Analysis of multi-level barriers to physical activity among nursing students using regularized regression.

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.

Frequent temporal patterns of physiological and biological biomarkers and their evolution in sepsis.

Sepsis is one of the most challenging health conditions worldwide, with relatively high incidence and mortality rates. It is shown that preventing sepsis is the key to avoid potentially irreversible organ dysfunction. However, data-driven early identification of sepsis is challenging as sepsis shares signs and symptoms with other health conditions. This paper adopts a temporal pattern mining approach to identify frequent temporal and evolving patterns of physiological and biological biomarkers in sepsis patients. We show that using these frequent patterns as features for classifying sepsis and non-sepsis patients can improve the prediction accuracy and performance up to 7%. Most of the temporal modeling approaches adopted in the sepsis literature are based on deep learning methods. Although these approaches produce high accuracy, they generally have limited model explainability and interpretability. Using the adopted methods in this study, we could identify the most important features contributing to the patients' sepsis incidence, such as fluctuations in platelet, lactate, and creatinine, or evolution of patterns including renal and metabolic organ systems, and consequently, enhance the findings' clinical interpretability.

Display and perception of risk: Analysis of decision support system display and its impact on perceived clinical risk of sepsis-induced health deterioration.

Despite acknowledging the value of clinical decision support systems (CDSS) in identifying risk for sepsis-induced health deterioration in-hospitalized patients, the relationship between display features, decision maker characteristics, and recognition of risk by the clinical decision maker remains an understudied, yet promising, area. The objective of this study is to explore the relationship between CDSS display design and perceived clinical risk of in-hospital mortality associated with sepsis. The study utilized data collected through in-person experimental sessions with 91 physicians from the general medical and surgical floors who were recruited across 12 teaching hospitals within the United States. Results of descriptive and statistical analyses provided evidence supporting the impact of display configuration and clinical case severity on perceived risk associated with in-hospital mortality. Specifically, findings showed that a high level of information (represented by the Predisposition, Infection, Response and Organ dysfunction (PIRO) score) and Figure display (as opposed to Text or baseline) increased awareness to recognizing the risk for in-hospital mortality of hospitalized sepsis patients. A CDSS display that synthesizes the optimal features associated with information level and design elements has the potential to enhance the quantification and communication of clinical risk in complex health conditions beyond sepsis.

Proximity of Cellular and Physiological Response Failures in Sepsis.

Sepsis is a devastating multi-stage health condition with a high mortality rate. Its complexity, prevalence, and dependency of its outcomes on early detection have attracted substantial attention from data science and machine learning communities. Previous studies rely on individual cellular and physiological responses representing organ system failures to predict health outcomes or the onset of different sepsis stages. However, it is known that organ systems' failures and dynamics are not independent events. In this study, we identify the dependency patterns of significant proximate sepsis-related failures of cellular and physiological responses using data from 12,223 adult patients hospitalized between July 2013 and December 2015. The results show that proximate failures of cellular and physiological responses create better feature sets for outcome prediction than individual responses. Our findings reveal the few significant proximate failures that play the major roles in predicting patients' outcomes. This study's results can be simply translated into clinical practices and inform the prediction and improvement of patients' conditions and outcomes.

The Available Criteria for Different Sepsis Scoring Systems in the Emergency Department-A Retrospective Assessment.

OBJECTIVE: 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). DESIGN: Retrospective data analysis study. SETTING: ED of MedStar Washington Hospital Center (MWHC), a 912-bed urban, tertiary hospital. PATIENTS: 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. MAIN OUTCOMES MEASURED: 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. RESULTS: 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. CONCLUSION: 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.

Informatics and interaction: Applying human factors principles to optimize the design of clinical decision support for sepsis.

In caring for patients with sepsis, the current structure of electronic health record systems allows clinical providers access to raw patient data without imputation of its significance. There are a wide range of sepsis alerts in clinical care that act as clinical decision support tools to assist in early recognition of sepsis; however, there are serious shortcomings in existing health information technology for alerting providers in a meaningful way. Little work has been done to evaluate and assess existing alerts using implementation and process outcomes associated with health information technology displays, specifically evaluating clinician preference and performance. We developed graphical model displays of two popular sepsis scoring systems, quick Sepsis Related Organ Failure Assessment and Predisposition, Infection, Response, Organ Failure, using human factors principles grounded in user-centered and interaction design. Models will be evaluated in a larger research effort to optimize alert design to improve the collective awareness of high-risk populations and develop a relevant point-of-care clinical decision support system for sepsis.

The value of missing information in severity of illness score development.

