Umberfield, Elizabeth E.

Abstract Objectives The primary objective of this research is to assess the content coverage of nursing data within a publicly available common data model (CDM), focusing on how nursing data, documented in flowsheets, are represented within the model. Materials and Methods This mapping study was informed by previous evaluation studies and serves as a framework for evaluating information resources, including to guide development and implementation. The overall research process consists of 4 steps: (1) identify a CDM; (2) define evaluation criteria; (3) map nursing flowsheet data; and (4) apply evaluation criteria. Results Overall, 65.5% (n = 1170) of the flowsheet concepts were mapped to Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT) and Logical Observation Identifiers Names and Codes (LOINC) target codes and 56.0% (n = 1831) of the flowsheet values were mapped to SNOMED CT and LOINC target codes. The flowsheet concepts had a higher average mapping time per concept/reviewer (1.19 min) as compared to the average mapping time per value/reviewer (0.64 min). Discussion This mapping study demonstrated the progress and ongoing challenges of mapping nursing data to a national common data model. However, the ability to use nursing data at scale in a national CDM remains limited until more comprehensive mapping is completed. Conclusion This mapping study identifies a significant gap in integrating nursing data into a national common data model, highlighting an opportunity to enhance patient care through improved real-time insights and evidence-based nursing practices. Addressing this gap can help shape policies that prioritize the inclusion of nursing data. Additionally, aligning nursing data at scale can advance research, increase efficiency, and optimize nurse-sensitive patient outcomes.

Abstract Background Mortality prediction data may aid in identifying seriously ill transferred patients at high risk of dying and facilitate early goals-of-care discussions (GOCD); however, this is rarely evaluated. We recently developed a model for predicting 30-day inpatient mortality, which may be useful for promoting early GOCD. Objectives Our objectives were to examine the effects of sharing model-generated mortality risk with hospitalists by assessing (1) if hospitalists agreed with the mortality risk prediction, (2) if they planned to conduct GOCD or consult palliative care within 72 hours of transfer, and (3) if the communication alert affected GOCD timing and other clinical outcomes. We also aimed to measure the association between both the model-generated and hospitalists' stratified risk assessments with patient mortality. Methods This was a nonrandomized quasi-experimental pilot study with a historical control group. On the second day of hospitalization, the model-generated risk was communicated to the hospitalists. Hospitalists were asked to answer questions via a HIPAA (Health Insurance Portability and Accountability Act)-compliant mobile communication system, and clinical outcomes were extracted via chart review. Results Eighty-four patients (42 in the control and 42 in the intervention group) were included in this study. Hospitalists agreed that all patients in the intervention group were at risk for inpatient mortality. Hospitalists were more likely to indicate a plan to conduct GOCD in the intervention group (n = 9) compared with the control group (n = 4, p < 0.001). In this subset of patients, GOCD was completed within 72 hours in 78% of intervention patients (n = 7) as compared with 50% in the control group (n = 2). The greater absolute value of the model-generated mortality risk was significantly associated with deaths (p = 0.01), similar to the hospitalists' prediction of the mortality risk (p = 0.02). Conclusion Communicating model-generated mortality risk to hospitalists is a promising approach to promote timely GOCD.

Abstract Nursing and informatics share a common strength in their use of structured representations of domains, specifically the underlying notion of ‘things’ (ie, concepts, constructs, or named entities) and the relationships among those things. Accurate representation of nursing knowledge in machine-interpretable formats is a necessary next step for leveraging contemporary technologies. Expressing validated nursing theories in ontologies, and in particular formal ontologies, would serve not only nursing, but also investigators from other domains, clinical information system developers, and the users of advanced technologies such as artificial intelligence that seek to learn from the real-world data and evidence generated by nurses and others. Such efforts will enable sharing knowledge and conceptualizations about phenomena across the domains of nursing and generating, testing, revising, and providing theoretically-based perspectives when leveraging contemporary technologies. Nursing is well situated for this work, leveraging intentional and focused collaborations among nurse informaticists, scientists, and theorists.