Bed utilisation trends in selected wards across eight district hospitals in the Cape Town district Leilah Najjaar Student Number: 2006009 A mini-thesis submitted in partial fulfilment
INTRODUCTION
In 2015 the UN General Assembly adopted the 2030 Development Agenda titled Transforming our world: the 2030 Agenda for Sustainable Development The Global Sustainable
The Sustainable Development Goals (SDGs) focus on promoting sustainable progress across social, economic, and environmental dimensions, with health as a central theme Comprising 17 interconnected goals, these objectives highlight the significant role of health in human development, as various determinants like education, income, and urbanization directly influence health outcomes To effectively meet the health-related SDG targets, it is essential to ensure the availability of adequate resources, which will enhance both access to and quality of healthcare.
The Sustainable Development Goals (SDGs) build upon the Millennium Development Goals (MDGs) established by UN member states in 2000, which focused on limited human development targets such as poverty eradication, health, education, and food security In contrast to the MDGs, the SDGs encompass the three dimensions of sustainable development: economic, social, and environmental Valuable lessons learned from the MDGs have been integrated into the new 2030 agenda, enhancing the approach to achieving these comprehensive goals.
• A broader approach to improving health in all countries, including addressing non- communicable diseases, mental health and injuries;
• Emphasis on strengthening health systems;
• Recognition that health affects and is affected by other sectors like education and urban planning
• A focus on improving equity within and between countries, and not just focusing on aggregate targets;
• Recognition that disease outbreaks, natural disasters and humanitarian crises are a threat to sustainable development
Achieving Sustainable Development Goal 3 (SDG 3) by 2030 necessitates sufficient resources for the health sector to enhance both access to and quality of healthcare for individuals of all ages.
With a population of approximately 56.52 million people (Stats SA, 2017), the country faces significant income inequality, as indicated by a Gini coefficient of 0.69, the highest globally (The World Bank, 2018) This figure has risen from 0.6 in 1995 and 0.679 in 2009 (Benatar, 2013).
Figure one illustrates household expenditure per capita across five quintiles, with quintile one representing the lowest and quintile five the highest expenditure In 2015, quintile five accounted for about 60% of total household expenditure, while quintile one comprised only around 5% This data highlights the significant disparity between the wealthy and the impoverished in South Africa.
Figure 1: Share of annual household consumption expenditure by quintiles (2006, 2009, 2011,
Significant progress has been achieved in expanding health programs aimed at preventing mother-to-child transmission of HIV and improving access to antiretroviral treatment This progress is reflected in the increase in life expectancy at birth, which rose to an estimated 61.2 years for males and 66.7 years for females by 2017 The rise in life expectancy may be linked to improvements in survival rates among infants and children under five, following HIV interventions initiated in 2005 Notably, the infant mortality rate (IMR) decreased from 48.1 deaths per 1,000 live births in 2002 to 32.8 in 2017, while the under-five mortality rate (U5MR) fell from 71.3 to 42.4 during the same period (Stats SA, 2017).
Global health indicators, such as the five mortality rate, are essential for tracking health changes in populations (Larson and Mercer, 2004) Notably, significant improvements in these indicators since 2006 and 2007 reflect the success of health programs implemented in South Africa (SA) However, despite these advancements, substantial disparities in health and wealth persist, with some of the largest gaps observed globally (Benatar).
The National Health Insurance (NHI) proposal was introduced in South Africa after the 1994 apartheid era to tackle disparities in healthcare, poverty, and access to medical services In the public sector, which serves 84% of the population, the annual per capita healthcare expenditure is around R1,200, compared to R12,000 in the private sector for 16% of the population The NHI aims to reduce this inequality by enhancing public healthcare services, but achieving this goal necessitates significant material and human resources in a financially constrained environment (Benatar, 2013).
The Health Care 2030 strategic document, developed by the Western Cape Government, aims to promote wellness and is aligned with both the Provincial and National Strategic plans Key focus areas for intervention include reducing infectious diseases like HIV and TB, improving healthy lifestyles, preventing injuries and violence, enhancing maternal and child health, strengthening women's health, and improving mental health The district health system model is essential for delivering these goals, with district hospitals serving as critical support for primary health care and facilitating access to specialized care Monitoring bed utilization, length of stay, and expenditure per patient day is vital for assessing hospital efficiency and quality of care Inadequate bed utilization can lead to poor health outcomes, increased spread of infectious diseases, and complications in treatment, ultimately affecting patient mortality rates.
