Collaborations That Expand Our Nursing Knowledge

"It's hard for me to believe I'm the first person to have thought of this," laughs associate professor Pamela DeGuzman (COLL `92, BSN `96, Darden `00, MSN `00, PhD `12) of the research she's just begun, which aims to demonstrate how many of Virginia's rural-dwelling toddlers, often far from a pediatrician's office, fall through the cracks when it comes to early autism screening.

In the best cases, DeGuzman explains, "we screen at 18 months, but the last early childhood vaccination lands at 15 months. Our hypothesis is that parents may finish their vaccinations and, if the child doesn’t get sick enough to need care, don’t return for autism screening until the pre-kindergarten required visit up to three years later"—a point, DeGuzman says, that may be too late.

“Child brain development is so plastic," says DeGuzman, "but when caught early, and with the right therapy, autism is something we can do something about—provided we get there early enough.”

Nurse scientists' work reflects a concept instilled across all levels of nursing education: holistic care. That means understanding patients' present, their future, and the barriers they face, including poverty, education, and finances—even the critical absence of transportation. Nurses are also able to visualize gaps in care, and perhaps most crucially, recruit the right resources to get the job done. For DeGuzman's research, the work also means enlisting the assistance of Guoting Huang, a Geographic Information System (GIS) specialist and assistant professor of urban planning at UVA’s School of Architecture.

"You have to learn each other’s languages because, even if you are talking about the same thing, you approach the topic from different angles.”

Virginia LeBaron, assistant prof, who's collaborating with engineer John Lach and palliative care MD Leslie Blackhall

GIS imaging is the visual representation of data within set borders—a state, county, ZIP code, or neighborhood, for example—and its visuals pack a powerful punch when trying to make a point about health, politics, or economics. But if reading a GIS map is easy, filling in that map with meaningful color blocks requires data analysis best left to an expert.

Says DeGuzman, “Dr. Huang is amazing with data. He is the one who can show the statistical significance needed for a study to be accepted as relevant and important. And as a nurse, he's the critical partner I need to prove my hypothesis—and potentially change the manner in which we screen kids.”

If DeGuzman’s research and Huang's analysis indeed shows a gap in rural Virginia's autism screenings, the work will continue, spurring a series of subsequent collaborations with Curry School of Education scientists, UVA statisticians, and others. It's just one example of how nurse scholars continue to partner across professions in ways that embolden and enrich their work.  

"For a long time it's been clear that to produce the best research we can no longer just talk amongst ourselves, or even just with our colleagues in medicine," says Dean Dorrie Fontaine. "Tapping the rich expertise that surrounds us not only expands the strength of our nursing science, it's efficient, logical, and yields powerful results."

While partnerships are nothing new, UVA nurse scientists' collaborations are expanding, as they unbrick the traditional silos that keep disciplines apart.

They might begin as cold calls, or tentative email queries, then expand into conversations over coffee, and, when the partnership's right, agreed-upon topics and purpose. They're encouraged by seed grants. And they're fortified as each scholar—a nurse, a mathematician, architect, engineer, psychologist, a computer science expert or a software engineer—comes to understand and appreciate a new scientific language and culture.

It all translates into a nursing paradigm in which many hands, many perspectives, and many nurse leaders bring exponentially greater opportunities to improve patient care. Together, these scholars of disparate professions come to move as one to urge health science forward with common purpose: improving care, bettering quality of life, and seeking—and seeing—positive health outcomes.

final+

60 to 90%

of cancer patients report poorly managed pain

Partners Against Pain

“The unpredictable nature of cancer pain is, for some patients, the worst part,” says Virginia LeBaron (BSN ‘96), an assistant professor of nursing who's built a career caring for patients suffering with cancer pain, work that's both affirming in its help, and confounding in its scope. “Anywhere from 60 to 90 percent of patients with cancer report poorly managed pain, which, of course, affects sleep, mood—even whether patients come back for treatment."

Discovering the nature of pain—the when, where, and why of it, as well as how the environment, sleep habits, and interactions with at-home caregivers play a role in its exacerbation or alleviation—has been on LeBaron’s mind for years. But it wasn't until she met UVA engineering professor John Lach and palliative care physician Leslie Blackhall that plans for an investigation began to coalesce.

