Using a geographic information system to enhance patient access to point-of-care diagnostics in a limited-resource setting

Abstract

Background: Rapid and accurate diagnosis drives evidence-based care in health. Point-of-care testing (POCT) aids diagnosis by bringing advanced technologies closer to patients. Health small-world networks are constrained by natural connectivity in the interactions between geography of resources and social forces. Using a geographic information system (GIS) we can understand how populations utilize their health networks, visualize their inefficiencies, and compare alternatives.

Methods: This project focuses on cardiac care resource in rural Isaan, Thailand. A health care access analysis was created using ArcGIS Network Analyst 10.1 from data representing aggregated population, roads, health resource facilities, and diagnostic technologies. The analysis quantified cardiac health care access and identified ways to improve it using both widespread and resource-limited strategies.

Results: Results indicated that having diagnostic technologies closer to populations streamlines critical care paths. GIS allowed us to compare the effectiveness of the implementation strategies and put into perspective the benefits of adopting rapid POCT within health networks.

Conclusions: Geospatial analyses derive high impact by improving alternative diagnostic placement strategies in limited-resource settings and by revealing deficiencies in health care access pathways. Additionally, the GIS provides a platform for comparing relative costs, assessing benefits, and improving outcomes. This approach can be implemented effectively by health ministries seeking to enhance cardiac care despite limited resources.

Fig. 1

Travel times to diagnosis (top frame) and care (middle frame) for current health care access. Histogram (bottom frame) shows distribution of travel time to care in 15 min bins

Fig. 2

Comparison of widespread integration strategies where POCT is placed in all health resource facilities (left frames) and only in hospitals (right frames). While both strategies indicate improved access when compared with current health care access, neither offer improvements over the other

Fig. 3

Travel time visual comparison between different low-resource implementation strategies

Fig. 4

Histogram comparison between different low-resource implementation strategies

Fig. 5

Location of the study area (green) used in this project as compared to the Isaan region. Khon Kaen (orange) contains Srinagarind Hospital, the available only cardiac care in the region. Therefore, patients originating from the study are must travel through the adjacent provinces (yellow)

Fig. 6

OpenStreetMap populated places (top frame) and MapMagic 13 health resource facility locations for the study are. Health resource facilities were included for the adjacent regions to account for edge effects

Fig. 7

Health resource facilities with point-of-care resources are mapped as well as Srinagarind Hospital. Populations in the western portion of Sakon Nakhon or Bueng Kan must decide to travel east or west to reach cardiac diagnostic resources

Fig. 8

Steps to create network used to determine. First, lines are created between points and existing roads. Second, lines are integrated using a 10 m tolerance to ensure network connectivity. Finally travel time is calculated using estimate travel speed in Table 1 and the network is compiled