Tag: teaching

  • Top Tips on Making Training Documents Accessible

    Top Tips on Making Training Documents Accessible

    Good morning, good afternoon, and good evening Compliance Rockstars, Clinical Researchers, Ethics Educators, and Investigators from around the globe!

    I hope you are doing well! This has been one crazy year so far. To think we are already in March. I wanted to take the time to have a step back from these current events that have occurred. Though the previous posts have relevance to the research and compliance world…

    I wanted to get back to the basics of this blog.

    As the name suggests, the purpose of this blog is to promote scientific trust and research ethics education. In light of this, I also find it important to discuss how we should be teaching research ethics.

    In today’s post, I want to describe how to make your training documents more accessible. If you have been following from the beginning, you may be thinking to yourself…

    What a minute…didn’t Tasha already write a post like this?

    Well, you’re right. I did write about a similar topic. However, I wanted to revamp this post to discuss what I have learned over the course of my careers.

    You may not know this about me, but I didn’t always work in research and compliance. I’ve also been trained as an auditor and an analyst. This may explain why I am detail oriented.

    Financial analysis and project management are tough topics. I remember being humbled my first semester of accounting. I thought to myself, if I can do calculus and differential equations, accounting should be a breeze. Boy, was I wrong! Though I had my tail between my legs, I still managed to rise to the occasion.

    Even within these careers, I have always been fascinated with training and outreach. I thrive on understanding how people learn and if folks are engaged with what I’m attempting to teach. I also look for ways to make complex topics easier. I don’t like to over-complicate things. The simpler I can make something for someone, the better I feel I understand it.

    They always say teaching something is the best way to become an expert at a topic. Even with this mindset, I prefer to “always be a student of my craft”. It’s natural to constantly be learning and evolving, as we all know life isn’t stagnant.

    Therefore, I want to share top tips I’ve learned to make documents more inclusive. You won’t find the old post as it has been deleted (bye-bye, gone). As a general reminder, these are my own interpretations. Any legal information discussed within this post should be discussed with your institution.

    Let’s get ready to learn!


    What is accessibility?

    When I look up what accessibility means, there are various definitions depending on the context. I really like how California State University Long Beach defines this term:

    Accessibility is about providing a means for users with disabilities to access the same information and services that users without disabilities are able to access.

    I like to think of accessibility as making training documents easy to comprehend and review for everyone. Accessibility makes it a level playing field. When it comes to folks with disabilities, the top ones to me are those with visual or hearing disabilities. Someone with color blindness or hard-of-hearing should be able to use the same training materials I create for someone who doesn’t have these disabilities.

    Why is accessibility important?

    Aside from the humanity aspect, it’s the law! Directly from the website itself, the Americans with Disabilities Act (ADA) protects people with disabilities from discrimination.

    It is a Federal civil rights law that protects people with disabilities from discrimination in many areas of life.

    Accessibility as a tenet of research ethics

    Circling back to a statement I made earlier:

    The purpose of this blog is to promote scientific trust and research ethics education.

    In light of this, I feel we should understand how accessibility can be viewed as a tenet of research ethics. When I think of human subjects research ethics, I automatically think of the Belmont Report. If you’ve conducted research with human subjects or taken a bioethics course, you may be familiar with this report. The Belmont Report has three principles:

    • Respect for persons
    • Beneficence
    • Justice

    With respect to accessibility, I’d like to discuss the Justice principle. Directly from the Belmont Report:

    Who ought to receive the benefits of research and bear its burdens? This is a question of justice, in the sense of “fairness in distribution” or “what is deserved”.

    Justice can also be understood as:

    Equals ought to be treated equally.

    The formulations of Justice include:

    • To each person an equal share,
    • To each person according to individual need,
    • To each person according to individual effort,
    • To each person according to societal contribution, and
    • To each person according to merit

    I really spent some time trying to find the perfect image to describe the Justice principle with respect to accessibility. I finally came up with the image below:

    McMaster University’s representation of “equality”, “accommodation”, and “accessibility” (also known as “equity”).

    Simply treating everyone as equals doesn’t fulfill the accessibility requirement. Referring to the picture, we can see the unfair distribution represented by “equality”. The Justice principle indicates that equals should be treated as equals. But what about those who are “unequal” to the “group of equals”? You can certainly provide accommodations, but accessibility is really about equity. Equity can be described as the formulations listed in the Justice principle.

    Equity is about treating people fairly in accordance with their needs.

    Top tips for training document accessibility

    Tip #1: Ensure you have documents available as a PDF and a Microsoft Word document

    I love this tip! This isn’t something I necessarily learned from a training session or workshop. Word documents are great because you can edit them and make notes.

    I used to do this all the time when I was learning something new (especially in college). It would be helpful for me to make my own annotations directly into a handout.

    Conversely, PDFs are great for distribution. If you just want a “clean copy” of a training handout, this is the best way to go. When you’re creating training handouts at your institution, you should strive to have both types of files available.

    This is especially important if you embed objects in your files. This is when you link a file into your working file. The user will be able to click on the file and access it. If you only have a PDF version of your working file, the user will not be able to access the embedded object. This is why you should have a Word version and a PDF version.

    Tip #2: Use Alt Text for images in your Microsoft Word document

    What is Alt Text?

    Short for alternative text, is a short description of an image in a training document. Typically, 1-2 sentences is the sweet spot for describing an image.

    You may be wondering to yourself…

    How is this different than adding a caption to your picture?

    Though I’ll define captions below, alt text is specifically for individuals who have visual disabilities. Alt text is meant to quite literally describe the image in the handout.

    Let’s review the picture used above from McMaster University. The text underneath is a caption adding a description of the image. An example of alt text of the image would be:

    There are three whiteboards at varying lengths and with standing prop stools. At each whiteboard, there are two people standing and one person in a wheelchair.

    As you can see, alt text literally describes the image itself. While a caption provides additional context or explanation about the image.

    Tip #3: Always use the headers feature to separate topics (and to use the cross-reference feature)

    The next three tips are related to formatting your training documents. Now, let me explain why I especially love this tip:

    I like to think of this tip as a “two-for-one” combo!

    Using headers in your training handouts is a great way to make different topics stand out. This helps folks visually see what you plan to discuss. Be sure to make your headers meaningful (i.e., the header should be concise and accurately describe the context beneath it).

    Headers also work well if your training handout has a table of contents. When you use headers, you will be able to use the cross-reference feature. This essentially serves as a hyperlink. When you go to your training handout’s table of contents and click on the link, it will take you directly to that section in the handout!