“We would have these great conversations about what could be possible with a tech approach to pain management," says LeBaron, of early meetings with Lach, "given that we are both interested in the health application of wireless and body-sensing technology. When we heard about the Engineering in Medicine (EIM) seed grant, it was the perfect opportunity to work together.”

Today, a $100,000 EIM seed pilot program grant is bringing together LeBaron's expertise with late stage cancer patients, Lach's tech savvy, palliative care physician Leslie Blackhall's patients, who were willing to volunteer as study subjects, and a device affectionately nicknamed "BESI."

BESI-C—which stands for Behavioral and Environmental Sensing and Intervention for Cancer—is a package of wireless and sensing technology developed by Lach that continuously records external data—such as light levels, temperature, and noise volume—coupled with readings from a smart watch worn on the wrist monitoring a patient's heart rate, activity level, and sleep duration, and quality. The smart watch is also used to collect data regarding a person’s mood or their pain levels through prompts with a simple question. To better understand cancer pain at home, when patients experience pain, or when their caregivers observe it, they mark the event on the watch, information that’s then integrated with environmental data to “create a rich context to help us understand not just that pain exists, but its context,” LeBaron explains.

The data will ultimately provide a foundation on which personalized pain management strategies may be built. So if a patient's pain appears linked to activity level in the home, or room temperature, for example, lighter clothing or the number of visitors may be appropriately shifted. In the high-interest field of precision medicine, BESI-C may be a game-changer for cancer patients being cared for at home.

“Our long-term goal for BESI-C is to actually help predict pain," explains LeBaron, "and, equally importantly, empower patients and caregivers to intervene before an episode of pain actually takes place.”

Looking at the same problem from the viewpoints of many disciplines also makes a difference in cracking the code of cancer pain, LeBaron says.

"You have to learn each other’s languages," she adds, "because, even if you are talking about the same thing, you approach the topic from different angles.”

final+

Data in Dreams

Taking on the number one cause of death in the United States requires expansive work from a world-class team, like the six UVA scholars currently linked across nursing, public health, mechanical engineering and medicine investigating the causes of cardiovascular disease.

Sorting through more than 5,000 sleep recordings from UVA's sleep lab—called polysomnographs, or PSGs—is Jeongok Logan, assistant professor of nursing and a scholar in cardiovascular disease, Min-Woong Sohn, a statistician, Jennifer Mason Lobo, a professor of public health sciences, Soyoun Kim, a social worker and research analyst in public health, Younghoon Kwon, a professor of medicine, and Hyojung Kang, a scholar in artificial intelligence and machine learning.

“Previous studies have overlooked the multi-dimensional nature of sleep and its complex interactions with cardiovascular risk factors,” says Kang, “typically focusing on a single domain of sleep—like sleep quality, duration, and circadian rhythm—rather than on the entire spectrum of sleep simultaneously.”

The yearlong, $132,000 study will drill down through the data using machine learning analytics to overcome the challenge of simultaneous assessment of the many variables contained in a good night’s sleep—first identifying which of the 5,000 patients have cardiovascular disease, and then seeking commonalities among them—to source those elements of disordered sleep that lead to hypertension and cardiovascular disease.

“We know sleep quality affects cardiovascular health, and know that blood pressure variability is an independent factor that predicts cardiovascular disease. So how can we connect the dots?”

Jeongok Logan, assistant prof, who's collaborating with MDs, MSWs, public health and AI experts and engineers

5,000+

number of PSGs (sleep recordings) that Logan and colleagues will comb through

“We want to know how sleep contributes to cardiovascular disease and health,” explains Logan. “We know sleep quality affects cardiovascular health, and know that blood pressure variability is an independent factor that predicts cardiovascular disease. So how can we connect the dots?”

The search for a causal link between sleep disorders and cardiovascular risk will look at the many variables recorded during a PSG, including brain waves, eye and muscle movement, heart rate, breathing, oxygen saturation, and blood pressure. Lobo, Sohn, and Kang are programming and managing the machine learning aspects of the data with engineering students tackling programming and input data alongside nursing students who use their clinical knowledge to validate data and identify outliers (a heart rate so far out of normal range that it indicates a device error, for example).

Because of the significant size of the data set, part of the work involves nursing, medical, and engineering students trimming away unnecessary numbers, yielding information that Logan—who's studied the sub-clinical markers of cardiovascular disease, and how people progress toward high blood pressure—will analyze.

