Effect of an Electronic Medical Record-Based Clinical Decision Support System on Adherence to Clinical Protocols: An Interrupted Time Series Study in Inflammatory Bowel Disease (Preprint)
Bibliographic record
Abstract
BACKGROUND Electronic medical record (EMR) (also called electronic health record (EHR)) embedded clinical decision support systems (CDSS) have the potential to improve the adoption of clinical guidelines. The University of Alberta Inflammatory Bowel Disease (IBD) Group developed a CDSS for IBD patients with suspected disease flare and deployed it within a clinical information system (CIS) in two continuous time periods. OBJECTIVE This study aims to evaluate the impact of the IBD CDSS on health care provider (physicians and nurses) adherence to institutionally agreed clinical management protocols. METHODS Two-period interrupted time-series (ITS) design, comparing adherence to a clinical flare management protocol during outpatient visits pre- and post-implementation of the CDSS. Each interruption was initiated with user training and a memo with instructions for use. 7 physicians, 1 nurse practitioner, and 4 nurses were invited to use the CDSS. 31,726 flare encounters were extracted from the CIS database, after which 9,217 were manually screened for inclusion. Each data point in the ITS analysis corresponds to one month of individual patient encounters, with a total of 18 months of data, 9 pre- and 9 post-interruption, for each period. The study was designed in accordance with STARE-HI guidelines for health informatics evaluations. RESULTS Following manual screening, 623 flare encounters were confirmed and designated for ITS analysis. The CDSS was activated in 198/623 of the encounters, most commonly in cases where the primary visit reason was a suspected IBD flare. In Period 1, before-and-after analysis demonstrates an increase in documentation of clinical scores from 3.5% to 24.1% (P<.001), which also showed a statistically significant level change on ITS analysis (P=.028). In Period 2, before-and-after analysis showed further increases in ordering of acute disease flare lab tests (47.6% to 65.8%, P <.001), including the biomarker fecal calprotectin (27.9% to 37.3%, P=.028), and stool culture testing (54.6% to 66.9%, P=.005), the latter which is a test used to distinguish a flare from an infectious disease. There were no significant slope or level changes on ITS analyses in Period 2. The overall provider adoption rate was moderate at approximately 25%, with greater adoption by nurse providers (used in 30.5% of flare encounters) than physicians (used in 6.7% of flare encounters). CONCLUSIONS This is one of the first studies to investigate the implementation of a CDSS for IBD designed with a leading EMR software (Epic Systems, Verona, WI, USA), providing initial evidence of an improvement over routine care. Several areas for future research were identified, notably the effect of CDSS on outcomes, and how to design CDSS with greater utility for physicians. CDSS for IBD should also be evaluated on a larger scale, which can be facilitated by regional and national centralized EMR systems.
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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 | 0.019 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".