Implementation of an Integrated, Clinical Decision Support Tool at the Point of Antihypertensive Medication Refill Request to Improve Hypertension Management: Controlled Pre-Post Study
Bibliographic record
Abstract
Background: Improving processes regarding the management of electronic health record (EHR) requests for chronic antihypertensive medication renewals may represent an opportunity to enhance blood pressure (BP) management at the individual and population level. Objective: This study aimed to evaluate the effectiveness of the eRx HTN Chart Check, an integrated clinical decision support tool available at the point of antihypertensive medication refill request, in facilitating enhanced provider management of chronic hypertension. Methods: The study was conducted at two Mayo Clinic sites-Northwest Wisconsin Family Medicine and Rochester Community Internal Medicine practices-with control groups in comparable Mayo Clinic practices. The intervention integrated structured clinical data, including recent BP readings, laboratory results, and visit dates, into the electronic prescription renewal interface to facilitate prescriber decision-making regarding hypertension management. A difference-in-differences (DID) design compared pre- and postintervention hypertension control rates between the intervention and control groups. Data were collected from the Epic EHR system and analyzed using linear regression models. Results: The baseline BP control rates were slightly higher in intervention clinics. Postimplementation, no significant improvement in population-level hypertension control was observed (DID estimate: 0.07%, 95% CI -4.0% to 4.1%; P=.97). Of the 19,968 refill requests processed, 46% met all monitoring criteria. However, clinician approval rates remained high (90%), indicating minimal impact on prescribing behavior. Conclusions: Despite successful implementation, the tool did not significantly improve hypertension control, possibly due to competing quality initiatives and high in-basket volumes. Future iterations should focus on enhanced integration with other decision support tools and strategies to improve clinician engagement and patient outcomes. Further research is needed to optimize chronic disease management through EHR-integrated decision support 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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".