    Say goodbye to doom-scrolling! With the cross-reference, you can get to the desired section in seconds.

    Tip #4: Use ordered (numbered) and/or unordered (bulleted) lists in lieu of long-winded paragraphs

    Now if you’ve seen my profile picture on the About Us page, you may have noticed that I wear glasses. Interestingly enough, this does NOT fall under the Americans with Disabilities Act.

    Even though I technically don’t have a visual disability, I do have ADHD!

    Therefore, I strongly appreciate this tip! I hate reading through long-winded sentences and paragraphs. Lists just make life easier! Regardless if you have ADHD or not. I’d take lists over long paragraphs any day. With my attention span, lists are much better for me (and in general…for everyone who also suffers from short attention spans).

    Tip #5: Use descriptive text when adding hyperlinks to your documents

    Now, I’m totally guilty of this…

    How many of you when writing an email or updated a website have written something along these lines?

    “Click here for more information”.

    That’s a HUGE NO NO! You should always use descriptive text when inserting a hyperlink to your training handouts. In lieu of the statement above, you could say:

    “Click the following link for Tasha’s Insights“.

    This way, the user of the file knows what the hyperlink is actually to!

    BONUS TIP: Get feedback on your training document’s accessibility by those with individuals with disabilities

    This is the ULTIMATE tip!

    I absolutely love getting feedback from folks. Especially if it’s a product I developed for them. You should also do this at your institution, especially from folks with disabilities. This way, if they find the document readable…or should I say accessible, then you know you’re on the right track.


    I hope you found this content useful in developing your training materials!

  • Top Tips for Medical Writing vs. Scientific Writing

    Top Tips for Medical Writing vs. Scientific Writing

    Authored/ Reviewed by Tasha Mohseni

    Contribution by Adnan Shaikh

    Good morning, good afternoon, and good evening Compliance Rockstars, Clinical Researchers, Ethics Educators, and Investigators from around the globe!

    I hope you are doing well! I can’t believe we are already in February 2025. It feels like just yesterday I was celebrating New Years Eve with my loved ones.

    In this post, I plan to define medical writing versus scientific writing. Then, Adnan will end the post with helpful tips for both medical and scientific writing.

    I would like to thank Adnan Shaikh for his willingness to contribute to this post!

    Let’s get ready to learn! As a general reminder, these are our own interpretations. Any legal information discussed within this post should be discussed with your institution.


    What is medical writing?

    Medical writing can be defined as the process of creating scientific and clinical documents related to:

    • Healthcare,
    • Medicine, and
    • Life sciences

    This type of writing typically yields the following types of content:

    • Regulatory documentation,
    • Research publications,
    • Educational materials related to healthcare, medicine, and life sciences, and
    • Promotional content for healthcare professionals and patients

    To be a successful medical writer, it is recommended to acquire skills in:

    • Understanding of medical and scientific concepts
    • Strong writing and communication skills
    • Ability to interpret and summarize complex data
    • Knowledge of regulatory guidelines (e.g., FDA)
    • Proficiency in literature searching and referencing

    What is scientific writing?

    Scientific writing is the structured dissemination of research findings, theories, and technical information in a clear and objective manner. This type of writing is commonly used in the following fields:

    • Academia,
    • Medical field, and
    • Technical fields (e.g., engineering)

    When I think about scientific writing, not only do I think about research publications I also think about research documentation.

    What do I mean by “research documentation”?

    Well, I mean your research protocol! To be a strong scientific writer, it is essential to:

    • Explain complex ideas in a concise format
    • Avoid technical jargon (i.e., write in a way that anyone who is not in your field would understand)
    • Use credible references to support your hypothesis
    • Let graphics and tables enhance the “research story” you’re trying to tell

    Is there truly a difference between these writing styles?

    To be honest here, I would say that I am well versed in scientific writing. I had to do some research and learn about what is considered medical writing. Even though I explained the differences here, if you’re scratching your head right now you maybe wondering…

    Tasha, there’s so much overlap. Aren’t these pretty much the same thing?

    I was feeling the same way! Even when I was doing my research on what constitutes medical writing, scientific writing would often appear! Therefore, I decided to go to my old friend, ChatGPT. First, I prompted ChatGPT to create a Venn diagram of medical writing versus scientific writing.

    For those who may not know, a Venn diagram is an illustration of two overlapping circles comparing two topics. In the area where the circles intersect, these are similarities between the two topics. Outside of the circle overlap, are the differences between the two topics.

    Below is the result from ChatGPT:

    Venn diagram comparing medical writing to scientific writing created by ChatGPT

    Though I technically haven’t done medical writing, I can see the significant overlap with scientific writing! It makes sense to me now why when I was researching medical writing, scientific writing often popped up.

    Next, I prompted ChatGPT to put this into a table for comparison:

    Tabular comparison of medical writing versus scientific writing created by ChatGPT

    What I love about this table even more than the Venn diagram is how ChatGPT defined these writing styles based on:

    • Content
    • Sentence structure
    • Audience

    I feel these components are so important! I almost wished I started out knowing this comparison.

    I wouldn’t have driven myself bonkers trying to figure it out for myself!

    Now that we have a better understanding of this, let’s see what top tips Adnan has to offer us!

    Top tips for medical writing

    Hello researchers, I am glad to be here once again. I’m excited to share my top tips for successful medical writing!

    Remember to rely on the following organizations below : 

    These associations provide many informational resources for professionals or novice learners, such as books and webinars. These materials will guide you on your journey to improve your medical writing skills. They will also help in understanding novel approaches in this field. 

    Other essential key skills for medical writing are:

    • Proficiency in the English language,
    • Ability to simplify medical and scientific terminologies,
    • Knowledge of clinical research, regulatory bodies, and ICH GCP Guidelines,
    • Proofreading of the content,
    • Ability to interpret complex research content, and
    • Knowledge of statistics

    I recommend writing in a daily gratitude journal and to connect with other seasoned medical writers. With knowledge sharing and daily practice, you will be on your way to mastering this skill!

    One bonus tip I’d like to offer is to complete an internship or get training by the organizations mentioned above. Whether you’re experienced or new to the field, these top tips will mold you to become a better medical writer.

