As AI becomes an integral part of healthcare call centers, ethical considerations are on the rise. While, as with most industries, AI is changing the industry for the better, from smarter call routing to predictive analytics and more personalized customer interactions, its integration also comes with risks.
Ethical AI implementation is important. Call centers must ensure that technology does not introduce bias or inequity into customer interactions. Clear frameworks guide how algorithms make decisions, promoting fairness, transparency, and accountability, which is essential in industries handling sensitive information.
Call centers should implement AI tools with security, ethics, and compliance as priorities. This approach allows call center teams to use technology to improve service while meeting regulatory and ethical standards.
In a call center without strong governance, the risks are great. If sensitive patient and customer information is not handled securely and consistently in accordance with HIPAA and other healthcare regulations, data breaches and associated fines, as well as loss of trust, can ensue. Regulatory compliance and the evolving rules for data privacy, automated communications, and patient consent must be integrated with AI systems to protect against these risks.
Here, we’ll explore how AI is reshaping the call center landscape and how organizations, particularly those in the healthcare industry, must determine the ethical path through data governance.
Medical Answering Services & Data Privacy
The ethical concerns of AI in a medical answering service include maintaining data privacy and security, addressing algorithmic bias that can result in inaccurate medical advice, and minimizing personalization that’s essential in patient communications.
Machine learning relies on algorithms to analyze data and make informed decisions. The first step is to provide the system with large data sets, enabling it to identify patterns and make predictions. One of the ethical concerns is the use of patient data to train AI models. Do its benefits supersede its risks?
When anyone in the healthcare industry who deals with protected health information (PHI) uses this information to train AI models, it must be de-identified. They must remove names, social security numbers, birth dates, and any other identifying markers. This ensures sensitive information is protected and secured. But it also introduces vulnerabilities.
Today, healthcare is the industry most frequently targeted by cyberattacks, including ransomware and data breaches. Its rapid adoption of AI-driven technologies and the new vulnerabilities it introduced make healthcare even more attractive to threat actors.
Because third-party vendors are often the entry point for hackers, medical practices using medical answering services must require the highest standards in PHI protection. One example of a security breach is ConnectOnCall, an answering service that reported a data breach in 2024 affecting over 900,000 people. Compromised data included personal and health information, including some Social Security numbers.
Ethical AI and Bias
When we talk about AI having ethnic and racial bias, are we really referring to AI? AI systems learn from large datasets. These datasets include patient demographics, health records, and treatment outcomes. It’s the data that is biased, whether gender-based or the underrepresentation of groups; AI is exposed to this information and perpetuates its existence.
According to Harvard Medical School, an AI in U.S. healthcare systems was used to prioritize patients for additional care management. It selected healthier white patients over sicker black patients because its training wasn’t based on a patient’s care needs, but rather, cost data. Predictive algorithms may predict lower health risks, not because a specific population is healthier, but because they have less access to healthcare.
This example illustrates the importance of taking proactive steps to identify and correct bias. Today, there are statistical techniques for adjusting for bias in a dataset by applying an increased weight to underrepresented population segments in a sample. Ethical AI requires that AI engineers and Data Scientists understand the inherent biases in their datasets due to sampling and how these biases may impact patient outcomes.
AI Bias and Growing Regulations
In May 2024, the US Department of Health and Human Services Office for Civil Rights (OCR) published a final ruling holding AI users legally responsible for managing and mitigating the risk of discrimination.
The Food and Drug Administration (FDA) has developed an action plan that includes the elimination of ML algorithm bias and improvement. Some states, such as Colorado and Utah, have enacted their own guidelines and laws regarding data security and the ethical challenges of AI.
Managing Sensitive Patient Information with Data Governance
Keeping sensitive patient data secure in an AI-enhanced medical answering service requires data governance frameworks and associated policies that encompass HIPAA compliance and robust security measures. Because of the advanced threats, many in the industry are ensuring their healthcare partners and patients by earning HITRUST certification.
HITRUST CSF is a framework for managing risks that incorporates healthcare-specific privacy, security, and regulatory requirements from existing regulations, including HIPAA, NIST, PCI, and many others. This integration creates a single overarching security platform and is considered the gold standard in health information privacy.
Over 80% of hospitals and health plans have adopted the HITRUST CSF framework. For healthcare organizations and medical answering services adopting AI tools, it’s essential to ensure these technologies also comply with this robust security framework.
Regulatory Compliance and AI in Healthcare
Regulatory compliance regarding the use of AI in the healthcare industry, including medical answering services, primarily revolves around HIPAA. It requires integrating this technology under its strict guidelines that mandate how PHI can be shared, accessed, and stored.
The core principle involves securing a patient’s health data, including that handled by AI systems, from storage through transmission. End-to-end data encryption makes this possible by transforming it into an unreadable format, keeping it safe from hackers.
Strict access controls, real-time monitoring, and automated compliance monitoring provide essential security measures to uphold HIPAA compliance.
The Role of AI in Medical Answering Services
When answering services turn strictly to AI to replace call agents, the human touch and empathy that reside there go dormant. In the healthcare industry and in medical answering services, that human touch is essential. AI should be a tool to support virtual medical call center agents, not replace them.
As a tool, it can provide relevant information, suggest responses, and send alerts regarding compliance requirements. Ultimately, when carefully integrated, it can provide more personalized and effective patient communications and support for medical practices. By focusing on transparency, integrity, inclusivity, and compliance, the two can work side-by-side.
