August 12, 2024
August 12, 2024
AI is a highly praised tool to implement across industries and service lines and Medical Information (MI) is no exception. In Medical Information, AI is embedded in a workflow for patients and healthcare providers to access the services more swiftly, and for the MI team to deliver timely and accurate information with elevated quality. When integrating AI into MI workflow, several risks should be carefully considered for patient privacy and safety. Addressing the risks proactively can help mitigate challenges and maximize the benefits of AI in enhancing MI services effectively and responsibly.
In Medical Information, advanced technological tools such as AI are integrated with various systems utilized to deliver information to patients and healthcare providers. MI specialists leverage these tools into their daily workflows to accelerate their response times and elevate the quality of services. The approach is to promote a collaboration of human expertise and AI capabilities to complement each other to enhance the quality of MI services.
AI can be part of Medical Information workflow from triaging, content search, response generation, quality control, and analytics. Natural Language Processing (NLP) plays a crucial role throughout the process, facilitating text-to-speech for IVR message recording, speech-to-text for transcribing calls and voicemails, and AI-driven translations. Advanced content search expands beyond single keywords, retrieving needed content more efficiently. Generative AI is used for response and content generation as well as document conversion from one format to another. AI can also assist in real-time conversation by analyzing customers’ intent and sentiment, supporting specialists in retrieving the right documents, and prompting for any adverse events or product quality reporting. The same mechanism can apply to the quality control process to expedite the review of cases. AI can enable an increase in sample sizes in quality control, providing deeper insights into performances and quality of services.
When evolving with technological advancements, especially AI, it is essential to consider associated risks and barriers.
Breakthrough AI technology has been around for years, yet there is a lack of comprehensive regulations worldwide. Aside from the European Union releasing guidelines in early 2024, most countries have not established robust AI regulations at the governmental level. Similarly, industries lack field-specific standardized guidelines for AI implementation. While one of AI's benefits is its versatility and exponential growth, these same qualities make it challenging for regulatory authorities to keep pace. This regulatory gap creates uncertainties and potential risks, emphasizing the need for a coordinated global effort to establish effective AI governance and standards.
AI relies heavily on data and it can be difficult to responsibly and securely handle sensitive data like personal and health information. Currently, the responsibility for safeguarding this data primarily falls on developers and businesses, who must implement stringent measures to protect it. However, without standardized regulations and oversight, there is a risk of inconsistent practices and potential data breaches.
AI models can inherit biases from their training data, which is generated by humans, who naturally have biases. Mitigating these biases presents a significant challenge due to their subjectivity. If not properly addressed, AI biases can lead to social impacts such as reinforcing inequalities and discrimination.
AI developers lack expertise in Medical Information, while MI operations teams may be interested in exploring AI but typically have limited knowledge of its workings. This knowledge gap can lead to numerous unforeseen scenarios that could impact operations and public safety, such as misinformed medical information or breaches of patient confidentiality. Additionally, the high costs associated with AI implementation can serve as a barrier for many businesses. The financial investment required for acquiring advanced technology, training staff, and maintaining AI systems can be substantial, deterring smaller organizations or those with limited budgets from adopting AI solutions.
Many risks and barriers surrounding AI share similar approaches to lower the threshold for successful implementation. One crucial component is involving a diverse range of representatives from the beginning of AI projects. This inclusivity allows for the identification of potential problems or solutions from different perspectives, reducing the knowledge gap and frequency of issues, and minimizing biases during the development and deployment. It is also essential to proactively support the adoption of robust data governance frameworks and for regulatory bodies to establish clear guidelines to ensure the ethical and secure use of AI-driven data. Additionally, fostering interdisciplinary collaboration and providing comprehensive, ongoing education and training for teams can spur innovative ideas, leading to the development of cost-effective AI solutions. These strategies collectively contribute to a more inclusive, fair, and effective AI use in Medical Information.
AI is a valuable tool in Medical Information, where it enhances workflows for quicker access to services for patients and healthcare providers and improves the delivery of accurate information by MI teams. Integrating AI into MI workflows brings significant benefits but requires careful consideration of risks, particularly regarding patient privacy and safety. Proactive management of these risks is crucial to maximizing AI's effectiveness in enhancing MI services responsibly. Advanced technological tools like AI are integrated into MI systems to accelerate response times and elevate service quality through collaborations between human expertise and AI capabilities. However, challenges such as regulatory gaps, data privacy concerns, biases in AI models, and limitations in developer and operational expertise must be addressed to foster effective and ethical AI implementation in MI. Strategies include inclusive project involvement, robust data governance frameworks, regulatory guidelines, interdisciplinary collaboration, and ongoing education to promote innovation and ensure equitable AI use in MI.
Ready to navigate the complexities of AI implementation in Medical Information? ProPharma is here to help. Our team of experts can guide you through the process, ensuring that your organization reaps the benefits of AI while effectively managing the associated risks.
Contact ProPharma today to learn how we can support you in integrating AI into your Medical Information workflows.
June 24, 2016
The core of my background was built upon analytical development science for biotechnology companies including Celera and Genentech. It became routine to develop analytical assays to test products...
June 10, 2015
If you are like the majority of those who have worked in the biotechnology and pharmaceutical industry for at least the last five years, your company has been part of a merger, acquisition or some...
June 10, 2022
The final version of ICH M10 “Bioanalytical Method Validation and Study Sample Analysis” was adopted on May 24, 2022. This is the harmonized guideline which has been ratified by participating...