Geo-mapping of government healthcare facilities providing diagnostic laboratory services in Delhi, India: a pilot study.

Background: Despite significant advances, the Indian public diagnostic healthcare system faces serious challenges, particularly in availability and accessibility, often affecting those most in need. There is a wide urban-rural disparity, necessitating long travel distances for patients. Geographic distance is a major determinant for any individual to access healthcare facilities. Strengthening the diagnostic system requires assessing its status by outlining the spatial distribution of facilities. Geo-mapping, using geographic information system (GIS) helps identify areas with limited accessibility to these facilities. This pilot study in Delhi aimed to provide evidence for placing new facilities based on population needs and create a centrally available free database for patient reference. Methods: This study mapped government diagnostic facilities in Delhi. All central and state government-run healthcare facilities providing diagnostic services were included. Data were collected online in real-time, and spatial data for each facility were derived. Variables included facility type and healthcare level (primary, secondary or tertiary). Data were integrated into a digital map and correlated with sociodemographic and health data. Three ratios were derived: government diagnostic facility density (LDR), facilities per 100 000 people and the population density/laboratory medicine facility (PALM) ratio (number of people per facility). A heat map was created based on facility density. The χ Results: Northeast Delhi has the highest population density (36 155 people/km²). There are eight facilities in New Delhi district and ninety in Northwest Delhi. The median government LDR in Delhi is 0.6 facilities/km². Northeast Delhi has only one facility per 100, 000 people. Northwest Delhi, with the largest rural and illiterate population, has an LDR of 0.7 facilities/km² and a PALM ratio of 11 538. Heat map data show that 45.1% of Delhi's area has scarce diagnostic facilities. LDR negatively correlates with district population (r=-0.453, p<0.05). Anaemia in children and women negatively correlates with the PALM ratio (r=-0.856, p<0.05). Higher facility density correlates with higher cancer screening rates (r=0.719, p<0.05). Conclusion: Matching service locations ensures laboratories are evenly distributed, and specialised services are accessible. This study highlights the need to use GIS to align healthcare service locations with population needs. Creating a free, centrally available public database of diagnostic facilities through geocoding can increase the utilisation of public healthcare facilities and improve the diagnostic healthcare system.