KAREN KOLEGA: Good afternoon, Joan. It's such an honor to be here, and like you said, I'm very excited for this series, especially for today's episode. Technology is something near and dear to me in the OB space, and we've got great, valuable information coming your way.
JOAN PORCARO: Thank you, Karen. We're also joined by Dr. Debbie Ketchum, and she serves as the Clinical Engagement Specialist at PeriGen. Welcome, Debbie.
DEBBIE KETCHUM: Thank you so much, Joan, and welcome, Dr. Kolega. I'm very happy to be here with you all and sharing this insightful information.
JOAN PORCARO: So, I asked you both to join me here today so that we can continue that discussion that we have brought forward in the other sessions for our podcast series. And we're looking at not only focusing on patient safety but also looking at avenues to reduce liability for the care team. So, my first question is this. How has the medical industry supported maternal health safety using technology?
KAREN KOLEGA: Joan that's a great question, and it's such a current and attractive topic and one in which I'm quite invested and very passionate. Technology and health care is quite a broad topic, and I'm really excited to see coming out of this podcast if there's certain focus areas that our listeners would like to hear more about. A foundational concept when considering technology and health care is that technology can reduce variance in care, both in real time and when we retrospectively review through reporting of data to support PI initiatives. Reduction of variance in care is key in improving quality and safety and, thus, reducing risk. I'm going to do my best here to hone in on technology associated with maternal health.
First off, I think it's really important to note that the obstetric service lines in the hospitals, during the late 1990s and early 2000s, were well ahead of the curve in their adoption of digital systems for capture of care. OB was one of the only areas-- the other one that comes to mind is surgical services-- that had niche electronic systems prior to adoption of the universal EMR. The advent of the universal EMR was a great step forward in health care in America. Yet, many of the universal EMR systems lack the precision for the specialty areas, like obstetrics.
There's a plethora of evidence that use of technology to provide telehealth care or remote care in obstetrics is feasible, it's cost effective, and delivers effective care, especially in those areas focusing on blood pressure management. This is very important, as hypertensive disorders are so prevalent during pregnancy, blood glucose management and weight management during pregnancy. Ochsner Health, in Louisiana, they offer a program called Connected Moms. They've been doing that since 2016, where patients can use digital tools, like blood pressure cuff and a scale, at home to manage pregnancy, with fewer visits to their health care providers.
A recent systematic review entitled "Effectiveness of remote fetal monitoring on maternal fetal outcomes" showed that remote fetal monitoring had a favorable effect on reducing neonatal asphyxia. Remote fetal monitoring can refer to either monitoring the pregnant patient in their home, during the prenatal period, or monitoring the patient who's laboring in the hospital from a central remote location. These study results are promising, but there was a significant limitation, in that it included only nine randomized controlled trials, and this itself demonstrates the lack of adoption of this modern technology to improve care and reduce risk.
DEBBIE KETCHUM: KK, I just love the information that you provided again, and I just want to summarize that AI machine learning technology does play a significant role in quality and safety innovations in the health care sector. And with the help of AI, health care professionals, including but not limited to our scientists, our researchers, our leaders, our nurses, doctors, independent providers, can process a significant amount of data to make systems more reliable, efficient, and improved diagnosis treatment, even staffing, patient placement, and care plan decision making. Health organizations and providers are using AI-driven digital first technology to optimize personalized and effective patient interactions.
AI and machine learning technology can include mitigating care and quality barriers in rural areas, something I'm very passionate about, for many reasons, including where I live in the United States, and establishing more efficient communication methods, such as voice recognition and natural language processing to improve the clinical documentation processes. Machine learning algorithms can be utilized in wearable heart monitors and mobile phone apps to monitor the patient's condition remotely. And I think of the machine learning algorithms, a year ago, when I had sepsis, and it really was my Apple Watch that alerted me that my vital signs and my heart rate was off the charts for me. And that was my first alert, and it really took me to the ED. And then everything got moving with the algorithms after that, and I'm here today to be able to speak to that.
KAREN KOLEGA: Well, and we're glad you are, Deb, and what you so succinctly said is this is broad. This is a really broad topic, and it covers so many areas. And I love so many of the examples that you bring up, because they're so real and can be applied to help improve that care of moms and babies and all those birthing families. So again, there's large opportunities for expansion in women's health care.
I think about some of the examples. It's certainly something I watch. There's a ton of blogs out there that you can participate in to find out what's going on in tech and AI and OB.
And think of some of the ones that like sparked my interest recently and some successes is Northwell Health. They have an AI-driven pregnancy chatbot. It's a pilot program that links the chat bot with a care management team that enables escalations to inpatient care, if a patient's responses indicate a serious issue.
