ArticleBMC health services research2026
Impact of linear programming-based optimization of pediatric hospital location and quantity on patient travel time in Germany.
Article in BMC health services research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccess to pediatric medical care is a critical factor in determining health outcomes. Hospital landscape restructuring processes need to consider the geographical accessibility of pediatric emergency and inpatient services. Public regulators in Germany aim for a travel time to the closest pediatric emergency hospital below a threshold of 40 min. This study investigates whether optimizing the allocation of pediatric hospital services to existing hospital sites by different approaches can improve accessibility by reducing travel time and enhancing threshold compliance.
methodsWe analyzed patient travel time by car to the nearest pediatric hospital using a comprehensive dataset of all hospitals in Germany, weighted by population density. Two optimization approaches were applied to minimize travel time by reallocating pediatric health services to existing hospital locations, k-means clustering and a p-median optimization method based on linear programming. We further evaluated the impact of adjusting the quantity of location-optimized pediatric hospitals on patient travel time.
resultsAllocation optimization using linear programming reduced weighted median travel time by 5.5% (p < 0.0001), unweighted median travel time by 15.0% (p < 0.0001) and the proportion of patients with travel time exceeding 40 min by 36.1%. Travel time improvement using k-means clustering algorithm was less pronounced. Location optimization led to greater gains in compliance with the 40 minute threshold in rural than in urban areas. Quantity of hospitals was inversely associated with travel time length and proportion of patients with travel time above 40 min threshold. In a model that prioritized existing pediatric hospital locations, increasing the number of hospitals led to more marked travel time changes than decreasing hospital quantity.
conclusionThe study highlights the potential of linear programming as an approach for optimizing countrywide hospital service allocation to enhance geographical accessibility of specialized care. It emphasizes the necessity of considering optimization methods for restructuring healthcare infrastructure and provides a model that may facilitate planning processes at a supra-regional or countrywide scale.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.