Application of machine learning approach in emergency department to support clinical decision making for SARS-CoV-2 infected patients.
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ABSTRACT:
SUBMITTER: Casano N
PROVIDER: S-EPMC10561065 | biostudies-literature | 2023 Jun
REPOSITORIES: biostudies-literature
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Application of machine learning approach in emergency department to support clinical decision making for SARS-CoV-2 infected patients.
Casano Nicolò N Santini Silvano Junior SJ Vittorini Pierpaolo P Sinatti Gaia G Carducci Paolo P Mastroianni Claudio Maria CM Ciardi Maria Rosa MR Pasculli Patrizia P Petrucci Emiliano E Marinangeli Franco F Balsano Clara C
Journal of integrative bioinformatics 20230307 2
To support physicians in clinical decision process on patients affected by Coronavirus Disease 2019 (COVID-19) in areas with a low vaccination rate, we devised and evaluated the performances of several machine learning (ML) classifiers fed with readily available clinical and laboratory data. Our observational retrospective study collected data from a cohort of 779 COVID-19 patients presenting to three hospitals of the Lazio-Abruzzo area (Italy). Based on a different selection of clinical and res ...[more]
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Project description:Background and aimsCollaboration has become a crucial element of effective healthcare delivery in the emergency department (ED). In high-pressure environments, healthcare providers can prioritize patients by consulting with other specialists to gain diverse perspectives and arrive at a shared understanding of the best course of action. It was conducted for the purpose of designing and evaluating the collaborative decision-making application for patient care in the ED.MethodsThe present applied research study was conducted between April 1, 2021 and May 31, 2023 at Imam Reza Hospital of Tabriz University of Medical Sciences. The study was conducted in three phases: exploration, development, and evaluation, utilizing modern technologies such as Flutter and Node.js to design and program the application. The effectiveness of the system was evaluated using established measures, including the think-aloud protocol, user experience questionnaire, and Likert-scale questionnaires developed by Ghadri et al.ResultsThe average scale for attractiveness was 2.03, perspicuity was 2.90, efficiency was 2.40, dependability was 1.93, stimulation was 2.48, and novelty was 2.78. Additionally, 71% of physicians gave a very good rating to the accessibility of necessary information at any time, motivation to use the system for accessing information, usefulness of the system compared to the time spent using it throughout the day. Furthermore, 57% of physicians gave a very positive rating to sharing information and knowledge, ease of using the search function and accessing the system, user control and monitoring, free access to the system, and support from colleagues and system users.ConclusionThe study suggests that introducing Information and Communication Technology such as medical apps can improve healthcare delivery by streamlining patient care, promoting effective teamwork, and reducing medical errors and treatment delays.
| S-EPMC10895157 | biostudies-literature
Project description:The emergency department (ED) is a fast-paced environment responsible for large volumes of patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the imperative to efficiently identify patients at imminent risk of acute deterioration. The aim of this study is to systematically compare the performance of machine learning algorithms based on logistic regression, gradient boosted decision trees, and support vector machines for predicting imminent clinical deterioration for patients based on cross-sectional patient data extracted from electronic patient records (EPR) at the point of entry to the hospital. We apply state-of-the-art machine learning methods to predict early patient deterioration, based on their first recorded vital signs, observations, laboratory results, and other predictors documented in the EPR. Clinical deterioration in this study is measured by in-hospital mortality and/or admission to critical care. We build on prior work by incorporating interpretable machine learning and fairness-aware modelling, and use a dataset comprising 118, 886 unplanned admissions to Salford Royal Hospital, UK, to systematically compare model variations for predicting mortality and critical care utilisation within 24 hours of admission. We compare model performance to the National Early Warning Score 2 (NEWS2) and yield up to a 0.366 increase in average precision, up to a [Formula: see text] reduction in daily alert rate, and a median 0.599 reduction in differential bias amplification across the protected demographics of age and sex. We use Shapely Additive exPlanations to justify the models' outputs, verify that the captured data associations align with domain knowledge, and pair predictions with the causal context of each patient's most influential characteristics. Introducing our modelling to clinical practice has the potential to reduce alert fatigue and identify high-risk patients with a lower NEWS2 that might be missed currently, but further work is needed to trial the models in clinical practice. We encourage future research to follow a systematised approach to data-driven risk modelling to obtain clinically applicable support tools.