OBJECTIVE: We aim to investigate the hypothesis that using information about which variables are missing along with appropriate imputation improves the performance of severity of illness scoring systems used to predict critical patient outcomes. STUDY DESIGN AND SETTING: We quantify the impact of missing and imputed variables on the performance of prediction models used in the development of a sepsis-related severity of illness scoring system. Electronic health records (EHR) data were compiled from Christiana Care Health System (CCHS) on 119,968 adult patients hospitalized between July 2013 and December 2015. Two outcomes of interest were considered for prediction: (1) first transfer to intensive care unit (ICU) and (2) in-hospital mortality. Five different prediction models were employed. Indicators were utilized in these prediction models to identify when variables were missing and imputed. RESULTS: We observed statistically significant gains in prediction performance when moving from models that did not indicate missing information to those that did. Moreover, this increase was higher in models that use summary variables as predictors compared to those that use all variables. CONCLUSION: When developing prediction models using longitudinal EHR data, researchers should explore the incorporation of indicators for missing variables along with appropriate imputation.

Risky Business: Predicting patient mortality at a glance.

Clinicians are constantly forecasting patient trajectories to make critical point of care decisions intended to influence clinical outcomes. Little is known, however, about how providers interpret mortality risk against validated scoring systems. This research aims to understand how providers forecast mortality specifically for that of patients with sepsis. Defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, sepsis is commonly hard to diagnose, progresses rapidly, and lacks a "gold standard" test. Participants were nurses and doctors from the general medical and surgical floors of six different hospitals. Each was presented with ten different patient cases, categorized into low and high severity sepsis, and were asked about care decisions, along with estimations of mortality risk. The resulting data provides a unique look into the differences of risk forecasting between profession and patient severity.

FUTURES: Forecasting the Unexpected Transfer to Upgraded REsources in Sepsis.

Sepsis is one of the most deadly and costly diseases. The Emergency Department (ED) is the initial point of care for most patients who become hospitalized due to sepsis. Quantifying the accuracy of ED clinician forecasting regarding patients' clinical trajectories and outcomes can provide insight into clinical decision making and inform sepsis management.

Objective measures of workload in healthcare: a narrative review.

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.

Microsimulation Model Using Christiana Care Early Warning System (CEWS) to Evaluate Physiological Deterioration.

While physiological warning signs prior to deterioration events during hospitalization have been widely studied, evaluating clinical interventions, such as rapid response team (RRT) activations, based on scoring systems remains an understudied area. Simulation of physiological deterioration patterns represented by scoring systems can facilitate testing different RRT policies without disturbing care processes. Christiana Care Early Warning System (CEWS) is a scoring system developed at the study hospital to detect the physiological warning signs and inform RRT activations. The objective of this study is to evaluate CEWS-triggered RRT policies based on patient demographics and policy structures. Using retrospective data derived from a subset of electronic health records between December 2015 and December 2016 (6000 patients), we developed a microsimulation model with integrated regression analysis to compare RRT policies on subpopulations defined by age, gender, and comorbidities to find score thresholds that result in the lowest percent of time spent above critical CEWS values. Policies that rely on average scores were more sensitive to threshold changes compared to policies that rely on current value and change in the CEWS. Policy using score threshold 10 provided the lowest percentage of time under the critical condition for majority of subpopulations. The proposed model is a novel framework to simulate individual deterioration patterns and systematically evaluate RRT policies based on their impact on health conditions. Our work highlights the importance of integration of data-driven models into personalized care and represents a significant opportunity to inform biomedical and health informatics research on designing and evaluating EWS-based clinical interventions.

Not all organ dysfunctions are created equal - Prevalence and mortality in sepsis.

PURPOSE: 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. MATERIALS AND METHODS: 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). RESULTS: 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. CONCLUSION: 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.

Data-driven approach to Early Warning Score-based alert management.

BACKGROUND: Increasing 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. METHODS: We 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. RESULTS: We 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. CONCLUSION: EWS-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.

Evaluation of User-Interface Alert Displays for Clinical Decision Support Systems for Sepsis.

BACKGROUND: Hospitals are increasingly turning to clinical decision support systems for sepsis, a life-threatening illness, to provide patient-specific assessments and recommendations to aid in evidence-based clinical decision-making. Lack of guidelines on how to present alerts has impeded optimization of alerts, specifically, effective ways to differentiate alerts while highlighting important pieces of information to create a universal standard for health care providers. OBJECTIVE: To gain insight into clinical decision support systems-based alerts, specifically targeting nursing interventions for sepsis, with a focus on behaviors associated with and perceptions of alerts, as well as visual preferences. METHODS: An interactive survey to display a novel user interface for clinical decision support systems for sepsis was developed and then administered to members of the nursing staff. RESULTS: A total of 43 nurses participated in 2 interactive survey sessions. Participants preferred alerts that were based on an established treatment protocol, were presented in a pop-up format, and addressed the patient's clinical condition rather than regulatory guidelines. CONCLUSIONS: The results can be used in future research to optimize electronic medical record alerting and clinical practice workflow to support the efficient, effective, and timely delivery of high-quality care to patients with sepsis. The research also may advance the knowledge base of what information health care providers want and need to improve the health and safety of their patients.

The design of decisions: Matching clinical decision support recommendations to Nielsen's design heuristics.

