Selective classification with machine learning uncertainty estimates improves ACS prediction: A retrospective study in the prehospital setting.

Accurate identification of acute coronary syndrome (ACS) in the prehospital sestting is important for timely treatments that reduce damage to the compromised myocardium. Current machine learning approaches lack sufficient performance to safely rule-in or rule-out ACS. Our goal is to identify a method that bridges this gap. To do so, we retrospectively evaluate two promising approaches, an ensemble of gradient boosted decision trees (GBDT) and selective classification (SC) on consecutive patients transported by ambulance to the ED with chest pain and/or anginal equivalents. On the task of ACS classification with 23 prehospital covariates, we found the fusion of the two (GBDT+SC) improves the best reported sensitivity and specificity by 8% and 23% respectively. Accordingly, GBDT+SC is safer than current machine learning approaches to rule-in and rule-out of ACS in the prehospital setting.

How Does Workplace Violence-Reporting Culture Affect Workplace Violence, Nurse Burnout, and Patient Safety?

BACKGROUND: Workplace violence (WPV) against nurses has a negative impact on the nurses and the care they provide. Formal reporting of WPV is necessary to understand the nature of violent incidents, develop proactive coping strategies, and provide support for nurses affected by WPV. PURPOSE: This study explored the relationships among nurses' WPV experiences, burnout, patient safety, and the moderating effect of WPV-reporting culture on these relationships. METHODS: This descriptive cross-sectional study used secondary data collected from 1781 nurses at a large academic medical center. RESULTS: Workplace violence increased nurse burnout, which in turn negatively affected patient safety. A strong WPV-reporting culture increased the negative effect of WPV on burnout but mitigated the negative effect of burnout on patient safety. CONCLUSIONS: The findings indicate that nurses may perceive WPV-reporting behavior as a stressor. Violence-reporting systems and procedures need to be improved to reduce the burden of reporting.

Patient Safety Culture: The Impact on Workplace Violence and Health Worker Burnout.

BACKGROUND: Patient and health care worker safety is an interconnected phenomenon. To date, few studies have examined the relationship between patient and worker safety, specifically with respect to work safety culture. Therefore, we examined patient safety culture, workplace violence (WPV), and burnout in health care workers to identify whether patient safety culture factors influence worker burnout and WPV. METHODS: This cross-sectional study used secondary survey data sent to approximately 7,100 health care workers at a large academic medical center in the United States. Instruments included the Hospital Survey on Patient Safety Culture, a WPV scale measuring physical and verbal violence perpetrated by patients or visitors, and the Emotional Exhaustion scale from the Maslach Burnout Inventory. FINDINGS: These analyses included 3,312 (47%) hospital staff who directly interacted with patients. Over half of nurse (62%), physician (53%), and allied health professional respondents (52%) reported experiencing verbal violence from a patient, and 39% of nurses and 14% of physicians reported experiencing physical violence from a patient. Burnout levels for nurses (2.67 ± 1.02) and physicians (2.65 ± 0.93) were higher than the overall average for all staff (2.61 ± 1.0). Higher levels of worker-reported patient safety culture were associated with lower odds of WPV (0.47) and lower burnout scores among workers (B = -1.02). Teamwork across units, handoffs, and transitions were dimensions of patient safety culture that also influenced WPV and burnout. CONCLUSIONS/APPLICATION TO PRACTICE: Our findings suggest that improvements in hospital strategies aimed at patient safety culture, including team cohesion with handoffs and transitions, could positively influence a reduction in WPV and burnout among health care workers.

Relational Coordination as a Predictor of Job Satisfaction and Intent to Stay Among Nurses and Physicians in the Military Health System.

