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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 aim of this research study is to develop a queue assessment model to evaluate the inflow of walk-in outpatients in a busy public hospital of an emerging economy, in the absence of appointment systems, and construct a dynamic framework dedicated towards the practical implementation of the proposed model, for continuous monitoring of the queue system.

The current study utilizes data envelopment analysis (DEA) to develop a combined queuing–DEA model as applied to evaluate the wait times of patients, within different stages of the outpatients' department at the Combined Military Hospital (CMH) in Lahore, Pakistan, over a period of seven weeks (23rd April to 28th May 2014). The number of doctors/personnel and consultation time were considered as outputs, where consultation time was the non-discretionary output. The two inputs were wait time and length of queue. Additionally, VBA programming in Excel has been utilized to develop the dynamic framework for continuous queue monitoring.

The inadequate availability of personnel was observed as the critical issue for long wait times, along with overcrowding and variable arrival pattern of walk-in patients. The DEA model displayed the “required” number of personnel, corresponding to different wait times, indicating queue build-up.

The current study develops a queue evaluation model for a busy outpatients' department in a public hospital, where “all” patients are walk-in and no appointment systems. This model provides vital information in the form of “required” number of personnel which allows the administrators to control the queue pre-emptively minimizing wait times, with optimal yet dynamic staff allocation. Additionally, the dynamic framework specifically targets practical implementation in resource-poor public hospitals of emerging economies for continuous queue monitoring.