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Purpose To explore how big data can be used to identify the contribution or influence of six specific workload variables: patient count, medication count, task count call lights, patient sepsis score, and hours worked on the occurrence of a near miss (NM) by individual nurses. Design A correlational and cross‐section research design was used to collect over 82,000 useable data points of historical workload data from the three unique systems on a medical‐surgical unit in a midsized hospital in the southeast United States over a 60‐day period. Data were collected prior to the start of the Covid‐19 pandemic in the United States. Methods Combined data were analyzed using JMP Pro version 12. Mean responses from two groups were compared using a t‐test and those from more than two groups using analysis of variance. Logistic regression was used to determine the significance of impact each workload variable had on individual nurses’ ability to administer medications successfully as measured by occurrence of NMs. Findings The mean outcome of each of the six workload factors measured differed significantly (p < .0001) among nurses. The mean outcome for all workload factors except the hours worked was found to be significantly higher (p < .0001) for those who committed an NM compared to those who did not. At least one workload variable was observed to be significantly associated (p < .05) with the occurrence or nonoccurrence of NMs in 82.6% of the nurses in the study. Conclusions For the majority of the nurses in our study, the occurrence of an NM was significantly impacted by at least one workload variable. Because the specific variables that impact performance are different for each individual nurse, decreasing only one variable, such as patient load, will not adequately address the risk for NMs. Other variables not studied here, such as education and experience, might be associated with the occurrence of NMs. Clinical Relevance In the majority of nurses, different workload variables increase their risk for an NM, suggesting that interventions addressing medication errors should be implemented based on the individual’s risk profile.

The primary focus of this teaching case is the patient journey, as facilitated and influenced by an e-system or electronic health record (EHR) system. The goal of this case is to provide the learner with the knowledge and skills needed to effectively incorporate patient-centered e-health (PCEH) principles into existing and planned e-health systems such as EHRs. This case can be used to help students understand a hospital experience from the perspective of a patient and her family. It is loosely based on an experience one of the authors had with an actual patient. This case is intended for use with upper level undergraduate and graduate health informatics, information systems, and nursing students. Students assigned to this case should have a working knowledge of clinical terms and the general workings of a hospital. This teaching case is best suited to an advanced course in a health informatics curriculum. Possible applications of the case include, but are not limited to, describing the patient journey, modeling the process flow, diagramming the data flow, and applying the principles of patient-centered ehealth.

More than half of practicing nurses have suboptimal physical or mental health. Impaired health is associated with a 76% higher likelihood that nurses will make medical errors. Improving the health habits of nursing students is essential to shaping and sustaining health prior to joining the workforce. Technology such as mobile health applications holds great promise in facilitating behavioral change and encouraging healthy habits in nursing students. Identifying the predictors of willingness to use mobile health is essential to creating mobile health applications that will engage nursing students and promote sustainable usage. Evaluation of psychological, attitudinal, and health-related correlates of mobile health can highlight predictors of willingness to use mobile health, which can influence nursing students' utilization and long-term engagement with mobile health applications. Analysis of these correlates shows that psychological attributes, such as hope, play a role in the willingness to use and may facilitate engagement in the utilization of a mobile health application. Development of a mobile health application that increases hope and helps establish healthy habits may enable nursing students to remain healthy throughout their lives, creating a new generation of happier, healthier nurses and, ultimately, improving safety for patients under their care.