In the health care industry, disparities are differences in treatment access and patient outcomes among various groups. They cause the avoidable loss of lives and dollars, both of which are unacceptable. In the long term, they lead to major consequences for everyone, not just those directly impacted. In the health care industry, disparities are differences in treatment access and patient outcomes among various groups. They cause the avoidable loss of lives and dollars, both of which are unacceptable. In the long term, they lead to major consequences for everyone, not just those directly impacted. Since this issue is so broad and complex, providers should address it with artificial intelligence. This technology has the intelligence of a human and the speed of a supercomputer, so it might be able to resolve pain points humanity has struggled with for decades. What Are Health Disparities? Health disparities are the differences in health status, life expectancy and access to care among different population groups. They extend to patient outcomes, including mortality and quality of life. For example, the infant mortality rate could be higher among marginalized groups because they may be more likely to lack prenatal care, live in unsafe areas or face bias during childbirth. Race and ethnicity are predominant factors. In the United States, around 42% of people identify as people of color, while 58% are white. Even though these groups are almost evenly split, their care is unequal. Hispanic individuals are more than twice as likely to be uninsured. Asian adults are less likely to receive mental wellness aid. Black infants are twice as likely to die. around 42% of people around 42% of people Looking Beyond Biological Factors Biological factors aren’t the only considerations. Although race, ethnicity and hereditary disease play a primary role, social elements — such as subpar living conditions — take center stage in this discussion because they are avoidable. Someone’s insurance status, employment situation and income level should not dictate their quality of care, but it happens often. In a perfect world, everyone receives the same level of care regardless of their background or genetics. However, that isn’t always how it works. Medical professionals aren’t purposefully preferential — everything from a hospital’s location to a person’s education level can inadvertently create unfair, systemic differences in patient outcomes. The Consequences of Inequity Inequitable treatment access and poor patient outcomes have broad ripple effects. For instance, providers may have difficulty containing the spread of an infectious disease. Also, people may turn to substance misuse to treat their conditions, which contributes to crime, poverty and unemployment rates — all of which are expensive to manage. A 2023 study from the National Institutes of Health was the first to estimate the total economic burden of health disparities for racial and ethnic minorities using a health equity approach. It found that they caused an estimated $451 billion loss in 2018, up 41% from 2014. Interestingly, the states with the highest burden were among the most populous and diverse. estimated $451 billion loss estimated $451 billion loss Most experts agree that the inequalities in the U.S. health care system cost hundreds of billions of dollars each year. Notably, the indirect effects of inequity could lead to generational health issues, making these losses grow exponentially. Some believe this loss will balloon within the next few decades. Deloitte predicts it could surpass $1 trillion annually by 2040 if left unaddressed, accounting for per capita spending and population changes. This increase would triple the average American’s yearly health care spend from $1,000 to $3,000. could surpass $1 trillion annually could surpass $1 trillion annually AI Can Help Eliminate Disparities AI can help reduce disparities and promote equity by improving affordability, supporting diversity and addressing common pain points. Personalizing Preventive Care This technology enables hyperpersonalization by analyzing demographic data, location and family history. It would inform providers and patients of health complications, disease screenings and relevant interventions, preventing minor problems from snowballing. Keeping everyone healthy helps eliminate downstream disparities. Automating Everyday Tasks A machine learning model can automate everyday tasks like intake information collection, billing and schedule coordination. Research shows technology could automate 30% of nurses’ administrative duties, helping them focus more on patient care and less on paperwork. They would have more time to address people’s concerns meaningfully. 30% of nurses’ administrative duties 30% of nurses’ administrative duties Removing Language Barriers Providers could use a large language model for on-demand translation, which would help them communicate with people of all nationalities and backgrounds. It would also simplify complex medical terminology for those with low health literacy levels. Long-Term AI-Powered Solutions Personalizing care, removing language barriers and automating administrative work are excellent uses of AI. However, addressing the root cause of health inequity in the U.S. requires an in-depth, multipronged approach. Diversifying Medical Education Since diversity impacts many aspects of success in health care, it is fundamental. However, students and residents often come from predominantly white, upper-class backgrounds. AI could help make medical school and residency training more inclusive by improving applicant reviews and scholarship rules. impacts many aspects of success Addressing systemic barriers lets underserved groups represent their peers in the medical field. Greater representation could lead to improved patient outcomes. Continuous monitoring is essential for the success of health care diversity, equity and inclusion initiatives since it shows how impactful changes are. is essential for the success is essential for the success Optimizing Hospital Placement Policymakers could use AI to analyze a vast amount of demographic, health and socioeconomic data to determine where to build a new hospital or how to strengthen emergency services. This would help improve patient outcomes in low, middle and high-income areas. Identifying Underlying Causes Generally, marginalized and underserved groups are more likely to experience inequity. While bias plays a role, social conditions are more predominant factors. For instance, someone who experiences food insecurity at a young age may develop malnutrition-related health issues. If they develop a secondary condition, their outlook worsens. This individual might need preferential treatment to avoid a poor outcome. As this example suggests, the difference between equality and equity is complex and easy to miss. AI can analyze billions of indirect variables to determine risk and priority levels, making health care more objective. Ethical and Technical Considerations The health care industry is trying to address health disparities. According to the World Health Organization, the rich-poor gap in health care coverage among women, newborns and children in lower-income countries has almost been halved in the last decade. This progress is monumental, but there’s still room for improvement. has almost been halved has almost been halved Since people can ask AI to analyze massive datasets or automate simple tasks in plain language, almost anyone can use it. However, that doesn’t mean implementation is straightforward. For one, professionals must prevent their models from propagating bias. Robust audit programs and transparency are essential for mitigating learned prejudice. Data drift is a similar problem. This mismatch occurs when the training data becomes less representative of real-world information, which impacts accuracy and validity. AI engineers should routinely test model performance and retrain if necessary. Data privacy and patient consent are important ethical issues to consider. Facilities must be transparent about their use of AI by telling people what information they collect and where they store it. The cybersecurity landscape is evolving exponentially, so keeping them informed about potential security risks is essential. AI Can Improve Health Equity AI isn’t a silver bullet. However, it could smooth out the wrinkles in existing health equity strategies, helping nurses, administrators, policymakers and patients find a solution. If enough hospitals adopt it, it could potentially eliminate inequities, saving the U.S. hundreds of billions of dollars annually.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

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