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Advancing Nursing Research Through Data Science

What is Data Science?

With advances in technologies, nurse scientists are increasingly generating and using large and complex datasets, sometimes called “Big Data,” to promote and improve the health of individuals, families, and communities. In recent years, the National Institutes of Health have placed a great emphasis on enhancing and integrating the data sciences into the health research enterprise.  New strategies for collecting and analyzing large data sets will allow us to better understand the biological, genetic, and behavioral underpinnings of health, and to improve the way we prevent and manage illness.

doc tablet handsNursing science is poised to make significant contributions to new advances that make use of the data sciences. The use of data science will enhance the efforts of scientists supported by the National Institute of Nursing Research (NINR) to: understand the biological basis of adverse symptoms of illness, such as pain and fatigue; examine new behavioral interventions for maintaining wellness and preventing chronic illness; and develop better strategies to help individuals and caregivers manage chronic illness.  

NINR-supported science, with its emphasis on multidisciplinary research, integrating the biological and behavioral sciences, and patient outcomes rather than disease-specific outcomes, is uniquely positioned to provide a platform for expanding the use of data science. The emergent field requires the development of new methods, tools, and skills, as well as additional researchers to pursue new applications of these methodologies. NINR is committed to supporting innovative nurse scientists as they integrate “Big Data” into nursing research through its intramural and extramural research programs, as well as through training and mentored research opportunities.

NINR’s Symptom Methodologies Boot Camp on Big Data

NINR annually sponsors a Symptoms Research Methodologies Boot Camp, a week-long intensive research training course that takes place on the NIH campus. In 2014 and 2015, the focus of this course was Big Data in Symptoms Research. The course provided a foundation in data science focusing on methodologies and strategies for incorporating novel methods into research proposals. More information is available at the Boot Camp webpage.

For the benefit of those individuals who were unable to attend in person, a video recording of the first day of the 2015 Boot Camp is available for viewing.

To watch videos from the first day of the NINR Big Data Boot Camp, click play on the YouTube playlist embedded below. Click on the horizontal bars at the top left to select individual speakers or segments; click play to watch the speakers in sequence.

Recent Advances and Current Activities

NINR supports numerous scientific activities in both its extramural and intramural programs that include data science as major components of the research programs. Here is a list of some of those activities:

  • At the P30 Center of Excellence for Biobehavioral Approaches to Symptom Management, nurse scientists pooled symptom data from research projects across diverse patient populations and different diseases and conditions to examine relationships between psychological states and nerve signaling chemicals. By using common data elements, these researchers were able to determine strong correlations among symptoms and perceived stress, but found weaker relationships between nerve signaling chemicals and symptoms, indicating the need for further research in the taxonomy of symptoms and biomarkers. PMID: 25218081
  • In the NINR Division of Intramural Research, researchers contribute to and share data with a range of sources. Dr. Jessica Gill and her team contribute data from their research on biomarkers for neurological and psychological impairments following traumatic brain injury to the Federal Interagency Traumatic Brain Injury Research (FITBIR) initiative. FITBIR is an informatics system and data warehouse developed to share data across the entire TBI research field and to facilitate collaboration between laboratories.

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  • Students enrolled in a nurse practitioner program routinely use a handheld mobile device in their practice to document their clinical encounters; this system has been shown to help assess student performance, as well as to strengthen evidence-based nursing practice. In a randomized controlled trial, decision support software was integrated into the devices that provided clinical practice guidelines for the screening and management of obesity and overweight, tobacco use, and depression in adults and children. The trial data included over 34,000 unique clinical encounters; data analysis of these encounters found that screening increased, but varied across condition in the development of management plans for the conditions diagnosed. By improving health care processes, nursing science is improving health care outcomes. PMID:25821418
  • In a large study funded in part by NINR that employed Big Data, a group of scientists investigated patient safety, decision-making, and nurse staffing characteristics. Examining discharge data from nine European countries, they found that an increased workload of one patient per nurse was associated with a 7% increase in the odds of surgical inpatient mortality within 30 days of admission. Based on this analysis, the researchers suggest that 1) nurse staffing cuts to save money might adversely affect patient outcomes and 2) an increased emphasis on  bachelor’s education for nurses could reduce preventable hospital deaths. PMID:24581683

Additional Data Science Activities

  • binary blueIn the field of data science, NINR has been particularly focused on the development of common data elements, which can allow for comparisons of variables across studies and patient populations. In 2011, NINR released a Request for Information (NOT-NR-11-010), which called for public input on an initiative to identify the best common measures for symptom assessment. Common Data Elements (CDEs) were also the focus of the 2014 NINR Center Directors Meeting: Integration, Leveraging, and Translating Common Data, and common data elements for symptom science were also the subject of sessions at the regional nursing research conferences in 2015. The meeting culminated in the publication of an article that identifies CDEs to be used across NINR-supported Centers to measure pain, sleep, fatigue, and affective and cognitive symptoms. PMID: 26250061A recent NINR funding opportunity announcement requires applicants for new NINR Center awards in self-management of symptoms to include CDEs in their research plan (RFA-16-NR-002). 
  • NINR also participates in the trans-NIH Big Data to Knowledge (BD2K) initiative, which was established to support new knowledge, maximize community engagement, and facilitate discovery. In addition to several funding opportunities listed below, NINR participated in the funding opportunities “Enhancing Diversity in Biomedical Data Science (R25)” (RFA-MD-15-005) and “Advancing Biomedical Science Using Crowdsourcing and Interactive Digital Media (UH2)” (RFA-CA-15-006), which both expired in early 2015.


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