Digital dashboards for direct oral anticoagulant surveillance, intervention and operational efficiency: uptake, obstacles, and opportunities
Bibliographic record
Abstract
Direct oral anticoagulants (DOAC) are the most widely prescribed oral anticoagulants in the United States. Despite advantages over warfarin, system-level improvements are needed to optimize outcomes. While Veterans Health Administration and others have described successful DOAC management dashboard implementation, the extent of use nationally is unknown. A survey of Anticoagulation Forum's members was conducted to assess access to digital tools available within a dashboard and to describe implementation models. An Expert Forum was subsequently convened to identify barriers to dashboard development and adoption. Responses were received from 340 targeted recipients (8.5% of invitees). Only a minority of inpatient (25/52, 48.1%) and outpatient (47/133, 35.3%) respondents outside of Veterans Health Administration were able to generate rosters of DOAC users on-demand, and fewer had the ability to digitally display key clinical data elements, identify drug-related problems, document interventions, or generate reports. The lack of regulatory requirements regarding Anticoagulation Stewardship was identified by the Expert Forum as the major barrier to widespread development of digital tools for improved anticoagulation management. While some health systems have demonstrated the feasibility of DOAC dashboards and described their impact on quality and efficiency, these tools do not appear to be widely available in the United States apart from Veterans Health Administration. The lack of regulatory requirements for Anticoagulation Stewardship may be the primary barrier to the development of digital resources to better manage anticoagulants. Efforts to secure regulatory requirements for Anticoagulation Stewardship are needed, and evidence of improvements in clinical and financial outcomes through DOAC dashboard use will likely bolster such efforts.
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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 | 0.064 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".