Integrating Adverse Outcome Metrics Into Quality Assurance Strategies for Improved Patient Outcomes
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.206 | 0.277 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".