Spatial methods for evaluating critical care and trauma transport: A scoping review.

J Crit Care

Kettering Medical Center, Departments of Emergency Medicine & Critical Care, 3535 Southern Blvd, Kettering, OH 45429, USA.

Published: February 2018

Purpose: The objective of this scoping review is to inform future applications of spatial research regarding transportation of critically ill patients. We hypothesized that this review would reveal gaps and limitations in the current research regarding use of spatial methods for critical care and trauma transport research.

Materials And Methods: Four online databases, Ovid Medline, PubMed, Embase and Scopus, were searched. Studies were selected if they used geospatial methods to analyze a patient transports dataset. 12 studies were included in this review.

Results: Majority of the studies employed spatial methods only to calculate travel time or distance even though methods and tools for more complex spatial analyses are widely available. Half of the studies were found to focus on hospital bypass, 2 studies focused on transportation (air or ground) mode selection, 2 studies compared predicted versus actual travel times, and 2 studies used spatial modeling to understand spatial variation in travel times.

Conclusions: There is a gap between the availability of spatial tools and their usage for analyzing and improving medical transportation. The adoption of geospatially guided transport decisions can meaningfully impact healthcare expenditures, especially in healthcare systems looking to strategically control expenditures with minimum impact on patient outcomes.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jcrc.2017.08.039DOI Listing

Publication Analysis

Top Keywords

spatial methods
12
spatial
8
critical care
8
care trauma
8
trauma transport
8
scoping review
8
studies
7
methods
5
methods evaluating
4
evaluating critical
4

Similar Publications

Background: Environmental change in coastal areas can drive marine bacteria and resulting infections, such as those caused by , with both foodborne and nonfoodborne exposure routes and high mortality. Although ecological drivers of in the environment have been well-characterized, fewer models have been able to apply this to human infection risk due to limited surveillance.

Objectives: The Cholera and Other Illness Surveillance (COVIS) system database has reported infections in the United States since 1988, offering a unique opportunity to both explore the forecasting capabilities machine learning could provide and to characterize complex environmental drivers of infections.

View Article and Find Full Text PDF

Single-cell decisions made in complex environments underlie many bacterial phenomena. Image-based transcriptomics approaches offer an avenue to study such behaviors, yet these approaches have been hindered by the massive density of bacterial messenger RNA. To overcome this challenge, we combined 1000-fold volumetric expansion with multiplexed error-robust fluorescence in situ hybridization (MERFISH) to create bacterial-MERFISH.

View Article and Find Full Text PDF

Data-driven discovery and parameter estimation of mathematical models in biological pattern formation.

PLoS Comput Biol

January 2025

Department of Anatomy and Cell Biology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Fukuoka, Japan.

Mathematical modeling has been utilized to explain biological pattern formation, but the selections of models and parameters have been made empirically. In the present study, we propose a data-driven approach to validate the applicability of mathematical models. Specifically, we developed methods to automatically select the appropriate mathematical models based on the patterns of interest and to estimate the model parameters.

View Article and Find Full Text PDF

Background: Peripheral nerve sheath tumors (PNSTs) encompass entities with different cellular differentiation and degrees of malignancy. Spatial heterogeneity complicates diagnosis and grading of PNSTs in some cases. In malignant PNST (MPNST) for example, single cell sequencing data has shown dissimilar differentiation states of tumor cells.

View Article and Find Full Text PDF

This study expands the original two-dimensional carbon footprint model into a three-dimensional model form. Introduce two indicators of carbon footprint depth (CF) and size (CF) to form a three-dimensional carbon footprint model (CF), which is used to respectively represent the occupation and consumption of natural capital reserves by human activities' carbon emissions. Based on the 3D carbon footprint model, this paper calculated the CF, CF, and CF for four different urban agglomerations of China (BTH, YRD, PRD, and CY) spanning from 2000 to 2017.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!