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Innovative nursing education methods became essential due to the COVID-19 pandemic. Immersive Virtual Reality (VR) education offers nursing students authentic patient encounters in a realistic simulated environment. A pilot study was conducted to identify nursing education clinical scenarios that should be developed for immersive VR and to assess students’ perception of immersive VR in education. We formed a focus group composed of nursing faculty (N=10) with expertise in the clinical setting and simulation. Faculty participants identified important topics and aspects of immersive VR scenarios during focus group discussions. We assessed nursing student participants’ (N=11) perception of immersive VR in nursing education using a VR game (Anatomy Explorer 2020). Most student participants indicated that a VR game was immersive and realistic and recommended using immersive VR to learn clinical nursing skills. Realistic immersive VR clinical education scenarios could result in effective clinical nursing education.

Accurate assessment and documentation of skin conditions facilitate communication among care providers and are critical to effective prevention and mitigation of pressure ulcer. We report developing a prototype mobile system called SAPPIRE (Skin Assessment for Pressure Ulcer Prevention, an Integrated Recording Environment) for an android device to assist nurses with skin assessment and documentation at bedside. SAPPIRE demonstrates (1) data documentation conforming to the relevant terminology standards, (2) data exchange using Continuity of Care Records (CCR) standard and (3) smart display of patient data relevant to risk parameters to promote accurate pressure ulcer risk assessment with the Braden scale. Challenges associated standardizing assessment data faced during this development and the approaches that SAPPIRE took to overcome them are described.

In recent years, Decision Support Systems (DSSs) have been developed and used to achieve "meaningful use". One approach to developing DSSs is to translate clinical guidelines into a computer-interpretable format. However, there is no specific guideline modeling approach to translate nursing guidelines to computer-interpretable guidelines. This results in limited use of DSSs in nursing. Unified modeling language (UML) is a software writing language known to accurately represent the end-users' perspective, due to its expressive characteristics. Furthermore, standard terminology enabled DSSs have been shown to smoothly integrate into existing health information systems. In order to facilitate development of nursing DSSs, the UML was used to represent a guideline for medication management for older adults encode with the International Classification for Nursing Practice (ICNP®). The UML was found to be a useful and sufficient tool to model a nursing guideline for a DSS.

Background. Advances in genetic science and biotechnology accumulated huge knowledge of genes and various genetic tests and diagnostic tools for healthcare providers including nurses. Genetic counseling became important to assist patients making decisions about obtaining genetic testing or preventive measures. Method. This review was conducted to describe the counseling topics, various interventions adopted in genetic counseling, and their effectiveness. Experimental studies (N=39) published between 1999 and 2012 were synthesized. Results. The most frequently covered topic was benefits and limitations of genetic testing on breast cancer ovarian and colorectal cancers. Most of researchers focused on evaluating cognitive aspect and psychological well-being. Conclusion. No single intervention was consistently reported to be effective. Decision aids enhanced with information technologies have potential to improve the outcomes of genetic counseling by providing tailored information and facilitating active engagement of patients in information uptake. Clinical Implication. When nurses are familiar with topics and interventions of genetic counseling, they are well positioned to provide genetic/genomic information to the patient and families.

Pain is a common but significant problem that is considered a high priority area of care. Although there are many pain assessment scales that can be applied to patients who can communicate, either verbally or non-verbally, pain assessment for minimally responsive patients is limited. In this preliminary work, we developed a novel approach for assessing pain in such patients using a principal component analysis (PCA)-based local detector. Our algorithm produce a single index to indicate the increase in pain level based on unsynchronized, sparse and noisy time series data collected from electronic flowsheets. Among 8032 patient cases collected, 53 cases that satisfied the data requirements for PCA were used in this experiment. Our preliminary results indicate high potential in this approach by yielding an average AUC of 0.76 for the 53 cases.

We have been developing a domain-level knowledge representation construct in GLIF called macro. A macro is a declarative specification of a high-level concept that is mapped to a procedural pattern of primitive GLIF steps. We developed macros for (1) appropriateness criteria for radiological examinations, and (2) toxicity criteria for cancer clinical trial protocols. Authoring tools for these macros were built using a Java software library we have designed for this purpose. The authoring tools provide an easy way to encode these guidelines that can then be automatically mapped to GLIF.