CORR Insights®: Can Surgeons Adequately Capture Adverse Events Using the Spinal Adverse Events Severity System (SAVES) and OrthoSAVES?
Notice bibliographique
Résumé
Where Are We Now? The Institute of Medicine has recommended systems for reporting medical errors. However, at a basic level, a standard language and consistent definitions for what constitutes an adverse event (AE) have been lacking [11]. Current terminology, such as “complication,” “AE,” “adverse occurrence,” or “near misses,” are often used interchangeably, but have different meanings (between or within institutions depending on case definition) and varying methods for identification and reporting [10]. In addition, concerns regarding malpractice, professional, and financial implications (personal, practice group, or institutional) of AEs can result in variable reporting of AEs [7]. The inconsistency of AE reporting in surgery reflects the complexity and challenges of this important issue, and currently is a focus of many surgical societies and institutions. Specific to the current article by Chen and colleagues, underreporting of AEs by physicians is one of the evidentiary challenges in patient-safety initiatives [12]. Chen and colleagues prospectively assessed the use of a Spinal Adverse Events Severity System (SAVES) (developed by this writer) for spine and orthopaedic surgery [10, 11]. They demonstrated relative under-reporting by surgeons regarding minor AEs and appropriately questioned the cost-benefit of using third-party reviewers. They also noted that specific training may have improved physician reporting. Where Do We Need To Go? A thorough discussion of the existing body of science on the broader needs, methods, and evaluation of processes for improving patient safety and quality initiatives (QIs) is beyond the scope of this brief commentary. But from my perspective, Chen and colleagues raise several critical questions regarding physician participation and consistent AE reporting across the healthcare sector: (1) As it relates to reporting AEs, is it about education or the broader need for cultural change regarding medical errors? (2) What are the key education components and implementation enablers that address specific system and regional barriers for change? (3) What are the system and regional incentives/drivers for change from the perspective of patients, providers, institution and payer? (4) What is the effectiveness (that is, the impact on reduction of AEs) and cost consequence(s) of current and future interventions? How Do We Get There? For Questions 1-3, the subjective and often-emotional aspects (such as perception of punitive action or professional “shame”) of reporting AEs requires the addition of qualitative research to traditional quantitative methods. Future studies can build on the current study by Chen and colleagues by adding a qualitative component that can add insight into why the surgeons did not feel it was necessary to report the more frequent “minor” AEs. For example, if the prevailing theme from such an exercise was that the more minor events, such as an UTI, were not perceived to have any serious clinical impact, then perhaps education regarding the cumulative financial impact of minor AEs and how it directly affects the surgeons’ practices (such as with respect to bed availability) would result in greater incentive to actively participate [3]. Furthermore, to determine relevant barriers and enablers for implementation of a safety initiative, the regional context requires more explicit exploration. For example, the scenario of reporting a minor event such as UTI will have very a different context in the United States as it pertains to issues such as “never events” and the shifting of the cost burden for such events from payers to providers [2]. This issue is further complicated by variable mechanisms and layers of accountability for a given payer, provider, and institution [8]. Comparatively, in the single-payer context of Canada, other than the need for treatment of a given AE, there is no real personal consequence for the surgeon or immediate actionable consequence for the institution [3]. Consequently, patient safety, QI research, educational efforts, and interventions must take these varying, context specific, factors into consideration and adapt the process to achieve appropriate alignment and incentive across multiple stakeholders, including patients [4]. As demonstrated in previously published studies [6, 13], SAVES functions well when the process of identification and classification of AEs is done by the healthcare team, rather than individual providers or institutional processes [1]. Utilization of the existing healthcare team (doctors, trainees, nurses, allied health providers, and pharmacists) partially addresses concerns regarding cost-benefit of third-party reviewers, but not the cost requirements of data entry and database management. In addition to patient safety and quality of care, AEs have a significant financial impact [3]. Cost-effectiveness analysis (CEA) is the preferred form of health economic assessment, however, large scale CEAs are challenging and costly to execute. Furthermore, the results of CEAs are often difficult to translate to a different regional context. A practical alternative may be cost-consequent analysis which comprises of a disaggregated presentation of relevant variables (including costs and benefits) that provides decision makers with detailed information about the intervention under evaluation, thus allowing different stakeholders to identify components most relevant to their perspective [9]. Regarding outcomes, a recent systematic review [5] demonstrated variable effectiveness in the ability of patient safety initiatives to reduce surgical harm and also implementation feasibility. Continued studies on effectiveness of a given patient safety initiative are paramount, however, studies should also concurrently evaluate the cost consequences.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,169 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,148 | 0,062 |
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 source (Gemma direct ou Codex distillé), 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 ».