P226 Assessing the effectiveness of the nurse navigator role in an inflammatory bowel disease medical home: A retrospective cohort study
Bibliographic record
Abstract
Abstract Background Patients living with chronic illnesses require long-term and often repeated interactions with the healthcare system. inflammatory bowel disease (IBD) is an incurable, chronic gastrointestinal disease which frequently flares and remits. The nurse navigator (NN) serves as the point of first contact for IBD connecting patients with their multidisciplinary care team in order to facilitate and expedite assessment, treatment and navigation through the healthcare system with the goal of improving disease-related outcomes while reducing healthcare system burden. The aim of this study was to assess the impact of implementation of an IBD NN role within a multidisciplinary IBD Medical home on access to care, disease-related outcomes, patient satisfaction with care, and healthcare resource use. Methods This was a retrospective cohort study comparing an IBD patient population that had access to a 24/7 NN-led helpline to a reference population who did not have access to such a service. Data between August 2017 and October 2019 were extracted from patient charts. Distribution of the number of flares and time to clinical assessment between the NN exposed cohort and a non-NN exposed cohort are planned using multivariate analysis. This is a preliminary description of the NN-exposed cohort only. Results Preliminary results identified a total of 643 patients in the NN-exposed cohort. The majority of our NN-exposed population were female (64.3%). The mean age was 46.42 ± 16.86 years. Sixty-five per cent of patients had CD, 33% UC and 2% IBDU. Of the 729 calls extracted, care coordination (39%) was the most frequent indication for calls followed by flare (25%), and medication education (16%). Patients made the majority (52.8%) of calls compared with NN initiated calls (47.2%). The mean number of calls per patient was 2.64 ± 2.51 (range 1–18) during the study period. Time to clinic assessment post flare call was on average 10.22 ± 8.51 days. Conclusion These results are descriptive of the NN-exposed cohort. Data comparing outcomes amongst the NN-exposed cohort to the non-exposed cohort will be presented at ECCO.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".