Early Warning Score Research Articles

Abstract Although the European Society of Cardiology (ESC) predicts mortality in acute pulmonary embolism (PE), it may overtriage the level of clinical monitoring needed. The National Early Warning Score (NEWS) is used to triage level of care in many diseases, but it is rarely reported in PE literature. In this retrospective, single-center, observational cohort study of consecutive adults with acute PE, between 2017 and 2020, we aim to assess the association between NEWS and the risk of hemodynamic (HD) deterioration or PE-related death in intermediate–high risk PE patients. The NEWS at admission and after 24 hours were determined. A baseline NEWS of ≥5 or the maximum score of a single parameter was considered an indication of high risk of our primary outcome (hemodynamic deterioration and/or PE-related mortality). ESC classified 99 of 318 patients with PE as intermediate–high risk; 8 patients (8%) met the primary outcome. A total of 52 (52%) patients had an elevated NEWS and 7 of these met the primary outcome (13%), while only 1 patient with a non-elevated NEWS (2.0%) met the primary outcome (negative predictive value of 98%; 95% CI 90–98%). Sensitivity of elevated NEWS in patients with intermediate–high risk was 88% (95% CI 74–90%) and the specificity was 51% (95% CI 41–61%). Using NEWS in intermediate–high risk, acute PE patients may improve accuracy in identifying patients with a higher risk of adverse outcomes and may guide the decision to monitor a patient in a high-care department, especially in patients with intermediate–high risk PE.

Background: Identifying patients at high risk for in-hospital cardiac arrest (IHCA) is critical for improving survival. While early warning scores (MEWS), metabolic derangements like dyskalemia, and diurnal patterns have been studied independently, a combined analysis of these pre-arrest characteristics is needed to create a more robust profile of patients at imminent risk. We aimed to identify predictors of IHCA by analysing physiological deterioration scores (MEWS), pre-arrest serum potassium(K), and timing of events in a multi-centre cohort. Methods: We conducted a retrospective chart review of 92 "Code Blue" events across three affiliated hospitals from November 1, 2024, to January 31, 2025. Subsequent codes within 20 minutes of ROSC were excluded. We collected data on: 1) MEWS scores within the 4 hours preceding the event; 2) the serum potassium level closest to the event, categorized as hypokalemia (<3.5 mEq/L), normokalemia (3.5-5.5), or hyperkalemia (>5.5); and 3) the time of day of the event. Analysis included descriptive statistics, binomial distribution analysis for MEWS, and Fisher’s Exact Test for K levels. Results/Data: Of 92 IHCA events (mean patient age 67.7 years), a distinct diurnal pattern was observed, with a primary peak in events between 08:00 and 11:00 and a secondary peak at 18:00. For patients with available data (n=63), a majority (57.1%) had a pre-arrest MEWS ≥ 4. Pre-arrest K was recorded in only 43 events (46.7%); of these, hypokalemia was present in 8 patients (18.6%) and hyperkalemia in 2 (4.7%), with no significant difference between these proportions (p=0.174). Conclusion: This multi-center analysis reveals potential opportunities for proactive intervention. A significant proportion of events cluster in the late morning, suggesting that resource allocation and heightened surveillance may be beneficial during these peak hours. Furthermore, the high prevalence of MEWS ≥ 4 pre-arrest, while not reaching statistical significance in this cohort, supports its use as a critical alert for potential clinical deterioration. Care escalation such as a rapid response in these patients with elevated MEWS score did not occur and cardiac arrest occurred. Given dyskalemia is present so frequently, further data is needed to explore whether intra-code measurement of K via point of care testing may change code outcomes. Combining temporal and physiological data may enhance risk stratification and help prevent in-hospital cardiac arrests.

