Browsing by Subject "intensive care unit"

Now showing 1 - 4 of 4
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    Coincidence Analysis: A Novel Approach to Modeling Nurses' Workplace Experience
    (Thieme, 2022-08) Womack, Dana M.; Miech, Edward J.; Fox, Nicholas J.; Silvey, Linus C.; Somerville, Anna M.; Eldredge, Deborah H.; Steege, Linsey M.; School of Nursing
    Objectives  The purpose of this study is to identify combinations of workplace conditions that uniquely differentiate high, medium, and low registered nurse (RN) ratings of appropriateness of patient assignment during daytime intensive care unit (ICU) work shifts. Methods  A collective case study design and coincidence analysis were employed to identify combinations of workplace conditions that link directly to high, medium, and low RN perception of appropriateness of patient assignment at a mid-shift time point. RN members of the study team hypothesized a set of 55 workplace conditions as potential difference makers through the application of theoretical and empirical knowledge. Conditions were derived from data exported from electronic systems commonly used in nursing care. Results  Analysis of 64 cases (25 high, 24 medium, and 15 low) produced three models, one for each level of the outcome. Each model contained multiple pathways to the same outcome. The model for “high” appropriateness was the simplest model with two paths to the outcome and a shared condition across pathways. The first path comprised of the absence of overtime and a before-noon patient discharge or transfer, and the second path comprised of the absence of overtime and RN assignment to a single ICU patient. Conclusion  Specific combinations of workplace conditions uniquely distinguish RN perception of appropriateness of patient assignment at a mid-shift time point, and these difference-making conditions provide a foundation for enhanced observability of nurses' work experience during hospital work shifts. This study illuminates the complexity of assessing nursing work system status by revealing that multiple paths, comprised of multiple conditions, can lead to the same outcome. Operational decision support tools may best reflect the complex adaptive nature of the work systems they intend to support by utilizing methods that accommodate both causal complexity and equifinality.
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    Hospital-Level Variation in Death for Critically Ill Patients with COVID-19
    (ATS, 2021) Churpek, Matthew M.; Gupta, Shruti; Spicer, Alexandra B.; Parker, William F.; Fahrenbach, John; Brennen, Samantha K.; Leaf, David E.; STOP-COVID Investigators; Medicine, School of Medicine
    Rationale: Variation in hospital mortality has been described for coronavirus disease 2019 (COVID-19), but the factors that explain these differences remain unclear. Objective: Our objective was to utilize a large, nationally representative dataset of critically ill adults with COVID-19 to determine which factors explain mortality variability. Methods: In this multicenter cohort study, we examined adults hospitalized in intensive care units with COVID-19 at 70 United States hospitals between March and June 2020. The primary outcome was 28-day mortality. We examined patient-level and hospital-level variables. Mixed-effects logistic regression was used to identify factors associated with interhospital variation. The median odds ratio (OR) was calculated to compare outcomes in higher- vs. lower-mortality hospitals. A gradient boosted machine algorithm was developed for individual-level mortality models. Measurements and Main Results: A total of 4,019 patients were included, 1537 (38%) of whom died by 28 days. Mortality varied considerably across hospitals (0-82%). After adjustment for patient- and hospital-level domains, interhospital variation was attenuated (OR decline from 2.06 [95% CI, 1.73-2.37] to 1.22 [95% CI, 1.00-1.38]), with the greatest changes occurring with adjustment for acute physiology, socioeconomic status, and strain. For individual patients, the relative contribution of each domain to mortality risk was: acute physiology (49%), demographics and comorbidities (20%), socioeconomic status (12%), strain (9%), hospital quality (8%), and treatments (3%). Conclusion: There is considerable interhospital variation in mortality for critically ill patients with COVID-19, which is mostly explained by hospital-level socioeconomic status, strain, and acute physiologic differences. Individual mortality is driven mostly by patient-level factors.
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    Hypothermia is Associated With Poor Prognosis in Hospitalized Patients With Severe COVID-19 Symptoms
    (2021) Maait, Yousef; El Khoury, Marc; McKinley, Lee; El Khoury, Anthony; Graduate Medical Education, Office of Educational Affairs, IU School of Medicine
    Rationale Hypothermia forms a part of the diagnostic criteria for Systemic Inflammatory Response Syndrome (SIRS), National Early Warning Score (NEWS) and has repeatedly been shown to be associated with worse outcomes when compared to normothermic and hyperthermic patients with sepsis. We evaluate whether this is the case in COVID-19 patients. Objective To determine whether there is an association between hypothermia and worse prognosis in COVID-19 patients in the intensive care unit. Methods Retrospective study of a cohort of patients (n = 57) admitted to the intensive care unit of a community hospital with a positive test for COVID-19. Measurements Data relating to mortality, comorbidities and length of stay was recorded from electronic medical records for each patient. Hypothermia was defined as ≥2 recorded body temperatures of less than 96.5℉ (35.83℃) at the time of admission. Main results Of the 57 patients enrolled in the study, 21 developed hypothermia during their stay and 36 did not. Our results show that patients who have hypothermia at the time of admission spend a longer time intubated (p < 0.01) and go through longer ICU stays (p < 0.01). These patients are also 2.18 times more likely to suffer a fatal outcome compared to patients that did not develop hypothermia while in the intensive care unit (Chi-squared = 8.6209, p < 0.01, RR = 2.18). Conclusions Hypothermia in patients with severe COVID-19 at the time of admission to the ICU is associated with poorer outcomes for patients. This manifests as a longer period of intubation, longer ICU stay, and increased risk of mortality.
