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. RESULTS: The constructed prototype creates a feasible patient appointment schedule by classifying patient examination times into different categories for different patients according to the four types of body parts, eight hospital departments, and 10 radiological technologists. CONCLUSION: The proposed patient appointment scheduling system can schedule appointment times for different types of patients according to the type of visit, thereby addressing the challenges associated with diversity and uncertainty in radiological examination services. It can also improve the quality of medical treatment.

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. A comparison of the total cost of each policy showed that the preempt policy performed better than the wait policy in the presence of unpunctuality. The study used sensitivity analyses to identify the different effects of patient unpunctuality on the system. The weights of the cost coefficient of both radiological technician's idle time and patient waiting time must be equal in order to achieve a lower cost. The patient's inter-arrival time must be close to the average total time in the system to achieve lower costs. Moreover, utilization decreases as the patient's inter-arrival increases. Therefore, the patient's inter-arrival time should be higher than, but close to, the service time to ensure less radiological technician's idle time and patient waiting time.

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. RESULTS: Based on the revised technology characteristics of the TTF model, this research develops flexible scheduling functions to satisfy daily manpower requirements and allow predetermined schedules and day-off reservations for a hospital's radiological technologists. Characterized by flexibility, customization, and scalability, the system can accommodate several algorithms to generate a better schedule that satisfies hard and soft constraints. Furthermore, the scheduler can choose the required hard and soft constraints from all constraints. The prototype of the scheduling system will be easily extended to add or modify constraints in the case of requirement or regulation changes. CONCLUSION: The results of this study provide a prototype for system developers to design a customized staff scheduling system for each medical unit.

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 (s, S) and established a simulation optimization model for the case hospital's outpatient pharmacy. This research used the simulation optimization software Arena OptQuest, developed by Rockwell Automation Inc (Rockwell Automation, Coraopolis, PA, USA), in order to determine the minimum and maximum values (s, S) of the best stock amounts for each drug under the considerations of cost and related inventory constraints. The research results showed that the minimum and maximum inventory settings for each drug in the simulation model were better than those set by the case outpatient pharmacy system. The average inventory cost was reduced by 55%, while the average inventory volume was reduced by 68%. The proposed method can improve management efficiency and inventory costs of hospital pharmacies without affecting patient services and increasing the inventory turnover rate of the drugs.

Using Simulation Optimization to Solve Patient Appointment Scheduling and Examination Room Assignment Problems for Patients Undergoing Ultrasound Examination.

This study investigates patient appointment scheduling and examination room assignment problems involving patients who undergo ultrasound examination with considerations of multiple examination rooms, multiple types of patients, multiple body parts to be examined, and special restrictions. Following are the recommended time intervals based on the findings of three scenarios in this study: In Scenario 1, the time interval recommended for patients' arrival at the radiology department on the day of the examination is 18 min. In Scenario 2, it is best to assign patients to examination rooms based on weighted cumulative examination points. In Scenario 3, we recommend that three outpatients come to the radiology department every 18 min to undergo ultrasound examinations; the number of inpatients and emergency patients arriving for ultrasound examination is consistent with the original time interval distribution. Simulation optimization may provide solutions to the problems of appointment scheduling and examination room assignment problems to balance the workload of radiological technologists, maintain high equipment utilization rates, and reduce waiting times for patients undergoing ultrasound examination.

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. To ensure workload balance, this study balances the workload among medical staff without increasing the objective function values. RESULTS: Based on the numerical results, the BA3 outperforms the BA1, BA2, and particle swarm optimization (PSO). The robustness of the BA1, BA2, and BA3 is verified. Finally, conclusions are drawn, and directions for future research are highlighted. CONCLUSIONS: The framework of this research can be used as a reference for other hospitals seeking to determine their future medical staff schedule.

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. RESULTS: Based on the 20 test instances, we verified the stability of this algorithm. The results show that the average magnetic resonance imaging (MRI) patient wait time reduced from 16 days to eight days. The hospital should increase the average total monthly MRI referral patients to 370 under the limitation of the daily referral patients to 25. CONCLUSIONS: This research investigated the two-hospital patient referral problems. We conducted and analyzed a simulation model and improved the case hospital's conditions, enhancing the quality of its medical care. The findings of this study can extend to other departments or hospitals.

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.e., number of patients) are dynamic. When a patient arrives, the nurse anesthetist and anesthesiologist are limited, but the medical service duration per patient is random. We introduce the drum-buffer-rope (DBR) scheduling approach to analyze which types of medical resources become bottleneck resources for optimizing operating room scheduling. After verifying the effectiveness of the DBR method in uncertain situations, the Monte Carlo simulation is demonstrated.

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. After the simulated model was verified and validated, three patient referral mechanisms to dispatch referring patients to the appropriate recipient hospitals were proposed. Based on the numerical results, the findings showed that Mechanism 2, transferring patients to the hospital with the shortest waiting time, had good performance in both scenarios: allowing patient referrals among all hospitals and limiting the patients' waiting time. Finally, this study presents the conclusions and some directions for future research.

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. The TTF model could be used to assess the 'match' between the task and technology characteristics. The patient-referral process involved an integrated information framework consisting of a hospital information system (HIS), radiology information system (RIS), and picture archiving and communication system (PACS). A formal interview was conducted with the director of the case image center on the applicable characteristics of TTF model. Next, the Icam DEFinition (IDEF0) method was utilized to depict the As-Is and To-Be models for CT patient-referral medical operational processes. Further, the study used the 'leagility' concept to remove non-value-added activities and increase the agility of hospitals. The results indicated that hospital information systems could support the CT patient-referral mechanism, increase hospital performance, reduce patient wait time, and enhance the quality of care for patients.