Reducing Unnecessary Respiratory Viral Testing to Promote High-Value Care

BACKGROUND AND OBJECTIVES: Viral respiratory infections are common in children, and practice guidelines do not recommend routine testing for typical viral illnesses. Despite results often not impacting care, nasopharyngeal swabs for viral testing are frequently performed and are an uncomfortable procedure. The aim of this initiative was to decrease unnecessary respiratory viral testing (RVT) in the emergency department (ED) and the pediatric medicine wards (PMWs) by 50% and 25%, respectively, over 36 months. METHODS: An expert panel reviewed published guidelines and appropriate evidence to formulate an RVT pathway using plan-do-study-act cycles. A multifaceted improvement strategy was developed that included implementing 2 newer, more effective tests when testing was deemed necessary; electronic order modifications with force functions; audit and feedback; and education. By using statistical process control charts, the outcomes analyzed were the percentage of RVT ordered in the ED and the rate of RVT ordered on the PMWs. Balancing measures included return visits leading to admission and inpatient viral nosocomial outbreaks. RESULTS: The RVT rate decreased from a mean of 3.0% to 0.5% of ED visits and from 44.3 to 30.1 per 1000 patient days on the PMWs and was sustained throughout the study. Even when accounting for the new rapid influenza test available in the ED, a 50% decrease in overall ED RVT was still achieved without any significant impact on return visits leading to admission or inpatient nosocomial infections. CONCLUSIONS: Through implementation of a standardized, electronically integrated RVT pathway, a decrease in unnecessary RVT was successfully achieved. Audit and feedback, reminders, and biannual education all supported long-term sustainability of this initiative.

Correction: Yau et al. Risk Factors for Early Return Visits to the Emergency Department in Patients Presenting with Nonspecific Abdominal Pain and the Use of Computed Tomography Scan. Healthcare 2021, 9, 1470

The authors would like to make corrections to their published paper [...]

COVID-19 Follow-App. Mobile App-Based Monitoring of COVID-19 Patients after Hospital Discharge: A Single-Center, Open-Label, Randomized Clinical Trial

Introduction: In the midst of a pandemic, apps can be used to provide close follow-up, ensure that patients are monitored at home, avoid excessive pressure on medical facilities, prevent the movement of people (both patients and health professionals), and reduce the risk of infection. Objective: To adapt and validate the use of a smartphone application for outpatient follow-up of COVID-19 patients after hospital discharge. Methods: We conducted an open-label clinical trial at Hospital Universitari Vall d’Hebron in Barcelona, Spain. Patients were randomly assigned in a 1:1 ratio to be followed by the Farmalarm app or by their primary care center. The primary endpoint was the reduction in the need for in-person return visits. Results: From 31 March to 4 May 2020, 150 patients were enrolled in the study at hospital discharge: 74 patients were randomized to the experimental group, and 76 to the control group. All patients in the control group and all except for six in the experimental group completed the study. During hospitalization, before study inclusion, all but 4 (97.3%) had viral pneumonia, 91 (60.7%) required supplemental oxygen, and 16 (10.7%) required intensive care unit (ICU) admission. COVID-19–related return visits to the emergency department were significantly higher in the control group (7.9% vs. 0%; p = 0.028) in the per-protocol analysis. Telephone consultations with the emergency department were performed by 12 (15.8%) patients in the control group and 0 (0%) in the experimental group (p < 0.001). Satisfaction with outpatient monitoring was rated higher by the experimental group (5 vs. 4 points; p < 0.001). Conclusions: Following COVID-19 hospital discharge, home follow-up via a mobile app was effective in reducing in-person return visits without undermining patient satisfaction or perception of health, compared with standard follow-up.

Risk factors for acute bronchiolitis-related return visits to the emergency department

Purpose: Acute bronchiolitis (AB)-related return visits incur overuse of emergency medicine resources, crowding of emergency departments (EDs), and deterioration of rapport with the guardians. The authors aimed to analyze factors associated with the return visits to the ED.Methods: This study was conducted based on the medical records of 447 children aged 24 months or younger with AB who visited the ED from January 2019 through December 2020. A return visit was defined as an AB-related visit to the ED within 7 days of index visit. According to the return visit, we compared the clinical features. Multivariable logistic regression was conducted to identify independent factors for the return visit.Results: Of the 323 children with AB, 77 (23.8%) made return visits. The returning children showed a younger median age (6 [interquartile range, 2-10] vs. 8 months [3-14]; P < 0.001), a lower mean oxyhemoglobin saturation (92.9 ± 4.3% vs. 97.1 ± 1.8%; P < 0.001), and higher frequencies of congenital heart diseases (22.1% vs. 10.6%; P = 0.009) and bronchopulmonary dysplasia (11.7% vs. 5.7%; P = 0.013), and respiratory syncytial virus infection (57.1% vs. 37.0%; P = 0.002). No other variables, such as the hospitalization rate, differed as per return visits. The factors associated with return visits were respiratory syncytial virus infection (adjusted odds ratio, 9.41; 95% confidence interval, 2.13-41.57), lower oxygen saturation (2.00; 1.64-2.43), and age younger than 3 months (1.25; 1.07-1.24).Conclusion: AB-related return visits may be associated with age younger than 3 months, lower oxygen saturation, and respiratory syncytial virus infection.

A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain

Seventy-two-hour unscheduled return visits (URVs) by emergency department patients are a key clinical index for evaluating the quality of care in emergency departments (EDs). This study aimed to develop a machine learning model to predict 72 h URVs for ED patients with abdominal pain. Electronic health records data were collected from the Chang Gung Research Database (CGRD) for 25,151 ED visits by patients with abdominal pain and a total of 617 features were used for analysis. We used supervised machine learning models, namely logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC), to predict URVs. The VC model achieved more favorable overall performance than other models (AUROC: 0.74; 95% confidence interval (CI), 0.69–0.76; sensitivity, 0.39; specificity, 0.89; F1 score, 0.25). The reduced VC model achieved comparable performance (AUROC: 0.72; 95% CI, 0.69–0.74) to the full models using all clinical features. The VC model exhibited the most favorable performance in predicting 72 h URVs for patients with abdominal pain, both for all-features and reduced-features models. Application of the VC model in the clinical setting after validation may help physicians to make accurate decisions and decrease URVs.