Adherence to Clinical Care Protocols for Inflammatory Bowel Disease and Evaluation of a Clinical Decision Support System to Improve Adherence
Bibliographic record
Abstract
Clinical care pathways have been developed with the goal to standardize and improve quality of care. At the University of Alberta, clinical care pathways have been developed, and are currently in use, for inflammatory bowel disease patents experiencing disease flare. However, there is limited literature available regarding the level of adherence of IBD practitioners to the published guidelines or best practices, such as those implemented through these clinical care pathways. The first part of this thesis is a retrospective, single-center chart review of 207 inflammatory bowel disease receiving steroid dispensations from inflammatory bowel disease specialists at the University of Alberta. Adherence to best practices for flaring IBD patients were determined by dividing the number of adherent encounters over the total number of encounters. Key gaps in care were found: documenting of clinical scores (33.5%), completion of standard flare lab tests (63.3%), testing for Clostridium difficile toxin (65.5%), testing for fecal calprotectin (17.6%), 2-4 week follow-up (22.2%), documentation of steroid consenting (24.6%), and provision of osteoprotective therapy (29.9%). Electronic clinical decision support systems (CDSS) have been shown to have potential to improve adoption of clinical guidelines. The second part of this thesis details the development, two-phase implementation, and evaluation of a CDSS integrated into the electronic medical record system, for inflammatory bowel disease patients suspected of having disease flares. In Phase 1, before-and-after analysis demonstrates an increase in documentation of clinical scores from 3.5% to 24.1% (p<0.001), which also showed a significant level change on interrupted time series analysis (p=0.028). In Phase 2, before-and-after analysis showed increases in ordering of flare lab tests (47.6% to 65.8%, p<0.001), fecal calprotectin (27.9% to 37.3%, p=0.028), and stool culture testing (54.6% to 66.9%, p=0.005). Interrupted time series analyses did not reach statistical significance in Phase 2. The overall system adoption rate was moderate at approximately 25%, with greater adoption by nurse providers than physicians. This study is one of the first to investigate the implementation of an Epic EMR-based CDSS in IBD and prompts many areas for future investigation, such as the effect of CDSS on outcomes, or how to design CDSS that have greater utility for physicians. Future iterations of CDSS for IBD should be evaluated on a larger scale, which can be facilitated by Connect Care, the coming provincial clinical information system for the province of Alberta.
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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 | 0.066 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".