Digital Diagnostics and Mobile Health in Laboratory Medicine: An International Federation of Clinical Chemistry and Laboratory Medicine Survey on Current Practice and Future Perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: A survey of IFCC members was conducted to determine current and future perspectives on digital innovations within laboratory medicine and healthcare sectors. METHODS: Questions focused on the relevance of digital diagnostic solutions, implementation and barriers to adopting digital technologies, and supplier roles in supporting innovation. Digital diagnostic market segments were defined by solution recipient (laboratory, clinician, patient/consumer, payor) and proximity to core laboratory operations. RESULTS: Digital solutions were of active interest for >90% of respondents. Although solutions to improve core operations were ranked as the most relevant currently, a future shift to technologies beyond core laboratory expertise is expected. A key area of potential differentiation for laboratory customers was clinical decision support. Currently, laboratories collaborate strongly with suppliers of laboratory integration software and information systems, with high expectations for future collaboration in clinical decision support, disease self-management, and population health management. Asia Pacific countries attributed greater importance to adopting digital solutions than those in other regions. Financial burden was the most commonly cited challenge in implementing digital solutions. CONCLUSIONS: Specialists in laboratory medicine are proactively approaching digital innovations and transformation, and there is high enthusiasm and expectation for further collaboration with suppliers and healthcare professionals beyond current core laboratory expertise.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it