Background: Postpartum hemorrhage (PPH) remains one of the leading causes of maternal morbidity and mortality worldwide. Rapid identification of women at risk of poor outcomes is essential in emergency settings to guide appropriate resuscitation and management. Aim: This study aimed to assess the prognostic value of lactate levels measured upon admission to the emergency department (ED) in women with PPH and to compare their predictive performance against other early warning scores, including the shock index, modified early warning score (MEWS), and modified rapid emergency medicine score (mREMS). Methods: We conducted a retrospective analysis of 424 women diagnosed with PPH who presented to a tertiary hospital emergency department in South Korea between January 2013 and December 2023. Clinical and laboratory data at ED admission were analyzed in relation to key outcomes: massive transfusion, hysterectomy, uterine artery embolization, and intensive care unit (ICU) admission. Receiver operating characteristic (ROC) curves and multivariable logistic regression were used to evaluate the predictive capacity of each indicator. Results: Elevated lactate levels were significantly associated with the need for massive transfusion (cut-off: 2.95 mmol/L; AUC = 0.86) and hysterectomy (cut-off: 3.05 mmol/L; AUC = 0.84). A combined model of lactate and shock index improved predictive performance(AUC = 0.88) for massive transfusion compared to lactate alone. In multivariable analysis, lactate (OR = 1.67), prothrombin time/international normalized ratio (OR = 1.86), and total CO2 (OR = 1.32) were independently associated with massive transfusion. Fibrinogen levels independently predicted embolization, while total CO2 and triage acuity (as assessed by Korean Triage and Acuity Scale) were associated with ICU admission. Conclusion: Lactate levels provide valuable prognostic information for identifying high-risk PPH patients in emergency care. When used in combination with the shock index, predictive accuracy is further improved, supporting the clinical use of composite indicators for early risk stratification.

Peripheral perfusion index versus NEWS score in prehospital non-trauma adults: A prospective cohort study.

Predictive performance and temporal dynamics of national early warning score 2 (NEWS2) in detecting clinical deterioration in general ward: A large-scale validation study in Singapore.

Are Pediatric early warning scores reliable in malnourished children? A prospective validation study in a resource-limited ED.

Purpose : This study aimed to examine the clinical progress of patients discharged from intensive care units (ICU) according to the method of proactive rounding, based on clinical decision-making or the National Early Warning Score (NEWS), and to identify the factors influencing their clinical progress. Methods : A total of 627 patients discharged from ICUs were included in this study. Data were collected from electronic medical records following a comprehensive literature review and analyzed using t-tests, χ<sup>2</sup>-tests, Fisher’s exact test, and logistic regression. Results : The likelihood of ICU readmission was significantly lower when proactive rounding was conducted using the NEWS compared with clinical decision-making (OR=0.40, <i>p</i> =.036). In addition, increases in oxygen demand were significantly reduced when rounding was guided by NEWS rather than the clinical judgment of the Rapid Response Team (OR=0.26, <i>p</i> =.009). Conclusion : These findings suggest that proactive rounding conducted by the Rapid Response Team based on NEWS significantly reduced ICU readmission rates and oxygen demand. Therefore, NEWS-based proactive rounding by nurses may help predict and detect clinical deterioration at an early stage.

The potential role of the renin-angiotensin-aldosterone system (RAAS) in the pathogenesis of coronavirus disease 2019 (COVID-19) is controversial, with concerns mainly about the part RAAS peptides play in the prediction of progression to more severe disease. Given the importance of COVID-19 prognostication at early disease stages, we established and validated a multivariable risk stratification tool for COVID-19 associated lung involvement by utilizing a combination of RAAS peptides. In this prospective study, circulating renin and angiotensin converting enzyme-2 (ACE-2) levels were measured in 116 COVID-19 patients who were admitted to our hospital from March 30, 2021 to January 24, 2022 and underwent a lung computed tomography (CT) scan. Clinical severity was measured with a national early warning score (NEWS). Associations among RAAS peptides, inflammation-dependent biomarkers, demographic variables, and clinical outcomes were studied using logistic regression and Cox proportional-hazards models. We assessed 116 COVID-19 patients (mean age 45.1 ± 12.6 years; 51.7% male), of whom 66 (56.9%) had COVID-19 associated pneumonia. Baseline circulating ACE-2 (2.63 ± 0.12 ng/mL) and renin levels (85.04 ± 6.8 ng/L) were lower in patients with COVID-19 related pneumonia compared to patients without pneumonia (6.4 ± 0.7 ng/mL and 211.6 ± 21.9 ng/L, respectively) (p < 0.001 for both). Both RAAS components were found to be significantly related to adverse outcomes, including COVID-19 associated pneumonia and intensive care unit (ICU) admission, in both crude and adjusted multivariable logistic regression analyses. Circulating ACE-2 and renin levels can predict lung involvement in COVID-19 patients, and they display good correlation and agreement with NEWS.