| S-EPMC10442440 | biostudies-literature
Project description:In an emergency, making the correct decision is vital. It is a necessary element of professional nursing care, and the ability of nurses to make successful clinical decisions is the most critical element influencing care quality. The purpose of this study was to assess the factors influencing nurses' clinical decision-making in the emergency department of Palestinan hospitals. A cross-sectional study was targeted at all nurses working in emergency departments at the Palestinian hospitals. The study was completed with 227 nurses, and collecting data was performed with the Clinical Decision Making in Nursing Scale. Results of the study revealed that the average score for the total clinical decision-making score was 3.3 (SD = 0.23). The subscales of clinical decision making were "search for alternatives or options," "canvassing of objectives and values," "evaluation and reevaluation of consequences," and "search for information and unbiased assimilation of new information." Furthermore, multiple linear regression analysis revealed that degree and work hours accounted for 11.7% of the variance in clinical decision-making. The study confirmed the average score for clinical decision-making was slightly higher than the average score. Also, it approved that nursing degree and work hours were predictors of clinical decision-making among nurses in emergency departments.
| S-EPMC9893345 | biostudies-literature
Project description:ObjectiveTo demonstrate the incremental benefit of using free text data in addition to vital sign and demographic data to identify patients with suspected infection in the emergency department.MethodsThis was a retrospective, observational cohort study performed at a tertiary academic teaching hospital. All consecutive ED patient visits between 12/17/08 and 2/17/13 were included. No patients were excluded. The primary outcome measure was infection diagnosed in the emergency department defined as a patient having an infection related ED ICD-9-CM discharge diagnosis. Patients were randomly allocated to train (64%), validate (20%), and test (16%) data sets. After preprocessing the free text using bigram and negation detection, we built four models to predict infection, incrementally adding vital signs, chief complaint, and free text nursing assessment. We used two different methods to represent free text: a bag of words model and a topic model. We then used a support vector machine to build the prediction model. We calculated the area under the receiver operating characteristic curve to compare the discriminatory power of each model.ResultsA total of 230,936 patient visits were included in the study. Approximately 14% of patients had the primary outcome of diagnosed infection. The area under the ROC curve (AUC) for the vitals model, which used only vital signs and demographic data, was 0.67 for the training data set, 0.67 for the validation data set, and 0.67 (95% CI 0.65-0.69) for the test data set. The AUC for the chief complaint model which also included demographic and vital sign data was 0.84 for the training data set, 0.83 for the validation data set, and 0.83 (95% CI 0.81-0.84) for the test data set. The best performing methods made use of all of the free text. In particular, the AUC for the bag-of-words model was 0.89 for training data set, 0.86 for the validation data set, and 0.86 (95% CI 0.85-0.87) for the test data set. The AUC for the topic model was 0.86 for the training data set, 0.86 for the validation data set, and 0.85 (95% CI 0.84-0.86) for the test data set.ConclusionCompared to previous work that only used structured data such as vital signs and demographic information, utilizing free text drastically improves the discriminatory ability (increase in AUC from 0.67 to 0.86) of identifying infection.
| S-EPMC5383046 | biostudies-literature
Project description:IntroductionMachine learning as a clinical decision support system tool has the potential to assist clinicians who must make complex and accurate medical decisions in fast paced environments such as the emergency department. This paper presents a protocol for a scoping review, with the objective of summarising the existing research on machine learning clinical decision support system tools in the emergency department, focusing on models that can be used for paediatric patients, where a knowledge gap exists.Materials and methodsThe methodology used will follow the scoping study framework of Arksey and O'Malley, along with other guidelines. Machine learning clinical decision support system tools for any outcome and population (paediatric/adult/mixed) for use in the emergency department will be included. Articles such as grey literature, letters, pre-prints, editorials, scoping/literature/narrative reviews, non-English full text papers, protocols, surveys, abstract or full text not available and models based on synthesised data will be excluded. Articles from the last five years will be included. Four databases will be searched: Medline (EBSCO), CINAHL (EBSCO), EMBASE and Cochrane Central. Independent reviewers will perform the screening in two sequential stages (stage 1: clinician expertise and stage 2: computer science expertise), disagreements will be resolved by discussion. Data relevant to the research question will be collected. Quantitative analysis will be performed to generate the results.DiscussionThe study results will summarise the existing research on machine learning clinical decision support tools in the emergency department, focusing on models that can be used for paediatric patients. This holds the promise to identify opportunities to both incorporate models in clinical practice and to develop future models by utilising reviewers from diverse backgrounds and relevant expertise.
