Relationship Between Waiting Time on Patient Satisfaction at Baloi Permai Batam Puskesmas Year 2021

Background: Medical records are an important part in assisting the implementation of service delivery to patients in hospitals. This research aimsMethods: Quantitative with survey research, a quantitative approach is used to find out how effective the relationship between waiting time and patient satisfaction is at the Baloi Permai Health Center.Results: The results of the chi square statistical test showed that the p-value of 0.001 was less than 0.050, so it can be said that there is a significant relationship between waiting time and patient satisfaction. The odds ratio for the relationship between waiting time and patient satisfaction is 7.263 with 95% CI between 2.143- 24.614. Patients with long waiting times are 7,263 or 7 times more likely to have a low level of satisfaction compared to patients whose waiting times are not too long.Conclusions: Based on the results of the study, it can be concluded that there is an effect of patient waiting time on outpatient satisfaction. The staff of the Baloi Perma Batam outpatient unit should further improve services, especially for waiting time for outpatients. Based on the results of the study, it can be concluded that there is an effect of patient waiting time on outpatient satisfaction. The staff of the Baloi Perma Batam outpatient unit should further improve services, especially for waiting time for outpatients.

Studying the appointment scheduling window considering patient no-show behavior in one public and one private outpatient clinics

Purpose This study is carried out in one public and one private health-care centers based on different probabilities of patient’s no-show rate. The present study aims to determine the optimal queuing system capacity so that the expected total cost is minimized. Design/methodology/approach In this study an M/M/1/K queuing model is used for analytical properties of optimal queuing system capacity and appointment window so that total costs of these cases could be minimized. MATLAB software version R2014a is used to code the model. Findings In this paper, the optimal queuing system capacity is determined based on the changes in effective parameters, followed by a sensitivity analysis. Total cost in public center includes the costs of patient waiting time and rejection. However, the total cost in private center includes costs of physician idle time plus costs of public center. At the end, the results for public and private centers are compared to reach a final assessment. Originality/value Today, determining the optimal queuing system capacity is one of the most central concerns of outpatient clinics. The large capacity of the queuing system leads to an increase in the patient’s waiting-time cost, and on the other hand, a small queuing system will increase the cost of patient’s rejection. The approach suggested in this paper attempts to deal with this mentioned concern.

Factors Associated With Patient Waiting Time at a Medical Outpatient Clinic: A Case Study of University of Nairobi Health Services

Background: Long waiting time in outpatient clinics is a constant challenge for patients and the health care providers. Prolonged waiting times are associated with poor adherence to treatment, missed appointment and failure or delay in initiation of treatment and is a major factor towards the perception of the patient towards the care received. Objective: To determine the waiting time and associated factors among out patients attending staff clinic at University of Nairobi health services. Method: A cross-sectional study design was used and data collected from 384 ambulatory patients over a period of four weeks using an interviewer administered pretested structured exit questionnaire with a time-tracking section. Simple random sampling was used to select respondents in a walk- in outpatient clinic set up. Data was cleaned and analysed using Statistical Package for Social Sciences (SPSS) 20. Analysis of variance (ANOVA), and cross tabulation was used to establish associations between the independent variable and dependent variables. Results: In total 384 patients were tracked and interviewed. The average patient waiting time was 55.3mins.Most respondents (52%) suggested that improving availability of staff at their stations would help to reduce patient waiting time. In this study, gender (P=0.005) and availability of doctors (p=0.000) were found to affect patient waiting time with women waiting longer than the male patients. Conclusion: Majority of the patients spent about an hour at the facility to be served. Inadequate number of health workers was the main cause of long waiting time.

Who Is Next: Patient Prioritization Under Emergency Department Blocking

In “Who Is Next: Patient Prioritization Under Emergency Department Blocking,” Li, Sun, and Hong study how physicians and nurses choose the next patient for treatment in hospital emergency departments (EDs). Using data from a tertiary hospital in Alberta, Canada, they conduct an empirical investigation and find that both clinical factors and resource constraints are considered in patient-prioritization decisions. In particular, discharged patients are prioritized when ED beds are increasingly occupied by boarding patients so as to avoid further blocking the ED. A stylized model is developed to explain the rationale behind the prioritization behavior. Using a simulation study, they show such behavior can improve ED operations by reducing the average patient waiting time and length of stay without adding extra capacity, which results in significant cost savings for hospitals.

Improving Chemotherapy Infusion Operations through the Simulation of Scheduling Heuristics: a case study

Over the last decade, chemotherapy treatments have dramatically shifted to outpatient services such that nearly 90% of all infusions are now administered outpatient. This shift has challenged oncology clinics to make chemotherapy treatment as widely available as possible while attempting to treat all patients within a fixed period of time. Historical data from a Veterans Affairs chemotherapy clinic in the United States and staff input informed a discrete event simulation model of the clinic. The case study examines the impact of altering the current schedule, where all patients arrive at 8:00 AM, to a schedule that assigns patients to two or three different appointment times based on the expected length of their chemotherapy infusion. The results identify multiple scheduling policies that could be easily implemented with the best solutions reducing both average patient waiting time and average nurse overtime requirements.