In hospitalised patients, clinical deterioration can be detected at an early stage using early warning scores (EWS), enabling timely interventions. However, it remains unclear to what extent EWS have been implemented within the network of the Initiative of Quality in Medicine (IQM), comprising 480 hospitals, and what facilitating and challenging factors have been reported in this context. An online survey within the IQM was conducted to assess EWS-specific structures and processes, barriers, and facilitators. The degree of implementation was self-rated by participants on anumerical rating scale from 1to 10(10 = maximum). Of the 480 individuals contacted, 18.7% (n = 90) responded; 6.3% (n = 30) reported using EWS in clinical practice for an average of 4.3years. Among these, the National-EWS (NEWS) was most frequently used (37.9%, n = 11), primarily on general medical wards (66.7%, n = 20). Rapid response teams were mostly formed by resuscitation teams (33.3%, n = 10). Electronic systems were used for EWS recording in 82.2% (n = 24), and documentation was most often performed in response to clinical abnormalities (56.7%, n = 17). The self-rated median implementation score was 3.5. The top three facilitating factors included committed staff, clear communication and training strategies, and technological support; barriers identified were lack of staff acceptance, insufficient technical infrastructure, and time required for training. The implementation of EWS within the IQM appears to be limited and might be achallenging and complex process. The low participation rate limits the informative value of this study.

BACKGROUNDPrimary liver cancer is a globally prevalent malignancy, with China accounting for approximately 55% of new cases, and is linked to hepatitis B, aflatoxin, and cirrhosis. Its rupture with hemorrhagic shock is a lethal complication with high mortality, and traditional triage struggles with timely risk stratification, necessitating better tools, such as the integrated shock index (SI)-early warning score (EWS).AIMTo study and analyze the combined effect of the SI and EWS in primary liver cancer patients with ruptured hemorrhage and shock.METHODSIn total, 118 patients who visited the Emergency Department of Nantong Third People's Hospital from January 2023 to December 2024 were selected and randomly divided into a control group (59 patients who received routine emergency treatment) and an observation group (59 patients who received condition assessment and intervention by combining the SI and EWS based on routine emergency treatment). The clinical treatment outcomes, respiratory function indicators, serological indicators, complications, and satisfaction with emergency intervention before and after the emergency intervention were compared between the two groups.RESULTSThe emergency, triage, waiting, and hemostasis times, as well as hospital stay were shorter in the observation group than in the control group (P < 0.05). After 48 hours of emergency intervention, blood oxygen saturation and partial pressure of oxygen in the observation group were higher than those in the control group (P < 0.05). Seven days after emergency intervention, the hemoglobin, prealbumin, and albumin levels were higher in the observation group than in the control group (P < 0.05). The complication rate in the observation group was 3.39%, lower than that in the control group (13.56%; P < 0.05). Satisfaction with emergency intervention in the observation group was 94.92%, higher than 83.05% in the control group (P < 0.05).CONCLUSIONThe combined application of the SI and EWS in patients with primary liver cancer rupture, hemorrhage, and shock can significantly shorten emergency treatment time, improve respiratory function and serological indicators, reduce the incidence of complications, and enhance patient satisfaction with emergency interventions, with higher clinical treatment efficiency and quality. Therefore, it is worthy of promotion and application.

