🧠 How AI Can Reinvent Aging Societies

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Welcome back AI prodigies!

In today’s Sunday Special:

• 📜The Prelude

• 🏥Spending on Seniors

• 🤖AI & Age-Related Diseases

• ⚙️Process Automation in Health Insurance

• 🔑Key Takeaway

Read Time: 7 minutes

🎓Key Terms

• Gross Domestic Product (GDP): The total value of all goods and services produced in a country.

• Deep Learning (DL): Mimics the human brain by creating multiple layers of “artificial” neurons to solve problems.

• Natural Language Processing (NLP): The ability of computers to understand, interpret, and generate human language.

• Robotic Process Automation (RPA): Uses software bots, or “bots,” to perform repetitive, rule-based digital commands without human intervention.

🩺 PULSE CHECK

📜THE PRELUDE

By 2050, more than 1 in 6 people on Earth will be over the age of 65, up from just 1 in 11 people in 2019. As longevity increases and birth rates decline, societies worldwide are grappling with the implications of aging populations.

This growing demographic shift presents complex economic challenges. Aging populations place increasing pressure on long-term healthcare infrastructure and public pension programs. In the U.S., federal spending on Medicare exceeded $1.5 trillion in 2024.

This current level of spending is likely unsustainable. So, what do expenditures on elderly populations look like? More importantly, how can AI help improve elder care while making it more cost-efficient?

🏥SPENDING FOR SENIORS

Better Life Expectancy = More Public Spending?

As life expectancy increases, age-related diseases raise individual healthcare needs, which, in turn, drive up public spending on long-term healthcare infrastructure.

The global population aged 65 and older will jump from 6% today to 16% by 2050. More specifically, the Organization for Economic Co-operation and Development (OECD) forecasts that the Support Ratio (i.e., the number of working people per elderly people) will fall from 4.1 in 2010 to 3.1 by 2050. This decline in the Support Ratio makes diseases like Dementia more challenging to manage because of the need for increased care demand with fewer caregivers available. In 2019, 57 million people worldwide lived with Dementia, which cost roughly $1.3 trillion to care for.

Wealthy nations face spending roughly 21% of their GDP on elderly populations by 2050, with 75% of this spending supporting direct care for elders (i.e., Medicare) and 25% of this spending supporting pension programs (i.e., Social Security). This results in an ever-increasing burden: more elderly people need long-term healthcare infrastructure, but fewer working-age people remain to fund it. Consequently, government debt across wealthy nations has already reached historically high levels; public debt as a percentage of GDP is 263% in Japan, 124% in the U.S., and 104% in the UK.

AI = Reduced Cost Burdens?

AI can’t reverse demographic shifts and the associated government spending to address them. However, it can target both sides of the equation by improving the efficiency of healthcare outcomes to reduce cost burdens.

🤖AI & AGE-RELATED DISEASES

DL Techniques to Predict Alzheimer’s.

Cutting-edge Dementia treatments rely on DL techniques for analyzing complex medical datasets to predict the onset of Alzheimer’s. These complex medical datasets are comprised of three main types of medical data:

1. Genetic Markers: Analyzing specific genetic sequences within DNA to discover gene variants that influence the likelihood of developing Alzheimer’s (i.e., APOE Gene).

2. Standardized Cognitive Tests: Evaluating memory, attention, language, and problem-solving abilities through clinically validated assessments to detect early cognitive impairments associated with Alzheimer’s.

3. Magnetic Resonance Imaging (MRI): A medical imaging technique that leverages a strong magnetic field and radio waves to create detailed images of the brain’s structure to identify abnormalities in brain regions linked to Alzheimer’s (i.e., Hippocampal Shrinkage).

DL techniques that process these three main types of medical data have achieved accuracy scores between 90% and 94% when predicting the onset of Alzheimer’s.

NLP Techniques to Analyze Speech Patterns.

Specialists like Audiologists also use NLP methods to identify subtle changes in speech patterns that may indicate Mild Cognitive Impairment (MCI), a common precursor of Dementia.

For example, a healthy person might say: “The electrician repaired the faulty wiring.” On the other hand, a cognitively impaired person might say: “The electrician repaired his faulty wiring.” So, what’s the difference?

The pronoun “his” introduces ambiguity. It’s unclear whether “his” refers to the electrician or someone else. While this subtle change might go unnoticed in everyday conversation, NLP methods excel at uncovering these subtle changes, flagging MCI with approximately 78% accuracy.

Why DL Techniques and NLP Methods Matter.

Delaying the onset of Dementia by just a single year could prevent approximately 500,000 Dementia cases in the U.S. by 2050, resulting in potential yearly cost savings of up to $640 billion.

⚙️PROCESS AUTOMATION IN HEALTH INSURANCE

How Process Automation Works.

When processing Medicaid paperwork, several simple tasks involve repetitive and standardized procedures, making them ideal candidates for RPA.

RPA employs software bots, or “bots,” to perform three key functions:

1. Data Extraction and Entry: The “bots” utilize Optical Character Recognition (OCR), which converts images of text into machine-readable formats, to extract patient information from medical records and insurance forms. Then, they transfer this patient information into Medicaid paperwork.

2. Validation and Verification: The “bots” cross-verify the entered patient information with external sources and validate medical codes to ensure the Medicaid paperwork complies with Medicaid policies.

3. Fraud Prevention: The “bots” analyze the Medicaid paperwork for unusual patterns or inconsistencies that may indicate potential fraud.

RPA Case Study.

Community Health Choice (CHC), a local, non-profit care organization that provides health insurance plans in Texas, implemented RPA to streamline the processing of Medicaid paperwork.

By automating repetitive and error-prone workflows, such as verifying patient details and authenticating provider information, CHC reduced labor costs by 69%, translating to millions of dollars in savings. Automating the process of identifying duplicate Medicaid paperwork alone saved 672 Full-Time Equivalent (FTE) hours per month, amounting to over $1.4 million in labor savings since 2016.

RPA reclaimed thousands of labor hours from manual processing. For example, when CHC faced backlogs due to the Coronavirus (COVID-19), a single “bot” processed 14,000 Medicaid claims in under two weeks, saving 1,167 FTE hours.

🔑KEY TAKEAWAY

Aging is often framed as a domestic challenge. However, the global ripple effects of aging, which include labor shortages, could indirectly cripple global supply chains and strain the developing economies that depend on them.

As the global population ages, wealthy nations face spending roughly 21% of their GPD on long-term healthcare infrastructure to treat age-related diseases like Dementia.

AI paired with automation frameworks like RPA can significantly reduce administrative overhead. Together, these technologies offer a scalable solution to reduce healthcare costs, enhancing elder care while improving the financial outlook for future generations.

📒FINAL NOTE

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