Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning.

Background: Emergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of ED patient arrivals can help management to better allocate staff and medical resources. In this study, we investigate the use of calendar and meteorological predictors, as well as feature-engineered variables, to predict daily patient arrivals using datasets from eleven different EDs across three countries.

Workspace Layout for Resilient Performance using Social Network Analysis: A Case Study.

To present the social network analysis-based approach used to create a new workspace layout for three hospital services as part of a campus expansion at a large tertiary care public hospital. To analyze the relationships among service members across four healthcare resilience abilities (monitor, anticipate, respond, and learn) and utilize network metrics to indicate the suitability of a shared workspace layout for the services. The hospital expanded by 70%, providing space for relocating key services-the rapid response team, medical on-call team, and nursing supervision.

Burnout and network centrality as proxies for assessing the human cost of resilient performance.

The extra effort of healthcare professionals to provide care is a manifestation of resilient performance (RP), usually going unnoticed due to successful outcomes. However, it is not clear how the human cost of RP can be assessed. This study addresses this gap by investigating the relationships between proxies of RP and its human cost.

Modelling the no-show of patients to exam appointments of computed tomography.

Background: Patients' no-shows negatively impact healthcare systems, leading to resources' underutilisation, efficiency loss, and cost increase. Predicting no-shows is key to developing strategies that counteract their effects. In this paper, we propose a model to predict the no-show of ambulatory patients to exam appointments of computed tomography at the Radiology department of a large Brazilian public hospital.

Playful interventions to promote the subjective wellbeing of pediatric cancer inpatients during laboratory and imaging exams: A qualitative study.

Purpose: We investigate the experience of pediatric oncology patients with objects and equipment involved in laboratory and image examinations during hospitalization for cancer treatment while generating guidelines for playful interventions to improve their subjective wellbeing.

Method: The study was carried out at a public tertiary referral teaching hospital in Southern Brazil. Data collection was based on participatory observations with six children aged 4-8 years.

Combining wavelength importance ranking to the random forest classifier to analyze multiclass spectral data.

Near Infrared (NIR) is a type of vibrational spectroscopy widely used in different areas to characterize substances. NIR datasets are comprised of absorbance measures on a range of wavelengths (λ). Typically noisy and correlated, the use of such datasets tend to compromise the performance of several statistical techniques; one way to overcome that is to select portions of the spectra in which wavelengths are more informative.

Dynamic capacity allocation in a radiology service considering different types of patients, individual no-show probabilities, and overbooking.

Background: We propose a mathematical model formulated as a finite-horizon Markov Decision Process (MDP) to allocate capacity in a radiology department that serves different types of patients. To the best of our knowledge, this is the first attempt at considering radiology resources with different capacities and individual no-show probabilities of ambulatory patients in an MDP model. To mitigate the negative impacts of no-show, overbooking rules are also investigated.

Hospital Investment Decisions in Healthcare 4.0 Technologies: Scoping Review and Framework for Exploring Challenges, Trends, and Research Directions.

Background: Alternative approaches to analyzing and evaluating health care investments in state-of-the-art technologies are being increasingly discussed in the literature, especially with the advent of Healthcare 4.0 (H4.0) technologies or eHealth.

Approaches to the rationalization of surgical instrument trays: scoping review and research agenda.

Background: Surgical Tray Rationalization (STR) consists of a systematic reduction in the number of surgical instruments to perform specific procedures without compromising patient safety while reducing losses in the sterilization and assembly of trays. STR is one example of initiatives to improve process performance that have been widely reported in industrial settings but only recently have gained popularity in healthcare organizations.

Methods: We conduct a scoping review of the literature to identify and map available evidence on surgical tray management.

Man vs. machine: Predicting hospital bed demand from an emergency department.

Background: The recent literature reports promising results from using intelligent systems to support decision making in healthcare operations. Using these systems may lead to improved diagnostic and treatment protocols and to predict hospital bed demand. Predicting hospital bed demand in emergency department (ED) attendances could help resource allocation and reduce pressure on busy hospitals.

Strategies for synchronizing chocolate conching batch process data using dynamic time warping.

In batch processing, process control is typically carried out comparing trajectories of process variables with those in an in-control set of batches that yielded products within specifications. However, one strong assumption of these schemes is that all batches have equal duration and are synchronized, which is often not satisfied in practice. To overcome that, dynamic time warping (DTW) methods may be used to synchronize stages and align the duration of batches.

Systematic Layout Planning of a Radiology Reporting Area to Optimize Radiologists' Performance.

Optimizing radiologists' performance is a major priority for managers of health services/systems, since the radiologists' reporting activity imposes a severe constraint on radiology productivity. Despite that, methods to optimize radiologists' reporting workplace layout are scarce in the literature. This study was performed in the Radiology Division (RD) of an 850-bed University-based general hospital.

[A data mining method for breast cancer identification based on a selection of variables].

In the majority of countries, breast cancer among women is highly prevalent. If diagnosed in the early stages, there is a high probability of a cure. Several statistical-based approaches have been developed to assist in early breast cancer detection.