If the study is successful, the researchers hope to pinpoint the specific variables in sleep that put people at risk for cardiovascular problems. But the benefits are broader than that. Engineering students will also come to understand the clinical environment, and its terminology, helping them design with patient care in mind, while nursing students will grow familiar with the algorithms and programming necessary for predictive medicine.

final+

 

“Nurses want to know what goes into any model that's been developed to augment their care. We are trained to look for the why before we intervene."

Jessica Keim-Malpass, assistant prof, who, with MD Randall Moorman, presented a predictive software program in the ICU

Visual Metaphors

A keen interest in technology, coupled with a background in oncology and pediatric critical care, led Jessica Keim-Malpass (MSN ‘05, CNL ‘08, PhD ‘11), an assistant professor of nursing, to join UVA’s Center for Advanced Medical Analytics, a group of clinicians and quantitative scientists dedicated to using clinical data to forecast illness.

Led by Randall Moorman, a physician with background in computational predictive models and physiological research who heads Advanced Medical Predictive Devices, Diagnostics, and Displays (AMP3D), UVA’s commercial partner, the group is piloting new software to predict patient deterioration through a CoMET® (Continuous Monitoring of Event Trajectories) score.

CoMET has already found early success in UVA's Surgical Intensive Care Unit, where it helped caregivers predict and initiate timely interventions for catastrophic events, like hemorrhage or intubation, before the need became a life-threatening emergency.

With $340,000 from UVA’s Translational Health Institute (THRIV), Keim-Malpass led the introduction of CoMET to UVA’s Surgical/Trauma ICU nurses, a natural fit given her in-depth understanding of ICU nursing, and work that’s helping shape the way the new system is conveyed to clinicians going forward.

"Tapping the rich expertise that surrounds us not only expands the strength of our nursing science, it's efficient, logical, and yields powerful results."

Dean Dorrie K. Fontaine

 

“Nurses want to know what goes into any model that's been developed to augment their care," explains Keim-Malpass, who notes that the CoMET visuals help clinicians get a quick read on what's likely to happen with their patients. "We are trained to look for the why before we intervene."

CoMET's why is based on data gathered every two seconds from vital monitoring equipment used in the ICU—heart and respiratory rate, blood pressure, body temperature, and so forth. The software then crunches that data, yielding a portrait of risk for emergent intubation (on the x-axis) and hemorrhage (on the y-axis). Patients with elevated risk appear as sky-streaking comets, labeled by their room numbers. As risk increases, the comets grow larger, taking on distinctive red and orange hues.

Still, a dramatic CoMET score doesn't automatically mean clinicians intervene before a critical event takes place. It does mean, however, they're likelier to be ready for it.

“It’s hard to act on proactive data,” explains Keim-Malpass. “It can happen that you’re looking at your patient and, even though their breathing, heart rate, blood pressure are all good, you’ve got a CoMET score showing high likelihood of intubation. It can be hard to make sense of that at first.”

Guided by principles of stakeholder engagement to inform CoMET’s introduction "recognizes nurses’ need to know what components go into the score, and how it turns out a number,” says Keim-Malpass.

Creating a revolutionary patient care technology is in itself a challenge, but even studied and proven, its ultimate success depends on buy-in and implementation. Nurses, with good reason, are inherently critical thinkers who won't act on interventions without rationale, Keim-Malpass notes. With the multitude of lessons learned from CoMET's pilot, Keim-Malpass, Moorman, and their colleagues are on a path toward better understanding the process of introducing new technology into the clinical context.

final+

 

More than ever, today's nurse scholars at UVA are eager collaborators, in stark contrast to centuries of independent scientists who toiled alone toward breakthroughs that cemented their name in history. And while there is a place for lone researchers, today's best scholarship is done hand-in-hand, combining the expertise and perspective of many, colleagues equal partners in the game.

Discoveries that better our health and human life on our delicate planet are needed now more than ever. Thanks to the interdisciplinary cooperation of our scholars, UVA is getting that work done.

Writer Melissa Crawford, MSN, RN - who graduated from UVA Nursing's Clinical Nurse Leader program in 2015 - is a freelance health writter and blogger based in Northern Virginia. An expereinced clinician, cancer survivor, mother, and health advocate, Crawford established her own company - Context Health - in 2016 and contracts with companies pushing forward innovative soluations to improve care quality and safety. Crawford's essay, "Putting on your 'professional hands,'" was published in the Washington Post in April, 2015.