Patient Journey Record Systems (PaJR) for Preventing Ambulatory Care Sensitive Conditions
Bibliographic record
Abstract
This chapter articulates key considerations for the translation of the concept of the Patient Journey Record Systems (PaJR) into real world systems. The key concept lies in the ‘discovery’ of the use of patient narratives to locate the phase of illness in a patient journey. We describe our developmental framework of in the context of Ambulatory Care Sensitive Conditions (ACSC) for older patients with multiple morbidity, who are at a high risk of hospitalizations and other adverse health outcomes. The framework addresses the feasibility and usability of an information technology based solution to avert adverse outcomes of hospitalization when this is potentially avoidable by activities in primary care. Key considerations in the PaJR knowledge systems are the design and implementation of robust expert knowledge and data support systems. The patient, caregiver, physician and care team perspectives drive clinical usability and functionality requirements. Experts from computer science domains in artificial intelligence, expert systems, and decision support systems ensure the requirements for the functionality of underlying systems architecture are met. We explore this transdisciplinary perspective and ways in which coherence might be achieved among the many practitioners and expert domains involved in a developmental framework for PaJR. We make a case for the implementation of PaJR systems as part of a universal move to electronic user driven health care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".