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To assess and plan alterations in outpatient clinic structure, produces a computer simulation of an outpatient clinic based on detailed time and role measurements from the authors’ clinic. The simulation which used an object‐oriented design method is able to indicate the impact of changes in clinic structure using patient and doctor waiting times in clinic as endpoint measures. Examines the effects of changes in clinic size, consultation time, patient mix, appointment scheduling and non‐attendance. Finds that patient waiting time could be shortened considerably by using an optimizing appointment scheduler to determine appointment intervals. Clinic mix influences patient waiting time, which was shorter with a 1 in 4 ratio of new to follow‐up patients. In mixed clinics, new patients appointments are optimally spread throughout the clinic to reduce patient waiting time. In all new or all follow‐up clinics, waiting time is improved if the appointment interval reflects the consultation time. Computer modelling can help in optimizing clinic management so improving the delivery of care in outpatient services.

In a study of 195 patients visiting the urgent care department of a hospital in the UK, we examined the effects of three elements of process control on patients’ fairness and satisfaction perceptions. Patients who believed they had a voice in the triage process had higher fairness perceptions and waited a shorter period of time than those who believed they did not have a voice in the triage process. In addition, patients who were told the expected waiting time and were kept busy while waiting had higher satisfaction perceptions. We identify implications for hospital employees in managing the patient waiting process.

Waiting time, as an important predictor of queue abandonment and patient satisfaction, is important for resource utilization and patient experience management. Medical institutions have given top priority to reforming the appointment system for many years; however, whether the increased information transparency brought about by the appointment scheduling mechanism could improve patient waiting time is not well understood. In this study, the authors examine the effects of information transparency in reducing patient waiting time from an uncertainty perspective.

Leveraging a quasi-natural experiment in a tertiary academic hospital, the authors analyze over one million observational patient visit records and design the propensity score matching plus the difference in difference (PSM-DID) model and hierarchical linear modeling (HLM) to address this issue.

The authors confirm that, on average, improved information transparency significantly reduces the waiting time for patients by approximately 6.43 min, a 4.90% reduction. The authors identify three types of uncertainties (resource, process and outcome uncertainty) in the patient visit process that affect patients' waiting time. Moreover, information transparency moderates the relationship between three sources of uncertainties and waiting time.

The authors’ work not only provides important theoretical explanations for the patient-level factors of in-clinic waiting time and the reasons for information technology (IT)-enabled appointment scheduling by time slot (ITASS) to shorten patient waiting time and improve patient experience but also provides potential solutions for further exploration of measures to reduce patient waiting time.

This is a national study which aims to determine the average waiting time in Malaysian public hospitals and to gauge the level of patient satisfaction with the waiting time. It also aims to identify factors perceived by healthcare providers which contribute to the waiting time problem.

Self‐administered questionnaires were the main method of data collection. Two sets of questionnaires were used. The first set solicited information from patients on their waiting time expereince. The second set elucidated information from hospital employees on the possible causes of lengthy waiting time. The questionnaires were administered in 21 public hospitals throughout all 13 states in Malaysia. A total of 13,000 responses were analysed for the patient survey and almost 3,000 were analysed for the employee survey.

The findings indicate that on average, patients wait for more than two hours from registration to getting the prescription slip, while the contact time with medical personnel is only on average 15 minutes. Employee surveys on factors contributing to the lengthy waiting time indicate employee attitude and work process, heavy workload, management and supervision problems, and inadequate facilities to be among the contributory factors to the waiting time problem.

Public healthcare in Malaysia is in a state of “excess demand”, where demand for subsidised healthcare far outstrips supply, due to the large fee differential between public and private healthcare services. There is a need for hospital managers to reduce the boredom faced by patients while waiting, and to address the waiting time problem in a more scientific manner, as has been carried out in other countries through simulation and modelling techniques.

Healthcare organisations are keen to address their waiting time problem. However, not much research has been carried out in this area. The study thus fills the lacuna in waiting time studies in healthcare organisations.

The purpose of this paper is to use Lean Six Sigma (LSS) to reduce patient waiting time in a Kuwaiti private hospital obstetrics and gynaecology clinic.

The define, measure, analyse, improve and control methodology was used. The “define” stage involved identifying patients’ needs, system capabilities and project objectives. The “measure” stage assessed the system’s current state through data collection on waiting times. Dunnett’s test, control charts and process capability analysis were used to ensure data accuracy. In the “analyse” stage, an Ishikawa diagram and Pareto chart were constructed, showing that overbooking appointments, doctors’ unscheduled breaks and doctors not arriving on time were the root causes of the problem. The “improve” stage used an Arena simulation model to represent current and improved system status. The proposed solutions were implemented and monitored in the “control” stage.

A sigma-level improvement of 300 per cent (0.5–2.0) was realized for appointment patients on Saturdays, with a 67 per cent reduction in waiting time. For walk-ins, the sigma level improved by 288 per cent (0.8–3.1), with a 55 per cent reduction in waiting time. For weekday appointments, the sigma level improved by 111 per cent (0.9–1.9), with a 63 per cent reduction in waiting time. For walk-ins, the sigma level improved by 69 per cent (1.6–2.7), with a 46 per cent reduction in waiting time. A cost–benefit analysis estimated the present project value at $656,459, leading to a total of $5,820,319 in savings by 2025.

