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Enregistrement W4399628319 · doi:10.1097/ccm.0000000000006294

Entrust But Verify…*

2024· editorial· en· W4399628319 sur OpenAlexaboutno aff
Cherylee W. J. Chang, Lewis J. Kaplan

Notice bibliographique

RevueCritical Care Medicine · 2024
Typeeditorial
Langueen
DomaineMedicine
ThématiqueInnovations in Medical Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésIntensivistMedicineCredentialingWorkforceSpecialtyNursingHealth careIntensive careMedical emergencyFamily medicine

Résumé

récupéré en direct d'OpenAlex

Whether seeking outpatient health maintenance, emergency care, inpatient management, or critical care, the preparation, skill, and competency of the clinician providing your care is paramount. When there is no preexisting relationship, one instead relies on education, credentialing, and licensing bodies to assess the clinician ability to render care that meets the existing standard before entering independent practice. One underlying assumption was that there was time spent during training that afforded regular and direct evaluation of knowledge and skills, including critical thinking. In complex care environments such as the ICU life-saving decisions and procedures are often time-sensitive events. Nonetheless, nearly half of all U.S. ICUs are devoid of an intensivist as the predicted shortage of the critical care physician workforce has materialized (1,2). This shortfall is addressed by a variety of physicians in facilities without intensivists, but intensivist-staffed and unstaffed ICUs are increasingly underpinned by a workforce augmented by, or wholly delivered by, advanced practice providers (APPs which include nurse practitioners [NPs] and physician assistants [PAs]). Indeed, APPs are essential team members who help fully staff and enhance the ICU care quality of care (3). Even in training programs, trainee ICU rotations seem increasingly sparse, leaving a workforce gap filled by APPs. Although trainees were always presumed to require oversight within the confines of a training program, APPs are increasingly viewed as independent practitioners. This raises the question of how APPs are prepared and assessed for practice, especially within a general or specialty-focused ICU. That question is what Harrison et al (4) have set forth to answer in this issue of Critical Care Medicine. Physician critical care training is guided by program accreditation entailing specific curriculum and faculty requirements, coupled with a certification process following graduation. APPs are also trained in competency models and require national certification. There is, however, significant variation in APP foundational discipline and background training between NPs and PAs, including their training-based clinical exposure. For example, some direct-entry NP programs do not require prior bedside nursing experience, and some NP programs are substantially delivered using an online format. Some—but clearly not all—NPs and PAs undertake a critical care fellowship pathway after completing their training. Therefore, the specific skill sets that an APP brings to the ICU are highly variable. This challenge has been well-recognized by ICUs integrating APPs into their staffing and has led to standardized ICU onboarding approaches to establish a similar set of core skills between practitioners (5,6). Although onboarding establishes a baseline of knowledge, skills, and processes, it does not provide a metric by which to assess competence and subsequent independent practice. The Dreyfus model of skill acquisition includes a transition from novice, advanced beginner, competent, and proficient to expert (Fig. 1). Within this model, the learner moves from a novice who requires close supervision and instruction to an expert who may readily and independently assess and resolve complex situations. Initially developed for the Air Force Office of Scientific Research, this model has been applied to clinical skills acquisition. The Accreditation Council for Graduate Medical Education (ACGME) recommends incorporating this model into training curricula preparation (7,8).Figure 1.: The Dreyfus model of skill acquisition—identifies five developmental stages from a novice gaining knowledge and experience to becoming competent and proficient. The learner eventually becomes a fully responsible expert with an intuitive understanding of the knowledge and skill acquired (7).A key challenge when assessing an individual along the Dreyfus continuum is to determine whether sufficient competency exists to entrust independent care. Older traditional educational strategies leveraged a fixed-time, hierarchical teacher-centered, and content-knowledge acquisition approach. In the 1990s, Canada set a precedent of curriculum reform (9) transitioning medical education to a competency-based model that is learner-centered and focused on outcome-knowledge application with accountability shared between teacher and learner. Because individuals learn at different rates, this model embraces that variation along the pathway to independent practice (10). The implication is that some will achieve independence more rapidly than the traditionally anticipated time would suggest; others may need a longer training period. The financial implications may be vast. In 1999, the ACGME adopted a six-domain educational model that uses “milestones” to reflect a stepwise progression using “defined, observable markers of an individual’s ability along a developmental continuum (11).” In 2014, the Association of American Medical Colleges (AAMC) adopted entrustable professional activities (EPAs) for medical students and their teachers. First introduced by Ten Cate (12), an EPA is “an essential task of a discipline (profession, specialty, or subspecialty) that an individual can be trusted to perform without direct supervision in a given healthcare context, once sufficient competency has been demonstrated (11).” EPAs differ from milestones in that they present discrete units of work that are directly observable and measurable to assess competency as a training outcome. For medical trainees, EPAs provide a practical framework to assess competency from initial medical education through post-graduate training (13). EPAs are also being explored within nurse, NP, and PA training (14,15). Given the objective aspect of EPA-based assessment, they may be appropriate for APPs working within the