The Symbiotic Collaboration Between Family Nursing and Data Science

Pruinelli, Lisiane; Farag, A.S.; Looman, Wendy S.; McKechnie, Anne Chevalier; Monsen, Karen A.; Gelderen, Stacey Van; Dunn-Lopez, Karen

doi:10.1097/cin.0000000000000675

Cited by 2 publications

(4 citation statements)

References 9 publications

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“…Therefore, a data science method was applied based on the Applied Healthcare Data Science Roadmap adapted from the CRISP‐DM tool (Figure 1). This method was developed and adapted specifically for nurse leaders, and it has been tested in real‐world scenarios around the world (Pruinelli, Farag, et al, 2020; Pruinelli, Johnson, et al, 2020).…”

Section: Design and Methodsmentioning

confidence: 99%

“…To achieve the necessary rigor based on data science knowledge, it is necessary to implement a process for data science application and development of problem solving, taking into account appropriate methods for exploratory data analysis techniques (Alharbi, Alyoubi, Hoque, & Almazmomi, 2019). Processes for data science exist, but there is a lack of frameworks specifically oriented and tested for nursing application; thus, the Applied Healthcare Data Science Roadmap adapted by Pruinelli, Johnson, et al (2020), derived from the Cross‐Industry Standard Process for Data Mining (CRISP‐DM), was optimized to guide the development of pragmatic experimental evidence using large clinical practice data sets, specifically in the context of health care, providing a guide to nursing leaders in the use of data science principles (Martinez‐Plumed et al, 2019; Pruinelli, Farag, et al, 2020; Pruinelli, Johnson, et al, 2020).…”

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confidence: 99%

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Pain Information Model and Its Potential for Predictive Analytics: Applicability of a Big Data Science Framework

Nomura

Almeida

Johnson

et al. 2021

J of Nursing Scholarship

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Purpose To describe the application of a big data science framework to develop a pain information model and to discuss the potential for its use in predictive modeling. Design and Method This is an application of a cross‐industry standard process for a data mining adapted framework (the Applied Healthcare Data Science Framework) to build an information model on pain management and its potential for predictive modeling. Data were derived from electronic health records and were composed of approximately 51,000 records of unique adult patients admitted to clinical and surgical units between July 2015 and June 2019. Findings The application of the Applied Healthcare Data Science Framework steps allowed the development of an information model on pain management, considering pain assessment, interventions, goals, and outcomes. The developed model has the potential to be used for predicting which patients are most likely to be discharged with self‐reported pain. Conclusions Through the application of the framework, it is possible to support health professionals’ decision making on the use of data to improve the effectiveness of pain management. Clinical Relevance In the long term, the framework is intended to guide data science methodologies to personalize treatments, reduce costs, and improve health outcomes.

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Section: Design and Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Pain Information Model and Its Potential for Predictive Analytics: Applicability of a Big Data Science Framework

Nomura

Almeida

Johnson

et al. 2021

J of Nursing Scholarship

Self Cite

View full text Add to dashboard Cite

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“…Organizations are inherently relational comprised of individuals with differing interests, goals, and preferences, interacting, communicating, and making decisions (Borgatti & Ofem, 2010). Scientific and professional groups endeavor to find ways to work better together, particularly if group members are not geographically colocated or come from distinct philosophical paradigms (Cohn, McCloskey, Kovner, Schiffman, & Mitchell, 2018;Gross, 2018;Pruinelli et al, 2020;Smith-Blair & Davis, 2016). Addressing these collaborative challenges can aid in promoting integrative approaches for designing research-related activities compromised by siloed or restricted structures.…”

Section: Purposementioning

confidence: 99%

“…A common barrier to scientific collaboration in both military and civilian settings is "siloed" thinking and "solo scientists" (Pruinelli et al, 2020), which is particularly problematic when members are dispersed globally within unique mission-based services (Bos et al, 2007;National Research Council, 2015). A brief description of the unique constraints experienced by military nurse scientists is important to fully appreciate the utility of capturing their social network and collaborations.…”

Section: Introductionmentioning

confidence: 99%

Social network analysis: Exploring connections to advance military nursing science

et al. 2021

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Background: Military nurse scientists are embedded in service-affiliated branches (Army, Navy, Air Force) with different missions, but with the singular purpose of generating and disseminating research impacting the health and well-being of DoD beneficiaries. Purpose: This project examines collaboration among TriService Nursing Research Program (TSNRP) members, seeking opportunities to strengthen, diversify, and expand research collaboration. Method: Social network analysis (SNA) is the empirical inquiry of relations among social actors at different levels of analysis. An electronic SNA assessment and total enumerative sampling were used to explore current collaborations among PhD-prepared military nurse scientists (N = 136). Findings: The TSNRP collaboration network has a complex service-driven structure with high status actors whose advice, knowledge, or skills are sought by others, and who function as connectors or bridges across service branches. Discussion: For military scientists, SNA is instrumental in identifying influential individuals, visualizing opportunities for intraservice mentoring, designing responsive policy, and directing career opportunities for novice scientists.

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The Symbiotic Collaboration Between Family Nursing and Data Science

Cited by 2 publications

References 9 publications

Pain Information Model and Its Potential for Predictive Analytics: Applicability of a Big Data Science Framework

Pain Information Model and Its Potential for Predictive Analytics: Applicability of a Big Data Science Framework

Social network analysis: Exploring connections to advance military nursing science

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