Analysis and development of bed optimisation model case study: Windhoek central hospital, Namibia.

Abstract

An efficient healthcare system aims to achieve optimal accessibility, exemplary treatments, and cost efficiency across all healthcare facilities within the system. In hospitals, a crucial factor is the bed capacity in individual wards, which not only incurs costs but also impacts the hospital's ability to accommodate and deliver exceptional care for patients. The shortage of Intensive Care Units (ICU) beds at Windhoek Central Hospital (WCH) has become a significant concern, resulting in delays in providing essential care for ICU patients and, unfortunately, patient fatalities while awaiting available beds. Current bed allocation decisions lack proper supporting evidence and quantitative model-based analyses. Despite adhering to standard guidelines from the World Health Organisation (WHO) and recommendations from the Indian Society of Critical Care Medicine (ISCCM), the number of ICU beds remains insufficient to meet the high demand in the Namibian healthcare system. To address this critical issue, there is an urgent need to implement an effective and optimised bed allocation model at WCH. This study aimed to develop a comprehensive approach for accommodating all admitted patients within the hospital, ensuring the availability of beds while remaining within the allocated budget. The primary research question focused on determining the average waiting time for ICU bed allocation at Windhoek Central Hospital in the context of patient admission. A positivist philosophy was chosen due to its structured approach in examining relationships and generalizing from data. Descriptive statistics were employed to determine the effectiveness of the bed optimisation model at Windhoek Central Hospital. The findings revealed that approximately 60% of beds were allocated to respiratory, general, and reserved bed patients, while car accident and COVID-19 patients each received 20% of the beds. On average, there were approximately 2 patients in each category. The category "Respiratory, General, Reserved Bed" showed the highest frequency, indicating a significant patient presence compared to other categories. The data points were closely clustered around the mean value, suggesting consistent allocation of beds. Based on the study's results, the hospital is recommended to invest in the development and implementation of a data-driven bed optimisation model for the ICU.