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Record W4411489983 · doi:10.1002/jdd.13961

Integrating Adverse Outcome Metrics Into Quality Assurance Strategies for Improved Patient Outcomes

2025· article· en· W4411489983 on OpenAlexaboutno aff
Fatemeh S. Afshari, Bin Yang, Susan Rowan, Danny Hanna

Bibliographic record

VenueJournal of Dental Education · 2025
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsnot available
Fundersnot available
KeywordsQuality assuranceOutcome (game theory)Adverse effectMedicineQuality (philosophy)MEDLINEIntensive care medicineMedical physicsInternal medicinePolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.206
metaresearch head score (Gemma)0.277
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.206
Threshold uncertainty score0.979

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2060.277
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0120.008
Science and technology studies0.0030.003
Scholarly communication0.0130.010
Open science0.0060.011
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0050.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.067
GPT teacher head0.504
Teacher spread0.437 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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