Monsen, Karen A.

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.

Objective: Liver transplantation is a costly and risky procedure, representing 25 050 procedures worldwide in 2013, with 6729 procedures performed in the United States in 2014. Considering the scarcity of organs and uncertainty regarding prognosis, limited studies address the variety of risk factors before transplantation that might contribute to predicting patient’s survival and therefore developing better models that address a holistic view of transplant patients. This critical review aimed to identify predictors of liver transplant patient survival included in large-scale studies and assess the gap in risk factors from a holistic approach using the Wellbeing Model and the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement. Data Source: Search of the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Medline, and PubMed from the 1980s to July 2014. Study Selection: Original longitudinal large-scale studies, of 500 or more subjects, published in English, Spanish, or Portuguese, which described predictors of patient survival after deceased donor liver transplantation. Data Extraction: Predictors were extracted from 26 studies that met the inclusion criteria. Data Synthesis: Each article was reviewed and predictors were categorized using a holistic framework, the Wellbeing Model (health, community, environment, relationship, purpose, and security dimensions). Conclusions: The majority (69.7%) of the predictors represented the Wellbeing Model Health dimension. There were no predictors representing the Wellbeing Dimensions for purpose and relationship nor emotional, mental, and spiritual health. This review showed that there is rigorously conducted research of predictors of liver transplant survival; however, the reported significant results were inconsistent across studies, and further research is needed to examine liver transplantation from a whole-person perspective.

Background: Electronic health records (EHRs) provide a clinical view of patient health. EHR data are becoming available in large data sets and enabling research that will transform the landscape of healthcare research. Methods are needed to incorporate wellbeing dimensions and strengths in large data sets. The purpose of this study was to examine the potential alignment of the Wellbeing Model with a clinical interface terminology standard, the Omaha System, for documenting wellbeing assessments. Objective: To map the Omaha System and Wellbeing Model for use in a clinical EHR wellbeing assessment and to evaluate the feasibility of describing strengths and needs of seniors generated through this assessment. Methods: The Wellbeing Model and Omaha System were mapped using concept mapping techniques. Based on this mapping, a wellbeing assessment was developed and implemented within a clinical EHR. Strengths indicators and signs/symptoms data for 5 seniors living in a residential community were abstracted from wellbeing assessments and analyzed using standard descriptive statistics and pattern visualization techniques. Results: Initial mapping agreement was 93.5%, with differences resolved by consensus. Wellbeing data analysis showed seniors had an average of 34.8 (range=22–49) strengths indicators for 22.8 concepts. They had an average of 6.4 (range=4–8) signs/symptoms for an average of 3.2 (range=2–5) concepts. The ratio of strengths indicators to signs/symptoms was 6:1 (range 2.8–9.6). Problem concepts with more signs/symptoms had fewer strengths. Conclusion: Together, the Wellbeing Model and the Omaha System have potential to enable a whole-person perspective and enhance the potential for a wellbeing perspective in big data research in healthcare.