    Top tips for scientific writing

    Scientific writing is completed by researchers from various backgrounds. Completing an academic project within my PharmD program provided the exposure I needed to grow in this area. You can review my publications here as a sample of scientific writing:

    1. Pemphigus Vulgaris: An Overview
    2. An Observational Prospective Study on Drugs Utilization Pattern in Cardiovascular Diseases at a Tertiary Care Hospital

    Once you’ve reviewed my work, take a look to see how I applied my top tips for scientific writing:

    The Do’sThe “Don’t”s
    1. Find gaps in published literature that could lead to your research question.
    2. Prepare content simultaneously. While conducting your literature search, use this time to also list your references.
    3. Use active and passive appropriately.
    4. Define abbreviations and acronyms when first introducing them in your paper.
    5. Always read the journal authors’ instructions in depth. This is to ensure your work is suitable for the journal of interest.
    1. Avoid filler words such as “like” or “you know”.
    2. Avoid using the same word repetitively; this often happens with transition words.
    3. Avoid directly copying and pasting as this leads to plagiarism. Further, if Artificial Intelligence (AI) tools were used to generate your content, you must credit this tool in your paper.
    4. Avoid errors by ALWAYS proofreading your content before submitting to the journal for review.

    Always remember that practice makes perfect! I’d like to express gratitude to Tasha for providing me this space to showcase my ideas and thought with you.


    We hope you found this post insightful!

  • ICH GCP E6(R3) vs. E6(R2):  Guideline Differences

    ICH GCP E6(R3) vs. E6(R2): Guideline Differences

    Tasha Mohseni

    (Author)

    Adnan Shaikh

    (Author & Reviewer)

    Good morning, good afternoon, and good evening Compliance Rockstars, Clinical Researchers, Ethics Educators, and Investigators from around the globe!

    It’s hard to believe that January is coming to a close. It feels like just yesterday folks were sending their New Year wishes. It felt as though I was reviewing countless study submissions. I prefer to be on my toes than idle!

    ICYMI, TikTok went dark on January 18, 2025 only to return on January 20, 2025. You can read about my insights here:

    Now, back to today’s post. On January 6, 2025 the final version of the ICH GCP E6(R3) guidelines. Whether you’re a novice to clinical research (like me) or an expert, this is a HUGE deal. The last revision for ICH GCP E6(R2) was back in November 2016. Without writing this post, I could guess that there are significant differences. But I don’t like to guess…I like to know and understand why.

    For background, I primary review IRB studies related to social, behavioral, and education research (SBER). I am extremely interested in learning more about clinical research ethics and clinical trials in general.

    Therefore, I would like to thank Adnan Shaikh for his willingness to review and co-author this post!

    In this post, we plan to describe in detail the differences between the E6(R2) version and the E6(R3) version. As a general disclaimer, these are our own interpretations of these guidelines. If you have any questions about these guidelines, you should always consult with your institution.


    Tasha Mohseni’s POV on General Guideline Differences

    Aside from document length, the table below summarizes the general differences between the guidelines:

    ICH GCP E6(R2) SummaryICH GCP E6(R3) Summary
    1. Published in November 2016.
    2. It emphasized updates needed to integrate modern technology and risk-based approaches into clinical trials.
    3. Key updates included:
    A. Adoption of risk-based monitoring.
    B. Enhanced guidance on electronic records and data management.
    C. Emphasis on quality management systems tailored to clinical trial complexities.
    1. Published in January 2025.
    2. Introduced broader revisions across clinical trial design, oversight, and ethical conduct, emphasizing:
    A. Greater focus on patient needs and preferences.
    B. Enhanced data reliability and transparency mechanisms.
    C. Encouragement of adaptive and innovative trial designs.
    D. Modernized guidance for handling big data and novel analytical tools.

    What Did ChatGPT Have to Say About The Guideline Differences?

    As you all know from my Show and Tell: How to Write a Clear SBER Protocol post, I have my own personal ChatGPT account.

    I can’t even quantify how long it took me to summarize the general differences for the two documents. Though this was interesting to review, I found it difficult to sift through the information. This might be because I haven’t had exposure in this area. I likely should have completed the GCP CITI training course to provide a foundation. Alas, here we are!

    Out of curiosity and efficiency, I decided to upload both guidelines to ChatGPT and asked for itemized differences. Below is a complete response from ChatGPT:

    “To provide a detailed, itemized comparison of the ICH E6(R2) and ICH E6(R3) documents, I will identify key differences across major sections and topics in both documents. This will include updates, additions, and revisions. Here’s an itemized list based on their content:


    Introduction and Scope

    • ICH E6(R2):
    • Introduced updates to integrate risk-based monitoring and electronic systems.
    • Focused on maintaining ethical principles while improving trial efficiency and data reliability.
    • ICH E6(R3):
    • Broader scope, emphasizing patient-centric approaches and advanced technologies like decentralized trials and real-world evidence.
    • Greater emphasis on stakeholder collaboration and global harmonization.

    Quality Management

    • ICH E6(R2):
    • Introduced a risk-based quality management system.
    • Focused on identifying and mitigating risks to critical data and processes.
    • ICH E6(R3):
    • Expands the quality management framework to include:
      • Adaptive approaches.
      • Continuous quality improvement throughout the trial lifecycle.
      • Integration of advanced analytics and real-world data for monitoring.

    Data Management

    • ICH E6(R2):
    • Introduced guidance on electronic systems and electronic data capture.
    • Added the requirement for an audit trail for all changes in electronic data.
    • ICH E6(R3):
    • Extends guidance on managing big data, artificial intelligence (AI), and machine learning (ML) tools.
    • Updates standards for data security, privacy, and transparency in decentralized trials.

    Trial Design

    • ICH E6(R2):
    • Focused on traditional trial designs, with some flexibility for centralized monitoring.
    • ICH E6(R3):
    • Encourages adaptive, innovative, and patient-centric trial designs.
    • Includes guidance for decentralized clinical trials (DCTs) and the use of real-world evidence (RWE).

    Investigator Responsibilities

    • ICH E6(R2):
    • Detailed investigator obligations, focusing on protocol adherence and oversight.
    • Introduced responsibility for supervising delegated tasks.
    • ICH E6(R3):
    • Expands the investigatorโ€™s role to ensure oversight in remote and virtual settings.
    • Adds accountability for new roles introduced in decentralized models (e.g., telemedicine).

    Monitoring

    • ICH E6(R2):
    • Introduced risk-based monitoring (RBM) for the first time.
    • Emphasized centralized monitoring over traditional on-site monitoring.
    • ICH E6(R3):
    • Refines RBM principles and promotes greater use of AI-driven monitoring systems.
    • Integrates remote monitoring for decentralized trials.

