Improving Patient Flow in a Primary Care Clinic

When patients visit primary care clinics, they can be subject to long wait times due to operational inefficiencies and bottlenecks, decreasing patient satisfaction and sometimes leading to worse health outcomes. The existing literature models primary care clinics primarily as agent-based models, which are excellent at tracking individual patients and their movements in a model of a clinic. While agent-based models can detect bottlenecks, a network flow model better detects bottlenecks in the model by correlating changes in patient flow and wait times in the healthcare network. In this paper, a network flow model is constructed, where patients flow along the capacitated edges of a network while receiving treatment at the nodes. This configuration easily identifies bottlenecks by analyzing the flow in and flow out of nodes through metrics such as efficiency and patient wait times. The capacities of the edges for this model are taken from an agent-based model of a case study of a primary care clinic and sampled as random variables. Ensemble runs of the network flow model are created to account for uncertainty in the synthetic data. By changing the topology of the network flow model, bottlenecks are removed, increasing the model efficiency and decreasing patient wait times. Finally, the model is subjected to a sensitivity analysis. The focus in this work is on the method rather than the results per se.

Waiting time and its associated factors in patients presenting to outpatient departments at Public Hospitals of Jimma Zone, Southwest Ethiopia

Background

Waiting time is defined as the total time that a patient spends in a facility from arrival at the registration desk until the time she/he leaves the facility or last service. In Ethiopia, the waiting time in the hospitals particularly in the outpatient department is lengthy. Studies at Jimma University specialized hospital indicated patients are forced to wait an average of 4.5 waiting hours to get service. Even there are many hospitals found in the zone, there is a paucity of information regarding waiting time and associated factors. Hence, this study is aimed to assess waiting time and associated factors at outpatient departments in Public Hospitals of Jimma zone, southwest Ethiopia.

Methods

An institution-based cross-sectional study design was used from March 22 to June 3, 2020. A total of 422 study subjects were included in the study and systematic random sampling methods were used. The data were collected by observing the whole service points of each patient. The exit interview was made at the last point of the service unit. Descriptive statistics, bi-variable and multi-variable logistic regressions were used.

Results

The whole waiting time patients spent in the hospitals before getting service was a minimum of 41 and a maximum of 185 min. Patients who came far from the hospitals were 1.93 times (AOR = 1.93; 95% CI, 1.16, 3.21) more likely to spend longer waiting time as compared to those who came from the hospital's area.

Patients visited on Monday were 2.64 times (AOR = 2.64; 95% CI, 1.45, 4.79) more likely to spend longer waiting time than those who visited the hospital on Friday. Patients who arrived early in the morning were 3.22 times (AOR = 3.22; 95% CI, 1.32, 7.86) more likely to spend longer waiting time than those who arrived in the afternoon.

Conclusions

The mean waiting time was higher than the average recommended time by Business Process Reengineering (BPR) and more than five out of every ten clients spent long waiting time at outpatient departments Waiting time was affected by Educational status, residence, arrival time, and date of the visit.

Productivity Improvement through Reengineering and Simulation: A Case Study in a Footwear-Industry

Process reengineering is a very useful tool, specifically in industrial engineering where technological advances, information systems, customer requirements, and more have led to the need for radical change in some or all areas of an organization. The objective of this work is to show the usefulness of applying reengineering in the case of the footwear industry to make a proposal to change the problem area and the production decoration line as well as compare it with the current process using models of simulation performed in the Arena™ software. The proposal consisted of merging two production lines and comparing the current design with the proposal as well as comparing different parameters such as the use of resources and the production rate. The results indicated that the production rate increases by approximately 29% with the new design, using the same resources. In addition, using the OptQuest tool of the Arena™ software, it was found that with the new process, the production rate could be increased by up to 41% compared to the current process.

A generic discrete-event simulation model for outpatient clinics in a large public hospital

The orthopedic outpatient department (OPD) ward in a large Thai public hospital is modeled using Discrete-Event Stochastic (DES) simulation. Key Performance Indicators (KPIs) are used to measure effects across various clinical operations during different shifts throughout the day. By considering various KPIs such as wait times to see doctors, percentage of patients who can see a doctor within a target time frame, and the time that the last patient completes their doctor consultation, bottlenecks are identified and resource-critical clinics can be prioritized. The simulation model quantifies the chronic, high patient congestion that is prevalent amongst Thai public hospitals with very high patient-to-doctor ratios. Our model can be applied across five different OPD wards by modifying the model parameters. Throughout this work, we show how DES models can be used as decision-support tools for hospital management.