Evaluation of the First Year(s) of Physicians Collaboration on an Interdisciplinary Electronic Consultation Platform in the Netherlands: Mixed Methods Observational Study
Bibliographic record
Abstract
BACKGROUND: Complexity of health problems and aging of the population create an ongoing burden on the health care system with the general practitioner (GP) being the gatekeeper in primary care. In GPs daily practice, collaboration with specialists and exchange of knowledge from the secondary care play a crucial role in this system. Communication between primary and secondary care has shortcomings for health care workers that want to practice sustainable patient-centered health care. Therefore, a new digital interactive platform was developed: Prisma. OBJECTIVE: This study aims to describe the development of a digital consultation platform (Prisma) to connect GPs with hospital specialists via the Siilo application and to evaluate the first year of use, including consultations, topic diversity, and number of participating physicians. METHODS: We conducted a mixed methods observational study, analyzing qualitative and quantitative data for cases posted on the platform between June 2018 and May 2020. Any GP can post questions to an interdisciplinary group of secondary care specialists, with the platform designed to facilitate discussion and knowledge exchange for all users. RESULTS: In total, 3674 cases were posted by 424 GPs across 16 specialisms. Most questions and answers concerned diagnosis, nonmedical treatment, and medication. Mean response time was 76 minutes (range 44-252). An average of 3 users engaged with each case (up to 7 specialists). Almost half of the internal medicine cases received responses from at least two specialisms in secondary care, contrasting with about one-fifth for dermatology. Of note, the growth in consultations was steepest for dermatology. CONCLUSIONS: Digital consultations offer the possibility for GPs to receive quick responses when seeking advice. The interdisciplinary approach of Prisma creates opportunities for digital patient-centered networking.
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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 | 0.025 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".