INTRODUCTION: Job satisfaction and retention of military and civilian nurses and physicians who work in military treatment facilities (MTFs) are critical to maintaining quality of care and operational readiness. Civilian nurses and physicians working in MTFs supplement staffing for active duty military nurses and physicians and support operational readiness when military nurses and physicians deploy in wartime crises or humanitarian efforts. Decreased retention of military and civilian nurses and physicians can negatively impact operational readiness and patient care outcomes. Although several factors (e.g., burnout, pay, and leadership) influence job satisfaction and retention among nurses and physicians in both military and civilian healthcare settings, high-quality communication and relationships between nurses and physicians are associated with better job satisfaction and retention. However, little is known about how high-quality communication and relationships affect job satisfaction and retention among nurses and physicians in MTFs. Relational coordination (RC) is a process of high-quality communication supported by relationships of shared knowledge, shared goals, and mutual respect among members of the healthcare team. By strengthening RC, hospital leaders can more effectively achieve desired outcomes. The purpose of this study was to explore how RC influences job satisfaction and intent to stay among nurses, residents, and physicians in an Army hospital, and whether job satisfaction mediated the relationship between RC and intent to stay. MATERIALS AND METHODS: We conducted an exploratory, cross-sectional study in a 138-bed MTF in the southeastern USA and invited a convenience sample of military and civilian nurses, residents, and physicians to complete a 47-item survey on RC, job satisfaction, and intent to stay. We used Pearson's correlation to explore relationships between RC, job satisfaction, and intent to stay and then employed multiple regression to explore whether RC predicts job satisfaction and intent to stay, after controlling for professional role, demographic characteristics, and other covariates. Furthermore, we explored whether job satisfaction mediates the relationship between RC and intent to stay. RESULTS: Two hundred and eighty-nine participants completed the survey. Seventy percentage of respondents were civilian, were Caucasian (61%), and had a mean age of 40 years old. The RCs within roles (ß = 0.76, P < .001) and between roles (ß = 0.46, P < .001) were both positively associated with job satisfaction. RCs within roles was associated with higher intent to stay (ß = 0.38, P = .005). Civilian nurses and physicians reported higher intent to stay, followed by officers and enlisted service members. Job satisfaction mediated the relationship between RC within roles and intent to stay. CONCLUSION: Our findings suggest that RC is a powerful workplace dynamic that influences job satisfaction and intent to stay, for nurses, residents, and physicians in MTFs. Specifically, we found that RC was positively associated with job satisfaction and intent to stay and that job satisfaction mediates the relationship between RC and intent to stay. We recommend that hospital leaders in MTFs explore interventions to strengthen RC among health professionals by including relational, work process and structural interventions as part of their strategy for retaining military healthcare professionals.

Relational coordination and staff outcomes among healthcare professionals: a scoping review.

Relational coordination (RC) is a process of coordinating work between professionals that can be used as a framework to enhance interprofessional collaborative practice (IPCP) in various healthcare settings. RC encompasses four communication dimensions (frequent, timely, accurate, problem-solving) and three relational dimensions (shared knowledge, shared goals, mutual respect). RC has been associated with better staff and patient outcomes; it has wide applicability, and it has been examined nationally and internationally in various healthcare settings. The aim of this scoping review is to identify and synthesize available evidence on RC and staff outcomes among healthcare professionals. Literature searches were conducted on articles published between May 2000 until February 2020. Sixteen abstracts were screened from four databases (PubMed, Psych Info, CINAHL, and Scopus). Eleven empirical studies fulfilled the inclusion criteria. Articles were excluded if they did not measure RC and staff outcomes. RC was reported as positively associated with higher job satisfaction, better work engagement, lower burnout, lower turnover, and reciprocal learning among healthcare professionals. Literature on this topic is scarce, despite RC being a promising framework for healthcare professionals in various disciplines to enhance IPCP and improve staff outcomes across healthcare settings.

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.

Identification of social determinants of health using multi-label classification of electronic health record clinical notes.

