Developing a prototype system of computer-aided appointment scheduling: A radiology department case study.

Background: Scheduling patient appointments in hospitals is complicated due to various types of patient examinations, different departments and physicians accessed, and different body parts affected.

Objective: This study focuses on the radiology scheduling problem, which involves multiple radiological technologists in multiple examination rooms, and then proposes a prototype system of computer-aided appointment scheduling based on information such as the examining radiological technologists, examination departments, the patient's body parts being examined, the patient's gender, and the patient's age.

Methods: The system incorporated a stepwise multiple regression analysis (SMRA) model to predict the number of examination images and then used the K-Means clustering with a decision tree classification model to classify the patient's examination time within an appropriate time interval.

Patient Unpunctuality's Effect on Appointment Scheduling: A Scenario-Based Analysis.

This study examined patient unpunctuality's effect on patient appointment scheduling in the ultrasound department of a hospital. The study created a simulation system incorporating the formulated F3 distribution to describe patient unpunctuality. After the simulation model passed verification and validation processes, what-if scenarios were conducted under two policies: The preempt policy and the wait policy.

Applying the task-technology fit model to construct the prototype of a medical staff scheduling system.

Background: Medical staff scheduling problems are complex and involve numerous constraints.

Objective: This research uses the task-technology fit (TTF) model to measure the technology characteristics of information technology (IT) systems as a reference for constructing a prototype for a medical staff scheduling system to identify function requirements and design human interfaces.

Method: After the evaluation of the proposed scheduling system, this research excludes compatibility from the 13 technology characteristics and adds two technology characteristics for consideration: customization and scalability.

Applying Simulation Optimization to Minimize Drug Inventory Costs: A Study of a Case Outpatient Pharmacy.

Drug inventory management is an important part of hospital management. The large amounts of drug data in hospitals bring challenges to optimizing the setting values for the safety stock and the maximum inventory of each drug. This study combined a two-stage clustering method with an inventory policy (, ) and established a simulation optimization model for the case hospital's outpatient pharmacy.

Developing three-phase modified bat algorithms to solve medical staff scheduling problems while considering minimal violations of preferences and mean workload.

Background: This research studies a medical staff scheduling problem, which includes government regulations and hospital regulations (hard constraints) and the medical staff's preferences (soft constraints).

Objective: The objective function is to minimize the violations (or dissatisfaction) of medical staff's preferences.

Methods: This study develops three variants of the three-phase modified bat algorithms (BAs), named BA1, BA2, and BA3, in order to satisfy the hard constraints, minimize the dissatisfaction of the medical staff and balance the workload of the medical staff.

Solving patient referral problems by using bat algorithm.

Background: A two-hospital patient referral problem intends to calculate an optimal value of referral patients between two hospitals and to evaluate whether or not the current number of referral patients is too low.

Objective: The goal of this study is to develop a simulation-based optimization algorithm to find the optimal referral between two hospitals with the unfixed daily patient referral policy.

Methods: This study applied system simulation and a bat algorithm (BA) to build a simulation model in accordance with the status of the two hospitals case and to calculate an optimal value of daily referral patients.

Dynamic configuration scheduling problem for stochastic medical resources.

Scheduling approaches for conventional surgery operating rooms in a hospital treat surgeons as bottleneck resources directly, but do not deal with stochastic medical resources, leading to an uneven human resource distribution in optimizing medical resource scheduling. Thus, this research focuses on the dynamic configuration scheduling problem for stochastic medical resources. In this paper, the surgical operating room is limited, and the arriving calls (i.

Development of simulation optimization methods for solving patient referral problems in the hospital-collaboration environment.

This research studied a patient referral problem among multiple cooperative hospitals for sharing imaging services' referrals. The proposed problem consisted of many types of patients and the uncertainty associated with the number of patients of each type, patients' arrival time, and patients' medical operation time, leading to a difficulty in finding solutions due to the uncertain environment. This research used system simulation to construct a model and develop a simulation optimization method, combining the heuristic algorithm (patient referral mechanism) with the particle swarm optimization (PSO) method, to determine a better way to refer patients from one hospital (referring hospital) to another (recipient hospital) to receive certain imaging services.

Applying Task-Technology Fit Model to the Healthcare Sector: a Case Study of Hospitals' Computed Tomography Patient-Referral Mechanism.

With the growth in the number of elderly and people with chronic diseases, the number of hospital services will need to increase in the near future. With myriad of information technologies utilized daily and crucial information-sharing tasks performed at hospitals, understanding the relationship between task performance and information system has become a critical topic. This research explored the resource pooling of hospital management and considered a computed tomography (CT) patient-referral mechanism between two hospitals using the information system theory framework of Task-Technology Fit (TTF) model.