    Patient-Centric Approaches

    • ICH E6(R2):
    • Focused primarily on data integrity and participant safety.
    • ICH E6(R3):
    • Strong emphasis on patient engagement, including:
      • Simplified informed consent processes.
      • Participant-centered trial design.
      • Use of digital tools to enhance patient experience and access.

    Ethical Considerations

    • ICH E6(R2):
    • Rooted in the principles of the Declaration of Helsinki, with updates for vulnerable populations.
    • ICH E6(R3):
    • Expands ethical considerations to include:
      • Diversity and inclusion in clinical trial participation.
      • Enhanced protection for participants in decentralized and global trials.

    Use of Technology

    • ICH E6(R2):
    • Introduced electronic systems, requiring validation and audit trails.
    • ICH E6(R3):
    • Extends to newer technologies like AI, blockchain, and wearable devices.
    • Requires validation and traceability for advanced digital systems.

    Regulatory Harmonization

    • ICH E6(R2):
    • Harmonized standards for the EU, U.S., and Japan.
    • ICH E6(R3):
    • Broader harmonization efforts, including additional regions (e.g., Canada, Switzerland).
    • Greater emphasis on mutual regulatory acceptance of trial data.

    Risk-Based Approaches

    • ICH E6(R2):
    • Introduced risk-based approaches for trial management and monitoring.
    • ICH E6(R3):
    • Refines risk-based approaches with continuous risk assessment throughout the trial lifecycle.
    • Incorporates new methodologies to address emerging risks in decentralized and digital trials.

    Terminology and Glossary Updates

    • ICH E6(R2):
    • Updated glossary to include terms like “risk-based monitoring” and “certified copy.”
    • ICH E6(R3):
    • Expands glossary to include modern terms like “decentralized trials,” “real-world data,” and “validation of AI systems.”

    These itemized differences highlight the evolution of clinical trial guidelines from E6(R2) to E6(R3), reflecting advancements in technology, trial design, and ethical considerations. Let me know if you want a deeper analysis of any specific section!”

    At face value, this seems to be an impressive comparative analysis.

    You might be wondering why I said the word “seems”.

    I say this because I am acknowledging that I am not a subject-matter expert. I can’t even say that I have direct experience with this other than reading the guidelines themselves. I can’t necessarily validate if ChatGPT assessed the information correctly. I could have done more iterative prompting to receive an even more detailed response…but for what? This is why it is so important to have the expertise to verify the information is correct. Whether you become knowledgeable or collaborate with someone who has the expertise. Let’s see what Adnan Shaikh has to say about ChatGPT’s analysis!

    Adnan Shaikh’s POV on the ChatGPT Analysis (in his own words)

    Hey everyone! I hope you’re enjoying reading Tasha’s content. Here’s my take on the ICH GCP E6(R2) versus ICH GCP E6(R3).

    The adoption ICH GCP E6(R3) introduces a transformative update to the principles and practices of clinical research:

    • The ICH GCP E6(R2) guidelines were more focused
      • They adhered to the protocol and followed the traditional approach in clinical trial studies, that has an impact of โ€œone size fits all”
    • While ICH GCP E6(R3) is more modern and advanced approach for clinical trial, it encourages โ€œfit for purposeโ€
      • Which means that proportionality and risk-based approaches focus on the quality of clinical trials
        • This is critical and fundamental to the safety of participants and the reliability of participants results

    I humbly recognize that I am not as expert and that I am still learning

    However, to summarize the overall concept of ICH GCP E6(R3), it focuses on the following:

    • Emphasize proportionality risk-based approach,
    • Incorporates innovative technologies, and
    • Promotes adaptability to modern clinical trial design.

    In the recent times, AI is claimed to be one of the best tools in helping with clinical research.

    Now, I would like to share my POV on ChatGPT.

    It is helpful in many ways, for example:

    • Preparing document with little to no grammatical errors,
    • Providing information on particular drug molecules, and
    • Providing information on ICH GCP guidelines

    When I prompted ChatGPT about the difference between ICH GCP E6(R2) vs ICH GCP E6(R3), I received the following graphical explanation:

    Image of ChatGPT’s graphic representation of the differences between ICH GCP E6(R2) and ICH GCP E6(R3)

    As you can see, this further illustrates my point that ICH GCP E6(R3) highly focuses on modern adaptation. By implementing this concept, it can provide more flexibility in clinical research studies. In the era of AI and emerging technologies, itโ€™s important to adapt the modern techniques in clinical trials. It is also criticial to note that this must be done without compromising confidentiality. We must adhere to ethical standards for the responsible use of these emerging technologies.

    I am expressing my sincere gratitude to Tasha for involving me in this post. I ‘m looking forward to working with her on future projects!


    We hope you find the content useful and thought-provoking! What do you think about current guidelines? Please lead the discussion and leave a comment below!

    Again, thank you Adnan Shaikh for your invaluable expertise! For those who would like to learn more about him, you can follow him on LinkedIn and read his brief biography below.

    You can follow Adnan on LinkedIn, Instagram, and WhatsApp!

    Adnan Shaikha is a Pharm.D (Doctor of Pharmacy) candidate. He is completing his clinical pharmacy internship at New Civil Hospital. His degree will be from the Shree Dhanvantary Pharmacy College, Kim, Surat, Gujarat, India. He is expecting to graduate with his PharmD degree in June 2025. Additionally, he is doing a certification course on medical writing in clinical research.

    We wish you the best of luck on this exciting endeavor and soon-to-be new chapter in your life!

  • How to Write a Clear SBER Protocol

    How to Write a Clear SBER Protocol

    Good morning, good afternoon, good evening IRBers, Clinical Research Educators, and Investigators from around the world!

    I’m so excited about sharing the very first Show-and-Tell post within a highly educational series!

    I’m sure by the title you can tell what this post is going to be about. But first…

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    And then…

    I want to tell you about my methodology of assessing 17 social, behavorial, education research (SBER) IRB protocol templates from various institutions.