OBJECTIVE: While general design heuristics exist for graphic user interfaces, it remains a challenge to facilitate the implementation of these heuristics for the design of clinical decision support. Our goals were to map a set of recommendations for clinical decision support design found in current literature to Jakob Nielsen's traditional usability heuristics and to suggest usability areas that need more investigation. MATERIALS AND METHODS: Using a modified nominal group process, the research team discussed, classified, and mapped recommendations, organized as interface, information, and interaction, to design heuristics. A previous narrative review identified 42 recommendations from the literature to define the design and functional characteristics that impact the performance of CDS in terms of provider preference, process of care, and patient outcomes. MAIN FINDINGS: We matched 20 out of 42 recommendations to heuristics. The mapping reveals gaps in both heuristics and recommendations, identifying a set of Nielsen's heuristics that are underrepresented in the literature and subsets of recommendations important to design not covered in Nielsen's heuristics. We attributed this, in part, to the evolution of technology since the inception of Nielsen's heuristics. The team created a new interaction heuristic: Integration into real-time workflow to consider the needs of the end-user in the clinical space. DISCUSSION: Clinical decision support has enabled clinicians to better address arising information needs; however there remains a lack of evidence-based guidelines in terms of functional and design requirements. CONCLUSION: Results from this review suggest that interaction design principles were not fully satisfied by the current literature of clinical decision support.

Interface, information, interaction: a narrative review of design and functional requirements for clinical decision support.

Objective: Provider acceptance and associated patient outcomes are widely discussed in the evaluation of clinical decision support systems (CDSSs), but critical design criteria for tools have generally been overlooked. The objective of this work is to inform electronic health record alert optimization and clinical practice workflow by identifying, compiling, and reporting design recommendations for CDSS to support the efficient, effective, and timely delivery of high-quality care. Material and Methods: A narrative review was conducted from 2000 to 2016 in PubMed and The Journal of Human Factors and Ergonomics Society to identify papers that discussed/recommended design features of CDSSs that are associated with the success of these systems. Results: Fourteen papers were included as meeting the criteria and were found to have a total of 42 unique recommendations; 11 were classified as interface features, 10 as information features, and 21 as interaction features. Discussion: Features are defined and described, providing actionable guidance that can be applied to CDSS development and policy. To our knowledge, no reviews have been completed that discuss/recommend design features of CDSS at this scale, and thus we found that this was important for the body of literature. The recommendations identified in this narrative review will help to optimize design, organization, management, presentation, and utilization of information through presentation, content, and function. The designation of 3 categories (interface, information, and interaction) should be further evaluated to determine the critical importance of the categories. Future work will determine how to prioritize them with limited resources for designers and developers in order to maximize the clinical utility of CDSS. Conclusion: This review will expand the field of knowledge and provide a novel organization structure to identify key recommendations for CDSS.

We all make choices: A decision analysis framework for disposition decision in the ED.

BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSION: 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.

A Framework to Tackle Risk Identification and Presentation Challenges in Sepsis.

Introduction: Sepsis trajectories, including onset and recovery, can be difficult to assess, but electronic health records (EHRs) can accurately capture sepsis as a dynamic episode. Methods: Retrospective dataset of 276,722 clinical observations (4,726 unique patients) during a two-month period in 2015 were extracted from the EHRs. A Cox proportional hazard model was built to test hazard ratios of risk factors to the first sepsis episode onset within 72 hours for patients with presumed infection. Predisposition, infection, response, and organ failure (PIRO) score-based framework was used in a logistic regression to identify factors associated with in-hospital mortality within the sepsis population. Results: 47.54% of patients with an infection episode experienced at least one sepsis episode (N=1,044 out of 2,196) within 72 hours of admission. The mortality rate was higher for patients with sepsis episodes (7.24%) compared to patient with only organ dysfunction episodes (4.84%) or only with infection episodes (3.96%). Analysis identified factors associated with the first sepsis episode onset and those associated with in-hospital mortality. Discussion: Our study addresses identification of infection, organ dysfunction, and sepsis as dynamic episodes utilizing EHR data and provides a systematic approach to detect risk factors related to sepsis onset and in-hospital mortality.

Optimizing usability and signal capture: a proactive risk assessment for the implementation of a wireless vital sign monitoring system.

Wearable vital sign monitors are a promising step towards optimal patient surveillance, providing continuous data to allow for early detection and treatment of patient deterioration. However, as wearable monitors become more widely adopted in healthcare, there is a corresponding need to carefully design the implementation of these tools to promote their integration into clinical workflows and defend against potential misuse and patient harm. Prior to the roll-out of these monitors, our multidisciplinary team of clinicians, clinical engineers, information technologists and research investigators conducted a modified Healthcare Failure Mode and Effect Analysis (HFMEA), a proactive evaluation of potential problems which could be encountered in the use of a wireless vital signs monitoring system. This evaluation was accomplished by focussing on the identification of procedures and actions that would be required during the devices' regular usage, as well as the implementation of the system as a comprehensive process. Using this method, the team identified challenges that would arise throughout the lifecycle of the device and developed recommendations to address them. This proactive risk assessment can guide the implementation of wearable patient monitors, optimising the use of innovative health information technology.