This paper fulfils the need for process improvement, increasing patients’ satisfaction and hospitals’ profitability using LSS.

The purpose of this paper is to evaluate the impact of extended waiting time on patients’ perceptions of provider communication skills and in-clinic satisfaction, in three major cities in India.

In total, 625 patients were interviewed. The multivariate general linear model was used to determine the causality and relationship between the independent and the dependent variable. A moderation analysis was also conducted to assess waiting time role as a potential moderator in doctor-patient communication.

Results show that patients with higher waiting time were less satisfied with health care quality. Male patients and patients of male providers were more affected by extended waiting time than female patients and patients of female providers. The advanced regression analysis, however, suggests weak support for waiting time and its effect on overall satisfaction with clinic quality. Waiting time did not moderate the relationship between satisfaction with dominant communication style, and overall satisfaction at the outpatient clinic.

A cross-sectional study does not easily lend itself to explaining causality with certainty. Thus, sophisticated techniques, such as structural equation modelling may also be utilized to assess the influence of extended waiting time on satisfaction with healthcare at outpatient clinics.

Findings are relevant for providers as the onus is on them to ensure patient satisfaction. They should initiate a workable waiting time assessment model at the operational level.

There has been a relatively lesser focus on patient waiting time in patient-provider satisfaction studies. In India, this aspect is still vastly unexplored especially in the context of outpatient clinics. Gender wise pattern of patient satisfaction and waiting time is also missing in most studies.

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).

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.

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).

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.

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.

Successful implementation of lean requires a supportive and cooperative culture from all stakeholders involved.

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.

The purpose of this paper is to assess causal relations between the implementation of the 5S approach and the reduction of patients’ waiting time at out patient departments (OPDs) of hospitals in Tanzania.

Patients’ waiting time was measured under the cluster randomized control trial (c-RCT). In all, 16 hospitals were chosen and divided into treatment and control groups using block randomization. Before the intervention, a baseline study was conducted at OPDs in all 16 hospitals. After one year of the intervention, the end-line study was carried out in both the groups. A comparison of the average waiting time reduction and Difference-in-Difference (DID) analysis was carried out to see the effect of the 5S approach on the reduction of patients’ waiting time.

Statistical significance in reduction of patients’ waiting time was seen in the medical records sections (p=0.002) and consultation rooms (p=0.020) in the intervention group. The same trend was also seen using DID analysis (−15.66 min in medical record, −41.90 min in consultation rooms).

This study has the following limitations in terms of the data. The data were collected for only three days at the time of baseline survey, and again for three days at the time of the end-line survey from 16 hospitals. Moreover, piloted areas for the implementation of the 5S approach vary from hospital to hospital. There might be a bias in the measurement of a patient’s waiting time. Caveats are therefore needed in extrapolating the study results to other settings. Despite these caveats, the findings will provide important insights for implementing quality improvement programs in Tanzania and in other African countries for improvement of time factors.

This study used c-RCT, and has proven the effectiveness of the 5S approach in improving the working environment and reducing patients’ waiting time at OPDs in several hospitals at district level in Tanzania.

Guidelines stating maximum waiting times fail to take cancer patients’ expectations into account. Therefore, the purpose of this paper is to assess patients’ expectations and experiences with their waiting time at a fast-track clinic.

Patients were selected using a purposeful sampling strategy and were interviewed four times: before the visit; one day after; two weeks after the visit; and one week after starting treatment. Interviews were audiotaped and independently coded by two researchers.

All patients (n=9) preferred a short waiting time before the first visit; they feared that their disease would spread and believed that cancer warrants priority treatment. Six patients experienced the waiting time as short, one had no expectations and two felt they waited longer than expected; three patients changed this evaluation during the study. Six patients received treatment – four preferred to wait before treatment and two wanted to start treatment immediately. Reasons to wait included putting one’s affairs in order, or needing to adjust to the diagnosis.

Cancer patients prefer a short waiting time before the first visit but have different expectations and needs regarding waiting time before treatment. Ideally, their expectations are managed by their treating physician to match waiting time reality.

This is the first study to assess cancer patients’ waiting time experiences and how these experiences change over time. This study paves the way for establishing a framework to better assess patient satisfaction with oncology care waiting time. An important aspect, is managing patients’ expectations.

The purpose of this paper is to explore healthcare waiting time and the negative and positive effects (i.e. the dual effects) it has on outpatient satisfaction.

Self-administered surveys with 334 outpatients and follow-up interviews with 20 outpatients in three large hospitals in Taiwan were conducted to collect data.

Quantitative surveys demonstrated that perceived waiting time correlated with satisfaction negatively first but then positively. Satisfaction also correlated with doctor reputation and patient sociability. Follow-up qualitative interviews further revealed that, for some patients, waiting contributed positively to patient evaluations through signaling better healthcare quality and facilitating social interaction.

This research demonstrated the possibility that waiting might have positive effects on healthcare satisfaction. It also identified variables that could produce greater positive perceptions during hospital waiting and underlying mechanisms that could explain how the positive effects work. This research may potentially help hospitals with a better understanding of how they can improve patients’ waiting experiences and increase satisfaction.