ObjectiveRapid identification of high-risk and low-risk patients presenting to the emergency department (ED) influences clinical management and can help optimise patient outcomes as well as resource allocation. This study aims to externally validate the Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RISE UP) score in adult patients in the ED with suspected infection. Furthermore, generalisability was assessed by comparing the discriminatory ability of the RISE UP with the quick Sequential Organ Failure Assessment (qSOFA) as well as the Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS).DesignRetrospective cohort study.SettingSingle-centre study in the ED of a tertiary, university-affiliated hospital.ParticipantsAdult patients with suspected infection presenting at the ED for internal medicine from 2016 to 2022.OutcomesThe primary outcome was all-cause 30-day mortality. Secondary outcomes were all-cause 14-day mortality, 7-day mortality and intensive care unit (ICU) admission.MethodsPrognostic performance was evaluated using discrimination (area under the receiver operating characteristic curve (AUC)) and a calibration plot.ResultsOf the included 5038 ED visits, there was a 30-day mortality of 7.1%. Discrimination of RISE UP for 30-day mortality was good (AUC 0.809; 95% CI 0.786 to 0.832) and significantly higher than that for the other risk scores: qSOFA (AUC 0.675; 95% CI 0.644 to 0.707), MEWS (AUC 0.688; 95% CI 0.658 to 0.718) and NEWS (AUC 0.725; 95% CI 0.696 to 0.754) (p<0.001). For 14-day and 7-day mortality, RISE UP had the highest AUC, but NEWS performed best for ICU admission. The RISE UP score was well calibrated and had significantly better discriminatory ability in older patients aged ≥65 years (AUC 0.772; 95% CI 0.738 to 0.806; p<0.001) and patients with sepsis (AUC 0.746; 95% CI 0.695 to 0.798; p<0.05) compared with the other scores. Low-risk patients with a RISE UP score of <5% yielded a negative predictive value of 97.7% (95% CI 97.2 to 98.1) and a sensitivity of 79.3% (95% CI 74.7 to 83.4).ConclusionsThe RISE UP score outperformed the qSOFA, MEWS and NEWS in predicting 30-day mortality. It is generalisable to an adult infection-specific cohort and may facilitate distinction between high-risk and low-risk patients in the ED, particularly to rule out poor outcomes.

Evaluating the national early warning score (NEWS) and NEWS-calcium for predicting adverse outcomes in acute pancreatitis: a prospective cohort study in Vietnam

PurposeTo develop and compare multiple machine learning (ML) algorithms with traditional logistic regression for predicting in-hospital cardiac arrest (IHCA) using comprehensive electronic health record data, with the goal of improving early risk stratification beyond conventional early-warning scores and providing potential integration into hospital early warning systems for timely clinical intervention.Patients and MethodsWe performed a retrospective case-control study at a large tertiary medical center, including 800 IHCA cases and 3,464 controls. Candidate predictors comprised demographics, comorbidities, vital signs, and laboratory measurements. Five models-logistic regression, decision tree, random forest, XGBoost, and multivariate adaptive regression splines (MARS)-were trained and validated. Performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score.ResultsXGBoost yielded strong discrimination and the highest accuracy (AUC 0.909; accuracy 0.883), while random forest showed comparable discrimination (AUC 0.910) with slightly lower accuracy (0.876). Logistic regression performed robustly but lower than ML models (AUC 0.895; accuracy 0.876). ML models consistently identified clinically meaningful predictors-including blood urea nitrogen, heart rate, and pre-existing heart failure-offering insights beyond traditional regression.ConclusionIntegrating ML approaches with conventional regression enhances IHCA risk prediction by capturing non-linear relationships and interactions while retaining the interpretability of regression. These approaches could strengthen hospital early-warning systems, enabling earlier detection and intervention, and ultimately improving patient outcomes.

BackgroundSeveral predictive scoring systems have been developed to improve the clinical management of patients with community-acquired pneumonia (CAP) and optimise resource allocation. None of the current scores have been validated for immunosuppressed patients and data on outcome of CAP in solid organ transplant recipients are scarce. What is the predictive value of pneumonia severity scores in solid organ transplant recipients with CAP?Study design and methodsWe retrospectively analysed 333 first CAP episodes after lung, kidney and liver transplantation between 1 January 2010 and 31 May 2021. We assessed clinical outcomes and evaluated six risk scores (CRB-65 (confusion, respiratory frequency ≥30 breaths·min−1, systolic blood pressure <90 mmHg or diastolic blood pressure ≤60 mmHg and age ≥65 years); CURB-65 (confusion, urea >7 mol·L−1, respiratory frequency ≥30 breaths·min−1, systolic blood pressure <90 mmHg or diastolic blood pressure ≤60 mmHg and age ≥65 years); qSOFA (Quick Sepsis-Related Organ Failure Assessment); SOFA; Infectious Diseases Society of America (IDSA)/American Thoracic Society (ATS) 2007 minor criteria; and the National Early Warning Score 2 (NEWS2)) for the prognosis of severe pneumonia (admission to intensive care unit) and in-hospital mortality. Risk scores were assessed in the first 24 h after admission.ResultsNone of the scores showed adequate prognostic values to guide clinical management of patients in our cohort. The IDSA/ATS 2007 minor criteria, SOFA and NEWS2 performed best in predicting both severe pneumonia and in-hospital mortality, with area-under-the-curve values between 0.72 and 0.67.ConclusionThis comprehensive analysis of CAP in solid organ transplant recipients emphasises the complexity of managing this population and the need for transplant-specific risk assessment tools to improve patient management.

