Sequence to Sequence ECG Cardiac Rhythm Classification Using Convolutional Recurrent Neural Networks.

This paper proposes a novel deep learning architecture involving combinations of Convolutional Neural Networks (CNN) layers and Recurrent neural networks (RNN) layers that can be used to perform segmentation and classification of 5 cardiac rhythms based on ECG recordings. The algorithm is developed in a sequence to sequence setting where the input is a sequence of five second ECG signal sliding windows and the output is a sequence of cardiac rhythm labels. The novel architecture processes as input both the spectrograms of the ECG signal as well as the heartbeats' signal waveform. Additionally, we are able to train the model in the presence of label noise. The model's performance and generalizability is verified on an external database different from the one we used to train. Experimental result shows this approach can achieve an average F1 scores of 0.89 (averaged across 5 classes). The proposed model also achieves comparable classification performance to existing state-of-the-art approach with considerably less number of training parameters.

Diffusing an Innovation: Clinician Perceptions of Continuous Predictive Analytics Monitoring in Intensive Care.

BACKGROUND: The purpose of this article is to describe neonatal intensive care unit clinician perceptions of a continuous predictive analytics technology and how those perceptions influenced clinician adoption. Adopting and integrating new technology into care is notoriously slow and difficult; realizing expected gains remain a challenge. METHODS: Semistructured interviews from a cross-section of neonatal physicians (n = 14) and nurses (n = 8) from a single U.S. medical center were collected 18 months following the conclusion of the predictive monitoring technology randomized control trial. Following qualitative descriptive analysis, innovation attributes from Diffusion of Innovation Theory-guided thematic development. RESULTS: Results suggest that the combination of physical location as well as lack of integration into work flow or methods of using data in care decisionmaking may have delayed clinicians from routinely paying attention to the data. Once data were routinely collected, documented, and reported during patient rounds and patient handoffs, clinicians came to view data as another vital sign. Through clinicians' observation of senior physicians and nurses, and ongoing dialogue about data trends and patient status, clinicians learned how to integrate these data in care decision making (e.g., differential diagnosis) and came to value the technology as beneficial to care delivery. DISCUSSION: The use of newly created predictive technologies that provide early warning of illness may require implementation strategies that acknowledge the risk-benefit of treatment clinicians must balance and take advantage of existing clinician training methods.

Impact of predictive analytics based on continuous cardiorespiratory monitoring in a surgical and trauma intensive care unit.

Predictive analytics monitoring, the use of patient data to provide continuous risk estimation of deterioration, is a promising new application of big data analytical techniques to the care of individual patients. We tested the hypothesis that continuous display of novel electronic risk visualization of respiratory and cardiovascular events would impact intensive care unit (ICU) patient outcomes. In an adult tertiary care surgical trauma ICU, we displayed risk estimation visualizations on a large monitor, but in the medical ICU in the same institution we did not. The risk estimates were based solely on analysis of continuous cardiorespiratory monitoring. We examined 4275 individual patient records within a 7 month time period preceding and following data display. We determined cases of septic shock, emergency intubation, hemorrhage, and death to compare rates per patient care pre-and post-implementation. Following implementation, the incidence of septic shock fell by half (p < 0.01 in a multivariate model that included age and APACHE) in the surgical trauma ICU, where the data were continuously on display, but by only 10% (p = NS) in the control Medical ICU. There were no significant changes in the other outcomes. Display of a predictive analytics monitor based on continuous cardiorespiratory monitoring was followed by a reduction in the rate of septic shock, even when controlling for age and APACHE score.

Advancing Continuous Predictive Analytics Monitoring: Moving from Implementation to Clinical Action in a Learning Health System.

In the intensive care unit, clinicians monitor a diverse array of data inputs to detect early signs of impending clinical demise or improvement. Continuous predictive analytics monitoring synthesizes data from a variety of inputs into a risk estimate that clinicians can observe in a streaming environment. For this to be useful, clinicians must engage with the data in a way that makes sense for their clinical workflow in the context of a learning health system (LHS). This article describes the processes needed to evoke clinical action after initiation of continuous predictive analytics monitoring in an LHS.

Exploring interpersonal behavior and team sensemaking during health information technology implementation.

