Happy May! We’ve returned from our newsletter spring break with several new tools to tell you about!
UpToDate
UpToDate, one of our primary clinical and drug information point of care tools now has two new features: Lab Interpretations and UpToDate Pathways. UpToDate Pathways are interactive guides that support clinical decision-making on 79 different topics. In cases where abnormal lab results may raise questions, Lab Interpretation monographs enable you to more quickly and accurately interpret and decide on next steps. Access UptoDate from our website or directly from Excellian.
Clinical Skills
Clinical Skills is our longstanding tool for keeping up on nursing skills and procedures, but unfortunately, until now, there wasn’t a way to get and save CE credit from the tool. Now all nurses, nursing and medical assistants, and respiratory therapists should automatically have Clinical Skills accounts set up which allows you to complete and save CE. To find out more about logging in and self-assigning courses see our guide.
Need More Help?
Allina Library Services is always looking for new ways to support your complex information needs. We’ve added two new resources available on our website. You now can Request a Librarian Consultation anytime you’d like to discuss your information needs. We’ve also developed several guides to help you use our resources on your own. They are available in the new How-to-Guides section of our website.
Pros and Cons of AI: the Medical Librarians Perspective The rapid expansion of artificial intelligence (AI) poses opportunities and challenges for healthcare professionals. Copilot, Microsoft’s new AI assistant for Windows 11 and Edge recently became available for all Allina Health employees, which features relevant and creative suggestions, insights, and tips for writing documents, planning projects, and research information for nearly any topic. The applications present exciting time-saving resources for professionals; however, our findings have shown significant limitations of AI tools.
For instance, our initial experiences have revealed AI provided limited context in answering complex questions. While algorithms are continuously improving, many AI models initially offered older or outdated, or incorrect information. AI learns from feedback and experience over time, and the data is only as good as the input. AI models insufficiently trained in certain areas may hallucinate data –presenting information that appears to be real and valid, but essentially is nonexistent.
Further analysis suggests AI algorithms do not exist neutrally or objectively. Numerous concerned researchers, including scientists from NEJM and NLM have raised red flags that AI algorithms reflect bias within social systems, especially in medicine. Inequities that persist in social realities such as race, sex, gender, disability status and other factors are often embedded in large language models for medical machine learning systems. These inequities endure as social underpinnings of algorithmic systems that cement discriminatory decisions in code.
The bottom line is AI models provide convenient and innovative solutions around the workplace, but the scope, relevancy, and underlying biases of AI systems are significant issues that will require long-term evaluation.
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Exciting New Library Tools Available
Happy May! We’ve returned from our newsletter spring break with several new tools to tell you about!
UpToDate
UpToDate, one of our primary clinical and drug information point of care tools now has two new features: Lab Interpretations and UpToDate Pathways. UpToDate Pathways are interactive guides that support clinical decision-making on 79 different topics. In cases where abnormal lab results may raise questions, Lab Interpretation monographs enable you to more quickly and accurately interpret and decide on next steps. Access UptoDate from our website or directly from Excellian.
Clinical Skills
Clinical Skills is our longstanding tool for keeping up on nursing skills and procedures, but unfortunately, until now, there wasn’t a way to get and save CE credit from the tool. Now all nurses, nursing and medical assistants, and respiratory therapists should automatically have Clinical Skills accounts set up which allows you to complete and save CE. To find out more about logging in and self-assigning courses see our guide.
Need More Help?
Allina Library Services is always looking for new ways to support your complex information needs. We’ve added two new resources available on our website. You now can Request a Librarian Consultation anytime you’d like to discuss your information needs. We’ve also developed several guides to help you use our resources on your own. They are available in the new How-to-Guides section of our website.
Pros and Cons of AI: the Medical Librarians Perspective
The rapid expansion of artificial intelligence (AI) poses opportunities and challenges for healthcare professionals. Copilot, Microsoft’s new AI assistant for Windows 11 and Edge recently became available for all Allina Health employees, which features relevant and creative suggestions, insights, and tips for writing documents, planning projects, and research information for nearly any topic. The applications present exciting time-saving resources for professionals; however, our findings have shown significant limitations of AI tools.
For instance, our initial experiences have revealed AI provided limited context in answering complex questions. While algorithms are continuously improving, many AI models initially offered older or outdated, or incorrect information. AI learns from feedback and experience over time, and the data is only as good as the input. AI models insufficiently trained in certain areas may hallucinate data –presenting information that appears to be real and valid, but essentially is nonexistent.
Further analysis suggests AI algorithms do not exist neutrally or objectively. Numerous concerned researchers, including scientists from NEJM and NLM have raised red flags that AI algorithms reflect bias within social systems, especially in medicine. Inequities that persist in social realities such as race, sex, gender, disability status and other factors are often embedded in large language models for medical machine learning systems. These inequities endure as social underpinnings of algorithmic systems that cement discriminatory decisions in code.
The bottom line is AI models provide convenient and innovative solutions around the workplace, but the scope, relevancy, and underlying biases of AI systems are significant issues that will require long-term evaluation.
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