    Being in the compliance field, I am also passionate about research. More importantly, passionate about doing research ethically and in a reproducible way. I am briefly going to summarize how I analyzed 17 SBER IRB protocol templates from various institutions for commonalities:

    1. I went to Google and typed in “IRB Templates”.
      • From here, I only reviewed templates with the following keywords:
        • “SBER”
        • “Social/Behavioral”
        • “Social/Behavioral/Educational”
    2. My initial plan was to stop once I browsed through 10 pages. Which I did and was hoping to have 50 records for review.
      • But…I encountered obstacles:
        • Some institutions had templates for consent and recruitment, but not for the protocol (perhaps this is an internal document in the electronic IRB submission system)
        • Some institutions relied on Federal agency templates (which is completely fine, but I said I would do my search on institutions)
        • Some institutions only had one type of protocol template (i.e., no difference between SBER or biomedical research) – again this is completely fine depending on the nature of the research at your institution
        • Some institutions required an institutional login to access their templates (hear, hear for additional security!)
        • Some institutions had protocol templates based on review type (exempt, expedited, vs. full board) – this is interesting to me because I wonder how these institutions have trained their investigators to know which template to use
        • Some institutions may have had SBER protocols, but I was limited by the keywords I selected
          • Could I have assumed that surveys/focus groups/observations fall under SBER? Sure…but how do I know they aren’t also applicable to a biomedical component? Further, I set my keywords and that does not fit the criteria
    3. After going through 20 (LONG) pages of results, I decided to stop once I reviewed 17 institutions’ SBER protocol templates.
    4. As I reviewed each template, I gathered the following data points:
      • A link to their PDF for future reference
      • Their institution only to avoid duplicate values (once I had 19 unique values – this column was deleted)
      • Sections within their protocol template
    5. Then, with iterative prompting in my personal ChatGPT-4 account, the GPT summarized the recurring themes within the protocol templates.
      • If you would like to review any of my references, please leave me a comment or email me at tmohseni@renovationinirbeducation.org.

    Now that you know my methodology, I also want to share a poll I created via Renovation in IRB Education’s LinkedIn page:

    Poll created in LinkedIn to determine an investigator’s problem area when creating a SBER protocol.

    Though only one person voted (thank you!), they gave me some great insight. This is a common issue I see. Providing too little detail doesn’t give the IRB reviewer an idea of what the proposed research is about. More importantly, by providing little detail, you lose the significance behind your very important research!

    Now, let’s take a deep dive into the various sections of a SBER protocol template. At the end, I will have a list of of applicable Office of Human Research Protections (OHRP) guidance documents and Secretaryโ€™s Advisory Committee on Human Research Protections (SACHRP) recommendations. You should consult with your institution’s IRB if you have any questions about the resources provided at the end of this post (or the recommendations within this post to ensure it is applicable to your institution):


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    Title

    This should hopefully be one of the easiest parts of your SBER IRB protocol! The title of your protocol should give the IRB reviewer a sense of what your IRB protocol will be about.

    Is there a grant associated with your study?

    If so, I recommend reviewing sponsor requirements. Some sponsors may require that your protocol title must be the same title as your grant.

    Principal Investigator (and Study Team)

    The institution must know who is the lead investigator (i.e., the principal investigator (PI)) for the SBER study. This is typically a faculty or staff member. If the research is student-led, then the student should check with their institution to see if they can be listed as the PI. Some institutions may be fine with this, but require a faculty member to be listed on the protocol as well. The protocol should also list all study team members associated with your study. Whether they are affiliated with your institution or an external collaborator, IRBs want to ensure that everyone has received the proper IRB/human subjects research training.

    Institution Team Members

    These are team members that are affiliated with your institution.

    External Collaborators

    These are team members that are not affiliated with your institution. If the collaborator is affiliated with an institution, they will need to reach out to their IRB. The collaborator’s IRB may require that the collaborator submit a study for review. Conversely, the collaborator’s IRB may not considered them engaged in research. You may not know this, but institutions have flexibility in how to apply the regulations.

    Stay tuned for an upcoming post that will discuss the reliance process and the single IRB mandate!

    Background and Objectives

    This is also known as:

    • The study significance and rationale (i.e., purpose) for the proposed research
    • The study aims and hypothesis

    This is where you briefly introduce the purpose of your proposed study. If your research is funded, the study aims should be consistent with your grant. This is also where you would include your research question(s) and hypothesis. You should also provide a summary of research currently available (i.e., publications) to provide justification for the proposed study. I caution you here to avoid any technical terms or jargon. Remember, IRB reviewers aren’t as connected to your project as you are.

    Therefore, this section should be written in a way that anyone can understand:

    • The main idea of the proposed research
    • Any published research that is related to the proposed research
    • Any current studies that are related to the proposed research (i.e., the ID number of any active studies related to the proposed research)
      • This helps IRB reviewers when they are making a determination for your study with respect to risk (i.e., no greater than minimal risk or above minimal risk)

    Inclusion and Exclusion Criteria

    As the section heading implies, this is where you would describe:

    • Criteria that makes the individual eligible for the proposed study
    • Criteria that makes the individual ineligible for the proposed study

    Special Populations

    If not obvious, you want to provide the scientific rationale for any exclusions of special populations. Special populations under The Common Rule (45 CFR 46) are:

    There are other examples of special populations that aren’t covered in the regulations. An individual can be part of a special population if they can be considered vulnerable in the research:

    • Individuals with impaired decision-making capacity
    • Economically or educationally disadvantaged persons
    • Socially disadvantaged
    • Terminally ill or very sick
    • Racial or ethnic minorities
    • Institutionalized persons (e.g., persons in correctional facilities, nursing homes, or mental health facilities)
    • Dual role relationships (the investigator could be a manager or professor to the employee and/or student participant)

    What is “vulnerability” in a research setting?

    Below is a summarized table of when vulnerability can occur in a research setting:

    Undue InfluenceCoercion
    Misusing a position of power to influence others to make a decision they would not normally makeA way to force or control someone

    Number of Participants

    In this section, you would indicate the number of anticipated participants you plan to enroll in your proposed study.

    Recruitment Methods

    Here, I like to take the “Five W’s” approach:

    • Who are you recruiting?
    • Why are you recruiting these potential participants?
    • What materials will be used for recruitment?
    • Where will potential participants be recruited?
    • When will recruitment begin?

    Let’s look at these components one piece at a time.

    The first question should be a concise statement of your inclusion criteria. When IRB reviewers ask who you will be recruiting, they aren’t looking for specific names. They are looking for the population of interest that will help answer your research question(s).

    The second question is aiming towards providing justification for the population of interest. How will this particular population help you answer your research question(s)? What is it about this population that would benefit from the proposed research?

