Integrating Adverse Outcome Metrics Into Quality Assurance Strategies for Improved Patient Outcomes
Notice bibliographique
Résumé
The Commission of Dental Accreditation (CODA) Standard 5-3 dictates that dental schools must conduct a formal system of continuous quality improvement and demonstrate a “mechanism to determine the cause(s) of treatment deficiencies and…implementation of corrective measures as appropriate” [1]. The core purpose of this standard has been central to the creation of the Adverse Outcomes model developed at the University of Illinois Chicago, College of Dentistry (UIC-COD). In a healthcare setting, adverse events refer to undesirable and unintended outcomes of patient management rather than the underlying condition [2, 3]. The UIC-COD defines “adverse outcomes” as unintended outcomes of routine care that necessitate the repetition of a procedure or an alternative treatment plan. In an educational context, adverse outcomes are not uncommon when care is provided by trainees, which often go unreported in the literature. Formal systems for documenting treatment deficiencies, causes, and corrective measures may help institutions set benchmarks, standardize operating procedures, and enhance training for students and faculty alike [4]. In 2019, UIC-COD created an innovative workflow within the electronic health record (axiUm, Exan, Coquitlam, BC, Canada) to formally document adverse outcomes. The process begins chairside when the student provider and attending faculty identify a deficiency in a recently completed procedure (within 2 years) and attach a completed adverse outcome form to the original procedure code in axiUm (Figure 1). The UIC Quality Assurance Committee regularly runs reports, consolidating the data from the forms and displaying results graphically, with a detailed list of the various deficiencies (Figure 2). Data from this form can be used to identify patient care trends, which can then be further investigated and addressed through adjustments in student education, faculty calibration, lab communication, dental material choices, or clinical care philosophies. Two workflows for all-ceramic, single-unit crowns in the UIC-COD predoctoral program were recently investigated. The study was exempted by the UIC Institutional Review Board (#STUDY2024-1431). The query aimed to investigate the rate of in-house fabricated (IHF) digitally scanned all-ceramic crown adverse outcomes as compared to commercial lab fabricated (CLF) and the associated causes over a time span of 5 years (August 16, 2019–August 15, 2024). Descriptive analysis revealed a comparable adverse outcome frequency of approximately 2.7% (Figure 3). The result has helped validate the in-house digital workflow within the curriculum. Since poor marginal adaptation following cementation was found to be the leading cause of failure, the program can now consider strengthening efforts in student training, faculty and lab technician calibration, and dental cement choices for improved patient care outcomes. The outcomes of this innovative workflow have proven to be valuable from a quality improvement standpoint. Successful implementation necessitated student and faculty training, clinic director oversight, and the initial effort of developing and creating the custom forms and reports. The adverse outcome system for documenting treatment deficiencies, causes, and corrective measures has helped UIC-COD set benchmarks, standardize operating procedures, focus calibration efforts, and enhance student education. The authors would like to thank Nish Shivnani and Ed Early, former and current Directors of Health Informatics Technology, respectively, at the UIC-COD.
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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 | 0,206 | 0,277 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,012 | 0,008 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,013 | 0,010 |
| Science ouverte | 0,006 | 0,011 |
| Intégrité de la recherche | 0,002 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
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 ».