A131 ELECTRONIC HEALTH RECORD–BASED SMARTSETS INTEGRATE VARIOUS ASPECTS OF FLARE MANAGEMENT IN OUTPATIENTS WITH INFLAMMATORY BOWEL DISEASE THEREFORE ENSURING CONTINUITY OF CARE
Bibliographic record
Abstract
Clinical course of inflammatory bowel disease (IBD) is characterized by periods of relapse (flares) and remission. Induction and maintenance of sustained endoscopic remission is the primary target in treatment of IBD resulting in improved quality of life, reduced complications rates, better health outcomes and less burden on the health care system. The aim of this project is to implement the Electronic Health Record (EHR) based IBD “Flare management” algorithm that will ensure adherence to guidelines and continuity of IBD care over time warranting better health and health care outcomes. In the IBD Unit at the University of Alberta Hospital (www.ibdunit.com) in collaboration with University of Calgary IBD specialists, we designed, developed and validated a set of the IBD Clinical Care Pathways (CCPs) (www.ibdclinic.ca/ibd-ccp/). These IBD CCPs are supported by systematic reviews of published evidence and are comprised of protocols, algorithms and checklists that help to harmonize clinical and administrative aspects, ensure stability of remission, and continuity of IBD care in outpatient setting. As a result of collaboration between the IBD Unit at the University of Alberta Hospital and Ambulatory Clinical Information Systems at the Alberta Health Services, the “Flare management” IBD CCPs have been successfully incorporated into eClinician® EHR system in a form of smartsets (documentation templates comprising of components relevant to the specific appointment and clinical situation). The “Flare management” IBD CCPs encompass three subsequent smartsets representing comprehensive stepwise approach: (1) Suspected flare, (2) 2–4 weeks’ Mid-flare, and (3) 16 weeks’ Post-flare assessments. The complete algorithm is presented on the Figure 1. While using the smartsets, system prompts the IBD specialist to provide educational resources and instructions to the patient. One-time clicks lead to issuing and immediate printing of the requisitions, prescriptions, referral letters, progress notes, and after-visit summaries. Implemented into real clinical settings, the “Flare management” IBD CCPs have many advantages including but not limited to: (1) maximizing compliance with up-to-date evidence-based guidelines; (2) streamlining the workflow by bridging various aspects of it; (3) helping to follow medication authorization more thoroughly to protect against misuse and overuse; (4) improving communication between IBD team members; (5) exercising “proactive care” by engaging patients into decision-making process, not just reacting to their needs; (6) accurately capturing data essential for measuring the process and outcome quality indicators and perform better reporting. CCC
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".