Does an emergency access button increase the patients' satisfaction and feeling of safety with the out-of-hours health services? A randomised controlled trial in Denmark

Identification of emergencies in the telephone queue and routing to a fast track (FAST): study protocol for a prospective, two-armed cohort study

BACKGROUND

In Germany, the telephone patient service 116,117 for callers with non-life-threatening health issues is available 24/7. Based on structured initial assessment, urgency and placement of suitable medical care offer have been offered since 2020. The service has been in increasing demand for several years: Depending on time and residence, this can result in longer waiting times.

METHODS

Prospective, two-armed cohort study with two intervention groups and one control group, alternating between blinding and unblinding for employees of 116,117 regarding prioritization status. Two interventions based on automated voice dialogues (1: Simple self-rating tool, 2: Automated brief query of emergency symptoms). In case of high level of urgency, callers are prioritized. Validation of urgency and need for care is carried out routinely based on structured initial assessment.

DISCUSSION

By creating and providing a largely reproducible documentation of the implemented solutions for a waiting queue management, the developed approach would be available for comparable projects in the German health care system or in the European context. This potentially leads to a reduction in the use of resources in the development of comparable technical solutions based on automated voice dialogs.

TRIAL REGISTRATION

DRKS00031235, registered on 10th November 2023, https://drks.de/search/de/trial/DRKS00031235 .

Prediction of outpatient waiting time: using machine learning in a tertiary children's hospital

Background

Accurately predicting waiting time for patients is crucial for effective hospital management. The present study examined the prediction of outpatient waiting time in a Chinese pediatric hospital through the use of machine learning algorithms. If patients are informed about their waiting time in advance, they can make more informed decisions and better plan their visit on the day of admission.

Methods

First, a novel classification method for the outpatient clinic in the Chinese pediatric hospital was proposed, which was based on medical knowledge and statistical analysis. Subsequently, four machine learning algorithms [linear regression (LR), random forest (RF), gradient boosting decision tree (GBDT), and K-nearest neighbor (KNN)] were used to construct prediction models of the waiting time of patients in four department categories.

Results

The three machine learning algorithms outperformed LR in the four department categories. The optimal model for Internal Medicine Department I was the RF model, with a mean absolute error (MAE) of 5.03 minutes, which was 47.60% lower than that of the LR model. The optimal model for the other three categories was the GBDT model. The MAE of the GBDT model was decreased by 28.26%, 35.86%, and 33.10%, respectively compared to that of the LR model.

Conclusions

Machine learning can predict the outpatient waiting time of pediatric hospitals well and ease patient anxiety when waiting in line without medical appointments. This study offers key insights into enhancing healthcare services and reaffirms the dedication of Chinese pediatric hospitals to providing efficient and patient-centric care.