2024 HRS perspective on advancing workflows for CIED remote monitoring
Bibliographic record
Abstract
Cardiac implantable electronic devices (CIEDs) generate substantial data, often stored in image or PDF formats. Remote monitoring, now an integral component of patient care, places considerable administrative burdens on clinicians and staff, in large part due to the challenge of integrating these data seamlessly into electronic health records. Since 2006, the Heart Rhythm Society, in collaboration with the CIED industry, has led an initiative to establish a unified standard nomenclature. This effort has harmonized terminology, aligning diverse terms with single terms approved by the Institute of Electrical and Electronics Engineers. With this foundational work complete, attention now turns to developing technical standards for interoperability, which would enable the smooth communication of CIED data between information technology systems used in clinical practice. In this article, by leveraging Health Level 7 Fast Healthcare Interoperability Resources, we present a road map for the technical committee to guide this endeavor. We identify critical data exchange points between remote transceivers, electronic health records, and third-party platforms commonly used for CIED patient data management. Our objective is to establish bidirectional communication among these resources, ensuring the accuracy, timeliness, and accessibility of clinical data for clinicians. We also anticipate substantial benefits for both clinical research and administrative efficiency through the implementation of this interoperability framework.
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 | 0.026 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.028 | 0.014 |
| Insufficient payload (model declined to judge) | 0.069 | 0.044 |
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".