S2208 Bridging Healthcare Gaps: Improving Access to Gastroenterology Services in Hard-to-Reach Areas Using an Integrated Care Approach
Bibliographic record
Abstract
Introduction: Access to gastroenterology (GI) services in hard-to-reach areas presents challenges such as care delays, high travel costs, and distance to tertiary hospitals. This project aimed to develop and trial a comprehensive model to improve the delivery of GI services in a rural community in Saskatchewan, Canada. Methods: This project was conducted at a rural community hospital in northern Saskatchewan, Canada. It utilized a model that incorporates telehealth services, the establishment of mobile GI clinics, and the integration of basic GI care into primary care practice. The study measured the impact of these interventions on 4 key issues: delays in initial diagnosis, disease progression, technological challenges, and travel frequency and costs. Data were collected from patient surveys, electronic health records, and system logs over a 12-month period. Results: Delay in Diagnosis: Time from symptom onset to diagnosis decreased by 30%, from an average of 90 days to 63 days. This included a decrease in; patient delay (from 30 days to 20 days), primary care provider delay (from 40 days to 28 days), and GI specialist delay (from 20 days to 15 days). Disease Progression: On a 10-point scale, symptom severity scores decreased by 32%, from an average baseline score of 7.8 to 5.3. Hospitalization rates for GI complications decreased by 35%, from 20 to 13 per year. Technological Challenges: 85% of patients and 90% of providers reported positive experiences. The rate of technical issues decreased by 40%. Travel Frequency and Costs: The number of out-of-community travels decreased by 75%, from an average of 4 visits per patient per year to 1 visit per patient per year. On average, patients saved 200 miles per year in travel distance. Conclusion: A 30% reduction in diagnostic delays indicates enhanced access, potentially improving patient outcomes by addressing GI conditions early. Clinical outcomes showed a 25% decrease in symptom severity, suggesting effective disease management and timely interventions. A 35% decrease in hospitalization rates for GI complications highlights the model's impact on preventing severe outcomes and reducing healthcare costs. High user satisfaction (85% patients, 90% providers) with telehealth services and a 40% decrease in technical issues signifies the model's feasibility and acceptance. The 75% reduction in in-person travel signifies enhanced access and convenience. Future studies should validate this model's effectiveness in improving GI care delivery in larger settings.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".