Take Control. Period. - Needs Assessment Leading to the Development of a Digital Quality Improvement Intervention Designed to Enhance Adherence to Tranexamic Acid and Iron Supplementation in Women with Heavy Menstrual Bleeding
Bibliographic record
Abstract
Introduction Heavy menstrual bleeding (HMB) affects up to 51% of all women and 93% of those with bleeding disorders. It is essentially universally associated with iron deficiency +/- anemia (ID/IDA), resulting in substantial fatigue and reduced quality of life. Despite the large body of evidence to support the use of tranexamic acid (TXA) in reducing HMB, in the real world, many patients report difficulty taking this medication on a consistent basis. Adherence to oral iron supplementation is similarly poor, despite its seemingly straightforward use. Our goal was to further understand the problem of adherence to these oral medications in women with HMB and to begin developing a potential digital solution. Methods We performed a retrospective practice audit of an outpatient hematology clinic between November, 2018 and January, 2019 to determine the baseline adherence to prescribed TXA and oral iron in women with HMB. We then conducted a root-cause-analysis to identify common themes affecting medication adherence and opportunities for patient education. Key contributing factors were determined based on quality improvement (QI) team brainstorming, practice audit, qualitative survey of patients and practitioners, and review of literature. Results There were 252 clinical encounters with women ≤55 years-old during this time frame. Of these, 85 involved active management of HMB, with 47 cases already prescribed TXA. Only including days with ≥2 patients on TXA, the median adherence rate per clinic was 33%. There were 114 encounters involving management of ID/IDA due to HMB, with 57 already on oral iron. The median adherence rate per clinic with ≥2 patients on oral iron was 40%. Common barriers to TXA adherence identified by the patient/practitioner survey included: need to take at least two large tablets three times per day during bleeding; premature cessation of TXA due to the perception of it interfering with "normal" blood loss; high cost of oral TXA; difficulty with dose and frequency titration; common side effects including nausea, diarrhea, and headache; concern regarding the potential thromboembolic risk of TXA, and the synergistic thromboembolic risk with TXA and estrogen based therapies. Common concerns with oral iron were: difficulty managing gastrointestinal side effects; not taking iron on an empty stomach or with acidic foods/beverages; cessation of therapy before iron stores are replete; forgetting to take doses; and high cost of polysaccharide and heme-based iron supplements. Conclusions HMB negatively impacts the quality of life and productivity of women of reproductive age. There is a robust body of evidence supporting TXA and iron supplementation in this context. Despite best efforts at counselling on proper utilization of these agents in clinic, baseline adherence was only 33% to TXA and 40% to oral iron. We identified numerous barriers to adherence that are amenable to QI initiatives. Using these results, we have begun to develop a multimodal, interactive website for women with HMB to improve adherence to TXA and oral iron, called "Take Control. Period." Future steps will be to evaluate changes in adherence to TXA and oral iron per patient per clinic week prospectively after digital intervention implementation. Disclosures Sholzberg: Novartis: Honoraria; Amgen: Honoraria, Research Funding.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".