Patient Decision and Experience in the Multi-channel Appointment Context: An Empirical Study (Preprint)

BACKGROUND Long waiting time for treatment in the outpatient department has long been a complaint and influence patient experience. It is critical to schedule patients for doctors to reduce patient waiting time. Nowadays, the multi-channel appointment has been provided for patients to get medical services, especially for those with severe illnesses and remote distance. OBJECTIVE This study aims to explore the factors influencing patient appointment channel choice in the context of multi-channel appointments, and how channel choice affects the waiting time for offline visiting. METHODS We collected outpatient appointment records from both online and offline appointment channels to conduct our empirical research. The empirical analysis is conducted into two steps. We first analyze the relationship between appointment channel choice and patient waiting time, and then the relationships between three determinants and appointment channel choice. The ordinary least squares and the logistic regression model are used to obtain empirical results. RESULTS Our results show that a patient with an online appointment decision has a shorter consultation waiting time compared with a patient with on-site appointment (β = -0.320, p<0.001). High-quality resource demand (β = 0.349, p<0.001), high-severity disease (β = 0.011, p<0.001), and high non-disease costs (β = 0.039, p<0.001) create an obvious incentive for patients to make appointments via the Internet. Further, only the effect of non-disease cost on channel choice is lower for patients with multiple visit histories (β = -0.021, p<0.001). CONCLUSIONS Our study confirms the effect of Internet use on reducing patient waiting time. Patients consider both health-related risk factors and cost-related risk factors to make decisions on appointment channels. Our study produces several insights, which have implications for channel choice and patient behavior literature. More importantly, these insights as a whole, contribute to the design of appointment systems of hospitals.

Predicting Patient Waiting Time in the Queue System Using Deep Learning Algorithms in the Emergency Room

Many hospitals consider the length of time waiting in queue to be a measure of emergency room (ER) overcrowding. Long waiting times plague many ER departments, hindering the ability to effectively provide medical attention to those in need and increasing overall costs. Advanced techniques such as machine learning and deep learning (DL) have played a central role in queuing system applications. This study aims to apply DL algorithms for historical queueing variables to predict patient waiting time in a system alongside, or in place of, queueing theory (QT). We applied four optimization algorithms, including SGD, Adam, RMSprop, and AdaGrad. The algorithms were compared to find the best model with the lowest mean absolute error (MAE). A traditional mathematical simulation was used for additional comparisons. The results showed that the DL model is applicable using the SGD algorithm by activating a lowest MAE of 10.80 minutes (24% error reduction) to predict patients' waiting times. This work presents a theoretical contribution of predicting patients’ waiting time with alternative techniques by achieving the highest performing model to better prioritize patients waiting in the queue. Also, this study offers a practical contribution by using real-life data from ERs. Furthermore, we proposed models to predict patients' waiting time with more accurate results than a traditional mathematical method. Our approach can be easily implemented for the queue system in the healthcare sector using electronic health records (EHR) data.

Appointment scheduling system in multi doctor/multi services environment

Appointment scheduling systems are used by health care providers to manage access to their services. In this paper an algorithm and a web application for automatic appointment scheduling is presented. Both are implemented using the concept of booking appointments for patients for a specific service offered by each doctor. The purpose of the application is to make signing up for a specific service easier for patients and to improve health tourism in Croatia by maximizing doctor’s efficiency and minimize patient waiting time. Medical providers are added to the system, they add the services which they provide, and each service offered has its own duration time. Users register, search for services matching their parameters, and schedule an appointment for the requested service. Available appointments are generated using the presented algorithm, which is the main part of this paper. The algorithm searches the database and returns possible appointments. If patient has more than one appointment, possible appointments time can be before the existing appointment, between two appointments, or at the end of the last appointment. Thus, web application enables the patient to reserve desirable appointment time.

Using lean techniques and discrete-event simulation for performance improvement in an outpatient clinic

Purpose Increased demand and the pressure to reduce health-care costs have led to longer waiting time for patients to make appointments and during the day of hospital visits. The purpose of this study is to identify opportunities to reduce waiting time using lean techniques and discrete-event simulation (DES). Design/methodology/approach A five-step procedure is proposed to facilitate the effective utilization of lean and DES to improve the performance of the Otolaryngology Head and Neck Surgery Outpatient Clinic at Cleveland Clinic Abu Dhabi. While lean techniques were applied to reduce the potential sources of waste by aligning processes, a DES model was developed to validate the proposed solutions and plan patient arrivals under dynamic conditions and different scenarios. Findings Aligning processes resulted in an efficient patient flow reducing both waiting times. DES played a complementary role in verifying lean solutions under dynamic conditions, helping to plan the patient arrivals and striking a balance between the waiting times. The proposed solutions offered flexibility to improve the clinic capacity from the current 176 patients up to 479 (without violating the 30 min waiting time policy) or to reduce the patient waiting time during the visit from the current 33 min to 4.5 min (without violating the capacity goal of 333 patients). Research limitations/implications Proposing and validating lean solutions require reliable data to be collected from the clinic and such a process could be laborious as data collection require patient and resource tracing without interfering with the regular functions of the clinic. Practical implications The work enables health-care managers to conveniently conduct a trade-off analysis and choose a suitable inter-arrival time – for every physician – that would satisfy their objectives between resource utilization (clinic capacity) and average patient waiting time. Social implications Successful implementation of lean requires a supportive and cooperative culture from all stakeholders involved. Originality/value This study presents an original and detailed application of lean techniques with DES to reduce patient waiting times. The adopted approach in this study could be generalized to other health-care settings with similar objectives.