Foreword from the Editor

This Open Educational Resource (OER) text focuses on the Pathophysiology, Pharmacology, and Physical (Health) Assessment aspects of commonly seen clinical diagnoses. As nurse practitioner students transition from being an expert registered nurse to a novice nurse practitioner (NP), they commonly feel a temporary loss of confidence as they adapt to the new role’s expectations. Their background in patient care is strong, but becoming an NP requires them to develop advanced diagnostic reasoning skills and make autonomous clinical management decisions. That adjustment is difficult! This process begins in the foundational courses of Pathophysiology, Pharmacology, and Health Assessment, where they start to develop an advanced practice-level understanding of disease mechanisms, medication actions, and patient assessments, enabling them to formulate differential diagnoses and manage complex cases accurately. Although the learning curve may be steep, giving students the opportunity to engage in simulation, case-based learning, and reflective clinical practice, while seeking guidance from experienced faculty and preceptors, helps build the competence and confidence needed to be a successful advanced practice provider. We have designed this text to be one tool in that process.

The text evolved in a unique manner. I was in my second semester teaching Pathophysiology for Advanced Practice Nursing (APN) to our graduate nurse practitioner students. It is a dense and challenging course, and I was looking for more ways to present the content in a clinically relevant manner to students who were registered nurses preparing for, but not yet enrolled in, their actual nurse practitioner clinical courses. They liked some of the case studies included in the course textbook but wanted more. I decided that including case studies related to the pathophysiology of diagnoses commonly seen in the early stages of NP practice would improve student learning, so I began creating them. At the same time, I joined a SUNY faculty learning community focused on how faculty can use artificial intelligence (AI) to enhance curriculum development and expand learning opportunities for students. Each member of the group identified a project they wanted to work on using AI to support student learning. I decided to learn how I could use AI in the development of pathophysiology case studies.

AI presented a significant challenge for me as I explored various options, including Claude, ChatGPT, Google LM, You.com, and others. Each system had advantages and disadvantages, and I found some completely insufficient, while others were better suited to what I was trying to achieve, especially at the graduate NP level. As I experimented with these AI tools, I narrowed down the AI platforms I utilized to two or three preferred ones. I discovered that if I controlled the content the AI system used to respond to my prompts, I was much more successful in creating appropriately leveled case studies. While developing the cases, I shared them as optional test review resources for the NP students in the course. Their feedback was overwhelmingly positive, and they kept asking for more. I added review questions to each case, and when I identified specific topics that were more challenging, I was able to generate cases that helped them understand the nuances of disease presentation and the underlying pathophysiology. As students communicated their needs, I actively adjusted and created more cases. The text, lectures, and skeletal notes were always available in the course, but these cases really seemed to help students connect difficult concepts in a practical way, and they liked them.

Ultimately, the graduate curriculum committee decided to make these resources available to all of our NP students as additional review options to help prepare for their clinical readiness exam, which they all take before starting clinical courses. My colleague, Dr. Renee Biedlingmaier, also assigned NP students to review a brief patient write-up and determine appropriate pharmacological treatment along with supporting guidelines in her course, Pharmacology for APN. In the Health Assessment for APN, our colleagues, Dr. Jackson and Dr. Burgoyne, presented several case studies applying the assessment techniques used by NP students. We decided to consolidate all these resources so our NP students could use them to review for the clinical readiness exam. We focused on cases that would be commonly seen in the early levels of NP practice. Later, we thought, if these resources are so helpful for our students, why not share them with others preparing for the same exam and, subsequently, clinical experiences? Because of our commitment to providing accessible and affordable education for all students, we have developed this as an Open Educational Resource (OER) text.

I would like to be clear that all the Pathophysiology case studies (and some others within the text) utilized AI extensively during the development process. All areas of the text that used AI have the AI resource that was utilized noted in the reference list. In some cases, multiple different platforms were used, particularly as I learned the pros and cons of each. After much experimentation, I found a process that worked for me and have listed it below. I will continue to learn about and work with AI as I have come to believe it is a tool that faculty can utilize to efficiently and proactively develop resources and support student learning. I am far from an expert, but as I learn more about it, I find that I am better able to efficiently help my students master and apply complex content, which will improve their clinical readiness and performance when caring for patients. Ultimately, that is the goal of all my instruction.

