Electronic consultation use by advanced practice nurses in older adult care—A descriptive study of service utilization data
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To describe characteristics of service utilization by advanced practice nurses (APNs) employing an electronic consultation (eConsult) service in their care for older adults. BACKGROUND: Canada's aging population is projected to place unprecedented demands on the healthcare system. APNs, which include clinical nurse specialists (CNSs) and nurse practitioners (NPs), are nurses with advanced knowledge who can independently provide age-appropriate care. eConsult is a secure web-based platform enabling asynchronous, provider-to-provider communication. APNs can send and receive eConsults to address patient-specific concerns. METHODS: This is a retrospective analysis of eConsult utilization and user survey data for cases completed in 2019, reported in line with the STROBE guidelines. Eligible eConsults included those that had APN involvement (as a referrer or responder) and were concerning an older patient (≥65 years). Descriptive statistics were used to analyse service utilization and survey response data. RESULTS: Of 430 eligible eConsults, 421 (97.9%) were initiated by NPs and the rest by physicians. 23 (5.3%) were received by a CNS, of which 14 (3.3%) involved an NP-to-CNS exchange. Median specialist response interval was 0.9 days. 53% of eConsults was for dermatology, haematology, cardiology, gastroenterology and endocrinology. 73% of eConsults avoided a face-to-face referral after the consultation. In 90% of eConsults, APNs rated the service as helpful and/or educational. CONCLUSIONS: Through eConsult, APNs can collaborate with each other and physicians to access and provide a breadth of advice facilitating timely specialist-informed care for older patients, thus helping to alleviate some of the demands placed on the healthcare system. RELEVANCE TO CLINICAL PRACTICE: There is an opportunity for APNs to further adopt eConsult into their clinical practice, and this can, in turn, support the integration of the APN role in the health workforce. PATIENT OR PUBLIC CONTRIBUTION: Current APN eConsult users were involved in the study design and interpretation of results.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".