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Physical inactivity is a growing societal concern with significant impact on public health. Identifying barriers to engaging in physical activity (PA) is a critical step to recognize populations who disproportionately experience these barriers. Understanding barriers to PA holds significant importance within patient-facing healthcare professions like nursing. While determinants of PA have been widely studied, connecting individual and social factors to barriers to PA remains an understudied area among nurses. The objectives of this study are to categorize and model factors related to barriers to PA using the National Institute on Minority Health and Health Disparities (NIMHD) Research Framework. The study population includes nursing students at the study institution (N = 163). Methods include a scoring system to quantify the barriers to PA, and regularized regression models that predict this score. Key findings identify intrinsic motivation, social and emotional support, education, and the use of health technologies for tracking and decision-making purposes as significant predictors. Results can help identify future nursing workforce populations at risk of experiencing barriers to PA. Encouraging the development and employment of health-informatics solutions for monitoring, data sharing, and communication is critical to prevent barriers to PA before they become a powerful hindrance to engaging in PA.

BackgroundIncreasing adoption of electronic health records (EHRs) with integrated alerting systems is a key initiative for improving patient safety. Considering the variety of dynamically changing clinical information, it remains a challenge to design EHR-driven alerting systems that notify the right providers for the right patient at the right time while managing alert burden. The objective of this study is to proactively develop and evaluate a systematic alert-generating approach as part of the implementation of an Early Warning Score (EWS) at the study hospitals.MethodsWe quantified the impact of an EWS-based clinical alert system on quantity and frequency of alerts using three different alert algorithms consisting of a set of criteria for triggering and muting alerts when certain criteria are satisfied. We used retrospectively collected EHRs data from December 2015 to July 2016 in three units at the study hospitals including general medical, acute care for the elderly and patients with heart failure.ResultsWe compared the alert-generating algorithms by opportunity of early recognition of clinical deterioration while proactively estimating alert burden at a unit and patient level. Results highlighted the dependency of the number and frequency of alerts generated on the care location severity and patient characteristics.ConclusionEWS-based alert algorithms have the potential to facilitate appropriate alert management prior to integration into clinical practice. By comparing different algorithms with regard to the alert frequency and potential early detection of physiological deterioration as key patient safety opportunities, findings from this study highlight the need for alert systems tailored to patient and care location needs, and inform alternative EWS-based alert deployment strategies to enhance patient safety.

Emergency Department (ED) providers' disposition decision impacts patient care and safety. The objective of this brief report is to gain a better understanding of ED providers' disposition decision and risk tolerance of associated outcomes. We synthesized qualitative and quantitative methods including decision mapping, survey research, statistical analysis, and word clouds. Between July 2017 and August 2017, a 10-item survey was developed and conducted at the study hospital. Descriptive and statistical analyses were used to assess the relationship between the participant characteristics (age, gender, years of experience in the ED, and level of expertise) and risk tolerance of outcomes (72-h return and negative outcome) associated with disposition decision. Word clouds facilitated prioritization of qualitative responses regarding information impacting and supporting the disposition decision. Total of 46 participants completed the survey. The mean age was 39.5 (standard deviation (SD) 10years), and mean years of experience was 9.6years (SD 8.7years). Decision map highlighted the connections between patient-, provider-, and system-related factors. Survey results showed that negative outcome resulted in less risk tolerance compared to 72-h return. Chi-square tests did not provide sufficient evidence to indicate that the responses are independent of participants characteristics – except age and the risk of 72-h return (p=0.046). Discharge decision making in the ED is complex as it involves interconnected patient, provider, and system factors. Synthesizing qualitative and quantitative methods promise enhanced understanding of how providers arrive to disposition decision, as well as safety and quality of care in the ED.

Purpose Workload is a critical concept in the evaluation of performance and quality in healthcare systems, but its definition relies on the perspective (e.g. individual clinician-level vs unit-level workload) and type of available metrics (e.g. objective vs subjective measures). The purpose of this paper is to provide an overview of objective measures of workload associated with direct care delivery in tertiary healthcare settings, with a focus on measures that can be obtained from electronic records to inform operationalization of workload measurement. Design/methodology/approach Relevant papers published between January 2008 and July 2018 were identified through a search in Pubmed and Compendex databases using the Sample, Phenomenon of Interest, Design, Evaluation, Research Type framework. Identified measures were classified into four levels of workload: task, patient, clinician and unit. Findings Of 30 papers reviewed, 9 used task-level metrics, 14 used patient-level metrics, 7 used clinician-level metrics and 20 used unit-level metrics. Key objective measures of workload include: patient turnover (n=9), volume of patients (n=6), acuity (n=6), nurse-to-patient ratios (n=5) and direct care time (n=5). Several methods for operationalization of these metrics into measurement tools were identified. Originality/value This review highlights the key objective workload measures available in electronic records that can be utilized to develop an operational approach for quantifying workload. Insights gained from this review can inform the design of processes to track workload and mitigate the effects of increased workload on patient outcomes and clinician performance.

