“I Know You'Re a Nurse or a Doctor but I'm the Expert on Hemophilia”: Insights into Healthcare Professional Knowledge and Patient Care Experiences in Hemophilia
Bibliographic record
Abstract
Introduction: Despite advancements in hemophilia treatment, people with hemophilia (PwH) face challenges seeking care outside Hemophilia Treatment Centers (HTC), often resulting in poorer health outcomes. Healthcare professionals' (HCPs) knowledge and skills are one of many contributing factors influencing care delivery. However, understanding how these factors impact PwH's care experiences remains limited. Gaining insights from the patient perspective can identify targets for interventions that impact health outcomes and care delivery. Therefore, we aimed to explore how HCP's knowledge and skills affect the care experiences of people with hemophilia. Methods: Informed by constructivist grounded theory, 29 people with inherited hemophilia A (17 men; 5 women) and B (5 men; 2 women), varying severity (11 severe; 18 non-severe), participated in semi-structured interviews about their experiences with hemophilia and the Canadian healthcare system. Data collection and analysis were iterative, using constant comparative analysis to identify codes and themes. Results: We identified important gaps between non-hemophilia HCPs' knowledge of hemophilia and the practical expertise required for effective management. In settings like emergency departments, primary care, and dentistry, participants encountered subtle and overt barriers due to HCP's superficial understanding, infrequent clinical exposure, and outdated misconceptions about sex-based differences and hemophilia classification. This often led to incorrect treatments, unnecessary delays, and ultimately suboptimal care experiences. Participants frequently anticipated non-hemophilia HCPs' knowledge gaps and used their experiences to educate and advocate for themselves and others, becoming the hemophilia expert in the room, even at times of vulnerability. However, this role, which many took on willingly, led to emotional exhaustion, feelings of isolation, helplessness, and frustration, especially when advocacy efforts were met with resistance. To bridge these gaps, participants relied on HTCs through direct contact or by seeking care at affiliated hospitals, reporting better experiences and reduced need for self-advocacy. Furthermore, improved communication between patients and HCPs, and among HCPs themselves, was suggested as essential for mitigating misunderstandings, ensuring prompt care, and enhancing patient satisfaction. Specifically, participants wanted HCP to acknowledge their lived experience with hemophilia as a legitimate form of expertise. Several key interrelationships were identified among the core themes. Despite progress in hemophilia care, the lack of practical experience among non-hemophilia HCPs increased the burden on patients to self-advocate and educate, perpetuating a cycle where patients must continuously manage and monitor their care. The absence of effective continuous education maintained the knowledge-experience gap, with non-hemophilia HCPs not equipped with the best practices for hemophilia management. The psychological and emotional strain of constant self-advocacy adversely impacted patients' well-being, potentially worsening their health outcomes. Access to specialized care mitigated these effects, as HTCs provided comprehensive, expert care aligned with patients' needs. Effective communication served as a key mediator to alleviate knowledge gaps, improve patient advocacy, and enhance care experiences. Discussion: Centered on the lived experiences of PwH, this study highlights the dynamic interplay between non-hemophilia HCPs' knowledge of hemophilia and the resultant barriers to optimal patient care. While non-hemophilia HCPs cannot be expected to be hemophilia experts, up-to-date baseline knowledge within their scope of practice is necessary to deliver appropriate care to PwH. Based on our findings, patients are an invaluable resource for both revealing knowledge gaps across various care settings and for using their expertise to advocate for better care. Practical and effective education interventions depend on collaborative effects that center the patient voice to bridge these knowledge gaps and reduce the burden on PwH. HTCs serve as a model for improving hemophilia care but may need to play a larger role within their community of practice to address these gaps for better outcomes, equitable care, and enhanced patient experiences.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".