From Data to Action

Health is a fundamental human right, and health equity is essential for society. Health and health equity are influenced by the conditions in which people are born, grow, live, work, play, and as well as by biological determinants (e.g., age, sex, race etc.). Health equity means ensuring everyone has an equal opportunity to attain their highest level of health. It entails removing unjust, preventable, or correctable discrepancies between groups of individuals, regardless of how those groups are classified—socially, economically, geographically, demographically, or according to other aspects of inequality like sex, gender, race, ethnicity, and any disabilities.

AI in healthcare must be precisely planned and executed with an emphasis on inclusivity and engagement with society to attain genuine health equity. AI has the potential to revolutionize preventive care and enhance the quality of life by generating more accurate diagnoses and personalized treatment plans, leading to better patient outcomes. It can also play a crucial role in predicting and tracking the spread of infectious diseases by analyzing data from government, healthcare, and other sources. AI has the potential to transform health equity by making healthcare more accessible, personalized, and efficient.

Digestive disorder is a global health issue. Research has focused on clinical aspects and quality of life, significant disparities in healthcare access impact the disorder’s prevalence and management. Screening could be biased by gender, age, race, and region. For example, during data analysis of claims from millions of patients for a digestive disorder disease, we found that while screening is more common among women, yet Hispanic, non-Hispanic Black and Asian populations are screened less frequently than non-Hispanic Whites. Regional differences are also stark, with the South region leading in screening rates and the Northeast region lagging in diagnosis times. Post-diagnosis follow-up care is inadequate, especially in the Midwest region. Contributing social factors include unemployment, reliance on social assistance, education, and social vulnerability.

Addressing health disparities involves tackling multifaceted challenges related to mortality rates influenced by race, ethnicity, sex, education, geographic location, and other social determinants of health (SDOH). Vulnerable groups often face inadequate screenings for risk factors and limited access to preventive health services, exacerbating early intervention disparities. These inequities persist and may worsen in complex medical cases, where diagnostic exams require coordination among multiple specialties. Patients from ethnically concentrated neighborhoods are less likely to receive necessary referrals, leading to artificially lower burden estimates. Even with appropriate diagnostic tools, certain race groups are significantly less likely to be diagnosed within a year, partly due to limited treatment options and provider experience. Access inequities are compounded by financial barriers, low socioeconomic status, regional referral differences, and systemic discrimination, resulting in reduced access to innovative treatments and worse health outcomes for marginalized populations. Addressing these challenges necessitates targeted interventions, policy reforms, and efforts to improve patient engagement and health literacy.

Although addressing health disparities can be quite challenging, AI can assist in managing this. Using Artificial intelligence concepts such as Machine Learning, Deep learning and natural language processing, Althea.ai can identify disparities in demographics related to age, sex, race, and ethnicity. Althea.ai can analyze large volumes of patient data to deliver individualized solutions that are tailored to individual needs. Based on demographic data, the AI will identify trends and forecast health hazards, allowing for early treatments for at-risk populations. This is especially beneficial for communities who may experience hurdles to regular healthcare access. AI algorithms can be designed to minimize bias by being trained on diverse datasets. This ensures that the AI provides equitable recommendations and treatment plans, reducing disparities caused by biased medical practices.

As AI continues to develop gradually, its integration into healthcare systems offers a promising avenue to tackle health disparities. By leveraging AI, healthcare providers can gain deeper insights into the social determinants of health, identify at-risk populations more effectively, and tailor interventions that address specific community needs. AI-driven tools can also facilitate better resource allocation, ensuring that underserved areas receive the attention and care they need.