A Novel Technique to Optimize Facility Locations of New Nephrology Services for Remote Areas
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Travel distance to healthcare facilities affects healthcare access and utilization. Using the example of patients with kidney disease and nephrology services, we investigated the feasibility and utility of using geographic information system (GIS) techniques to identify the ideal location for new clinics to improve care for patients with kidney disease, on the basis of systematically minimizing travel time for remote dwellers. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Using a provincial laboratory database to identify patients with kidney disease and where they lived, we used GIS techniques of buffer and network analysis to determine ideal locations for up to four new nephrology clinics. Service-area polygons for different travel-time intervals were generated and used to determine the best locations for the four new facilities that would minimize the number of patients with kidney disease who were traveling >2 hours. RESULTS: We studied 31,452 adults with living in Alberta, Canada. Adding the four new facilities would increase the number of patients living <30 minutes from a clinic by 2.2% and reduce the number living >120 minutes away by 72.5%. Different two- and three-clinic scenarios reduced the number of people living >120 minutes away by as much as 65% or as little as 32%, emphasizing the importance of systematic evaluation. CONCLUSIONS: GIS techniques are an attractive alternative to the current practice of arbitrarily locating new facilities on the basis of perceptions about patient demand. Optimal location of new clinical services to minimize travel time might facilitate better patient care.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".