Utilization of big data to improve management of the emergency departments. Results of a systematic review

Background. The emphasis on using big data is growing exponentially in several sectors including biomedicine, life sciences and scientific research, mainly due to advances in information technologies and data analysis techniques. Actually, medical sciences can rely on a large amount of biomedical information and Big Data can aggregate information around multiple scales, from the DNA to the ecosystems. Given these premises, we wondered if big data could be useful to analyze complex systems such as the Emergency Departments (EDs) to improve their management and eventually patient outcomes. Methods. We performed a systematic review of the literature to identify the studies that implemented the application of big data in EDs and to describe what have already been done and what are the expectations, issues and challenges in this field. Results. Globally, eight studies met our inclusion criteria concerning three main activities: the management of ED visits, the ED process and activities and, finally, the prediction of the outcome of ED patients. Although the results of the studies show good perspectives regarding the use of big data in the management of emergency departments, there are still some issues that make their use still difficult. Most of the predictive models and algorithms have been applied only in retrospective studies, not considering the challenge and the costs of a real-time use of big data. Only few studies highlight the possible usefulness of the large volume of clinical data stored into electronic health records to generate evidence in real time. Conclusion. The proper use of big data in this field still requires a better management information flow to allow real-time application

Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU

Machine learning approaches have been effective in predicting adverse outcomes in different clinical settings. These models are often developed and evaluated on datasets with heterogeneous patient populations. However, good predictive performance on the aggregate population does not imply good performance for specific groups. In this work, we present a two-step framework to 1) learn relevant patient subgroups, and 2) predict an outcome for separate patient populations in a multi-task framework, where each population is a separate task. We demonstrate how to discover relevant groups in an unsupervised way with a sequence-to-sequence autoencoder. We show that using these groups in a multi-task framework leads to better predictive performance of in-hospital mortality both across groups and overall. We also highlight the need for more granular evaluation of performance when dealing with heterogeneous populations.Comment: KDD 201

Impact of Analytics Applying Artificial Intelligence and Machine Learning on Enhancing Intensive Care Unit: A Narrative Review

Introduction. The intensive care unit (ICU) plays a pivotal role in providing specialized care to patients with severe illnesses or injuries. As a critical aspect of healthcare, ICU admissions demand immediate attention and skilled care from healthcare professionals. However, the intricacies involved in this process necessitate analytical solutions to ensure effective management and optimal patient outcomes. Aim. The aim of this review was to highlight the enhancement of the ICUs through the application of analytics, artificial intelligence, and machine learning. Methods. The review approach was carried out through databases such as MEDLINE, Embase, Web of Science, Scopus, Taylor & Francis, Sage, ProQuest, Science Direct, CINAHL, and Google Scholar. These databases were chosen due to their potential to offer pertinent and comprehensive coverage of the topic while reducing the likelihood of overlooking certain publications. The studies for this review involved the period from 2016 to 2023. Results. Artificial intelligence and machine learning have been instrumental in benchmarking and identifying effective practices to enhance ICU care. These advanced technologies have demonstrated significant improvements in various aspects. Conclusions. Artificial intelligence, machine learning, and data analysis techniques significantly improved critical care, patient outcomes, and healthcare delivery

Predicting Factors of Re-Hospitalization After Medically Managed Intensive Inpatient Services in Opioid Use Disorder

IntroductionOpioid use disorder has continued to rise in prevalence across the United States, with an estimated 2.5 million Americans ailing from the condition (NIDA, 2020). Medically managed detoxification incurs substantial costs and, when used independently, may not be effective in preventing relapse (Kosten & Baxter, 2019). While numerous studies have focused on predicting the factors of developing opioid use disorder, few have identified predictors of readmission to medically managed withdrawal at an inpatient level of care. Utilizing a high-fidelity dataset from a large multi-site behavioral health hospital, these predictors are explored. MethodsPatients diagnosed with Opioid Use Disorder and hospitalized in the inpatient level of care were analyzed to identify readmission predictors. Factors including patient demographics, patient-reported outcome measures, and post-discharge treatment interventions were included. Patients re-hospitalized to the inpatient level of care were binary labeled in the dataset, and various machine learning algorithms were tested, including machine learning techniques. Methods include random forest, gradient boosting, and deep learning techniques. Evaluation statistics include specificity, accuracy, precision, and Matthew\u27s Coefficient. ResultsOverall, there was a wide variation if correctly predicting the class of patients that would readmit to a medically managed level of inpatient detoxification. Out of the six models evaluated, three of the six did not converge, thus not producing a viable feature ranking. However, of the other three models that did converge, the deep learning model produced almost perfect classification, producing an accuracy of .98. AdaBoost and the logistic regression model produced an accuracy of .97 and .61, respectively. Each of these models produced a similar set of features that were important to predicting which patient profile would readmit to medically managed inpatient detoxification. ConclusionsThe results indicate that overall reduction in the Quick Inventory of Depressive Symptomology, discharge disposition, age, length of stay, and a patient\u27s total number of diagnoses were important features at predicting readmission. Additionally, deep learning algorithms vastly outperformed other machine learning algorithms

Signatures of Subacute Potentially Catastrophic Illness in the ICU: Model Development and Validation

Objectives: Patients in ICUs are susceptible to subacute potentially catastrophic illnesses such as respiratory failure, sepsis, and hemorrhage that present as severe derangements of vital signs. More subtle physiologic signatures may be present before clinical deterioration, when treatment might be more effective. We performed multivariate statistical analyses of bedside physiologic monitoring data to identify such early subclinical signatures of incipient life-threatening illness. Design: We report a study of model development and validation of a retrospective observational cohort using resampling (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis type 1b internal validation) and a study of model validation using separate data (type 2b internal/external validation). Setting: University of Virginia Health System (Charlottesville), a tertiary-care, academic medical center. Patients: Critically ill patients consecutively admitted between January 2009 and June 2015 to either the neonatal, surgical/trauma/burn, or medical ICUs with available physiologic monitoring data. Interventions: None. Measurements and Main Results: We analyzed 146 patient-years of vital sign and electrocardiography waveform time series from the bedside monitors of 9,232 ICU admissions. Calculations from 30-minute windows of the physiologic monitoring data were made every 15 minutes. Clinicians identified 1,206 episodes of respiratory failure leading to urgent unplanned intubation, sepsis, or hemorrhage leading to multi-unit transfusions from systematic individual chart reviews. Multivariate models to predict events up to 24 hours prior had internally validated C-statistics of 0.61-0.88. In adults, physiologic signatures of respiratory failure and hemorrhage were distinct from each other but externally consistent across ICUs. Sepsis, on the other hand, demonstrated less distinct and inconsistent signatures. Physiologic signatures of all neonatal illnesses were similar. Conclusions: Subacute potentially catastrophic illnesses in three diverse ICU populations have physiologic signatures that are detectable in the hours preceding clinical detection and intervention. Detection of such signatures can draw attention to patients at highest risk, potentially enabling earlier intervention and better outcomes