How to Enhance Clinical Research with Environmental Data

How do new forms of environmental insights enhance patient analytics, achieve better patient outcomes, and reduce healthcare costs in the long run? Let’s explore the value of health-focused environmental intelligence for clinical research in detail.

The Environment Has a Tremendous Impact on Chronic Disease 

In the US alone, chronic diseases affect 6 out of 10 adults. Airborne environmental hazards like air pollution and pollen can significantly exacerbate chronic conditions such as asthma, COPD, seasonal allergy, heart disease, and even diabetes.

  • Small increases in PM2.5 concentrations have been associated with a 4.5% increase in Ischaemic Heart Disease events.
  • A study on asthma, which is responsible for 10 million daily outpatient visits in the US, associated lengthening of pollen season with a 17% increase in hospitalizations.
  • A Canadian study on air pollution discusses the impact on chronic conditions such as COPD and diabetes. The study showed an increase of up to 5% for each measured increase in pollution (based on the air quality health index of British Columbia).

Understanding the Historical Lack of Environmental Context in Healthcare

While harmful to everyone, exposure to airborne pollutants can affect people differently based on age, health conditions, level of exposure, and personal sensitivities. Subsequently, failing to monitor a patient’s exposure to specific pollutants and allergens can cause major information gaps in clinical research. A lack of environmental information may skew datasets and bias the way treatment or medication efficacy appears in the results.

In the past, clinicians were required primarily to work with one-size-fits-all treatment models, because they were forced to rely on patient tests, reports, and demographic information to understand chronic disease triggers, symptoms, and exacerbation causes. Actionable environmental information at the personal level just wasn’t available.

Why Wasn’t Actionable Environmental Information Available to Clinical Researchers?

Reporting on air quality based on location and in real-time is difficult and complex. While some organizations like the EPA produce annual air quality reports, most air quality providers do not provide hyperlocal, personalized, hourly environmental data, so clinicians have been limited in terms of understanding a patient’s true environmental exposure.

Common limitations include:

  • Huge gaps of unmonitored areas between monitoring stations.
  • A limited list of pollutants monitored, providing no information about other environmental hazards and variations.
  • No data personalization capabilities. No ability to monitor air quality at a hyperlocal resolution and lack of hourly reporting.

Similar challenges have long applied to pollen monitoring. Many parts of the world have no devices to measure pollen counts, or are utilizing outdated and inefficient methods. In addition, many sources of pollen information lack specificity and don’t differentiate between pollen types, which is problematic, since different plant types affect patients differently based on their sensitivities.

The High Cost of Incomplete Healthcare Information

Unmanaged exposure to pollution and pollen has been found to directly increase healthcare costs and patient outcomes:

  • An analysis of 21 studies concluded that minimal increases in pollution could increase hospitalization for pneumonia by 4.3%.
  • Short-term exposure to air pollution has been associated with a significant increase in both hospitalization costs and length of stay for type-2 diabetes patients.
  • Exposure to pollen has been linked to increased hospitalizations due to asthma, even up to 5 days after exposure.
  • A 2020 report released by Greenpeace estimates that air pollution results in $2.9 trillion annual costs and 1.8 billion work absence days. Pollution is also linked to 4 million new cases of childhood asthma, and 7.7 million annual ER visits.

How Health-focused Environmental Intelligence Expands Clinical Research Capabilities

Leading clinical research and public health organizations, including the FDA, already understand that big data modeling is the future for the healthcare industry. However, when it comes to environmental monitoring, installing myriads of pollution sensors and pollen traps is costly and logistically complex, and time-consuming. Worse, sensors don’t ensure reliable and accurate data in the event that a patient moves away from sensor range, unit failure, data reader variations, and other practical issues involved with sensor-only reporting.

For this reason, clinical researchers are turning to new forms of health-focused environmental intelligence to understand the impact of the environment on vulnerable groups: 

1. Correlating Individual Environmental Triggers With Symptoms on a Global Scale

Clinicians at the Icahn School of Medicine at Mt. Sinai hospital launched a global asthma study that leveraged personalized environmental data for over 8,600 participants. They were able to create extremely comprehensive patient analytics by accurately monitoring each patient’s daily exposure and correlating this exposure with their reported symptoms.

Mount Sinai Hospital’s study opened the door to further health-focused clinical research using big data and environmental information to engage huge patient pools which crossed borders.

2. Understanding the Interplay Between Different Environmental Triggers

Clinicians can understand the impact of the environment on clinical trial results in a more holistic way with a more complete picture of patient exposure.

For example, the presence of air pollution has been linked to increased pollen allergen concentrations, and evidence suggests pollutants like Nitrogen Oxide and PM2.5 can worsen allergic rhinitis symptoms. Therefore, pollution data combined with pollen information can assist clinical researchers in better understanding the environmental impact on seasonal allergy sufferers.

Researchers can also use historical air quality data analysis to create datasets and statistics for future studies.

3. Predicting Patient Demand for Healthcare Treatment

Healthcare providers and researchers can incorporate environmental intelligence into their AI models to better forecast and predict patient demand for treatment.

To better understand future demands, they’re analyzing historical sales performance and environmental data. This enables them to identify the patterns in changes to the environment that lead to increases in product demand.

The ability to store historical environmental data timelines enables new in-depth analysis and clinical research possibilities later on. Examining old pollution timelines with new AI tools lets researchers extract insights from the past to predict the future.

4. Richer Insights Power Smarter Group-targeted Therapies

Using personalized environmental intelligence, analysts can examine the impact of environmental exposure based on specific demographic attributes, such as children with asthma, pregnant women, patients with allergic asthma, and more.

By creating richer insights this way, researchers open up the way to smarter group-targeted forms of therapy based on unique contextual factors which make up a patient’s profile, rather than one-size-fits-all treatments based on clinical diagnosis alone.

The Bottom Line

Without accurate air quality or pollen information, chronic disease treatment methods are not put to the test against real-world environmental conditions. Ineffective drugs and treatments fail to prevent or alleviate chronic disease symptoms and also fail to reduce the economic impact.

Health-focused environmental intelligence paves the roadmap towards better patient outcomes through more comprehensive clinical research.

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Tamir Kessel
Tamir Kessel

Head of Business & Corporate Development @BreezoMeter. I'm a multicultural growth leader with experience in management consulting and market analysis. I enjoy developing new technology businesses and ecosystems - and have plenty of experience when it comes to applying environmental intelligence to drive business growth.