    For the third question, IRB reviewers are trying to understand what materials you will be using to recruit participants. Will potential participants be contacted via email? What about social media? Do you plan to do in-person recruitment where you will distribute fliers related to your study?

    Typically, the fourth and fifth questions are a combined statement. Something to keep in mind is in-person recruitment. Say you are going to an event where you plan to distribute recruitment fliers for the proposed research. Do you have permission to distribute recruitment fliers for research purposes? You will want to make note of any site permissions you have obtained for the proposed research. The IRB reviewer will likely want to review the site permission documentation as well. Be sure to include any explicit permissions within your submission.

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    The consent process should outline key information from the investigator that should be provided to participants. The key information here should facilitate the participantโ€™s comprehension and voluntariness of potential participation in your study. Though there are standard required elements of consent (and additional requirements depending on your study), at minimum, your consent form should include:

    • The study activities
    • The duration of time the activities will take
    • Explanation of risks and benefits
    • Compensation and any limits to receiving it
    • Protections and limits of confidentiality

    Stay tuned for a blog post describing the consent process in MUCH greater detail!

    Risk and Benefit Assessment

    Risk

    Remember when I said IRB reviewers look at previous applications similar to the proposed research?

    To reiterate, IRB reviewers when they are making a determination for your study with respect to risk (i.e., no greater than minimal risk or above minimal risk). Minimal risk (as defined by 45 CFR 46.102(j)) means that the probability and magnitude of harm or discomfort anticipated in the research are not greater in and of themselves than those ordinarily encountered in daily life or during the performance of routine physical or psychological examinations or tests. When the proposed research has risks, the investigator must have risk mitigation measures in place.

    Let’s look at the example below:

    Say you are conducting an anonymous survey regarding the mental health of college students. The IRB reviewer will review the survey questions to see if any of these questions could cause distress to participants. Then, the IRB reviewer will look at your protocol to see if:

    • The possibility of distress from answering these survey questions was disclosed
    • Distress mitigation measures (e.g., a list of mental health/therapy resources)

    The key here is that any potential benefits (whether direct or indirect) should outweigh the proposed study’s risks.

    Direct Benefit and Indirect Benefit

    First, let’s define direct benefit and an indirect benefit:

    Direct BenefitIndirect Benefit
    Refers to a positive outcome that directly results from the intervention being studied and is experienced by the research participants themselvesA positive outcome that arises from the research process but is not directly related to the intervention itself, often benefiting society at large or future research, rather than the individual participant in the study

    Now, let’s look at a couple of examples:

    • Direct benefit: In a behavioral intervention testing a new stress relieving technique, a direct benefit would be the participant experiencing symptom relief from applying said technique to their daily routine
    • Indirect benefit: In a study on a new educational program, an indirect benefit could be the increased awareness of the topic among the wider community due to the research dissemination

    It is also important to note that compensation is not considered a benefit.

    Study Design and Procedures

    The IRB reviewer should be able to read and understand exactly what you are proposing to do with participants. Let’s pretend you’re wanting to conduct a survey to see how folks feel social media impacts their mental health. This section should include the following details:

    • A statement of how the survey will be disseminated (paper or electronic)
      • If electronic, indicate if the collection of IP addresses or email addresses will be disabled (as this is identifiable information)
    • A statement regarding how long it should take to complete the survey
    • A statement of whether participants are allowed to skip questions they are not comfortable answering

    Stay tuned for the blog post related to online research considerations for a more detailed explanation of this!

    For all study procedures, consider the five questions below:

    • Who on the study team is conducting the specific procedure?
    • What is the specific procedure?
    • How will the specific procedure occur?
    • When will the specific procedure occur?
    • Where will the specific procedure occur?

    It is only important to note how the data collected from the specific procedures will be analyzed.

    Compensation

    As mentioned above, compensation is not a benefit. Rather, compensation is a token of appreciation for participating in the research. It is not required to provide compensation. If you do not plan to offer compensation, you would simply include a statement regarding this. If you do plan to offer compensation, ensure the following details are included:

    • The amount of compensation
    • The form of compensation (e.g., if it is a gift card, identify where the gift card is to)
    • The justification for the amount of compensation
    • When and how compensation will be provided to participants

    It is important to note that IRBs are not reviewing the amount of compensation to determine if it’s “enough”. IRBs review compensation amounts to ensure the amount is not coercive. As we know from the section above, coercion can make any individual vulnerable in research.

    Privacy, Confidentiality, Data Management, and Future Use of Data

    Privacy and Confidentiality

    First, let’s define the difference between “privacy” and “confidentiality”:

    PrivacyConfidentiality
    Refers to the right to control access to ourselves and our personal informationRefers to agreements made between investigators and participants, through the consent process, about if and how researchers will protect
    participant’s information

    Now, this section of a research protocol is vital for ensuring ethical standards are upheld and participant trust is maintained. Below are key elements researchers should address to create a robust plan for protecting participant data:

    • Outline how participant privacy will be safeguarded throughout the project
      • For instance, provide private and secure environments for interviews or survey completion
    • Specify where and how all data typesโ€”paper, electronic, or multimediaโ€”will be stored and managed
      • Data must be stored securely, such as in password-protected databases or locked filing cabinets in restricted-access areas
      • Highlight additional security measures like data encryption for electronic records
      • Clearly state who will have access to the data, limiting it to essential study personnel to minimize risk
    • For studies involving audio or video recordings, specify the retention period and handling procedures
      • For example, recordings may be deleted after transcription and verification or within six months of collection
      • During retention, secure storage methods, such as encrypted drives or locked cabinets, should be employed to prevent unauthorized access
    • If a master list or key is used to link participant identities to data, describe its management
      • Explain how it will be securely stored separately from study data (e.g., on a different encrypted server or in a separate locked cabinet)
      • Identify who will have access to the master list and ensure access is limited to essential personnel
      • Additionally, specify when the master list will be destroyed
    • State the minimum retention period for study data, typically three years after project completion, as per regulatory requirements
      • Except for master lists or keys and audio/video recordings, which should be destroyed at the earliest opportunity, all other data should be securely retained until the retention period ends
      • To maintain confidentiality, include methods for secure destruction, such as shredding paper files or securely wiping electronic data
    • If data will be shared or moved outside your institution, provide a detailed plan (such as a data use agreement)
      • Specify the type of data that will be shared, the recipient(s), the circumstances under which sharing will occur, and the timeline for these actions
      • Include any additional security measures to ensure data confidentiality during transfer

    Data Management

    For this aspect, the IRB reviewer will look for:

    • A statement of the types of study data collected
    • A statement of who has access to study data
    • A statement of how data will be stored
    • A statement of when data will be destroyed
    • A statement of how study data will be de-identified (if applicable)
    • A statement of how identifiable study data will be managed

    Stay tuned for a future blog post regarding methods on how to de-identify your study data!

