Digital Diagnostics and Mobile Health in Laboratory Medicine: An International Federation of Clinical Chemistry and Laboratory Medicine Survey on Current Practice and Future Perspectives
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".