Evaluating the Perception of Artificial Intelligence Among Healthcare Professionals and the Public in Bangladesh

A recent study was done with 399 healthcare workers and public members in Bangladesh. It looked at how people see AI in healthcare there. Bangladesh faces different challenges in infrastructure, education, and technology than the U.S. The study used questionnaires to measure how ready people are to use AI in healthcare. Results showed that social media and knowledge about technology helped people get ready to use AI. Social media had a strong effect (0.354), and technology knowledge had a smaller positive effect (0.162) on readiness.

Concerns about risks like privacy or AI failing had a smaller but positive effect (0.123) on readiness. Some things, like being open to new ideas or feeling likely to be affected by AI problems, did not affect attitudes much. The study pointed out that good rules and laws are very important to build trust and deal with privacy concerns. This is especially important in the U.S., where laws like HIPAA protect patient privacy.

AI Adoption Factors and Relevance to the United States

Even though the study was done in Bangladesh, some findings relate to the U.S. Social media’s influence points to a chance for education and clear communication about AI tools in healthcare. Raising knowledge about technology helps too. In the U.S., technology is a big part of medical care, but not all healthcare workers feel confident using AI.

Privacy and ethics are important in the U.S. and need careful handling. Clear rules for data safety and patient permission are required by law. These rules build confidence in using AI safely. This helps healthcare workers and patients accept AI more easily.

Challenges in Rural Healthcare and AI’s Potential Role

Research also shows AI could help rural healthcare. These places often lack good infrastructure, enough trained workers, and preventive care. This is true both in Bangladesh and in rural areas of the U.S. AI tools like machine learning and natural language processing can help make diagnoses better and speed up patient care.

AI mixed with Internet of Things devices and mobile health apps helps with remote monitoring and virtual visits. This is useful for chronic diseases like diabetes, which many Americans have. AI can spot warning signs early and help prevent bigger problems. But challenges like poor internet and low digital skills remain in rural areas. These need support from policy-makers and healthcare workers.

Regulatory and Ethical Considerations for AI in Healthcare

Bangladesh and the U.S. both face similar problems with AI about ethics and privacy. Because patient data is sensitive, AI needs careful planning and clear laws. The study says ethics rules should make AI decisions clear and honest. For U.S. healthcare managers, this means AI tools must meet current healthcare rules and maybe new ones made for new AI tech.

Patients need to know how AI affects their care and be able to say no or ask for a human if they want. This helps stop distrust and resistance to AI. Also, regular training and education for staff helps them learn about AI and feel less worried, making adoption easier.

AI in Workflow Automation: Streamlining Front-office Operations

One of the first places AI is used in U.S. healthcare is in front-office tasks. Companies like Simbo AI work on using AI for phone answering and managing appointments. These tools help reduce work for staff, avoid missed calls, and keep patients involved without needing humans all the time.

AI phone systems use natural language processing and smart call routing. Patients get quick answers about appointments, bills, and symptoms. For healthcare managers, this cuts staff costs and makes operations run better. Patients also wait less, which makes them happier.

Automation helps staff communicate better by logging patient calls and scheduling automatically. This lowers errors and stops appointments from overlapping. In places with few staff, especially rural areas, such automation lets clinical staff spend more time caring for patients instead of doing paperwork.

Implications for Healthcare Administration and IT Management in the United States

• Focus on Education and Awareness: Because social media and technology knowledge affect readiness, U.S. healthcare groups should provide training for staff and patients. Workshops and online lessons about AI benefits and limits are important.
• Regulatory Compliance Emphasis: AI tools must follow HIPAA and other rules about data safety. Healthcare groups should get clear privacy and data use information from AI providers.
• Evaluate AI for Workflow Automation: Tools like Simbo AI’s phone answering systems show how AI can reduce work and improve patient access. Testing and slowly adding these tools can help staff adjust without stress.
• Address Ethical Concerns Proactively: Make rules about patient permission and open AI use. Talk openly with staff to handle worries and build trust.
• Consider Rural and Underserved Settings: Healthcare leaders managing many clinics, including rural ones, should use AI to support remote care and telehealth. This can help fill gaps in services.
• Collaborate for Broader Impact: Partnerships among healthcare providers, tech companies, and policy makers can create better standards and systems to use AI responsibly.

Summary of Influential Factors Impacting AI Adoption

The study from Bangladesh highlights key factors affecting AI readiness:

• Social Media Influence (SMI): Strong positive effect (0.354); information sources help acceptance
• Technological Awareness (TA): Positive effect (0.162); knowing about technology supports readiness
• Perceived Risk (PR): Weak positive effect (0.123); concerns lead to cautious acceptance
• Personal Innovativeness (PI): No significant effect; being open to new ideas didn’t change readiness
• Perceived Susceptibility (PS): No significant effect; feeling vulnerable to AI issues didn’t affect readiness

These points suggest U.S. healthcare leaders should focus on education and clear information, not just pushing for new ideas.

Future Research and Practical Applications

Both studies call for more real-world research on AI in healthcare. In the U.S., this means trying pilot projects that check how AI affects health outcomes, patient feelings, and workflow.

Working closely with developers and users will help make sure AI fits with what clinics and patients need. Healthcare managers in the U.S. should watch global AI progress, learn from other countries, and use AI carefully in their systems. Lessons from places like Bangladesh, where there are big gaps in infrastructure and education, help show what steps are needed to make AI work in complex U.S. healthcare settings.

Artificial intelligence offers both chances and challenges in healthcare today. Knowing how culture, technology, and laws affect its use in places like Bangladesh helps U.S. healthcare managers make better choices. As AI develops in front-office work, telehealth, and clinical support, education, following rules, and ethical care will be key to using AI well. Learning from other countries’ experiences gives useful advice for improving healthcare with AI in the United States.