ObjectiveTo investigate the current status of pain control in trauma patients in the emergency department, as well as nurses’ attitudes, behaviors, and influencing factors regarding pain management, with the aim of improving the quality of emergency care.MethodsA single-center cross-sectional study was conducted. Using convenience sampling, 245 trauma patients admitted to the emergency department of Suzhou Research Center of Medical School, Suzhou Hospital, Affiliated Hospital of Medical School, between January and December 2024 were enrolled, along with 79 emergency nurses. Patients were assessed using the Modified Early Warning Score (MEWS) and the Facial Rating Scale (FRS) for pain. Questionnaires were administered to both patients and nurses. Statistical analyses included descriptive statistics, t-tests, logistic regression, and multiple linear regression.ResultsThe majority of patients were male (68.16%) and aged 18–60 years (80.82%). The most common injuries were limb (41.63%) and chest-abdominal (31.43%), with traffic accidents as the leading cause (40.00%). Analgesic administration rates increased with MEWS scores (0% in MEWS 0–2, 44.0% in 5–6, and 67.9% in ≥9). However, patient satisfaction did not increase correspondingly (29.21% in MEWS 3–4, 34.00% in 5–6). Nurses expressed strong concern that analgesia may mask clinical conditions (mean score 4.29 ± 0.56). Logistic regression showed that main injury site (OR = 0.69, p = 0.014), injury type (OR = 2.18, p = 0.001), analgesia request (OR = 1.68, p = 0.004), and injury manifestation (OR = 1.62, p = 0.003) were independent predictors of satisfaction. Multiple linear regression confirmed analgesia request (β = 0.32, p < 0.001) and obvious injury manifestation (β = 0.25, p = 0.002) as positive predictors, while limb injuries predicted lower satisfaction (β = −0.19, p = 0.008).ConclusionPain control in emergency trauma patients is influenced by injury severity, nurses’ attitudes, and patient-related factors. Comprehensive pain assessment, nurse training, and consideration of patient requests and injury characteristics are essential to improving emergency pain management.

ObjectiveTo identify patients with sepsis-associated acute kidney injury (SA-AKI) at high risk of respiratory failure within 48 h of admission and enable timely intervention to improve patient prognosis.MethodsData from SA-AKI patients admitted to Dongyang People's Hospital between June 2012 and October 2024 were collected, including gender, age, and blood biochemical indicators at admission. Patients were randomly divided into training and validation groups. Independent risk factors for respiratory failure were identified in the training group, and a nomogram prediction model was developed. The model's discriminative ability was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), and its calibration was evaluated using the GiViTi calibration plot. Clinical effectiveness was examined using decision curve analysis (DCA). Cross-validation was performed to test the model's stability using kappa value. The model was subsequently validated in the validation group. Sequential Organ Failure Assessment (SOFA)-based, National Early Warning Score (NEWS)-based, and various other machine learning models were also established and compared with the proposed model using DeLong's test after Bonferroni correction.ResultsA total of 702 patients were included in the study. Independent risk factors for respiratory failure included D-dimer, lactate, pro-BNP, albumin, globulin, transcutaneous blood oxygen saturation, and pulmonary infection. The AUC values for the training and validation groups were 0.818 and 0.795, respectively, with calibration plot p values of 0.973 and 0.864. The DCA curves for both groups indicated superior clinical utility compared with extreme scenarios. This model owned a kappa value of 0.628, indicating for good stability. The SOFA model achieved AUC values of 0.583 (training group) and 0.763 (validation group), while the NEWS model had AUC values of 0.628 (training) and 0.618 (validation). DeLong's test confirmed that the proposed model outperformed SOFA and NEWS models (p < 0.05). In the validation group, the AUC values for SVM, C5.0, XGBoost, and integrated models were 0.781, 0.757, 0.759, and 0.778, respectively, with comparable discriminative ability to the nomogram (p > 0.05).ConclusionThe nomogram developed in this study based on D-dimer, lactate, pro-BNP, albumin, globulin, transcutaneous blood oxygen saturation, and pulmonary infection was found to effectively predict respiratory failure risk in SA-AKI patients within 48 h of admission.