Data collection utilized survey administration to ED residents and board-certified ED physicians within the final hour of their shift using Research Electronic Data Capture (REDCap) [22]. The average NASA-TLX score components of participants whose response was “no” to the question assessing certainty in MDM, showed higher mental workload (58.1 vs 50.3), lower performance (67.9 vs 75.5), greater effort (65.5 vs 61.3), greater frustration (54.3 vs 47.6) and greater total NASA-TLX scores (55.8 vs 53.4) when compared with those who stated certainty in clinical decisions (Fig. 2). Medical decision making Were you certain in all your clinical decision made during this shift? (Yes/No) Medical decision making Rate your ease of clinical decision making during this shift (Visual Analog Scale) Number of patients Can you estimate the number of patients seen during this shift?

While organ dysfunctions within sepsis have been widely studied, interaction between measures of organ dysfunction remains an understudied area. The objective of this study is to quantify the impact of organ dysfunction on in-hospital mortality in infected population. Descriptive and multivariate analyses of retrospective data including patients (age ≥ 18 years) hospitalized at the study hospital from July 2013 to April 2016 who met the criteria for an infection visit (62,057 unique visits). The multivariate logistic regression model had an area under the curve of 0.9. Highest odds ratio (OR) associated with increased mortality risk was identified as fraction of inspired oxygen (FiO2) > 21% (OR = 5.8 and 95% Confidence Interval (CI) 1.8–35.6), and elevated lactate >2.0 mmol/L (OR = 2.45 (95% CI = 2.1–2.8)). Most commonly observed measures of organ dysfunction within mortality visits included elevated lactate (> 2.0 mmol/L), mechanical ventilation, and oxygen saturation (SpO2)/FiO2 ratio (< 421) at least once within 48 h prior to or 24 h after anti-infective administration. There exist differences in measures of organ dysfunction occurrence and their association with mortality. These findings support increased clinical efforts to identify sepsis patients to inform diagnostic decisions. •Understanding how many and which organ systems fail impact outcomes in sepsis.•We quantified and visualized organ system dysfunction in sepsis and mortality.•There are intra- and inter-system differences in acute organ system dysfunction.•Changes in renal, metabolic and respiratory systems are essential for surveillance.

The primary cause of preventable death in many hospitals is the failure to recognize and/or rescue patients from acute physiologic deterioration (APD). APD affects all hospitalized patients, potentially causing cardiac arrest and death. Identifying APD is difficult, and response timing is critical - delays in response represent a significant and modifiable patient safety issue. Hospitals have instituted rapid response systems or teams (RRT) to provide timely critical care for APD, with thresholds that trigger the involvement of critical care expertise. The National Early Warning Score (NEWS) was developed to define these thresholds. However, current triggers are inconsistent and ignore patient-specific factors. Further, acute care is delivered by providers with different clinical experience, resulting in quality-of-care variation. This article documents a semi-Markov decision process model of APD that incorporates patient and provider heterogeneity. The model allows for stochastically changing health states, while determining patient subpopulation-specific RRT-activation thresholds. The objective function minimizes the total time associated with patient deterioration and stabilization; and the relative values of nursing and RRT times can be modified. A case study from January 2011 to December 2012 identified six subpopulations. RRT activation was optimal for patients in “slightly concerning” health states (NEWS > 0) for all subpopulations, except surgical patients with low risk of deterioration for whom RRT was activated in “concerning” states (NEWS > 4). Clustering methods identified provider clusters considering RRT-activation preferences and estimation of stabilization-related resource needs. Providers with conservative resource estimates preferred waiting over activating RRT. This study provides simple practical rules for personalized acute care delivery.