Metro Health Services (MHS) provides essential health services to the City of Cape Town Municipality, which has a population of around 4 million, with approximately 87% depending on public healthcare The health services are organized into eight geographic sub-districts, offering a range of care including home and community-based services, primary health care, and hospital services While there are eight district hospitals, each sub-district is served by at least one hospital, except for the northern sub-district.
District/Subdistrict 2016 Total projected population
Figure 2: Map of City of Cape Town Health Boundaries
The primary health challenges in MHS are led by HIV/AIDS, with significant contributions from interpersonal violence, ischemic heart disease, and tuberculosis, as reported by WCGH in 2016 Additionally, there is a concerning rise in individuals experiencing multiple, interconnected health issues.
Table 2: Burden of Disease, Cape Town
The quadruple burden of disease significantly strains Cape Town's health system, highlighting the urgent need for innovative approaches to deliver quality health services Continuous monitoring at all levels is essential to optimize work processes and consistently identify opportunities for improving efficiencies.
The National Department of Health (NDOH) establishes yearly goals for key performance indicators, including the hospital inpatient bed utilization rate (BUR), average length of stay (ALOS), and expenditure per patient day equivalent (PDE) These metrics are systematically set and tracked across national, provincial, district, and hospital levels.
The Cape Town district is home to eight district hospitals, offering a total of 1,574 beds (MDHS 2016) In the financial year 2015-2016, the annual inpatient bed utilization rate (BUR) reached 93.9%, surpassing the target of 86.9% set for the district (WCGH, 2015; WCGH, 2017).
Trachea/Bro nchi/Lung (2.8%) COPD (2.7%)
Trachea/Bro nchi/Lung (3.3%) COPD (2.80)
The eight district hospitals reported an Inpatient Bed Utilization Rate (BUR) ranging from 66.2% at Hospital B to 127.2% at Hospital C, indicating that some hospitals are accommodating more patients than their bed capacity allows, potentially leading to patients waiting in chairs The Average Length of Stay (ALOS) for the district in 2015/16 was 3.7 days, with variations from 2.2 days at Hospital H to 4.3 days at Hospitals F and E Additionally, the number of usable beds across the district hospitals varied significantly, from just 31 beds at Hospital H to 391 beds at Hospital F.
Table 3: Number of beds, Average Length of Stay, Bed Utilization Rate [%] (District Hospital) 2015-2016 (MDHS, 2016)
LITERATURE REVIEW
The literature review examines bed utilization and length of stay in hospitals, highlighting various factors that influence these metrics It also addresses the ideal bed occupancy rates and the consequences of occupancy levels that are either excessively high or low.
Average length of stay, inappropriate admissions, delays in discharge and overcrowding are all factors affecting bed occupancy rates and is discussed in this chapter
The Bed Utilisation Rate (BUR) is a key metric in the District Health Barometer that measures hospital bed occupancy, reflecting the efficiency of a hospital's capacity usage It is calculated by adding the number of inpatient days to half the number of day patients, then dividing by usable bed days, with results expressed as a percentage BUR, often referred to as bed occupancy rates, serves as an important indicator of patient care quality According to Keegan (2010), to effectively use bed occupancy for planning and managing hospital resources to enhance patient outcomes, two critical questions must be addressed: the impact of bed occupancy rates on patient outcomes and the ideal occupancy level to target Keegan also noted that maintaining occupancy above 85% could negatively affect safety and efficacy in the management of public hospitals in Australia.
In a study done in South Africa where five district hospitals were measured on cost, Olukoga
(2007) stated that the hospitals operate most effectively and efficiently at 80%-90% bed occupancy rates, although the bed occupancy for the hospitals in the study only ranged between 39% and 68%
Jones (2011) described how bed occupancy across wards can differ based on their size, function
Understanding bed occupancy rates in hospitals is crucial, particularly when considering variations across different wards For instance, maternity wards, which experience low turn-away levels, necessitate lower bed occupancy rates to maintain sufficient available beds In contrast, surgical wards can accommodate higher occupancy rates due to the scheduling of elective surgeries The economies of scale related to patient turn-away levels are essential for analyzing bed queuing models that determine occupancy According to Jones (2011), maintaining a maximum occupancy between 82% and 85% is vital for minimizing hospital-acquired infections.
