The adoption of AI certainly has the potential to transform the healthcare industry. However, the importance of ethical and responsible AI in healthcare cannot be overstated as it is essential for ensuring patient safety, equity and trust.
In part one of this blog, we highlighted the pros of responsible AI in enhancing patient support. Read on to learn more about what challenges can be encountered in this process and some of the top applications available when those obstacles are overcome.
Key Challenges and Risks with Responsible AI
The advantages for healthcare providers adopting responsible AI in their operations far outweigh the risks. But, being aware of those risks is crucial to preventing them. A proactive strategy to use AI responsibly from the start will help healthcare providers better prepare to manage risk and comply with emerging regulations.
Algorithm Bias
Also referred to as AI bias, algorithm bias describes how artificial intelligence technology can be negatively impacted by social, economic and systemic biases. A reason for this occurrence is that medical researchers cannot easily procure large, diverse medical data sets.
Algorithm bias is a problem affecting a healthcare system that already displays barriers to healthcare equity, including unequal testing and treatment, bias in medical research and data and institutional racism. Research data and medical records are less likely to represent Black patients’ information adequately and accurately due to socioeconomic inequalities in healthcare delivery. Also, racial minorities and those living in poverty tend to receive lower-quality care than non-Hispanic Whites and people with higher levels of disposable income and accumulated wealth.
So, how can you mitigate AI bias? It can be achieved by implementing and maintaining strict data standards that promote accuracy and fairness. Healthcare providers using AI to interact with patients also should develop a training framework – for patients and staff – that accounts for a wide variety of social determinants of health (SDOH).
Lack of Data Privacy and Security
As we outlined in a previous blog, key privacy concerns with AI in healthcare include unauthorized access and use of sensitive patient data, re-identification and the limitations of de-identification protections, lack of continuous improvement and data breaches and cyberattacks. That’s why physician practices utilizing conversational AI and other types of artificial intelligence in their operations must proactively develop, implement and maintain strict security measures.
Healthcare providers employing AI should ensure robust data governance, employ stringent guidelines for sharing of PHI and continuously monitor the efficacy of such safeguards. They must embrace strategies that not only mitigate risks but also cultivate a culture of data responsibility and ethical data management. Other ways to ensure privacy and security through AI tools consist of:
- Consistently assessing data practices and evaluating compliance with stringent regulations like HIPAA
- Training healthcare staff on AI privacy and security best practices
- Securing networks connecting patients with their care and any external access points
- Involving third-party experts to conduct independent audits and assessments of your AI systems to identify vulnerabilities and provide unbiased feedback
Regulatory Non-Compliance
Any responsible AI solution offered by providers must ensure compliance with current government healthcare regulations while monitoring future ones. This commitment to security helps alleviate mistrust among doctors and healthcare consumers, one of the main barriers to the adoption of generative AI technology. 
Along with regularly auditing AI models to assess compliance, healthcare providers must ensure their patients’ data is secured with the appropriate privacy protection. This includes being prepared to comply with government AI standards such as those listed in Executive Order 13960. The Department of Health and Human Services (HHS) established an AI Task Force to regulate AI in accordance with the principles of the executive order by 2025.The standards include confidentiality and security, transparency, governance and non-discrimination.
Practical Use Cases of Responsible AI for Patient Support
The most practical applications of responsible AI for patient support are already in use and providing benefits to medical groups of all sizes. Along with the benefits they provide to providers, they also meet the preferences central to healthcare consumerism.
Patient Education
Patient education informs healthcare consumers about conditions and treatment and how to access primary and preventive services. When providers effectively educate patients, those individuals understand their health conditions and choices and are actively engaged in their care.
Healthcare providers have a responsibility to not only care for their patients but also ensure that they understand the information being offered to them. But, a study of adult healthcare consumers found that 33 percent of patients are not offered educational materials by their provider to help answer their questions, even though roughly 95 percent stated they would likely access these materials if they were supplied.
Unlike some chatbots, conversational AI provides a more human-like interaction for patients while giving them information specific to their healthcare. This personalized communication engages patients by being able to provide meaningful guidance and information, all without the need for a lengthy phone call that doesn’t meet their communication preferences.
Conversational AI tools also enable providers to manage education on the value of preventative care and screening by utilizing healthcare automation to follow up with patients who have open gaps in care. These solutions can share information in the healthcare consumer’s preferred language and adjust the information based on health literacy levels.
Medication Adherence
Prescription medications offer an effective method for treatment, but many patients do not take their prescriptions as directed. In addition to being associated with higher rates of hospital admissions, suboptimal health outcomes and increased morbidity, medication nonadherence is estimated to be linked to 125,000 avoidable deaths annually.
By analyzing patient data, such as prescription histories and vital signs, AI algorithms can help healthcare providers improve medication management and reduce the risk of adverse drug events. Voice-enabled conversational AI can be used to proactively remind patients to take their medicine, offer dosage instructions, answer questions about potential side effects or drug interactions, alert them when a refill is needed or recommend over-the-counter (OTC) medications based on their profile — all of which alleviate medication non-adherence and improve health outcomes.
Appointment Scheduling
Missed appointments by patients because of no-shows and cancellations do more than lead to loss of expected revenue. They typically result in longer wait times for other patients, lower satisfaction, wasted resources, reduced productivity and clinical effectiveness, added stress on practice staff and an overall decrease in the quality of care.
Effective patient scheduling is essential for providing patients with a better experience, improving physician utilization, improving staff productivity and decreasing no-shows and cancellations. Utilizing conversational AI gives them 24/7 access to their provider’s schedule and enables them to book appointments when and where they prefer. This drives patient satisfaction and engagement while improving practice revenue through fewer no-shows and appointment gaps.
Automatic appointment reminders are proven to result in fewer no-shows, better patient compliance, fewer unfilled appointment times and the ability to see more patients. Conversational AI tools are increasingly being employed by physician practices to conduct patient outreach efficiently and cost-effectively for appointment reminders because this secure technology meets patients’ expectations for a seamless healthcare experience while reducing the administrative burden on provider staff.
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