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The purpose of this study was to report a secondary analysis of data collected through a primary study. The primary study was a, randomized, control trial that used a team‐designed (nursing, speech language hearing, engineering, communication sciences, and biostatistics), nurse‐led, electronic communication intervention (Speak for Myself Voice) and measured patient outcomes of symptoms of anxiety and depression in five intensive care units at a regional, magnet‐status, academic medical center. A secondary analysis of data using the Hospital Anxiety and Depression scale is reported here. The extant literature supports patient expressions of frustration, anger, anxiety, and depression when unable to communicate. This secondary analysis study report adds information about Hospital Anxiety and Depression subscales in the communication‐vulnerable population. Implications include emerging awareness of potential feelings of depression and anxiety in patients who are receiving mechanical ventilation or who are unable to verbally communicate for any reason (e.g. obstruction, trauma, head and neck cancer) in the intensive care unit. [ABSTRACT FROM AUTHOR]

Educators are challenged to engage in dramatic reformation and innovation including learning, teaching, and curriculum design. A PhD level quantitative nursing research course was redesigned using the Andersen Behavioral Model of Health Services (ABMHS) to guide data assignments. Students navigated through levels of theoretical abstraction from constructs to concepts to variables. Subsequent assignments offered students the opportunity to operationalize, code, and manage the variables in their models. Outcomes were positive and resulted in student confidence as they progressed to the dissertation phase. This article adds novel information to the literature about attention to theory, models, or frameworks, and specifically the ABMHS, in a doctoral quantitative course. [ABSTRACT FROM AUTHOR]

Objectives: Our goal was to develop predictive models for sepsis and in-hospital mortality using electronic health records (EHRs). We showcased the efficiency of these algorithms in patients diagnosed with pneumonia, a group that is highly susceptible to sepsis.Methods: We retrospectively analyzed the Health Facts® (HF) dataset to develop models to predict mortality and sepsis using the data from the first few hours after admission. In addition, we developed models to predict sepsis using the data collected in the last few hours leading to sepsis onset. We used the random forest classifier to develop the models.Results: The data collected in the EHR system is generally sporadic, making feature extraction and selection difficult, affecting the accuracies of the models. Despite this fact, the developed models can predict sepsis and in-hospital mortality with accuracies of up to 65.26±0.33% and 68.64±0.48%, and sensitivities of up to 67.24±0.36% and 74.00±1.22%, respectively, using only the data from the first 12 hours after admission. The accuracies generally remain consistent for similar models developed using the data from the first 24 and 48 hours after admission. Lastly, the developed models can accurately predict sepsis patients (with up to 98.63±0.17% accuracy and 99.74%±0.13% sensitivity) using the data collected within the last 12 hours before sepsis onset. The results suggest that if such algorithms continuously monitor patients, they can identify sepsis patients in a manner comparable to current screening tools, such as the rulebased Systemic Inflammatory Response Syndrome (SIRS) criteria, while often allowing for early detection of sepsis shortly after admission.Conclusions: The developed models showed promise in early prediction of sepsis, providing an opportunity for directing early intervention efforts to prevent/treat sepsis. [ABSTRACT FROM AUTHOR]

Abdominal wall hernia repair, including ventral hernia repair, is one of the most common general surgical procedures. Nationally, at least 350 000 ventral hernia repairs are performed annually, and of those, 150 000 cases were identified as incisional hernias. Outcomes are reported to be poor, resulting in additional surgical repair rates of 12.3% at 5 years and as high as 23% at 10 years. Healthcare costs associated with ventral hernia repair are estimated to exceed $3 billion each year. Additionally, ventral hernia repair is often complex and unpredictable when there is a current infection or a history of infection and significant comorbidities. Accordingly, a predictive model was developed using a retrospectively collected dataset to associate the pre- and intra-operative characteristics of patients to their outcomes, with the primary goal of identifying patients at risk of developing complications a priori in the future. The benefits and implications of such a predictive model, however, extend beyond this primary goal. This predictive model can serve as an important tool for clinicians who may use it to support their clinical intuition and clarify patient need for lifestyle modification prior to abdominal wall reconstruction. This predictive model can also support shared decision-making so that a personalized plan of care may be developed. The outcomes associated with use of the predictive model may include surgical repair but may suggest lifestyle modification coupled with less invasive interventions.

