MON-118 Reducing Unnecessary Repeat HbA1c Testing in a Tertiary Academic Hospital
Bibliographic record
Abstract
Abstract Background Glycated hemoglobin (HbA1c) is a surrogate marker of glycemia over the preceding three months, where the last 30 days contributes to 50% of the value (1). Therefore guidelines often recommend repeating HbA1c only after 3 months in most situations (2), but repeat testing of HbA1c is often conducted earlier when not warranted (3). We aimed to conduct a Quality Improvement (QI) initiative to reduce unnecessary repeat testing of HbA1c at a large tertiary care academic hospital in Toronto, Ontario by 50% by May 2020. Methods: The Model for Improvement Quality Improvement (QI) framework was used in the design of the QI project to reduce repeat HbA1c. Problem characterization was conducted to understand root causes and iterative Plan-Do-Study-Act cycles were used to develop a change intervention. Unnecessary HbA1c tests were the primary outcome and defined as repeat HbA1c testing within 60 days; the top three specialities that ordered unnecessary HbA1c tests were targeted for education prior to implementation of the change intervention. Results: Baseline data on all HbA1c tests in 2018 revealed repeat testing in approximately 10% of 15,290 HbA1c tests, with estimated potential savings of more than $11,000 based on the provincial reimbursement rate. The top 3 ordering specialities targeted for education included Nephrology (n=410 repeat HbA1c tests), Cardiology (n=246 repeat HbA1c tests), and Endocrinology (n=136 repeat HbA1C tests). Root cause analysis revealed that providers often ordered repeat HbA1c tests due to being unaware of prior results and a knowledge gap of testing recommendations. A laboratory forced function will be implemented on December 1, 2019 to cancel any repeat HbA1c tests within 60 days and calls to the lab to add HbA1c testing will be tracked. Conclusions: Repeat HbA1c testing is frequent in hospital settings and can be an important target for QI efforts. A forced function to cancel processing of repeat HbA1c may be an appropriate QI intervention to reduce repeat testing to promote high-value care. Ongoing data analysis will be conducted to assess the impact of this intervention. References (1) Goldstein DE, Little RR, Lorenz RA, Malone JI, Nathan D, Peterson CM, Sacks DB. Tests of Glycemia in Diabetes. Diabetes Care 2004;27(7): 1761-1773. (2) Berard LD, Siemens R, Woo V. Diabetes Canada 2018 Clinical Practice Guidelines for the Prevention and Management of Diabetes in Canada: Monitoring Glycemic Control. Can J Diabetes 2018;42(Suppl 1):S47-S53. (3) Chami N, Simons JE, Sweetman A, Don-Wauchope AC. Rates of inappropriate laboratory test utilization in Ontario. Clinical Biochemistry 2017;50: 822-827.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".