City skyline transforming into a circulatory system, representing healthcare accessibility.

Is Your City Ready? New Tools to Measure Healthcare Accessibility

Kai Mendoza in Health & Wellness November 2025 • 4 min read.

"Big data and real-time analysis are revolutionizing how we understand and improve access to emergency medical services."

Imagine needing urgent medical care and wondering if you can quickly reach the nearest emergency department (ED). For city planners and healthcare administrators, ensuring optimal access to medical facilities is a constant challenge. Traditional methods of measuring accessibility often fall short, relying on outdated data and static models that don't reflect the dynamic realities of urban life.

But what if you could tap into real-time data streams to understand exactly how accessible healthcare is at any given moment? A new wave of research is doing just that, leveraging unconventional big data sources like web scraping, crowdsourcing, and the Internet of Things (IoT) to create more dynamic and responsive accessibility measures.

This article explores these innovative approaches, drawing insights from a recent study that proposes new ways to quantify and monitor healthcare accessibility using real-time data. We'll delve into how these measures can help us understand the impact of supply-side shocks, improve resource allocation, and ultimately ensure that everyone has timely access to the medical care they need.

Beyond Gravity: Rethinking Accessibility in the Age of Big Data

City skyline transforming into a circulatory system, representing healthcare accessibility.

Traditionally, accessibility to healthcare has been evaluated using spatial econometric frameworks. These models consider three primary factors: the supply of healthcare infrastructure (e.g., hospitals), the demand from individuals needing care, and the cost of mobility between demand locations and supply locations. Distance decay functions and threshold trip times are often incorporated to reflect people's willingness to travel for care.

While these gravity models and two-step floating catchment area (2SFCA) methods have been valuable, they often rely on aggregated and static data. The rise of big data offers a chance to refine our understanding of accessibility. Think about it: data collected automatically through web scraping, IoT devices, and other unconventional sources can provide a real-time, granular view of healthcare access.

Web Scraping: Gathering real-time data on ED wait times, capacity, and available resources.
Crowdsourcing: Collecting patient-reported data on travel times, perceived barriers to access, and satisfaction with services.
Internet of Things (IoT): Utilizing data from connected devices (e.g., wearable sensors) to monitor patient flow, predict demand surges, and optimize resource allocation.

By integrating these data sources, researchers can create accessibility indicators that are continuously updated, providing a more accurate and timely picture of healthcare access than traditional methods allow. This is especially crucial for real-time monitoring, surveillance, and the dynamic definition of health policies.

The Future of Accessible Healthcare: Real-Time, Responsive, and Equitable

The journey towards accessible healthcare has entered a new era, fueled by real-time data and innovative analytical methods. As we continue to refine these approaches, we can create healthcare systems that are not only more efficient but also more equitable, ensuring that everyone receives the care they need when they need it. The key lies in embracing the power of big data and using it to build a healthier future for all.

About this Article -

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This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2401.1337,

Title: New Accessibility Measures Based On Unconventional Big Data Sources

Subject: econ.em

Authors: G. Arbia, V. Nardelli, N. Salvini, I. Valentini

Published: 24-01-2024

Everything You Need To Know

What are the traditional methods used to evaluate healthcare accessibility, and what are their limitations?

Traditionally, healthcare accessibility has been evaluated using spatial econometric frameworks, including gravity models and the two-step floating catchment area (2SFCA) method. These models consider the supply of healthcare infrastructure, demand from individuals needing care, and the cost of mobility between demand and supply locations. However, they often rely on aggregated and static data, which doesn't capture the dynamic realities of urban life. These models may not accurately reflect real-time conditions or the impact of sudden changes, such as supply-side shocks or unexpected surges in demand.

How can big data sources like web scraping, crowdsourcing, and the Internet of Things (IoT) be used to improve healthcare accessibility measures?

Big data sources offer a more granular and real-time view of healthcare access. Web scraping can gather real-time data on Emergency Department (ED) wait times and capacity. Crowdsourcing can collect patient-reported data on travel times and perceived barriers to access. The Internet of Things (IoT), utilizing data from connected devices like wearable sensors, can monitor patient flow and predict demand surges. Integrating these data sources allows for continuously updated accessibility indicators, providing a more accurate picture compared to traditional methods. Missing from this is how to effectively validate the crowd-sourced data and ensure data privacy.

What are 'accessibility indicators,' and how do they differ when created using real-time data versus traditional methods?

Accessibility indicators are metrics that quantify how easily individuals can access healthcare services. When created using real-time data from sources like web scraping, crowdsourcing, and the Internet of Things (IoT), these indicators are continuously updated, reflecting current conditions such as ED wait times, patient-reported travel times, and real-time patient flow. In contrast, traditional methods rely on aggregated and static data, providing a less timely and accurate picture of healthcare access. The advantage of real-time indicators lies in their ability to support dynamic monitoring and policy adjustments. The downside is the added infrastructure needed to support the real-time capabilities.

In what ways can real-time healthcare accessibility measures contribute to more equitable access to medical care?

Real-time healthcare accessibility measures can contribute to more equitable access by enabling healthcare systems to be more responsive and efficient. By using data from web scraping, crowdsourcing, and the Internet of Things (IoT), resources can be allocated more effectively based on actual demand and real-time conditions. This ensures that everyone, regardless of their location or socioeconomic status, has timely access to the medical care they need. This also allows for proactive interventions to address disparities and improve outcomes for underserved populations. Not explored is how these measures can be customized to specific geographic communities.

What are the implications of using real-time data for defining health policies, and what are some potential challenges?

Using real-time data for defining health policies allows for dynamic and responsive adjustments based on current conditions. With data from web scraping, crowdsourcing, and the Internet of Things (IoT), policies can be tailored to address immediate needs and emerging trends, leading to more efficient resource allocation and improved health outcomes. Potential challenges include ensuring data privacy, managing data quality and validation (especially with crowdsourced data), addressing potential biases in data collection, and establishing the necessary infrastructure to support continuous data monitoring and analysis. Furthermore, ethical considerations regarding the use of personal data and the potential for misuse or discrimination must be carefully addressed.