40 min
Episode 284: How AI Is Influencing Cancer Care and Oncology Nursing The Oncology Nursing Podcast
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- Medicine
“We incorporate nurses and clinicians and users for any tool from the very beginning. They say, ‘You know, we need help with this.’ And then we start ideation: We start understanding the problem, we meet with them, we try to see what is it that they’re trying to do, is it feasible given the data we have? We go back, we do some research, feasibility study. We say we think this is something we can predict with decent performance. Now let’s do it,” Nasim Eftekhari, MS, executive director of applied artificial intelligence (AI) and data science at the City of Hope National Medical Center in Duarte, CA, told Lenise Taylor, MN, RN, AOCNS®, BMTCN®, oncology clinical specialist at ONS, during a discussion about how the use of AI in cancer care affects an oncology nurse’s daily work.
Music Credit: “Fireflies and Stardust” by Kevin MacLeod
Licensed under Creative Commons by Attribution 3.0
The advertising messages in this episode are brought to you by LUNGevity.
Episode Notes
Oncology Nursing Podcast:
Episode 281: Nursing’s Role in AI in Health Care
Episode 131: NLM Is Changing Health Care Through the Power of Data
ONS Voice articles:
New Technology Tools Help Oncology APRNs Improve Patient Outcomes
AI Ultrasound Is Nearly 100% Accurate in Detecting Thyroid Cancers
Nursing Informaticists Are the Backbone of Technology-Driven Care
What ChatGPT Says About Belonging and Oncology Nursing
Clinical Journal of Oncology Nursing article: Technology and Humanity
Oncology Nursing Forum article: Artificial Intelligence for Oncology Nursing Authors: Potential Utility and Concerns About Large Language Model Chatbots
Primers for AI concepts and terminology:
Introduction to Artificial Intelligence for Beginners
12 Important Model Evaluation Metrics for Machine Learning Everyone Should Know
To discuss the information in this episode with other oncology nurses, visit the ONS Communities.
To find resources for creating an ONS Podcast Club in your chapter or nursing community, visit the ONS Podcast Library.
To provide feedback or otherwise reach ONS about the podcast, email pubONSVoice@ons.org.
Highlights From Today’s Episode
“So, there is a lot of applications of AI in cancer care, so I can't possibly give you an exhaustive list. But the ones that come to my mind, at least the ones that we are actively working on are early detection and diagnosis, treatment planning, predictive modeling for predicting unwanted outcomes, remote monitoring, radiology applications, pathology applications, improving operations and helping the resource allocation, precision medicine, and research. And we also started a year or so incorporating AI and helping with drug discovery.” TS 2:13
“We’ve been using AI for a very, very long time. Recently, we just hear more about AI, but AI is in our lives, in health care or not, all day, every day. Google Maps, Google search, all of this is enabled by AI, but we may not realize even that we’re using it.” TS 8:27
“So, for technical challenges, you have to always consider: Is this model performing in a decent manner for this application? And depending on the use case, that’s different. If you’re providing a decision support to someone that is impacting patient care, then you have to be very careful about model performance. So, model performance is one technical consideration, then how do you really technically integrate with the EMR system? It’s not easy, EMR systems are not usually very open, and that’s a whole challenge in itself to be able to read from any EMR system in real time and feed data back into it in real time.” TS 10:16
“For nurses to successfully approach and adopt this work, I think the most important thing is to keep an open mind to really realize that these technologies can, at best, tak
“We incorporate nurses and clinicians and users for any tool from the very beginning. They say, ‘You know, we need help with this.’ And then we start ideation: We start understanding the problem, we meet with them, we try to see what is it that they’re trying to do, is it feasible given the data we have? We go back, we do some research, feasibility study. We say we think this is something we can predict with decent performance. Now let’s do it,” Nasim Eftekhari, MS, executive director of applied artificial intelligence (AI) and data science at the City of Hope National Medical Center in Duarte, CA, told Lenise Taylor, MN, RN, AOCNS®, BMTCN®, oncology clinical specialist at ONS, during a discussion about how the use of AI in cancer care affects an oncology nurse’s daily work.
Music Credit: “Fireflies and Stardust” by Kevin MacLeod
Licensed under Creative Commons by Attribution 3.0
The advertising messages in this episode are brought to you by LUNGevity.
Episode Notes
Oncology Nursing Podcast:
Episode 281: Nursing’s Role in AI in Health Care
Episode 131: NLM Is Changing Health Care Through the Power of Data
ONS Voice articles:
New Technology Tools Help Oncology APRNs Improve Patient Outcomes
AI Ultrasound Is Nearly 100% Accurate in Detecting Thyroid Cancers
Nursing Informaticists Are the Backbone of Technology-Driven Care
What ChatGPT Says About Belonging and Oncology Nursing
Clinical Journal of Oncology Nursing article: Technology and Humanity
Oncology Nursing Forum article: Artificial Intelligence for Oncology Nursing Authors: Potential Utility and Concerns About Large Language Model Chatbots
Primers for AI concepts and terminology:
Introduction to Artificial Intelligence for Beginners
12 Important Model Evaluation Metrics for Machine Learning Everyone Should Know
To discuss the information in this episode with other oncology nurses, visit the ONS Communities.
To find resources for creating an ONS Podcast Club in your chapter or nursing community, visit the ONS Podcast Library.
To provide feedback or otherwise reach ONS about the podcast, email pubONSVoice@ons.org.
Highlights From Today’s Episode
“So, there is a lot of applications of AI in cancer care, so I can't possibly give you an exhaustive list. But the ones that come to my mind, at least the ones that we are actively working on are early detection and diagnosis, treatment planning, predictive modeling for predicting unwanted outcomes, remote monitoring, radiology applications, pathology applications, improving operations and helping the resource allocation, precision medicine, and research. And we also started a year or so incorporating AI and helping with drug discovery.” TS 2:13
“We’ve been using AI for a very, very long time. Recently, we just hear more about AI, but AI is in our lives, in health care or not, all day, every day. Google Maps, Google search, all of this is enabled by AI, but we may not realize even that we’re using it.” TS 8:27
“So, for technical challenges, you have to always consider: Is this model performing in a decent manner for this application? And depending on the use case, that’s different. If you’re providing a decision support to someone that is impacting patient care, then you have to be very careful about model performance. So, model performance is one technical consideration, then how do you really technically integrate with the EMR system? It’s not easy, EMR systems are not usually very open, and that’s a whole challenge in itself to be able to read from any EMR system in real time and feed data back into it in real time.” TS 10:16
“For nurses to successfully approach and adopt this work, I think the most important thing is to keep an open mind to really realize that these technologies can, at best, tak
40 min