During the pilot, one woman's responses prompted immediate intervention. She was found to have severe preeclampsia and required hospitalization. Another patient shared thoughts of self-harm and reported that she had not shared her symptoms with a provider but felt comfortable sharing with a chatbot because of that feeling of anonymity. She was connected to mental health support within 24 hours, and that is like I hadn't thought about that, that anonymity that is brought by working with a chatbot.
A similar AI-driven chatbot program, Healing at Home, is utilized at UPenn to support postpartum needs of parents and babies. A quick Google search demonstrates several health systems have similar chatbots that are in development of those services for both that prenatal space and that postpartum care. An AI-driven early warning system for mom and baby in the intrapartum space, while the mom is on the ENFM is also in use at multiple hospitals and health systems across this nation.
Ochsner Health, in Louisiana, again, is an example and has published outcomes using a centralized, remote, fetal monitoring command center model like this. And they were able to publish outcomes of decreased unexpected admissions to the NICU, decreases in their C-section rates, and decrease in operative vaginal delivery. It's based on an early warning system that's AI driven. Early warning systems have been utilized in many health care service lines for over a decade with improved outcomes. We need to be utilizing this kind of technology to reduce risk for the vulnerable population of moms and babies.
The AI-driven technology systems for intrapartum care empowers health care providers with objective and automated, real-time insights and decision support. These help enhance patient safety and promote better outcomes for moms, birthing families, and babies. This patented and FDA-approved technology is currently available, in the industry, out there, commercially, by PeriGen.
DEBBIE KETCHUM: I love that we're focusing our conversation here on reducing the risk for a vulnerable population of moms and babies and preventing any more harm in this area. And I know there's a lot of questions around AI, because we hear of voice-over scams and et cetera. So, I just wondered if we could take a minute to go back to basics. There are differences between generative AI, ChatGPT, and you talked about the chatbot. And Joan, was wondering if you could go over some of those details with us, and maybe our listeners have the same questions.
JOAN PORCARO: Oh, thank you, Deb. I would agree. There's still a lot of confusion out there as to what AI really is, and I think we're all still learning.
When you look at what's out in the literature and on websites, well, everyone's definition of AI is different, depending on who you ask. Generally speaking, AI is a broad branch of computer science, and it's concerned with creating those systems and the applications. Because machines are capable of performing tasks, and it achieves this really by processing and analyzing volumes of data, just tremendous volumes of data. And it learns from the computer-- the system learns from the data that it's gathering, and it enables it to understand and helps it learn from past data points through specifically designed AI algorithms.
And the important thing I want to mention is, those designs and those algorithms, the human touch is still needed. Essentially, when we're looking at AI, various terms, they're used interchangeably. You'll hear AI, predictive analytics, machine learning. When we think about predictive analytics, it's a common tool that we've all used in our careers that looks to data science, and it helps to interpret historical data. And it helps to make informed predictions about the future, and it employs techniques, such like data mining, modeling, again, machine learning, statistics, aiding in identifying upcoming risks and opportunities for organizations.
Now, when you think about it, when we think about examples of predictive analytics and action that we might experience every day, we think about weather forecasting. We think about Amazon's recommendations for purchases, and how is it that it seems to know a lot more about us than we really would like it to know, and modeling of flu trends, and then even from the perspective of insurance risk assessment. So, when we look at AI, we look at machine learning, to some extent, they overlap, and again, I'm not an expert here.
I'm sure many folks listening in could probably speak to this as well. But with machine learning, it's sort of a subset of AI, but there's key differences. And beyond just the fact that AI is a broader term than ML, Machine Learning, AI aims to create computer systems that can imitate the human brain-- not eliminate it but imitate it-- and focus on broad and complex problems.
So, in contrast, machine learning is more task focused, training machines to perform specific tasks and learn in the process. Whereas AI tends to focus on solving broad, complex problems. Whereas machine learning focuses on streamlining a certain task to maximize performance.
Machines can learn from experience and can be trained to accomplish those specific tasks. So, some applications of AI, again, seeing it in your everyday life, clinical pathway decision making, the wearable tech, Apple Watch. And offsite, you both spoke about home-based patient monitoring.
AI can enhance efficiency, support workforce shortages, and provide accurate work product and reduce some training costs. There's some really neat programs out there where AI is involved in training. So, in addition, there's concerns, though, about the use of AI, such as biased decision making, socioeconomic inequality, privacy violations-- it's still something we're all concerned about-- and just lack of clear guidelines on informed consent that may result in further ethical dilemmas.
