omaha system
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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.

The purpose of this study was to examine nurse coach scope of practice in relation to existing evidence-based guideline interventions using the Omaha System. The majority of interventions were within scope for nurse coach practice, and problem, category, and target terms showed differential nurse coach practice applicability across interventions. The Omaha System terminology was aligned with nurse coach practice in that both represent and employ comprehensive and holistic perspectives. This study provides a platform for multiple initiatives in nurse coach quality and documentation and provides a methodology for examining the Omaha System guidelines and interventions for other interprofessional roles.

In order to study the application of doppler ultrasound in monitoring the changes of important vascular hemodynamic indexes in pregnant women with Pregnancy-induced Hypertension (PIH) during pregnancy under the Omaha systematic nursing intervention (NI), in this study, 83 PIH pregnant women were taken as the study object. First, 60 normal pregnant women were taken as the control group (CG). Ultrasonic images of the uterine artery (UA) and middle cerebral artery (MCA) were collected. The machine learning (ML) algorithm was used to detect the edge of the target vessel in the UA image. The hemodynamic indexes of UA and MCA were compared between the two groups. Then, according to the routine NI and the NI method based on the Omaha system, 83 PIH pregnant women were divided into the routine group (RG) (n = 35) and the observation group (OG) (n = 48). The differences in UA hemodynamic parameters and quality of life (QOL) scores were compared between the two groups at 6 d after surgery. The results showed that pre-pregnancy BMI, mean artery pressure (MAP), and uric acid content in the CG were significantly lower than those in the PIH group (P < 0.05); the content of serum albumin in the CG was significantly higher than that in the PIH group (P < 0.05); compared with Sobel operator, edge detection (ED) based on ML algorithm was more coherent; in the CG, the systolic peak velocity (PSV), acceleration index (AI), pulse index (PI), and resistance index (RI) were significantly lower than those in the PIH group (P < 0.05); the values of PI and RI in MCA of CG were significantly higher than those in PIH group (P < 0.05); after NI, PSV, PI, RI, and AI values in the OG were significantly lower than those in the RG (P < 0.05); the health status, trunk pain, social function, emotional function, vitality, mental health, and total score of the OG were significantly higher than that of the RG (P < 0.05), which indicated that ultrasonic detection based on ML algorithm can be used in monitoring the changes of hemodynamic indexes of PIH pregnant women during NI, and the NI based on the Omaha system can effectively improve the QOL of PIH pregnant women.