Promoting head CT exams in the emergency department triage using a machine learning model

Klang, Eyal; Barash, Yiftach; Soffer, Shelly; Bechler, Sigalit; Resheff, Yehezkel S.; Granot, Talia; Shahar, Meir; Klug, Maximiliano; Guralnik, Gennadiy; Zimlichman, Eyal; Konen, Eli

doi:10.1007/s00234-019-02293-y

Cited by 20 publications

(13 citation statements)

References 36 publications

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“…A vast majority of studies used EHR data, while two studies used administrative and claims as the primary dataset. 27 28 Study populations included adults in the ED, 26 27 28 29 30 31 32 33 34 35 36 37 home care patients, 38 and a mixture of adult and pediatric ED patients. 39 Most studies were based in the United States, but other study locations included Hong Kong, 27 Germany, 32 Italy, 39 Portugal, 37 and South Korea.…”

Section: Resultsmentioning

confidence: 99%

“…27 28 Study populations included adults in the ED, 26 27 28 29 30 31 32 33 34 35 36 37 home care patients, 38 and a mixture of adult and pediatric ED patients. 39 Most studies were based in the United States, but other study locations included Hong Kong, 27 Germany, 32 Italy, 39 Portugal, 37 and South Korea. 34 35 Sample size ranged from 199 to 2,910,321 observations.…”

Section: Resultsmentioning

confidence: 99%

“…Outcomes addressed in studies included mortality, 29 30 future posttraumatic stress disorder (PTSD) sequelae, 26 the novel coronavirus disease 2019 (COVID-19) infection status, 31 35 39 ED wait time, 27 intensive care unit (ICU) admission from the ED, 37 need for head computed tomography (CT), 32 cardiac arrest, 34 stroke severity, 28 and ED utilization. 30 33 36 38 ML methods were used in several studies, including the use of logistic regression, generalized linear models, neural network, and decision tree–based models to apply statistical learning to the prediction of deterioration, 30 37 stroke severity, 28 COVID-19 diagnosis, 31 35 39 wait time, 27 and need for head CT, 32 while the other study used autoregressive integrated moving average to explore time-dependent patient flow. 33 36 NLP was an alternative method used in four studies.…”

Section: Resultsmentioning

confidence: 99%

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Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

et al. 2022

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Background The term “data science” encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications. Objectives This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature. Methods We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care–acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture. Results Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing. Conclusion This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

et al. 2022

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“…However, recent literature reports that CT scan utilization has decreased since 2008 [ 15 ]. The use of the Pediatric Emergency Care Applied Research Network (PECARN) for head injury and Alvarado score for abdominal pain and the introduction of other imaging modalities including the “ultrasound first” approach have contributed to this decrease in the use of the CT scan [ 16 ].…”

Section: Discussionmentioning

confidence: 99%

The Pattern of CT Scan Use in the Diagnosis of Abdominal Pain in Children Presenting to the Emergency Department of a Tertiary Community Hospital

Khan¹,

Subedi²,

Kumar³

et al. 2021

Cureus

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Background and objective Pediatric populations are highly sensitive to ionizing radiations and, therefore, are more at risk of their harmful outcomes. Our study aimed to determine the percentage of children who underwent a CT scan after presenting to the ED with abdominal pain. The secondary aim was to determine the change in management related to the CT results. In addition, we also wanted to determine the predictors associated with the use of abdominal CT scans in the evaluation of children presenting to ED with abdominal pain as well as the predictors associated with positive CT scan results in children with abdominal pain. Materials and methods We retrospectively reviewed the medical records of children with abdominal pain seen in our ED from 01/01/2011 through 12/30/2012. Patients aged 4-18 years presenting with abdominal pain were identified from the medical records. Data on demographics, clinical characteristics, associated factors, CT use, CT findings, and change in management were collected. Data were analyzed using Chi-square (χ 2 ) analysis and Student’s t-test. Results A total of 1,780 charts were reviewed and 1,272 children were included in the study. The mean age of the cohort was 12.6 ± 4.6 years; 62.6% were female and 68.7% were African American. Of note, 14% (181/1,272) of the study group had received a CT scan; change in medical management was noted in 34.8% (63/181) of the scanned patients. Predictors of CT use included older age (p<0.0001), male gender (p<0.0001), white race (p<0.0001), an attending without advanced training in pediatric emergencies (p=0.001), acute onset of symptoms (p<0.0001), higher pain score (p<0.0001), right lower quadrant pain (p<0.0001), abdominal wall rebound tenderness (p<0.0001), abdominal tenderness (p<0.0001), fever (p<0.0001), and absence of constipation (p=0.04). Positive CT scan results were predicted by the presence of fever (p=0.013), lack of constipation (p=0.025), and white race (p=0.022). A multivariate analysis could not be done because not all data were available for each patient. Conclusion The use of the CT scan in children with abdominal pain affected the management in one out of three patients (34.8%). Fever, constipation, and white race were the factors associated with an increased likelihood of performing a CT scan and were also linked to positive results.

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“…1), potentially contributing to harmful and costly care delays. Timely identification of patients requiring NSICU admission may improve ED overcrowding and resource allocation.Driven by increases in electronic health data availability 13 and computing power, machine learning is increasingly used to automate processes in healthcare [14][15][16][17][18][19][20] . While many such approaches make use of structured, or "tabular", data, free text constitutes a large proportion of electronic health record (EHR) data 21 , and may capture…”

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confidence: 99%

Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach

Klang

Kummer

Dangayach

et al. 2021

Sci Rep

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Early admission to the neurosciences intensive care unit (NSICU) is associated with improved patient outcomes. Natural language processing offers new possibilities for mining free text in electronic health record data. We sought to develop a machine learning model using both tabular and free text data to identify patients requiring NSICU admission shortly after arrival to the emergency department (ED). We conducted a single-center, retrospective cohort study of adult patients at the Mount Sinai Hospital, an academic medical center in New York City. All patients presenting to our institutional ED between January 2014 and December 2018 were included. Structured (tabular) demographic, clinical, bed movement record data, and free text data from triage notes were extracted from our institutional data warehouse. A machine learning model was trained to predict likelihood of NSICU admission at 30 min from arrival to the ED. We identified 412,858 patients presenting to the ED over the study period, of whom 1900 (0.5%) were admitted to the NSICU. The daily median number of ED presentations was 231 (IQR 200–256) and the median time from ED presentation to the decision for NSICU admission was 169 min (IQR 80–324). A model trained only with text data had an area under the receiver-operating curve (AUC) of 0.90 (95% confidence interval (CI) 0.87–0.91). A structured data-only model had an AUC of 0.92 (95% CI 0.91–0.94). A combined model trained on structured and text data had an AUC of 0.93 (95% CI 0.92–0.95). At a false positive rate of 1:100 (99% specificity), the combined model was 58% sensitive for identifying NSICU admission. A machine learning model using structured and free text data can predict NSICU admission soon after ED arrival. This may potentially improve ED and NSICU resource allocation. Further studies should validate our findings.

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Promoting head CT exams in the emergency department triage using a machine learning model

Cited by 20 publications

References 36 publications

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

The Pattern of CT Scan Use in the Diagnosis of Abdominal Pain in Children Presenting to the Emergency Department of a Tertiary Community Hospital

Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach

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