The goal of the study was to assess the criteria availability of eight sepsis scoring methods within 6 hours of triage in the emergency department (ED). Retrospective data analysis study. ED of MedStar Washington Hospital Center (MWHC), a 912-bed urban, tertiary hospital. Adult (age ≥ 18 years) patients presenting to the MWHC ED between June 1, 2017 and May 31, 2018 and admitted with a diagnosis of severe sepsis with or without shock. Availability of sepsis scoring criteria of eight different sepsis scoring methods at three time points-0 Hours (T0), 3 Hours (T1) and 6 Hours (T2) after arrival to the ED. A total of 50 charts were reviewed, which included 23 (46%) males and 27 (54%) females. Forty-eight patients (96%) were Black or African American. Glasgow Coma Scale was available for all 50 patients at T0. Vital signs, except for temperature, were readily available (>90%) at T0. The majority of laboratory values relevant for sepsis scoring criteria were available (>90%) at T1, with exception to bilirubin (66%) and creatinine (80%). NEWS, PRESEP and qSOFA had greater than 90% criteria availability at triage. SOFA and SIRS consistently had the least percent of available criteria at all time points in the ED. The availability of patient data at different time points in a patient's ED visit suggests that different scoring methods could be utilized to assess for sepsis as more patient information becomes available.

As health-care organizations transition from paper to electronic documentation systems, capturing the nursing assessment electronically can play a fundamental role in transforming health-care delivery. Especially in preventive health, electronic capture of nursing assessment, combined with vital sign-based monitoring, can support early detection of physiological deterioration of patients. While vital sign-based Early Warning Systems have the potential to detect signals of physiological deterioration, their clinical interpretation and integration into the workflow in hospital-based care setting remain a challenge. This study presents a clinical early recognition algorithm using electronic health records (EHRs) coupled with an electronic Nurse Screening Assessment (NSA) that targets various health assessment categories and its integration into the nursing workflow. Data was collected retrospectively from a single institution (N=2,405 visits). χ 2 tests showed significant differences between algorithms with and without NSA (P<0.01). This study provides a practical framework for facilitating the meaningful use of EHRs in hospitals.

Hospitalized patients are at risk of unexpected acute and persistent physiological deterioration (APD), which is identified by disturbance in one or multiple physiological measures. Unanticipated APD may result in respiratory instability, cardiopulmonary arrest and death. Early warning scores (EWSs) are recommended as part of the early recognition and response to inpatient deterioration, including APD. EWSs not only provide a standardized method for clinical assessment, they also suggest which patients may require attention outside of an intensive care unit (ICU) provided by a critical care team, also called a Rapid Response Team (RRT). Currently used EWSs differ in the included physiological measures, and in the thresholds for triggering a response, e.g., RRT activation. At this time, there is no consensus on clinical guidelines for selection of physiological measures, and their thresholds to inform acute care decisions. There is a growing need to use the EWSs for acute care decision making to avoid failed or delayed response to APD. Bedside providers in the general ward commonly have to rely on subjective evaluations to trigger a response to APD. In addition, the current use of EWSs relies on fixed thresholds for a response without considering patient characteristics. Exploring the relationship between patient characteristics and APD, and identifying the patients who may benefit from an increased level of acute medical care would provide guidance in RRT activation, and help to personalize acute medical care. In this research, we collaborate with the Division of Health Care Policy & Research, Department of Health Sciences Research, Mayo Clinic, Rochester. We seek to individualize the response to APD by using electronic medical records (EMRs). We use EWSs to capture the stochastic changes in a patient’s physiological condition during a stay in the general ward. Our methodology includes statistical analysis, dynamic programming, random variable generation, clustering analysis, and a robust modeling approach. We segment the data into patient subpopulations, and apply the Chi-square and Kruskal-Wallis tests to identify statistically significantly different subpopulations. We develop subpopulation-specific infinite-horizon semi-Markov decision process (SMDP) models to optimize the care metrics related to stabilization and failure to recognize APD, while capturing the provider resource use as a function of EWSs and RRT activation. The optimal policies identify the subpopulation-specific RRT thresholds. We provide theoretical insights into the optimal total expected costs, and identify the framework to prove the existence of a control-limit policy. Finally, we address the uncertainty in cost parameters, because the fixed costs include time-based provider resource use, and may be subject to errors. In addition, the same resource time may be valued differently depending on the providers’ expertise level and the patients’ needs. We use an experimental design with eight scenarios, combined with a robust SMDP framework, to explore the impact of uncertainty in costs on the model results. The results of this research will allow bedside providers to make informed decisions regarding triggering an individualized response to APD in the general ward, and provide a baseline for future research in acute care decision making.