    Future Use of Data

    This section requires explanation only if you plan to share data outside of your institution. The investigator should provide a plan for any data movement or sharing outside of their institution. This section should also specify what data will be provided, to whom, under what circumstances, and when. This should also be disclosed in the consent form.

    Withdrawal of Participants

     If a participant requests to withdraw from the study, the investigator should describe:

    • The scenarios under which they will be able to delete the participantโ€™s data
    • The scenarios under which they will not be able to delete the participantโ€™s data

    Let’s look at a couple of examples:

    • If you are conducting an anonymous survey, you likely would have no way of identifying the participantโ€™s individual responses. Therefore, when a participant opts to withdraw, there would be no way to delete their data (i.e., their responses).
    • If you are conducting interviews, you could delete a participantโ€™s interview transcript when a master list is in existence. However, you may not be able to do so until after the master list is destroyed.

    I hope you found the first post of this blog-series helpful! Did you find this post informative for your SBER IRB protocol application? If so, please scroll on down and leave a comment below!

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    OHRP and SACHRP Resources

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  • FDA Artificial Intelligence (AI) Guidance Highlights

    FDA Artificial Intelligence (AI) Guidance Highlights

    Good morning, good afternoon, and good evening IRBers, clinical research educators, and investigators from around the world!

    I hope everyone had a great first of full week back to work! Now, I can officially say, back to the grind. I should be used to how busy the start of the semester is with outreach training efforts. I was also busy reviewing submissions. We typically see an increase in submissions around this time since it is the start of Spring semester.

    Besides being busy with work, I was also working on the Show-and-Tell Series of blog posts. You can read about the series here:

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    Well, I wasn’t the only one busy this week. The FDA went on a guidance posting frenzy! Below is a list of guidance documents relevant to IRB and clinical research that were issued between Monday, January 6, 2025 – Friday, January 10, 2025:

    Though all these guidance documents should be reviewed, I plan to only deep dive into the following guidance documents:

    You may be wondering why I’m solely focusing on these two guidance documents.

    I’ll tell you why I’m into AI!

    • AI can optimize productivity. I canโ€™t tell you how many times this has saved me. For people like me who have trouble reading long documents, AI is great for summarizing key concepts. Of course, I will read all documents in their entirety. However, itโ€™s nice to have a general idea of what I plan to read. This way, if there are any concepts or terms Iโ€™m unfamiliar with, I can simply look them up. Then, when Iโ€™m reading the document in full, I wonโ€™t have to waste time looking up terms and concepts.
    • AI can help you be creative. I canโ€™t wait to share a post related to this! Before I think about automating a task, I ask ChatGPT if itโ€™s possible. Then, with my skills Iโ€™ve acquired over the years, I can attempt to act on my efficiency idea. I also love the DALL-E aspect. This is great for visual folks. I especially love to have DALL-E create flowcharts or diagrams. This helps me understand complex topics (such as the ethical codes in human subjects research).
    • AI can be used in any facet. Whether youโ€™re a writer, an artist, in IT, or even compliance, AI can be helpful anywhere! I will say that itโ€™s important to make your audience aware when AI was used. I always love to give credit where credit is due. I feel that AI can even make the least creative personโ€ฆa creator.

    Though I sound pro-AI, I do see there are downsides.

    • Privacy and confidentiality are major concerns. This likely goes without saying, but I will say it anyway. Compliance personnel such as myself likely know you shouldnโ€™t place any personal information into ChatGPT. Well, others may not know this. What about ways to withdraw your data? Can you do that in ChatGPT? I know there is a way to export data that was entered into the tool via your ChatGPT settings. But can ChatGPT unlearn data that has been withdrawn? Iโ€™m not sure, but I hope to learn more about this.
    • Machine learning bias is real. For those who may not be familiar with machine learning, this is a subset of AI. There isnโ€™t actual programming (e.g., with Python). It learns from datasets and makes inferences based on patterns. This is why it is called โ€œmachine learningโ€ because the tool learns over time. I feel this is GREAT for a highly specific function (such as a customer service chatbot). But what about when AI is being used for biomarker analysis or drug development? How can we ensure that we are applying the Belmont principle of Justice (subjects must be fairly selected)? How do we ensure we are fairly selecting datasets that are representative of the population of interest?
    • Compliance professionals are in an arms race in how to regulate the rapid use of AI in research. Again, this likely goes without saying it. You can probably start calling me โ€œCaptain Obviousโ€ now. Even as I learn about AI, it is hard to keep track of everything thatโ€™s going on. I follow federal agencies for guidance documents or if strategic plans are released discussing the ethical uses of AI. To me, the problem seems to be that everyone is developing their own guidance documents and best practices. It seems to me that something like this should have a best practice standard that agencies can adopt. I plan to learn about the EU AI Act in further detail as this seems like a great start. I recently completely GDPR trainings and felt this regulation really covered everything. We desperately need something like this in the United States.

    In this post, I plan to highlight key takeaways from these lengthy AI guidance documents (90 pages total).

    Then, I plan to analyze the documents even further as the request for comment is due Monday, April 7, 2025.

    If you are interested in leaving a public comment with me, please email me at tmohseni@renovationinirbeducation.com.

    Let’s make our voice count TOGETHER!


    Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products

    Per the FDA, this guidance provides recommendations to sponsors and other interested parties on the use of AI to produce informations or data intended to support regulatory decision-making regarding the safety, effectiveness, or quality of drugs. Though I am not an expert in drug development, I will say that I have intermediate understanding of AI. Letโ€™s see what the FDA has to say.

    The guidance provides a risk-based credibility assessment framework that may be used for establishing and evaluating the credibility of an AI model for a particular context of use (COU).

    The COU defines the specific role and scope of the AI model to address a specific question. As a former auditor, I also appreciate that the FDA has defined the word โ€œshouldโ€. The definition of โ€œshouldโ€ means that the FDA recommends actions within this guidance, but they arenโ€™t required. I remember carefully reviewing policies for words like โ€œshouldโ€, โ€œshallโ€, or โ€œmustโ€. Itโ€™s important for institutions to define this as well. This way, when folks are reviewing their institutionโ€™s policy or guidance, they know what is required versus what is recommended.

