Does Informatics Enable or Inhibit the Delivery of Patient-centred, Coordinated, and Quality-assured Care: a Delphi Study
Bibliographic record
Abstract
BACKGROUND: Primary care delivers patient-centred and coordinated care, which should be quality-assured. Much of family practice now routinely uses computerised medical record (CMR) systems, these systems being linked at varying levels to laboratories and other care providers. CMR systems have the potential to support care. OBJECTIVE: To achieve a consensus among an international panel of health care professionals and informatics experts about the role of informatics in the delivery of patient-centred, coordinated, and quality-assured care. METHOD: The consensus building exercise involved 20 individuals, five general practitioners and 15 informatics academics, members of the International Medical Informatics Association Primary Care Informatics Working Group. A thematic analysis of the literature was carried out according to the defined themes. RESULTS: The first round of the analysis developed 27 statements on how the CMR, or any other information system, including paper-based medical records, supports care delivery. Round 2 aimed at achieving a consensus about the statements of round one. Round 3 stated that there was an agreement on informatics principles and structures that should be put in place. However, there was a disagreement about the processes involved in the implementation, and about the clinical interaction with the systems after the implementation. CONCLUSIONS: The panel had a strong agreement about the core concepts and structures that should be put in place to support high quality care. However, this agreement evaporated over statements related to implementation. These findings reflect literature and personal experiences: whilst there is consensus about how informatics structures and processes support good quality care, implementation is difficult.
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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 | 0.135 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| 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".