Animated Patient's guide to Acute Myeloid Leukemia: Assessing patient understanding, shared decision-making, and health outcomes.
Bibliographic record
Abstract
Abstract Background: Acute myeloid leukemia (AML) patient education resources that address barriers to health literacy, elucidate the many complexities of AML, and demonstrate improved patient outcomes are limited. Poor health literacy is well-recognized as a major impediment in patient understanding of disease, and is an obstacle to patient self-management, shared decision-making, and achievement of optimal health outcomes. The challenges to overcoming barriers of limited health literacy are immense – the AMA cites a mere 12% of Americans are considered health literacy proficient; yet most learning resources for patients utilize a text-based information format, often beyond the comprehension of most patients. To mitigate the barriers of poor understanding and inadequate health literacy, the MDS Foundation developed a highly visual format of learning for patients, using animations, and image-rich educational communication tools. We provide a synopsis of the impact, utility, and benefits of “An Animated Patient's Guide to AML for Best Health Outcomes (APG-AML)” accessible at . Our study's retrospective evaluation demonstrates the value of this format of learning, by assessing knowledge gains, improved self-management skills, as well as enabling discussions between patients and providers to validate the merits and outcomes benefits of the APG-AML website and YouTube resources. Methods: Our APG-AML study reports on data collected from November 2019 to June 2025 (~67 months). We monitored audience outreach, demographics, learner identity (patient/family member/caregiver), knowledge improvement, learner-reported commitment to change, and patients who planned to implement self-management actions into their AML management. Our data comprised information collected on unique visitors, country origins, areas of interest; we measured responses by learners who self-reported intention to discuss new management options with their providers. We also monitored usage of learning resources, such as top views by multimedia type (animations, slide shows, expert videos, patient videos, infographics). We assessed learner impact and commitment to change through surveys, which assessed areas of interest, intention to self-modify behaviors, and intention to discuss disease management opportunities with providers. We followed an adapted approach of Moore's outcomes performance criteria utilized in continuing medical education among the user audiences to assess effectiveness, and learner responses. Results: Our data reports on learner activities based on 565,131 total views globally. There were 319,173 global unique visitors; of which 159,587 (~50%) were unique visitors from the USA. Of US visitors, 47% (n=75,006) identified as AML patients; 24% (n=38,301) identified as family or caregivers; 13% (n=20,746) identified as ‘other’; and 16% (n=25,534) identified as healthcare providers. Of patients who reported actions related to commitment to change, 93% (n=670) reported they “will use information learned to better self-manage their AML”; 94% (n=597) reported they “plan to discuss information learned with their doctor”; 98% (n=737) reported they “plan to discuss AML medication and treatment options with their doctor”. In terms of knowledge improvement among213 respondents, 93% (n=198)indicated “improved understanding of AML”; 91% reported “better understanding of AML diagnosis”; and 94% (n=200)reported they “learned new treatment option information for AML”.Conclusion: Our AML patient education study provides insights into the learning needs, areas of importance and self-reported knowledge gains among learners and suggests that such learning resources can inform patients effectively and facilitate actionable behaviors leading to improved outcomes. Moreover, visual formats of learning can help overcome health literacy barriers and enable greater patient participation in achieving shared-decisions and optimal outcomes.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".