    A Risk-Based Credibility Assessment Framework

    This is a 7-step process:

    • Step 1: Define the question of interest that will be addressed by the AI model.
    • Step 2: Define the COU for the AI model.
    • Step 3: Assess the AI model risk.
    • Step 4: Develop a plan to establish credibility of AI model output within the COU.
    • Step 5: Execute the plan.
    • Step 6: Document the results of the credibility assessment plan and discuss deviations from the plan.
    • Step 7: Determine the adequacy of the AI model for the COU.

    Okay, so we know the steps. What do we do for each of these steps?

    Step 1 should describe the specific question, decision, or concern being addressed by the AI model. For step 2, the description of the COU should describe in detail what will be modeled and how model outputs will be used. It should also be notated on whether other information will be used in conjunction with the AI modelโ€™s output to answer the question of interest determined in step 1. Examples of other information include animal studies and/or clinical human research studies. In step 3, model risk is assessed by two factors: model influence and decision consequence. Model influence, like it sounds, compares data derived from the AI model to other evidence used to inform the question of interest in step 1. Decision consequence is the significance of an adverse outcome resulting from an incorrect decision concerning the question of interest in step 1. To appropriately assess these components of model risk, subject-matter expertise is strongly advised.

    Step 4 describes what information should be in your credibility assessment plan. Below is a summarized list of information that should be considered:

    • Describe the datasets used for training and tuning the AI model and which model development activities were performed using these datasets
    • Describe how the development data have been or will be collected, processed, annotated, stored, controlled, and used for training and tuning the AI model
    • Describe how the development data is fit for the COU
    • Describe whether the development data are centralized
    • Describe how the AI model was trained
    • Specify if a pre-trained model was used
    • Describe the use of ensemble methods
    • Explain any calibration of the AI model
    • Describe the quality assurance and control procedures of computer softwares and how version changes were tracked (as well as code verification)
    • Describe the applicability of the test data to the COU to minimize data drift
    • Describe the agreement between the model prediction and the observed data
    • Provide rationale for the chosen model evaluation method
    • Describe any model limitations and biases

    For step 5, discussing the credibility assessment plan with the FDA prior to execution may be helpful. The last section of the document describes early engagement options s with the FDA. Step 6 should involved documenting the results and deviations from steps 1-4. Once this is complete, you can proceed to step 7. Step 7 is where you determine if the AI model is appropriate for the COU. Finally, the document concludes with life cycle maintenance of the credibility of the AI model output in certain COUs. This can be referred to as the management of changes to an AI model (whether incidentally or deliberately).

    Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendation

    Per the FDA:

    This draft guidance, when finalized, will represent the current thinking of the FDA on this topic.

    Though this document will represent FDAโ€™s thoughts, I appreciate FDAโ€™s flexibility in approaches towards these recommendations. So long as the applicable statutes and regulations are met and it has been discussed with the FDA, you can use an alternative approach. The guidance provides recommendations on the contents of marketing submissions for devices that include AI-enabled device software functions including documentation and information that will support FDAโ€™s review. Similar to the previous guidance, the FDA defines the word โ€œshouldโ€ as โ€œsuggestedโ€ or โ€œrecommendedโ€. Nowโ€ฆletโ€™s get into this document!

    The FDA promotes a total product life cycle (TPLC) approach to the oversight of medical devices. You can read more about TPLC here: Total Product Life Cycle for Medical Devices. They also discussed the recent efforts made such as the 10 tenets of Good Machine Learning Practice (GMLP). The document further defines terminology used by the FDA versus the general AI community. For example, using the term โ€œvalidationโ€ to represent โ€œtrainingโ€ or โ€œtuningโ€ should be avoided in medical device marketing submissions. Instead, the word โ€œdevelopmentโ€ should be used. The FDA Digital Health and Artificial Intelligence Glossary โ€“ Educational Resource provides a compilation of commonly used AI Terms and how the FDA defines them.

    The next few sections within this guidance are what the FDA recommends including in marketing submissions. Each section provides a reason as to why it must be included, what must be included, and where to include it. Below is a general outline of what is recommended for submission:

    General Outline for Marketing Submissions

    • Device description
      • A statement that AI is used in the device
      • A description of device inputs and outputs
      • An explanation of how AI is used to achieve the deviceโ€™s intended use
      • A description of the intended users, their characteristics, and the level and type of training they are expected to have and/or receive
      • A description of its intended use environment(s)
      • A description of the intended workflow for the use of the device
      • A description of installation and maintenance procedures
      • A description of any calibration and/or configuration procedures
      • If the device can be configured by a user, then the submission should include information about:
        • All configurable elements of the AI-enabled device
        • How these elements and their settings can be configured
        • The potential impact of the configurable elements on user decision-making
      • If a device contains multiple connected applications with separate interfaces, then the device description should address all these applications
    • User Interface
      • A graphical representation of the device and its user interface
      • A written description of the device user interface
      • An overview of the operational sequence of the device and the userโ€™s expected interactions with the user interface
      • Examples of the output format
      • A demonstration of the device
    • Labeling
      • The following should be included at the age-appropriate reading level for the intended user:
        • Inclusion of AI
        • Model input
        • Model output
        • Automation
        • Model architecture
        • Model development data
        • Performance data
        • Device performance metrics
        • Performance monitroing
        • Limitations
        • Installation and use
        • Customization
        • Metrics and visualization
        • Patient and caregiver information
    • Risk assessment
      • Risk management file
    • Data management for both training and testing data
      • Data collection
      • Data processing and cleaning
      • Reference standard
      • Data annotation
      • Data storage
      • Management and independence of data
      • Representativeness
    • Model description and development
    • Performance validation
    • Device performance monitoring
    • Cybersecurity
      • Cybersecurity risk management report
      • How cybersecurity testing addresses the risks in the report
      • A security use case view(s) that covers the AI-enabled considerations for the Debi e
      • A description of controls
    • Publication submission summary
      • A statement that AI is used in the device
      • An explanation of how AI is used as part of the deviceโ€™s intended use
      • A description of the class of model and its limitations
      • A description of development and validation datasets
      • A description of the statistical confidence level of predictions
      • A description of how the model will be updated and maintained over time

    I hope you found this content enlightening and useful! I strive to provide my readers “food for thought”. What did you think of my interpretation of the guidance documents? Please leave a comment below and let’s get this discussion started!

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