Patient perceptions of in‐hospital laboratory blood testing: A patient‐oriented and patient co‐designed qualitative study
Bibliographic record
Abstract
BACKGROUND: Indiscriminate use of laboratory blood testing in hospitals contributes to patient discomfort and healthcare waste. Patient engagement in low-value healthcare can help reduce overuse. Understanding patient experience is necessary to identify opportunities to improve patient engagement with in-hospital laboratory testing. OBJECTIVES: To understand patient experience with the process of in-hospital laboratory blood testing. METHODS: We used a qualitative study design via semistructured interviews conducted online or over the phone. Participants were adult patients or family members/caregivers (≥18 years of age) with a recent (within 12 months of interview) experience of hospitalization in Alberta or British Columbia, Canada. We identified participants through convenience sampling and conducted interviews between May 2021 and June 2022. We analysed transcripts using thematic content analysis. Recruitment was continued until code saturation was reached. RESULTS: We interviewed 16 participants (13 patients, 1 family member and 2 caregivers). We identified four themes from patients' experiences of in-hospital laboratory blood testing: (i) patients need information from healthcare teams about expected blood testing processes, (ii) blood draw processes should consider patient comfort and preferences, (iii) patients want information from their healthcare teams about the rationale and frequency of blood testing and (iv) patients need information on how their testing results affect their medical care. CONCLUSION: Current laboratory testing processes in hospitals do not facilitate shared decision-making and patient engagement. Patient engagement with laboratory testing in hospitals requires an empathetic healthcare team that provides clear communication regarding testing procedures, rationale and results, while considering patient preferences and offering opportunities for involvement. PATIENT OR PUBLIC CONTRIBUTION: We interviewed 16 patients and/or family members/caregivers regarding their in-hospital laboratory blood testing experiences. Our findings show correlations between patient needs and patient recommendations to make testing processes more patient-centred. To bring a lived-experience lens to this study, we formed a Patient Advisory Council with 9-11 patient research partners. Our patient research partners informed the research design, co-developed participant recruitment strategies, co-conducted data collection and informed the data analysis. Some of our patient research partners are co-authors of this manuscript.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".