| S-EPMC10653406 | biostudies-literature
Project description:BackgroundDisadvantaged minority children are disproportionately affected by asthma. This group is also known to frequently use the emergency department (ED) for asthma care. Understanding decisions for use of the ED is important to prevent high cost.ObjectiveTo examine caregiver factors associated with the decision to use the ED for asthma care in inner-city children with asthma.MethodsOne hundred fifty participants in a randomized clinical trial testing the effectiveness of a home-based asthma intervention were enrolled, and questionnaires were administered to caregivers during the child's ED asthma visit. Sociodemographics, health characteristic data, and caregiver interview data were examined to ascertain factors that affected caregiver decision making to use the ED for asthma care. A cluster analysis was performed to correlate caregiver reasons for the decision to use the ED for asthma care.ResultsThree clusters emerged for decision making: urgency, preference for the use of the ED, and access to care issues. The perception of urgency was the most common reason reported by caregivers (91%) followed by reporting a preference for the ED for care (37%) and reporting access to care issues (31%). Access to care was primarily attributable to the inability to get a same-day appointment with their primary care practitioner (24%).ConclusionThe caregiver factors involved in the decision to use the ED can provide a basis for further intervention and investigation. Such factors include caregiver asthma home management, improvement in relationships with primary care practitioners, and access to care-related issues.
| S-EPMC5117360 | biostudies-literature
Project description:BackgroundCoprescribing naloxone with opioid analgesics is a Centers for Disease Control and Prevention (CDC) best practice to mitigate the risk of fatal opioid overdose, yet coprescription by emergency medicine clinicians is rare, occurring less than 5% of the time it is indicated. Clinical decision support (CDS) has been associated with increased naloxone prescribing; however, key CDS design characteristics and pragmatic outcome measures necessary to understand replicability and effectiveness have not been reported.ObjectiveThis study aimed to rigorously evaluate and quantify the impact of CDS designed to improve emergency department (ED) naloxone coprescribing. We hypothesized CDS would increase naloxone coprescribing and the number of naloxone prescriptions filled by patients discharged from EDs in a large health care system.MethodsFollowing user-centered design principles, we designed and implemented a fully automated, interruptive, electronic health record-based CDS to nudge clinicians to coprescribe naloxone with high-risk opioid prescriptions. "High-risk" opioid prescriptions were defined as any opioid analgesic prescription ≥90 total morphine milligram equivalents per day or for patients with a prior diagnosis of opioid use disorder or opioid overdose. The Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework was used to evaluate pragmatic CDS outcomes of reach, effectiveness, adoption, implementation, and maintenance. Effectiveness was the primary outcome of interest and was assessed by (1) constructing a Bayesian structural time-series model of the number of ED visits with naloxone coprescriptions before and after CDS implementation and (2) calculating the percentage of naloxone prescriptions associated with CDS that were filled at an outpatient pharmacy. Mann-Kendall tests were used to evaluate longitudinal trends in CDS adoption. All outcomes were analyzed in R (version 4.2.2; R Core Team).UnlabelledBetween November 2019 and July 2023, there were 1,994,994 ED visits. CDS reached clinicians in 0.83% (16,566/1,994,994) of all visits and 15.99% (16,566/103,606) of ED visits where an opioid was prescribed at discharge. Clinicians adopted CDS, coprescribing naloxone in 34.36% (6613/19,246) of alerts. CDS was effective, increasing naloxone coprescribing from baseline by 18.1 (95% CI 17.9-18.3) coprescriptions per week or 2,327% (95% CI 3390-3490). Patients filled 43.80% (1989/4541) of naloxone coprescriptions. The CDS was implemented simultaneously at every ED and no adaptations were made to CDS postimplementation. CDS was maintained beyond the study period and maintained its effect, with adoption increasing over time (τ=0.454; P<.001).ConclusionsOur findings advance the evidence that electronic health record-based CDS increases the number of naloxone coprescriptions and improves the distribution of naloxone. Our time series analysis controls for secular trends and strongly suggests that minimally interruptive CDS significantly improves process outcomes.