OBJECTIVES: Social determinants of health (SDH), key contributors to health, are rarely systematically measured and collected in the electronic health record (EHR). We investigate how to leverage clinical notes using novel applications of multi-label learning (MLL) to classify SDH in mental health and substance use disorder patients who frequent the emergency department. METHODS AND MATERIALS: We labeled a gold-standard corpus of EHR clinical note sentences (N = 4063) with 6 identified SDH-related domains recommended by the Institute of Medicine for inclusion in the EHR. We then trained 5 classification models: linear-Support Vector Machine, K-Nearest Neighbors, Random Forest, XGBoost, and bidirectional Long Short-Term Memory (BI-LSTM). We adopted 5 common evaluation measures: accuracy, average precision-recall (AP), area under the curve receiver operating characteristic (AUC-ROC), Hamming loss, and log loss to compare the performance of different methods for MLL classification using the F1 score as the primary evaluation metric. RESULTS: Our results suggested that, overall, BI-LSTM outperformed the other classification models in terms of AUC-ROC (93.9), AP (0.76), and Hamming loss (0.12). The AUC-ROC values of MLL models of SDH related domains varied between (0.59-1.0). We found that 44.6% of our study population (N = 1119) had at least one positive documentation of SDH. DISCUSSION AND CONCLUSION: The proposed approach of training an MLL model on an SDH rich data source can produce a high performing classifier using only unstructured clinical notes. We also provide evidence that model performance is associated with lexical diversity by health professionals and the auto-generation of clinical note sentences to document SDH.

Identifying Patient Phenotype Cohorts Using Prehospital Electronic Health Record Data.

Objective: Emergency medical services (EMS) provide critical interventions for patients with acute illness and injury and are important in implementing prehospital emergency care research. Retrospective, manual patient record review, the current reference-standard for identifying patient cohorts, requires significant time and financial investment. We developed automated classification models to identify eligible patients for prehospital clinical trials using EMS clinical notes and compared model performance to manual review.Methods: With eligibility criteria for an ongoing prehospital study of chest pain patients, we used EMS clinical notes (n = 1208) to manually classify patients as eligible, ineligible, and indeterminate. We randomly split these same records into training and test sets to develop and evaluate machine-learning (ML) algorithms using natural language processing (NLP) for feature (variable) selection. We compared models to the manual classification to calculate sensitivity, specificity, accuracy, positive predictive value, and F1 measure. We measured clinical expert time to perform review for manual and automated methods.Results: ML models' sensitivity, specificity, accuracy, positive predictive value, and F1 measure ranged from 0.93 to 0.98. Compared to manual classification (N = 363 records), the automated method excluded 90.9% of records as ineligible and leaving only 33 records for manual review.Conclusions: Our ML derived approach demonstrates the feasibility of developing a high-performing, automated classification system using EMS clinical notes to streamline the identification of a specific cardiac patient cohort. This efficient approach can be leveraged to facilitate prehospital patient-trial matching, patient phenotyping (i.e. influenza-like illness), and create prehospital patient registries.

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.

Reflective practices: meaningful recognition for healthy work environments.

Nurses' decisions about their intent to remain in the workforce are based on various factors. A healthy work environment in which work done well is recognised and appreciated contributes to nurses' satisfaction and better patient outcomes. This article examines the American Association of Critical-Care Nurses framework for a healthy work environment, focusing on standards for meaningful recognition. Reflective practice, which provides a self-analytical approach to appreciate and value one's work, is viewed as self-recognition. Neither boastful nor arrogant, reflective self-recognition is part of progression to professional maturity. It involves examining events at work continuously and systematically to learn, appreciate and move to higher levels of contribution in the workplace.

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.

Adopting best practices: "Agility" moves from software development to healthcare project management.

It is time for a change in mindset in how nurses operationalize system implementations and manage projects. Computers and systems have evolved over time from unwieldy mysterious machines of the past to ubiquitous computer use in every aspect of daily lives and work sites. Yet, disconcertingly, the process used to implement these systems has not evolved. Technology implementation does not need to be a struggle. It is time to adapt traditional plan-driven implementation methods to incorporate agile techniques. Agility is a concept borrowed from software development and is presented here because it encourages flexibility, adaptation, and continuous learning as part of the implementation process. Agility values communication and harnesses change to an advantage, which facilitates the natural evolution of an adaptable implementation process. Specific examples of agility in an implementation are described, and plan-driven implementation stages are adapted to incorporate relevant agile techniques. This comparison demonstrates how an agile approach enhances traditional implementation techniques to meet the demands of today's complex healthcare environments.