AIMC Topic: Personnel Staffing and Scheduling
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Integrating Nurse Preferences Into AI-Based Scheduling Systems: Qualitative Study.
JMIR formative research
Jun 4, 2025
BACKGROUND: Nurse scheduling is a complex challenge in health care, impacting both patient care quality and nurse well-being. Traditional scheduling methods often fail to consider individual preferences, leading to dissatisfaction, burnout, and high ...
Integrating autonomously navigating assistance systems into the clinic: guiding principles and the ANTS-OR approach.
International journal of computer assisted radiology and surgery
Apr 2, 2020
PURPOSE: Autonomously self-navigating clinical assistance systems (ASCAS) seem highly promising for improving clinical workflows. There is great potential for easing staff workload and improving overall efficiency by reducing monotonous and physicall...
ICU staffing feature phenotypes and their relationship with patients' outcomes: an unsupervised machine learning analysis.
Intensive care medicine
Oct 8, 2019
PURPOSE: To study whether ICU staffing features are associated with improved hospital mortality, ICU length of stay (LOS) and duration of mechanical ventilation (MV) using cluster analysis directed by machine learning.
Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation.
Computational intelligence and neuroscience
Jul 10, 2019
In this paper, a hybrid deep neural network scheduler (HDNNS) is proposed to solve job-shop scheduling problems (JSSPs). In order to mine the state information of schedule processing, a job-shop scheduling problem is divided into several classificati...
An Effective Solution for Large Scale Single Machine Total Weighted Tardiness Problem using Lunar Cycle Inspired Artificial Bee Colony Algorithm.
IEEE/ACM transactions on computational biology and bioinformatics
Feb 4, 2019
Single machine total weighted tardiness problem (SMTWTP) is one of the fundamental combinatorial optimization problems. The problem consists of a set of independent jobs with distinct processing times, weights, and due dates to be scheduled on a sing...
Machine learning to anticipate delivery room activity?
Journal of gynecology obstetrics and human reproduction
Dec 1, 2018
A Hyper-Heuristic Ensemble Method for Static Job-Shop Scheduling.
Evolutionary computation
Apr 27, 2016
We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and-conquer approach in which each heuristic solves a unique subset of the instance...
Optimising Nurse-Patient Assignments: The Impact of Machine Learning Model on Care Dynamics-Discursive Paper.
Nursing open
Apr 1, 2025
BACKGROUND: Machine learning (ML) models can enhance patient-nurse assignments in healthcare organisations by learning from real data and identifying key capabilities. Nurses must develop innovative ideas for adapting to the dynamic environment, mana...
Machine Learning in Optimising Nursing Care Delivery Models: An Empirical Analysis of Hospital Wards.
Journal of evaluation in clinical practice
Feb 1, 2025
OBJECTIVE: This study aims to assess the performance of machine learning (ML) techniques in optimising nurse staffing and evaluating the appropriateness of nursing care delivery models in hospital wards. The primary outcome measures include the adequ...
Development of a Data Model to Predict Nursing Workload Using Routine Clinical Data.
Studies in health technology and informatics
Aug 22, 2024
The effective management of human resources in nursing is fundamental to ensuring high-quality care. The necessary staffing levels can be derived from the nursing-related health status. Our approach is based on the use of artificial intelligence (AI)...
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