ICU. Determining competency, particularly in a subspecialty that not only requires general critical care knowledge, but specialized neurologic and neurosurgical knowledge and skills is essential for safe bedside care in a neurocritical care unit (16). Harrison et al (4) address this topic using a modified Delphi consensus approach to assess the importance of specific clinical activities or skills essential for APPs with immediately available supervision within a NeuroICU and craft a set of core EPAs. Their EPAs were assessed for quality and structure using a novel tool—EQual rubric—developed and revised by a team of health professions educational scholars experienced in EPA development. Using descriptive anchors in a rating scale, the EQual rubric demonstrates excellent interrater reliability and cut points that identify EPAs that are appropriate or in need of revision (17). Two external expert neurocritical care educators, one with and one without academic medical center affiliation applied the EQual rubric to the consensus EPAs and deemed them of sound structure and quality. Harrison et al (4) identified core EPAs specific to neurocritical care, but could be the core EPA essentials reflected in any critical care subspecialty if one replaces “neurologic” with a subspecialty (i.e., cardiac, trauma). Unsurprisingly, core NICU EPAs include: 1) taking a history, conducting a neurologic examination, and determining illness acuity for patients and consults, 2) recommending neurologic imaging and identifying the need for advanced monitoring, 3) recognizing core neuropathologic states and initiating resuscitation, 4) communicating disease severity and diagnostic/therapeutic steps to care teams, consultants, and family members/surrogates, 5) performing general critical care and specialized neurocritical care diagnostic and therapeutic procedures, and 6) recognizing common systemic physiologic derangements and initiating resuscitation. “Nested” EPAs are tasks that require proficiency in a subset of the knowledge, skills, and attitudes required for the broader parent EPA. Accordingly, entrustment for a nested task precedes entrustment for the parent EPA (11). Nested EPAs form the basis of a focused curriculum that may be customized for a specific patient population or setting. Interestingly, neither experience nor profession affected grading entrustment expectations. Programmatic EPA use typically includes achieving a “Statement of Awarded Responsibility” (STAR), a formal recognition of entrustment for all of the EPA’s critical activities. The authors specifically did not target consensus for independent practice (i.e., STAR) but sought a practical approach to consensus of deploying a learning provider into an ICU that has immediate or readily available assistance. Panelists were predominantly from academic medical centers, a composition that impedes generalizability. The authors attempted to mitigate this by distributing core EPAs for grading by private or community-based physicians and APPs. These two groups “deemphasized” the importance of: 1) ordering neurologic imaging and 2) recognizing the need for advanced monitored based, at least in part, on their lack of device availability. These elements emphasize that ICUs with different staffing patterns and resources can adjust EPAs to suit their practice environment. A corollary is that a palette of EPAs may greatly vary between facilities within a health system or network and solely inform practice within a specific setting. That the EPAs were designed to occur in a setting where there was immediate intensivist availability also threatens generalizability since half of all U.S. ICUs have no intensivist, and many of the remainder have an out-of-house intensivist. Telecritical care may qualify as “immediate availability” for cognitive oversight—but not procedural aid—and is not addressed. Given that APP training is not subject to requirements akin to AAMC- and ACGME-based training, deploying EPAs may be ideal for APPs new to practice. The intent is to favorably influence care quality and safety while nurturing a developmental trajectory toward independent practice. Whether EPAs will achieve these goals remains to be discovered. Nonetheless, this study generates more questions than solutions. Although some APPs will readily earn entrustment, others may do so, but slowly, or not at all. How the latter two events are to be handled from an educational, financial, and legal perspective remains unclear. In some states, NPs can engage in independent practice in an ICU. When applying the features of the Dreyfus continuum, the EPA model described by Harrison et al (4) appears to fall between “advanced beginner” and “competent.” By intent, the EPAs that the authors generated are not sufficiently robust to ensure competency in independent practice and should be used accordingly. PAs are not guided by regulations such as the Consensus Model for APRN Regulation to guide state nursing boards regarding NP licensure, accreditation, credentialing, and education (18). Therefore, if a PA shifts practice, it is unclear if the new practice environment EPAs apply to the PA since they would arrive as a seasoned practitioner. Finally, if there is one set of EPAs for APPs and another set for physician trainees, the time burden of entrustment may be substantial, especially if there are multiprofessional trainees with program-specific, rather than rotation-specific, EPAs. Although the authors are to be applauded for providing a framework for APP education, and one is inclined to trust the EPA generation process, it seems prudent to verify the success—or lack thereof—during APP-focused EPA practical implementation.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,037
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,225
Score d'incertitude au seuil0,753

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,037
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0030,002
Communication savante0,0050,007
Science ouverte0,0020,006
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,2250,134

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 GPT0,388
Écart entre enseignants0,373 · 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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

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

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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