklists and incorporated the AJCC 7th edition staging criteria. Subsequent releases will follow in the coming months as more cancer protocols are released by the CAP Cancer Committee. SNOMED CT encoding for histology and tumor site will be included in this release; subsequently, additional checklist sections will receive SNOMED CT mappings.In the first quarter of 2010, PERT will offer a preview release of its next-generation XML format incorporating several of the newly published 2009 CAP Cancer Committee Cancer Checklists. This will introduce PERT's new inclusive framework for creation and distribution of structured (sometimes referred to as “synoptic”) medical diagnostic reports. Initially designed for cancer reporting in pathology, this framework is intended to be scalable to other medical specialties, such as radiology, for their structured reporting needs. The cornerstone design concept is to maintain a modular, loosely coupled relationship among the user interface, the underlying data model and model extensions, the sharable semantics (ie, terminology binding), and the transport format. The design framework will incorporate the following components:The time has come to move from paper to electronic reporting in pathology. Electronic reporting tools will dramatically facilitate incorporation of cancer checklist information into the health care workflow. The monumental accomplishments of the CAP Cancer Committee in producing the revised 2009 checklists move us closer to that goal. Conversion of the CAP Cancer Committee's content into the electronic cancer checklist versions relies on close consultation with the committee members, voluntary participation of numerous experts from the pathology, registrar, epidemiology, and IT communities, and support from the CAP's DIHIT department. We hope that by assisting in the accurate determination of stage, facilitating data transmission, and increasing patient safety, the eCCs will assist the community of pathologists in making an ever-growing impact on cancer care.

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 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)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche
Catégories consensuellesIntégrité de la recherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,723
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,011
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 Opus0,015
Tête enseignante GPT0,301
Écart entre enseignants0,286 · 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

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations8
Publié2010
Routes d'admission1
Résumé présentoui

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