AI process I followed in Pathophysiology Case Studies

1. The basis for each of the case studies was either my own video lecture recorded for the class or the video lecture recorded by my colleague, Dr. Molly Fuehrer, who co-teaches the course with me. We both utilized an excellent textbook written and edited by Dr. Tkacs and her colleagues. It is listed on the reference list for all cases.
2. To utilize these lectures, I first extracted the audio files from the lecture videos we created.
3. Then I took the audio files and converted them to transcripts using Microsoft Word.
4. I used the lecture transcripts as the primary source within the AI platform and added additional reliable resources for the AI generator to utilize (noted in the reference list for the case).
5. Then I prompted the AI to generate a patient scenario and provided it with specific details I wanted to include about the patient, the condition, and any relevant parameters.
6. I edited and modified the scenario, adding details, removing content I didn’t want, expanding patient characteristics, etc., until it was where I wanted it to be.
7. Then I asked the AI to use the provided sources to explain the pathophysiology behind aspects of the case that I thought were particularly important for students to understand.
8. I modified and edited the pathophysiology explanations until they included essential accurate content.
9. When I began using AI, the platforms used some additional content I did not provide. I have checked all of those resources and verified the accuracy of each. They are also listed in the reference list for the case studies.
10. Last, I developed review questions for students to use. After using the case studies in class, students requested additional review questions for practice.
11. Again, I uploaded the case studies and the original lecture transcripts into AI to generate additional review questions.
12. I reviewed, edited, and modified the review questions generated. I shared some of them with students so they could use them for practice, and others were added exclusively to the instructor’s test bank for use on future clinical readiness exams.

In addition to the three sections of the text that focus on cases applying content for each of the distinct courses (Pathophysiology, Pharmacology, and Physical Assessment), we included summative case studies at the end of the book that connect all three areas within one case. We know that these three courses contain the building blocks for the development of diagnostic reasoning, which is essential to clinical decision-making. The ability to connect knowledge from all three courses is an excellent indicator of clinical readiness and provides a solid base for the development of future clinical skills. Development of these summative case studies created an opportunity to involve other NP faculty experts both within and outside of our university. We are pleased to have nurse practitioner clinical faculty members, with a wide range of expertise and affiliations, share their experience in these case studies. I would like to thank Dr. Lopez, Dr. Jackson, Dr. Peterson, Dr. Cumella, and Dr. Burgoyne for their willingness to create these more extensive cases. During one semester, I also had the privilege of working with a very talented doctor of nurse practice (DNP) student, Jennifer Ring. Jennifer is a Board-Certified Adult Geriatric Primary Care Nurse Practitioner (AGPCNP-C). She works in a rural practice, where she has experience managing chronic metabolic, cardiac, and geriatric health issues. She helped create the metabolic/cardiac and osteoporosis pathophysiology case studies, and her contributions are greatly appreciated. I want to thank the entire team for this truly collaborative effort!

Additionally, I am grateful to the expert reviewers who contributed their time, energy, and expertise to evaluate the case study drafts before publication. The feedback and clinical guideline reviews were sincerely appreciated and strengthened the work. Thank you to the OER publishing team from SUNY Geneseo, who believed in this project from the start.

Also, thank you to the SUNY Brockport administration and the entire School of Nursing at SUNY Brockport. This project, and many others, would not have been possible without the support each of them has given and continues to give to every big idea I have had during my 20+ years on campus.

I also want to thank all of the students who have provided feedback and helped me learn how to be a better teacher, mentor, author, and editor. Your stories and efforts make me so proud of all of you. I am inspired by the incredible human beings you are. I know the world is a better place because of you, and I am truly honored to join you on your professional journey.

Finally, I want to thank my friends and family who share all the ups and downs with me on this wild ride of life. I am grateful to be with you through it all. I hope we have many more years together to learn, grow, and celebrate along the way. I love you all.

Beth