Reporting Observational Studies of the Use of Information Technology in the Clinical Consultation. A Position Statement from the International Medical Informatics Association Primary Care Informatics Working Group (IMIA PCI WG)
Bibliographic record
Abstract
Objectives: To develop a classification system to improve the reporting of observational studies of the use of information technology (IT) in clinical consultations. Methods: Literature review, workshops, and development of a position statement. We grouped the important aspects for consistent reporting into a “faceted classification”; the components relevant to a particular study to be used independently. Results: The eight facets of our classification are: Theoretical and methodological approach: e.g. dramaturgical, cognitive; Data collection: Type and method of observation; Room layout and environment: How this affects interaction between clinician, patient and computer. Initiation and Interaction: Who starts the consultation, and how the participants interact; Information and knowledge utilisation: What sources of information or decision support are used or provided; Timing and type of consultation variables: Standard descriptors that can be used to allow comparison of duration and description of continuous activities (e.g. speech, eye contact) and episodic ones, such as prescribing; Post-consultation impact measures: Satisfaction surveys and health economic assessment based on the perceived quality of the clinician-patient interaction; and Data capture, storage, and export formats: How to archive and curate data to facilitate further analysis. Conclusions: Adoption of this classification should make it easier to interpret research findings and facilitate the synthesis of evidence across studies. Those engaged in IT-consultation research shouldconsider adopting this reporting guide.
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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 | 0.588 | 0.717 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".