Evaluating the Barriers and Facilitators to Implementing a Novel Referral System for Outpatient Geriatric Services: The Geri-Hub Quality Improvement Initiative
Bibliographic record
Abstract
Background: In healthcare systems prioritizing care of older adults, resource limitations and escalating demand often impede access to outpatient specialized geriatric services. Objectives: This study, theoretically guided by the Consolidated Framework for Implementation Research (CFIR), aimed to explore barriers and facilitators in implementing a centralized "Geri-Hub." The Geri-Hub is a centralized intake system established within 2 hospital systems to coordinate outpatient and community-based services for older adults, aiming to connect them with the most appropriate care in a timely manner. Methods: Qualitative insights were gathered from healthcare professionals at 2 academic institutions in the process of consolidating services. Through open-ended surveys and semi-structured interviews, we solicited feedback on referral management, waiting times, and overall work experiences. Results: Thirteen frequently referring providers and a cohort of 9 geriatricians, along with 4 administrators, contributed to the study. Geriatricians emphasized streamlined referrals, flexible scheduling for urgent cases, and a target wait time of 3 months. Administrators stressed standardized referral procedures, defined roles, and accessible referral information. Discussion: The findings underscored the need for straightforward referral processes, enhanced communication on referral statuses, and reduced wait times. Optimizing these processes could potentially mitigate resource utilization issues and improve patient outcomes in healthcare systems. This research highlights the critical role of timely access to geriatric services during transformative phases in healthcare delivery.
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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 | 0.090 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".