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    Reducing Diagnostic Error in the ICU: A Novel Approach to Clinical Workflow—Visualization-Communication Integration
    (Office of the Vice Chancellor for Research, 2014-04-11) Faiola, Anthony; Srinivas, Preethi; Karanam, Yamini; Koval, Olesia
    Background and Aim: The ICU holds the critically ill who require continuous and coordinated monitoring and frequent intervention. ICUs have the highest annual mortality rate of any hospital unit (12-22%), impacting nearly one-quarter of all admissions [1, 2]. Although ICU patients are the most monitored, tested, and examined of all hospital patients, medical conditions are missed. Studies consistently demonstrate that the complexities of ICU clinical workflow and decision-making directly impact patient safety [3], in spite of the advances in health information technology (HIT) such as clinical decision support (CDS) and smart bedside devices. The ICU is an intensely challenging and complex clinical environment, with each provider being inundated with thousands of independent pieces of information daily from multiple sources [4] including HIT and electronic medical records (EMR) systems [5]. Previous research identifies nearly 80% of HIT “user error” from cognitive overload [6], resulting in incorrect use or user error in analyzing medical data and 91% of all medical mishaps resulting from inefficient team collaboration and communication among the intensivists [7]. Although the key factor of user error can be attributed to poor or inadequately designed system interfaces or interaction sequences, research shows that without a comprehensive understanding of the context in which care occurs, it is improbable that systemic factors leading to error will be adequately understood. Hence, it is imperative to understand the underlying mechanisms of workflow error, from which innovative HIT/CDS systems can be designed to more effectively improve ICU care delivery. Although CDS systems have received increasing attention in biomedical informatics and humanfactors engineering literature, none has taken an integrated workflow approach that considers the following five factors as closely interrelated: (1) Patient status, involving continuous monitoring of patient organ function and vital sign function; (2) Patient data, such as that generated from treatment and bedside devices; (3) Medical cognition and cognitive resources of intensivists; (4) Communication among ICU team-members; and (5) Need for collaborative decision-making [8, 9]. The objective of our research is to investigate the root causes of and solutions to ICU error related to the effects of clinical workflow by: Aim 1: Identifying and comparing existing medical cognitive load, workflow, clinician happiness/challenge, and team communication/collaboration in the context of HIT/CDS system use. Aim 2: Constructing and validating several ICU workflow strategies that will be modeled for use with existing CDS systems, but primarily with the proposed novel VizCom technology, MIVA. Aim 3: Designing and building the next stage of the (formally prototyped) VizCom application MIVA that integrates patient data visualization and intensivist inter-communication into a single mobile technology (US Patent 2/4/2014, #8,645,164). Proposed Research: Based on two prior studies [10 11], our future work will identify intensivist cognitive load, workflow, and CDS system use by means of data collection methods that will take place in the ICUs of three Indianapolis hospitals, including: a) rapid ethnography: shadowing and group observation), b) self-reporting: survey, one-on-one interview, and social network analysis, and c) the experience sampling method. We propose a workflow model where MIVA will be used by intensivists who are spread across different zones, defined by location as: inside the ICU (Zone 1), inside the hospital but outside the ICU (Zone 2), and outside the hospital and on-call (Zone 3). According to our model, data will flow from bedside devices to the EMR to MIVA. The MIVA visualization-communication components will enable clinicians across all three zones to collaboratively diagnosis in unison. Broader Impact and Conclusion: Based on the aforementioned research, we believe that clear, rapid, appropriate, and accurate communication is essential to developing human-centered technology that will deliver safe and effective patient care, from which seamless collaboration among clinical professionals is vital [12]. Existing studies consistently suggest that medical cognition should focus on complex social systems that constitute distributed knowledge, collaborative performance and clinical group workflow. Our project will inform the design of a clinical decision support tool that will provide the intensivists with capabilities for greater control of ICU data and inter-communication at the point-of-care.