A Novel Framework to Assess Clinical Information in Digital Health Technologies: Cross-Sectional Survey Study
Bibliographic record
Abstract
Background: Digital health is a critical driver of quality, safety, and efficiency in health care. However, poor quality of clinical information in digital health technologies (DHTs) can compromise the quality and safety of care. The Clinical Information Quality (CLIQ) framework was developed, based on a systemic review of literature and an international eDelphi study, as a tool to assess the quality of clinical information in DHTs. objectives: The aim of this study is to assess the applicability, internal consistency, and construct validity of the CLIQ framework. Methods: This study was conducted as a cross-sectional survey of health care professionals across the United Kingdom who regularly use SystmOne electronic health records. Participants were invited through emails and social media platforms. The CLIQ questionnaire was administered as a web-based survey. Spearman correlation coefficients were computed to investigate the linear relationship between the dimensions in the CLIQ framework. The Cronbach α coefficients were computed to assess the internal consistency of the global scale (ie, CLIQ framework) and the subscales (ie, the informativeness, availability, and usability categories). Confirmatory factor analysis was used to assess the extent to which the survey data supported the construct validity of the CLIQ framework. Results: A total of 109 health care professionals completed the survey, of which two-thirds (67, 61.5%) were doctors and a quarter (26, 23.9%) were nurses or advanced nurse practitioners. Overall, the CLIQ dimensions had good quality scores except for portability, which had a modest score. The inter-item correlations were all positive and not likely due to chance. The Cronbach α coefficient for the overall CLIQ framework was 0.89 (95% CI 0.85-0.92). The confirmatory factor analysis provided a modest support for the construct validity of the CLIQ framework with the comparative fit index of 0.86 and standardized root mean square residual of 0.08. Conclusions: The CLIQ framework demonstrated a high reliability and a modest construct validity. The CLIQ framework offers a pragmatic approach to assessing the quality of clinical information in DHTs and could be applied as part of information quality assurance systems in health care settings to improve quality of health information.
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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 | 0.026 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".