Emergency conditions require prompt and appropriate treatment to prevent clinical deterioration. The Early Warning Score (EWS) is a tool used to monitor vital signs and support early detection of critical conditions in Emergency Departments (ED). This study aimed to describe the EWS assessment results in adult patients at the Emergency Department of RSU Siaga Medika Purbalingga. A descriptive quantitative method with a retrospective approach was used. A total of 338 respondents were selected using Slovin's formula from the population of inpatient ED patients from October to December 2024. Data were collected from medical records and analyzed using univariate analysis. The results showed that most patients were elderly (&gt;60 years old), with a higher proportion of males. The majority of patients had low EWS scores (57.99%) and were categorized in yellow triage (73.37%). These findings indicate that most patients arrived in conditions requiring close monitoring, especially among the elderly group. The study concludes that EWS provides an early clinical overview of patient conditions. It is recommended that hospitals improve training and optimize EWS implementation in the triage process to enhance patient safety and the quality of emergency care services.

PurposeAcute exacerbations of chronic obstructive pulmonary disease (AECOPD) are a major cause of hospital admissions and are associated with significant morbidity and mortality. Several scoring systems are available for early risk stratification, such as DECAF, BAP-65, and NEWS, each incorporating parameters from different clinical domains. This study aimed to validate and compare established risk scores for predicting in-hospital mortality in patients with AECOPD and to assess the accuracy of a novel composite model.Patients and MethodsThe BAP-65 score, a modified version of the DECAF score (DECAFm), and the National Early Warning Score (NEWS) were calculated using admission data of patients hospitalized for AECOPD in a Swiss hospital in 2022 and 2023. Predictive power for in-hospital death was compared using receiver operating characteristic (ROC) curves and the respective area under the curve (AUC). A novel scoring system, AECOPD-COMBI, combining parameters used in the three scores, was validated in the same cohort.Results314 patients (mean age 73 years (range 48–94), 47% female) were included, of whom 7 died during hospitalization (2.2%). Patients who died had significantly higher scores at admission across all validated tools. Among the established scores, the BAP-65 performed best in the prediction of in-hospital death (AUC 0.79), followed by DECAFm (AUC 0.72) and NEWS (0.64). The novel AECOPD-COMBI reached the highest AUC of 0.9 and, when setting the high-risk score threshold to S=18.5, it demonstrated strong classification accuracy (sensitivity 100%, specificity 81%, accuracy 81%).ConclusionThe AECOPD-COMBI score showed promising potential in identifying patients at risk of in-hospital death, potentially outperforming established scores. While the cohort’s low event rate may have influenced predictive estimates and performance differences were not statistically significant, these findings still highlight the score’s potential value in clinical decision-making. Given the small sample size and preliminary nature of the study, these results should be interpreted with caution. Larger studies are needed to validate the score’s applicapbility and assess its performance for other relevant outcomes.

This research focuses on establishing reliable machine learning models for clinical decision support by highlighting the crucial roles of data preprocessing and quality. Using the MIMIC-IV database, we developed and validated algorithms based on vital physiological indicators, including blood pressure, temperature, heart rate, respiratory rate, and blood oxygen saturation (SpO2).. The study reveals that the quality of the data provided to machine learning models significantly impacts their performance and reliability in clinical environments. To preserve data accuracy and quality, we have enforced rigorous data preprocessing and quality control guidelines, involving univariate and multivariate analyses. The refined data was utilized to educate an artificial neural network (ANN), which formulated an Early Warning Score (EWS) system. Remarkable performance was displayed by the model, with perfect classification scores (precision, recall, F1 score, and accuracy all equaling 1.0) for individual vital sign predictions. Additionally, the model's MSE and MAE were close to zero, indicating negligible error in the regression metrics. The AUC curve's area was consistently high across all parameters (ranging from 0.992 to 1.000), while the validation accuracy ranged from 94.6% to 100%. Such results are achievable when using high-quality data. Conversely, they also illustrate the negative effects of compromised data quality on performance. In conclusion, the successful development and trustworthy deployment of machine learning systems in healthcare settings rely on robust data preprocessing and quality control, as this research illustrates.

Impact of the EAGLEcareACT Telehealth Program on Emergency Department Outcomes Among Nursing Home Residents: A Retrospective Cohort Study.