Effective two-way patient-provider communication is challenging and is even more difficult when patients are communication vulnerable. The results of being unheard and unacknowledged can contribute to negative feelings and may manifest as symptoms of anxiety and depression. Researchers explored symptoms of anxiety and depression when using a team-developed, patient-centered, and nurse-led intervention called Speak for Myself-Voice (formerly published as Speak for Myself) in five intensive care units at a Magnet status, university-affiliated medical center in East Tennessee. This was an equivalent control group design. The data were analyzed with a mixed-effect analysis of variance (between and within groups) with repeated measures to see if the treatment group changed differently than the control group across time (48 hours). This study report adds information about anxiety and depression in patients who are communication vulnerable and using communication technology.

The purpose of this article is to report usability of a computer application, Speak for Myself, from nurses' perspectives. This was a one-group exploratory survey. Nurses included patients in the study who were older than 18 years, could write and speak English, were unable to verbalize needs for any reason, and had a specified sedation-agitation scale (-1 to +1). Patients were excluded if they were younger than 18, could not write and/or speak English, or had a Richmond Agitation-Sedation Scale score exceeding -1 to +1. Twelve RNs from various ICUs in two hospitals in South Florida participated in the study. A person who was a supervisor or clinical specialist was chosen in each participating unit and at each hospital for recruitment of patient participants. Five nurses (41.6%) stated their patients were able to communicate better with the use of Speak for Myself, and all 12 nurses (100%) indicated they would use Speak for Myself again. Suggestions for further development of Speak for Myself were offered from the nurses. Limitations include a small sample in South Florida. The results of this and previous studies about Speak for Myself will be used in further development and testing of the computer application.

Voice is crucial for communication in all healthcare settings. Evidence-based care highlights the need for clear communication. Clear communication methods must be applied when caring for special populations in order to assess pain effectively. Communication efforts also should be offered to patients who are in end-of-life care and would like to make independent decisions. A computer communication application was offered to patients in intensive care/critical care units in three hospitals in South Florida. Inclusion criteria included the age of 18 years or older, Richmond Agitation Sedation Scale between -1 and +1, ability to read and write English, and willingness to use the computer application. Exclusion criteria included inability to read and write English, agitation as defined by the Richmond Agitation Sedation Scale, and any patient on infection isolation protocol. Four qualitative themes were revealed, which directly relate to two published evidence-based guidelines. These are the End of Life Care and Decision Making Evidence-Based Care Guidelines and the Pain Assessment in Special Populations Guidelines. This knowledge is important for developing effective patient-healthcare provider communication.

Purpose: To describe the creation and initial feasibility study of a new computer application to improve communication with people who cannot communicate by customary means during their hospitalization., Design: This was a mixed-methods, quasi-experimental design., Methods: This exploratory feasibility study obtained data about the experiences of 20 intensive care patients in three South Florida hospitals who were unable to speak due to mechanical obstruction., Findings: Study participants (20), who ranged in age from 45 to 91 years (M=67.4, SD=12.88) and between -1 to +1 (SD=-0.15) on the Richmond Agitation Sedation Scale, used Speak for Myself from 4 to 16 hours with a mean of 8.86 (SD=2.12). Ninety-five percent of the participants stated that Speak for Myself was helpful for communication., Conclusions: Speak for Myself was helpful to patients who used it. This was a small study (n=20). It warrants further investigation., Clinical Relevance: Patients who are unable to communicate their needs through conventional methods still want to make their preferences and needs known. Speak for Myself is a new application that facilitates ensuring the patient's voice is heard., (© 2014 Association of Rehabilitation Nurses.)