Preventive Health Using Big Data

Stakeholders in the healthcare industry are turning to big data analytics for help with preventive medicine and care. Big Data provides enhanced capabilities such as analyzing it effectively when needed to prevent missed opportunities on intervention strategies. However, collecting, storing, and sharing the correct information among stakeholders is a significant challenge.

However, as technologies that enable its collection and processing improve, we see it creating deeper inroads towards preventive healthcare. This post covers four ways big data contributes to preventive care and medicine.

Improving population health at scale

Big data analytics has the potential to transform population health management. Big data sets are vast and highly varied, making it difficult to draw meaningful conclusions. Some healthcare providers use big data to understand patient metrics and develop risk scores for use in prediction models. These scores help them gain insight into patient needs and use that information to guide resource allocation and management.

As healthcare costs continue to rise, more providers are moving toward value-based care. Value-based care aims to improve patient experience, population health, and costs of care all at the same time. And there is evidence that real-time data can help make value-based care more successful.

Providers can use data to help with staffing shifts that can accommodate a value-based care strategy. Big data also plays a role in addressing social determinants of health (SDOH). SDOH are factors outside those traditionally evaluated in the healthcare setting that impact an individual’s health. For example, providers increasingly consider environmental factors, social isolation, or food insecurity when treating patients.

Measured social determinants of health can help better understand how they impact a population segment. Care initiatives led by providers often use this information.

Chronic disease management and care

Chronic disease management is an essential aspect of healthcare. The use of big data analytics can help clinicians identify high-risk patients and provide them with the treatment needed before their illnesses get worse. This preventive care means not needing long-term care or medications after being diagnosed early.

With the help of artificial intelligence, researchers found that data analytics is effective for managing care in type-2 diabetes mellitus (T2DM) patients. The most structured information was demographic, while semi-structured data included prescriptions, imaging films, handwritten notes, videos, audio recordings, and other multimedia assets.

The researchers found that there is a need for more research about the factors which impact big data analytics’ usability in managing T2DM. They thus developed a model to identify these elements and measure their usability as they relate specifically to diabetes management. However, this needs further study before being adopted clinically.

Identifying biomarkers for preventive diagnoses

A biomarker is a defining characteristic that can predict and prevent disease. In 2021, the Cleveland Clinic’s ongoing project aimed to identify brain disease biomarkers to prevent neurological illness before any symptoms occur. They gathered data from more than 200,000 neurologically healthy patients over 20 years.

More than a billion people worldwide live with brain disorders, and it is difficult to predict them early on. In addition, clinicians have difficulty stopping or slowing the progression of these diseases, making developing prevention methods even more critical.

With this data, researchers can identify genetic risk factors that will guide preventive and diagnostic strategies.

Better use of genomics for wider testing

Like cholesterol checks and cancer screenings, DNA screening has the potential to advance precision medicine by identifying patients at high risk for certain conditions. The use of genomic sequencing is becoming more common as healthcare providers seek ways to enhance routine clinical care with additional information about their patient’s health outcomes that could lead them down a path toward better diagnosis earlier on.

In November 2019, Geisinger reported that 2 to 4 percent of patients they tested present a genetic mutation that causes heart disease or cancer. Consumers appear interested in AI and genetics testing for precision medicine, but cost concerns about such tests are ever-present. Disease prevention through genomic sequencing programs can reduce these costs, making access easier.

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