Developing expert consensus for specialist e-consult response: A delphi study to inform graduate medical education
Notice bibliographique
Résumé
Abstract Introduction To meet the demands of a growing number of specialty referrals, outpatient electronic consultations (e-consults) have emerged as a rapid access strategy for specialist consultation, yet no framework currently exists to standardize specialist response to e-consults. With growing implementation of e-consult platforms throughout North America, it is essential to establish criteria for high-quality correspondence between specialists and primary care providers (PCP) and develop a formal e-consult curriculum to integrate into Accreditation Council for Graduate Medical Education (ACGME) subspecialty training programs. Methods We used a systematic, consensus-building methodology (modified Delphi) to develop expert recommendations for key elements of specialist response to e-consults. An expert group consisting of 14 clinicians (13 physicians, 1 physician assistant) from across the U.S. and Canada was selected to form the Delphi panel. Panelists were purposefully chosen to balance clinical expertise, practice location, and setting. Selection criteria included recognition as an expert in e-consults based on academic and/or clinical contributions. Panelists participated in an initial synchronous meeting to review and discuss a set of objectives identified through comprehensive literature review using MEDLINE/PubMed, followed by two rounds of anonymous and iterative voting. Consensus was determined a priori as ≥ 80% of panelists agreeing that an objective was essential. All objectives that achieved consensus were mapped to ACGME core competencies, including Patient Care (PC), Medical Knowledge (MK), Professionalism (P), Interpersonal and Communication Skills (ICS), Practice-Based Learning and Improvement (PBLI), and Systems-Based Practice (SBP). Results Two PCP and 12 specialty providers (7 hematology, 1 infectious diseases, 2 endocrinology, 1 gastroenterology, 1 cardiology) representing a range of geographic regions in the U.S. and Canada (7 East, 1 Central, and 6 West) and practice settings (12 academic, 1 private, and 1 integrated healthcare) were included in the Delphi panel. Three panelists serve in leadership roles for their e-consult program and 6 panelists have five or more e-consult peer-reviewed publications. After two survey rounds, 8 essential objectives of specialist e-consult responses were identified and achieved consensus (≥ 80%), including:Briefly summarize patient-specific descriptives and pertinent workup (e.g., key labs, imaging studies, procedures, etc.), specifying the time period of data reviewed and any pertinent missing data (PC).Review the differential diagnosis and suspected etiology, if pertinent (MK).Communicate specific recommendations (e.g., additional tests, monitoring, and/or treatment, including duration and administration), and explicitly state what the specialist will order or arrange if applicable (PC, ICS).Include a brief rationale for recommendations applied to the clinical scenario to improve educational value and encourage guideline adherence (e.g., cite guidelines, relevant data, etc.) (PBLI).Provide a clear contingency plan based on expected results (e.g., if results are positive/negative, proceed with treatment X) and document when a face-to-face referral or recontacting the specialist would be indicated/necessary (SBP, ICS).Communicate in a professional and supportive tone, acknowledging the referring provider's efforts and recognizing that the patient may review this communication (P, ICS).Delineate if/how the referring provider can communicate with the specialist, especially to ask an additional question or provide clarity about a patient (e.g., in basket message, chat function, repeat e-consult, etc.) (SBP, ICS).Understand the local context, including test and treatment availability and ordering, timeliness of e-consult completion, and clear role-delineation between referring and specialty providers (SBP). Conclusions An expert panel of PCP and medical specialists established consensus on a set of key components of effective specialist e-consult correspondence. These objectives align with ACGME core competencies and should inform future medical education curricula aimed to build competence in providing e-consult services.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».