| S-EPMC11560079 | biostudies-literature
Project description:Emergency Departments (EDs) can serve as surveillance sites for infectious diseases. Our purpose was to determine the burden of SARS-CoV-2 infection and prevalence of vaccination against COVID-19 among patients attending an urban ED in Baltimore City. Using 1914 samples of known exposure status, we developed an algorithm to differentiate previously infected, vaccinated, and unexposed individuals using a combination of antibody assays. We applied this testing algorithm to 4360 samples ED patients obtained in the springs of 2020 and 2021. Using multinomial logistic regression, we determined factors associated with infection and vaccination. For the algorithm, sensitivity and specificity for identifying vaccinated individuals was 100% and 99%, respectively, and 84% and 100% for naturally infected individuals. Among the ED subjects, seroprevalence to SARS-CoV-2 increased from 2% to 24% between April 2020 and March 2021. Vaccination prevalence rose to 11% by mid-March 2021. Marked differences in burden of disease and vaccination coverage were seen by sex, race, and ethnicity. Hispanic patients, though 7% of the study population, had the highest relative burden of disease (17% of total infections) but similar vaccination rates. Women and White individuals were more likely to be vaccinated than men or Black individuals (adjusted odds ratios [aOR] 1.35 [95% CI: 1.02, 1.80] and aOR 2.26 [95% CI: 1.67, 3.07], respectively). Individuals previously infected with SARS-CoV-2 can be differentiated from vaccinated individuals using a serologic testing algorithm. SARS-CoV-2 exposure and vaccination uptake frequencies reflect gender, race and ethnic health disparities in this urban context. Using an antibody testing algorithm, we distinguished between immune responses from SARS-CoV-2-infected and vaccinated individuals. When applied to blood samples from an emergency department in Baltimore, disparities in disease burden and vaccine uptake by sex, race, and ethnicity were identified.
| S-EPMC8528087 | biostudies-literature
Project description:Study objectiveOur institution experienced a change in SARS-CoV-2 testing policy as well as substantial changes in local COVID-19 prevalence, allowing for a unique examination of the relationship between SARS-CoV-2 testing and emergency department (ED) length of stay.MethodsThis was an observational interrupted time series of all patients admitted to an academic health system between March 15, 2020, and September 30, 2020. Given testing limitations from March 15 to April 24, all patients receiving SARS-CoV-2 tests were symptomatic. On April 24, testing was expanded to all ED admissions. The primary and secondary outcomes were ED length of stay and number needed to test to obtain a positive, respectively.ResultsA total of 70,856 patients were cared for in the EDs during the 7-month period. The testing change increased admission length of stay by 1.89 hours (95% confidence interval 1.39 to 2.38). The number needed to test was 2.5 patients and was highest yield on April 1, 2020, when the state positivity rate was 39.7%; however, the number needed to test exceeded 170 patients by Sept 1, 2020, at which point the state positivity rate was 0.5%.ConclusionAlthough universal SARS-CoV-2 testing of ED admissions may meaningfully support mitigation and containment efforts, the clinical cost of testing all admissions amid low community positivity is notable. In our system, universal ED SARS-CoV-2 testing was associated with a 24% increase in admission length of stay alongside the detection of only 1 positive case every other day. Given the known harms and risks of ED boarding and crowding, solutions must be developed to support regular operational flow while balancing infection prevention needs.
| S-EPMC8424016 | biostudies-literature
Project description:ObjectivesThe objective was to describe perceptions of practicing emergency physicians (EPs) regarding the appropriateness and medicolegal implications of using shared decision-making (SDM) in the emergency department (ED).MethodsWe conducted a cross-sectional survey of EPs at a large, national professional meeting to assess perceived appropriateness of SDM for different categories of ED management (e.g., diagnostic testing, treatment, disposition) and in common clinical scenarios (e.g., low-risk chest pain, syncope, minor head injury). A 21-item survey instrument was iteratively developed through review by content experts, cognitive testing, and pilot testing. Descriptive and multivariate analyses were conducted.ResultsWe approached 737 EPs; 709 (96%) completed the survey. Two-thirds (67.8%) of respondents were male; 51% practiced in an academic setting and 44% in the community. Of the seven management decision categories presented, SDM was reported to be most frequently appropriate for deciding on invasive procedures (71.5%), computed tomography (CT) scanning (56.7%), and post-ED disposition (56.3%). Among the specific clinical scenarios, use of thrombolytics for acute ischemic stroke was felt to be most frequently appropriate for SDM (83.4%), followed by lumbar puncture to rule out subarachnoid hemorrhage (73.8%) and CT head for pediatric minor head injury (69.9%). Most EPs (66.8%) felt that using and documenting SDM would decrease their medicolegal risk while a minority (14.2%) felt that it would increase their risk.ConclusionsAcceptance of SDM among EPs appears to be strong across management categories (diagnostic testing, treatment, and disposition) and in a variety of clinical scenarios. SDM is perceived by most EPs to be medicolegally protective.
| S-EPMC5308213 | biostudies-literature