As healthcare uses more technology, artificial intelligence (AI) is playing a bigger role in improving patient care and office work in the United States. For medical practice managers, owners, and IT staff, using AI that respects patients’ cultural backgrounds is very important. Since the U.S. has many cultures, making sure AI tools understand different backgrounds helps improve diagnosis, treatment, and patient involvement.
This article looks at why it is important, the challenges, and chances of using AI that understands culture in U.S. medical offices. It focuses on combining data, reducing bias, and automating tasks to make care better.
More than 70% of Americans believe the healthcare system does not fully meet their needs in some way. These problems often happen because care is the same for everyone, but patients come from many cultures, ethnic groups, and languages. This can cause wrong communication, wrong diagnosis, and less following of treatment advice.
Using AI that understands these differences by including cultural information and ways of talking can help fix these problems. AI can study different data, adjust how it talks in other languages or dialects, and give advice based on culture and social status.
Researchers from Regent Business School in South Africa, Nivisha Parag, Rowen Govender, and Saadiya Bibi Ally, say AI tools that use rules about cultural respect work better in diverse healthcare places. This is very important in the U.S., where many immigrant and ethnic groups need healthcare that fits their special needs.
A big problem for culturally aware AI is bias in the data and computer programs. AI systems that learn mostly from data not representing all groups do worse for some minorities. For example, AI for heart disease makes mistakes 47.3% of the time with women but only 3.9% with men. Skin disease AI makes 12.3% more errors for darker skin than lighter skin.
Bias comes from different places:
Matthew G. Hanna and others say ethical checks must be part of making and using AI in healthcare. Without them, biased AI could make health unfairness worse instead of better.
To fight this, healthcare centers should look hard for bias and fix it while creating AI and also keep checking AI after it is used. Collecting data that covers different ethnic, social, and language groups in the U.S. is key for good AI training.
Good communication between doctors and patients is very important for correct diagnosis and following treatments. Many patients in the U.S. have trouble because of language differences. AI tools that translate languages in real-time can help by offering support in many languages. These tools can turn medical words and patient questions into other languages so providers talk easier with people who do not speak English well.
But some problems still exist. Medical words can be hard, and AI may misunderstand the meaning or culture behind what is said. Humans need to check translations to make sure they are right, especially for consent and treatment directions.
In places with many immigrants, like California, Texas, Florida, and New York, AI translation can help give fair health info in patients’ first languages. Training staff to use these AI tools well improves how patients and doctors talk.
Long-term illnesses like diabetes show how AI can use culture to help health. Some AI apps made for indigenous and minority groups include cultural food habits and traditional healing along with medical rules. By changing advice to fit patient life and beliefs, these AI tools help patients follow treatment and manage illness better.
These AI tools matter especially in cities and rural areas where normal Western diet advice might not work for all groups. But these apps must follow strict privacy laws, like HIPAA, since patients may not want to share personal info if they worry about privacy.
The U.S. healthcare system is moving to value-based care. This means focus is more on patient results and costs, not how many services are given. AI helps by analyzing data for decisions made for each patient. AI needs info not just from medical records but also from social parts of life, like money, home, and education.
Rick Gates from Walgreens says pharmacies are changing into places that offer more than medicine. They give vaccines, screening tests, disease help, and counseling. AI in these places helps manage stock based on health trends and seasons and talks to patients in ways that fit who they are.
Practice managers who use AI connected to social data can work better with insurance and doctors to improve care and control cost.
Healthcare work has many admin tasks that can tire staff. AI automation can help work go smoother while still caring about patient culture.
Simbo AI, a company that works on phone automation and AI answering, shows how AI can help. Automated phone systems with AI bots can sort patient calls, give health info, and book appointments in several languages. This cuts wait times and helps staff, while respecting patient language.
Also, AI checking systems and robot pill dispensing, used by Walgreens, make work more correct and free health workers to spend time with patients, using cultural knowledge.
By automating routine tasks, AI lets healthcare teams give more time to care that fits patient culture and learning. IT staff are important in adding AI systems safely and making sure they don’t disturb daily work.
AI can improve healthcare quality, but using it in places with fewer resources or vulnerable people has ethical questions. Poor infrastructure and access can make unfairness worse if AI is used without thinking about these limits.