PURPOSE: We examine how interpersonal behavior and social interaction influence team sensemaking and subsequent team actions during a hospital-based health information technology (HIT) implementation project. DESIGN/METHODOLOGY/APPROACH: Over the course of 18 months, we directly observed the interpersonal interactions of HIT implementation teams using a sensemaking lens. FINDINGS: We identified three voice-promoting strategies enacted by team leaders that fostered team member voice and sensemaking; communicating a vision; connecting goals to team member values; and seeking team member input. However, infrequent leader expressions of anger quickly undermined team sensemaking, halting dialog essential to problem solving. By seeking team member opinions, team leaders overcame the negative effects of anger. PRACTICAL IMPLICATIONS: Leaders must enact voice-promoting behaviors and use them throughout a team's engagement. Further, training teams in how to use conflict to achieve greater innovation may improve sensemaking essential to project risk mitigation. SOCIAL IMPLICATIONS: Health care work processes are complex; teams involved in implementing improvements must be prepared to deal with conflicting, contentious issues, which will arise during change. Therefore, team conflict training may be essential to sustaining sensemaking. RESEARCH IMPLICATIONS: Future research should seek to identify team interactions that foster sensemaking, especially when topics are difficult or unwelcome, then determine the association between staff sensemaking and the impact on HIT implementation outcomes. VALUE/ORIGINALITY: We are among the first to focus on project teams tasked with HIT implementation. This research extends our understanding of how leaders' behaviors might facilitate or impeded speaking up among project teams in health care settings.

Innovative health information technology training: exploring blended learning.

Healthcare staff members are faced with an ever-increasing technology-enabled care environment as hospitals respond to financial and regulatory pressures to implement comprehensive electronic health record systems. Health information technology training may prove to facilitate user acceptance and overall adoption of advanced technologies. However, there is little evidence regarding best methods of providing health information technology training. This study retrospectively examined the difference in staff satisfaction between two training methods: traditional instructor-led and blended learning and found that participants were equally satisfied with either method. Furthermore, regardless of how much time was provided for practice, participants expressed a desire for more. These findings suggest that healthcare staff are open to new methods of training delivery and that, as adult learners, they desire increased opportunities to engage in hands-on activities.

Making sense of health information technology implementation: A qualitative study protocol.

BACKGROUND: Implementing new practices, such as health information technology (HIT), is often difficult due to the disruption of the highly coordinated, interdependent processes (e.g., information exchange, communication, relationships) of providing care in hospitals. Thus, HIT implementation may occur slowly as staff members observe and make sense of unexpected disruptions in care. As a critical organizational function, sensemaking, defined as the social process of searching for answers and meaning which drive action, leads to unified understanding, learning, and effective problem solving -- strategies that studies have linked to successful change. Project teamwork is a change strategy increasingly used by hospitals that facilitates sensemaking by providing a formal mechanism for team members to share ideas, construct the meaning of events, and take next actions. METHODS: In this longitudinal case study, we aim to examine project teams' sensemaking and action as the team prepares to implement new information technology in a tiertiary care hospital. Based on management and healthcare literature on HIT implementation and project teamwork, we chose sensemaking as an alternative to traditional models for understanding organizational change and teamwork. Our methods choices are derived from this conceptual framework. Data on project team interactions will be prospectively collected through direct observation and organizational document review. Through qualitative methods, we will identify sensemaking patterns and explore variation in sensemaking across teams. Participant demographics will be used to explore variation in sensemaking patterns. DISCUSSION: Outcomes of this research will be new knowledge about sensemaking patterns of project teams, such as: the antecedents and consequences of the ongoing, evolutionary, social process of implementing HIT; the internal and external factors that influence the project team, including team composition, team member interaction, and interaction between the project team and the larger organization; the ways in which internal and external factors influence project team processes; and the ways in which project team processes facilitate team task accomplishment. These findings will lead to new methods of implementing HIT in hospitals.

Birds of a feather: introducing a virtual learning community for geriatric nurse educators.

The Duke Geriatric Nursing Education Virtual Learning Community (Gero-VLC) is a newly developed online inquiry network that enables geriatric nurse educators to borrow, share, and collaborate to promote in-depth learning and optimal communication among instructors. Recently launched, the Gero-VLC website was developed to meet the needs of nurse educators who face increasing demands to develop quality, learner-centered online instruction focused on evidence-based geriatric care. Through the Gero-VLC, nurse educators can connect with nurse clinicians expert in geriatric care; access state-of-the-science information and learning opportunities; participate in collaborative projects; and publish their work on the North Carolina Learning Object Repository. The authors present the Gero-VLC as a best practice for online geriatric nursing education, describe its theoretical underpinnings, and outline a strategy for evaluation.