DEBBIE KETCHUM: Joan, I appreciate your summary and your context, because as AI is such a hot topic, and there's a lot of resources to sift through, just like KK had mentioned, she looks at a variety of different areas. There's a variety of information being sent to us too and trying to sift out the good, the bad, and really seeing how it does help us in the health care sector. So, there's a lot of definitions and classifications and differentiators. There are resources with MIT that agree, when people typically talk about AI, they're referring to machine learning models that can learn to make predictions based on data. And generative AI describes a machine learning model that is trained to create new data, rather than making predictions about specific data sets.
An example of generative AI includes ChatGPT. This is an artificial, intelligent chatbot that uses natural language processing and deep learning models to generate human-like conversation and dialogues, such as text. I reflect on how often I've been encountering the chatbots, customer service when making travel arrangements, search inquiries, and shopping. Others include Amazon and Grammarly.
JOAN PORCARO: So, I'll throw this question out to either one of you. Would there be further benefits that could be achieved in low-resource areas, such as the rural setting?
DEBBIE KETCHUM: Oh, great question and something that I've been thinking a lot about. It's an area of passion for me, like I mentioned before, as we continue to evaluate equitable quality care and access. And being in a state where 35 out of the 44 counties are rural, and 18 of the 35 counties go even further and are categorized as frontier, with fewer than six people per square mile, and our country continues to increase in our care deserts.
We learned from the pandemic how to reach people through telemedicine, and it's exciting to continue these innovations for our communities to increase compliance to their appointments, being able to ask and answer questions, improve convenience. For example, in remote care, there are some people-- and this is so true-- that they have to take an entire day off for just a 20-minute appointment, because the distance they have to drive. Two hours to an appointment, then you have your appointment, and then you come back. Before you know it, four or five hours has gone by, and they had to take a day off of work and lose income.
And you can also use it for staffing support for critical access with tertiary centers, not to replace the people but to provide staffing support at critical access. When maybe a nurse has a question for a tertiary center, as they contemplate whether it needs to be transported or not, we can actually improve with our technology and have face-to-face interactions through telemedicine.
KAREN KOLEGA: Deb, you bring up some great points there. Joan, we could do a whole other podcast on how to support critical access, rural settings, because certainly, telehealth is an element of an answer to that. Honestly, traditional prenatal care is just not friendly to underserved regions, and technology offers great promise for the delivery of service to those areas that are rural that have critical access.
Recurrent prenatal visits require multiple resources and costs, and as you already noted, Deb, there was a massive adoption of remote care during the COVID pandemic, obviously, due to the restrictions. But an important factor there was there was modifications made in public policy to expand reimbursement for telehealth services, and those emergency measures stayed for quite some time. I think they are timing out the end of this year.
Many private commercial insurance companies also made those changes. Studies have shown high satisfaction with virtual and hybrid models of care. Satisfaction scores were likely affected by, as you said, that reduced travel time, and you get to do it in the comfort of your home, in your pajamas.
DEBBIE KETCHUM: Right.
KAREN KOLEGA: Reduce time away from work and reduced wait times. Hybrid models with a combination of both virtual and in-person care are quickly becoming the reality in our post-pandemic environment. Recently, the Avera health system was one of four health systems in the United States that received a Health Resource and Service Administration-- we call that HRSA-- grant to improve maternal rural care. Part of their program will implement an OB command center model that uses AI-driven technology for every mom in the health system that's on a fetal monitor.
This model offers 24-hour support for nursing staff and all hospital OB units, and this is vital resource in limited settings, like rural and critical care access hospitals, and in settings that have less experienced OB nursing workforce. You already mentioned that bi-directional, where somebody on an outsite could then use that experience and that expertise that's remote from them to help guide some of those decisions for care. So just great opportunity coming forward.
JOAN PORCARO: Thank you both. Throw another question out there. What can we advise the care team, the front line, that support, and provide women's care with the use of technology? We want to improve outcomes, we want to decrease risk, but how do we help support our team members?
DEBBIE KETCHUM: Great question.
KAREN KOLEGA: Yeah. It is another great question, Joan. And first and foremost, there are certainly front-line workers out there that have interest in technology, and they need to become more aware and versed in what's currently available. And I have to say you're listening to this, so kudos, because it's a great way to help increase your knowledge.
The future of health care includes advanced AI technology, and we need to embrace this. Sometimes, with adoption of change, health care, we're not so great about it. I do a presentation across the nation on innovation and AI in obstetrics, and I can tell you, one of the slides in my deck has changed over the years.
But I've left all of the content on it, and it basically said, five years ago, it's like, AI is coming, and now that's crossed out. And then about two years ago, it said, AI is here, and now that's crossed out. And now it says, AI is taking off, and my fear is, if we don't get on the train, we're going to be left behind. And nurses need to be part of those decisions.