In end-of-life care, for example, ethics questions include informed consent, privacy, and the danger of losing human contact if AI replaces people. Abiodun Adegbesan and others say explainable AI (XAI) is important. XAI shows clear reasons for AI choices so doctors and patients understand why AI makes certain recommendations.
Medical leaders must balance using technology with keeping patient dignity and respecting culture. Experts, ethicists, tech people, and community members should work together to make fair and clear AI rules.
Besides technology, using AI well in diverse healthcare depends on training healthcare workers and AI users about cultural differences. Training should cover different health beliefs, ways people communicate, and what they expect from care.
Knowing how AI works, its limits, and possible biases helps doctors use AI tools carefully. This training builds trust in AI decisions for both providers and patients. Support in many languages and culturally careful consent are important for patient involvement.
Personalized medicine means giving treatment that fits a person’s genes, lifestyle, and environment. AI helps by studying complex data and thinking about culture. Also, AI helps engage patients in clinical trials by considering their social and demographic details. This helps make sure new treatments work for groups that have been left out before.
Ramita Tandon, Chief Clinical Trials Officer, says pharmacies and healthcare providers can use AI to connect with patients based on their situations. This is very important for clinical research and overall healthcare.
For managers and IT staff in U.S. medical offices, here are some steps to help put in culturally aware AI healthcare tools:
Medical offices that use culturally aware AI with automated workflows can improve patient satisfaction, following treatment, and health results. Recognizing patient diversity and using AI and data together can help deal with many challenges seen in healthcare for 2024.
Using these systems needs careful planning, ethical review, and ongoing work to ensure fairness. The result can be a healthcare system that better meets the needs of many different people in the United States.
Pharmacies are evolving into holistic healthcare hubs by offering vaccinations, screenings, disease management, and counseling. This transformation meets patient demand for convenience and integrated care by combining retail services with clinical support, promoting proactive health and deepening patient engagement in a single location.
AI leverages large datasets, including weather patterns and social media trends, to predict consumer needs and optimize inventory placement. This capability enhances customer experience by ensuring timely availability of products, such as over-the-counter medicines during flu season, demonstrating how technology can forecast demand accurately in healthcare retail.
AI-driven chatbots and symptom checker apps provide preliminary health information and guidance, reducing physician workload. These tools empower patients to self-manage certain health concerns, thereby easing the burden on healthcare providers and expanding access to care through convenient, non-doctor telehealth services.
2024 will see improved delivery pathways for diabetes-related weight loss medications, emphasizing holistic services beyond medication alone. Insurers, providers, and pharmacies are focusing on comprehensive patient education, support, and resources to ensure sustainable access and effective use, particularly for vulnerable populations.
Historically neglected, women’s health research is gaining momentum with technology-driven, data-informed, and community-centric approaches. Collaborative efforts in 2024 aim to fill research gaps, improve understanding of diseases predominantly affecting women, and develop targeted solutions that enhance care and outcomes.
AI holds promise to enhance culturally competent care by combining the right data with effective prompts, enabling more personalized patient interactions that respect cultural nuances. The healthcare industry is encouraged to integrate suitable datasets allowing AI to support providers in delivering care that aligns with patients’ cultural backgrounds.
Emerging technologies like AI-driven verification systems, robotic dispensing, and micro-fulfillment centers are enhancing operational efficiency and accuracy. These innovations free pharmacists to focus more on patient interaction, adherence, and engagement, thereby improving the quality and personalization of healthcare services.
Value-based care models prioritize patient outcomes and cost control over service volume, encouraging collaboration among payers, providers, and health systems. This shift promotes patient-centered, community-focused approaches and fosters partnerships that address rising costs while improving care quality.
Personalized patient engagement now leverages insights into social determinants of health and demographic-specific data. Pharmacy teams act as community researchers to bridge gaps between clinical trials and care, enhancing representation and tailoring healthcare solutions to improve quality of life and wellbeing.
Despite economic constraints, consumers prioritize health spending, prompting retailers to focus on delivering quality, innovative, and trusted products. Expanding owned brands with an emphasis on value helps meet consumer demand for affordable, effective health and fitness options without compromising quality.
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