Austin, Robin R

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.

AbstractObjectivesIn this study we aimed to describe and compare groups formed by a rules‐based algorithm to prospectively identify clients at risk of poor outcomes in order to guide tailored public health nursing (PHN) intervention approaches.DesignData‐driven methods using standardized Omaha System PHN documentation.SampleClients ages 13–40 who received PHN home visiting services for both the Caretaking/parenting and Mental health problems (N = 4109).MeasurementWe applied a theory‐based algorithm consisting of six rules using existing Omaha System data. We examined the groups formed by the algorithm using standard descriptive, inferential statistics, and Latent Class Analysis.ResultsClients (N = 4109) were 25.1 (SD = 5.9) years old and had an average of 7.3 (SD = 3.2) problems, 250 (SD = 319) total interventions, and 32 (SD = 44) Mental health interventions. Overall outcomes improved after PHN interventions (p < .001 for all) and having more Mental health signs/symptoms was negatively associated with outcome scores (p < .001 for all).ConclusionsThis algorithm may be helpful in identifying high‐risk clients during a baseline assessment who may benefit from more intensive mental health interventions. Findings show there is value using the Omaha System for PHN documentation and algorithm clinical decision support development. Future research should focus on algorithm implementation in PHN clinical practice.

As a new era of healthcare advocates a more valuable and intelligent approach to care management and delivery based on values and outcomes, shifts toward risk management to boost performance should be considered that encompass the capitalization of health assets or health strengths. To make full use of individuals’ or populations’ health assets, data capture and representation are needed. This paper uses a strengths-oriented case study mapped to an inter-disciplinary standardized terminology, the Omaha System, to illustrate and compare the conventional problem-based approach to care management with the strengths-oriented approach to care that demonstrates whole-person data capture of an individual’s health and health assets leveraged to promote health values and performance. The Omaha system provides a standardized framework to organize the concepts of all of health from a whole-person perspective for documentation to enable data analysis, interoperability, and health information exchange.

AbstractThe active involvement of citizen scientists in setting research agendas, partnering with academic investigators to conduct research, analyzing and disseminating results, and implementing learnings from research can improve both processes and outcomes. Adopting a citizen science approach to the practice of precision medicine in clinical care and research will require healthcare providers, researchers, and institutions to address a number of technical, organizational, and citizen scientist collaboration issues. Some changes can be made with relative ease, while others will necessitate cultural shifts, redistribution of power, recommitment to shared goals, and improved communication. This perspective, based on a workshop held at the 2018 AMIA Annual Symposium, identifies current barriers and needed changes to facilitate broad adoption of a citizen science-based approach in healthcare.