Nurses and specifically nurse informatics professionals, they play a vital role in the purchase, implementation, and adoption of these technologies. The knowledge that we bring to the table regarding the implications for clinical care, how it can affect our key performance objectives, and the nursing practice indicators is vital. Many of us have story after story of technology. We've all lived this.
The technology has been implemented, and we were never asked. There was not appropriate clinical input, and the disconnect and the downstream that happens after that, and there's a lot of loss of value and a loss of trust. We must use our voice and expertise to focus on the value-based outcomes that can be realized for patients, their families, the staff, hospitals, health systems, and the communities that we serve.
DEBBIE KETCHUM: KK, I really appreciate you saying that. We need to be at the table. Our expertise matters. Right? And there's key indicators that really impact nursing, and so the more that we are involved, our voice and expertise to focus on these value-based outcomes, really can be realized. And thank you for pointing that out.
We use technology for the holistic care for our patients. Joan, these were the points that I was thinking of with your question as well, that technology will provide the data, such as labs, the vital sign alerts, best practice algorithms for improving outcomes. However, again, our human factor is essential to augment these tools for improving quality decision making, care planning, and reducing risk, thereby also reducing our liability.
JOAN PORCARO: Thank you, Deb. So, we're coming to a close to our session already. So, what are some key takeaway points you'd want to leave with our listeners?
KAREN KOLEGA: Thanks for asking that, Joan, and I can tell you, it makes me think about your last question too. Because I always want to make sure that you have that whole continuum, something that can help leaders, something that can help bedside nurses. And I don't know if many are aware that there is a National Consortium of Telehealth Resource Centers, and one of the reasons I go there is its web based. So 15, 20 minutes, just go on and search a little bit. You will learn.
But there's 12 centers across the United States that are funded by the Department of Health and Human Services, and these centers, they serve us. They serve to advance the effective use of telehealth and support access to telehealth services in rural and underserved communities. There is no fee. There's no-- you're not required to do anything; except they will continue to feed you then resources of maybe upcoming meetings or webinars that they're having. So hopefully, we're able to put the link into some of the content here, so you can see how easy it is to just.
KAREN KOLEGA: Click your state, and yeah, and you can learn more. it's a wonderful resource out there to help support the adoption and driving of this technology. And then, of course, if you'd like to learn anything more about the modern AI-driven perinatal technology for moms and babies, then you can contact PeriGen for more information in that intrapartum space.
DEBBIE KETCHUM: KK, those are wonderful takeaways, and I'm excited to go look at that Telehealth Resource Center in more detail and see what they feed me. Right?
KAREN KOLEGA: Yeah.
DEBBIE KETCHUM: And an additional resource that I have appreciation for is the National Institute of Health's National Center for Biotechnology Information Resources, and it's around AI transforming the practice of health care. The NIH is a helpful resource, as our listeners research this topic. Technology innovation and AI solutions are transforming care at a scale by leveraging real-world, data-driven insights and achieving the quadruple aim of improving patient health, improving the patient's experience of care, enhancing our caregiver experience, and reducing the rise and cost of care.
I would be remiss if I didn't share my takeaway regarding advocacy for safety and quality innovation. As I mentioned to you before, almost a year ago today, I was hospitalized with a life-threatening diagnosis of sepsis. When I came into the emergency department, the care team-- almost immediately when I walked in and stated that I really honestly all I could say to them is I feel like I was falling apart.
They utilized their empathy and critical thinking skills to initiate the AI-supported screening tool, quickly diagnosed me with sepsis, and swiftly initiate the treatment protocol. Precious time was saved, and my family and I are so thankful. And as a thoughtful continuous health care leader and learner, I am thrilled how technology, innovation, and AI solutions are evolving to support our care team's passion and mission to provide safe, equitable, efficient care and improve access and its growth in the perinatal and neonatal specialties.
JOAN PORCARO: Thank you, Deb. Well, first, I want to acknowledge Karen for joining us today. Thank you, Karen.
KAREN KOLEGA: Thank you, Joan, for having me. It's always a pleasure.
JOAN PORCARO: And always, Deb, I appreciate your time and your expertise and all that you've shared with our listeners today. So, thank you for joining us today.
DEBBIE KETCHUM: Thank you so much, Joan. It was wonderful to be here with you and KK to talk about such an important topic.
JOAN PORCARO: And in closing, I want to thank our audience and those who have tuned into our discussion. I hope you'll be joining us for future discussions in the coming week, and again, thank you for listening to our podcast, WTW "Vital Signs."
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