Problem-Oriented Medical Records for Describing Care Cases Using Multi-Tenants
Bibliographic record
Abstract
There are countless applications in healthcare for digital personal assistants helping clinicians as well as patients and their families. However, there is no conversational application to understand patient encounters and assist clinicians to describe their clinical cases in association with their medical record like the HL7 FHIR. Building such conversational service requires understanding of the charting schema used to record clinical cases in the clinical setting like the SOAP note as well as having the federated APIs to exchange messages not only between tenants (e.g. Patients and Physicians) but also with different servers including the FHIR electronic healthcare record server. In this paper we described the extension that we have added to our existing QL4POMR framework to have extended ability to represent clinical cases via the use of multi-tenants chatbots based on the SOAP note and the connectivity to the FHIR server. The extension to the QL4POMR uses the Google DialogFlow and the GraphQL-Yoga APIs. This research work is a continuation of our MITACS and NSERC funded work that has started in 2020.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".