Low bed occupancy rates in hospitals signal operational inefficiency and potential opportunity costs, as noted by Olukoga (2007) Factors contributing to this issue include low demand for hospital services, the presence of alternative healthcare providers perceived as offering superior care, a limited catchment area, and ineffective referral mechanisms.
Accurate data is essential for determining the appropriate number of beds in hospitals and wards McClean and Millard (1993) utilized reliable data to create tools that evaluated performance measures related to hospital activity and bed usage, leading to enhanced bed management efficiency and more effective resource utilization.
A study by Holm et al (2013) conducted a simulation and optimization experiment on hospital bed utilization in a Norwegian general hospital By reallocating beds based on demand, the optimization reduced patient overcrowding from 6.5% to 4.2%.
In their 1993 study, Mclean and Millard developed a mathematical bed occupancy modeling package that effectively distinguishes between acute, rehabilitative, and long-stay components of bed occupancy This tool enables planners to evaluate the potential impacts of changes before they are implemented.
A very simple model using routine data to generate a forecast was used in the Koa and Tung
(1981) study done in a public health setting The model focused on minimising overflow and more accurate bed allocation would thus still require analyses over the forecast
Kumar and Mo (2010) used a combination of three different models in a hospital in Singapore
The turn-away level reflects the operational challenges faced by hospitals, including ambulance diversions, inappropriate bed allocations, and the need for schedule adjustments among staff It is crucial for maternity and intensive care units to maintain a turn-away level between 0.1% and 1% To effectively manage bed occupancy, hospitals can employ regression modeling for forecasting, a Poisson model for optimizing bed allocation based on patient data, and simulation modeling to analyze patient flow Together, these three models serve as essential tools for effective bed planning and management.
Average Length of Stay (ALOS) is a key metric that indicates the average duration patients remain in a hospital It is calculated by taking the total number of inpatient days in a year, adding half the number of day patients, and then dividing this sum by the total number of separations, which includes deaths, discharges, and transfers out (Massyn et al., 2016).
A persistently long ALOS could indicate that patients are kept in hospital for too many days
A short Average Length of Stay (ALOS) may indicate inadequate patient care, leading to premature discharges without necessary support It could also suggest that an excessive number of patients are being transferred to other facilities without thorough evaluation Therefore, it is crucial to maintain a consistent ALOS, as both excessively long and short stays require further scrutiny (Massyn et al.).
2016) A too long or too short ALOS is poorly defined in the literature but the WCGH used 3.5 days as the ideal ALOS
A study by Fine et al (2000) evaluated the costs associated with hospital care for patients with community-acquired pneumonia, distinguishing between room and non-room costs While room costs remained stable, non-room costs were high initially and decreased over time The findings indicated that even a one-day reduction in hospital stay could lead to significant cost savings Similarly, Silva (2013) highlighted that reducing the length of stay not only improves the efficiency of the patient flow system but also enhances patient satisfaction and reduces overall costs.
Clarke (1996) argues that a reduction in the average length of stay (ALOS) does not necessarily lead to cost savings for healthcare institutions, as the total costs may remain the same or even increase due to a higher number of patients being treated While the cost per patient may decrease, the overall expenses for the institution may not reflect significant savings Similarly, Fine et al (2000) highlight that the initial daily costs of hospitalization are typically higher than those incurred during the final days of a patient's stay Consequently, any cost savings from reducing the length of stay are primarily associated with the last few days in the hospital, which may not be substantial when compared to the total cost of patient care.
Factors Affecting Bed Utilisation and Average Length of Stay
The demand for beds is influenced by various factors, including shifts in population size and demographics, evolving treatment protocols, epidemiological trends, budget constraints, and the availability of alternative patient care facilities.
Patient factors significantly influence the average length of stay (ALOS) in hospitals A study by Schoetz et al (1997) revealed that patients over 65 years had an ALOS of 11 days, compared to 9 days for those younger than 65 Similarly, research by Rissanen, Aro, and Paavolainen (1996) involving over 10,000 hip and knee replacement patients confirmed that both age and gender affect ALOS Additionally, Gruskay et al (2015) found that elderly patients with widespread systemic disease tend to have longer hospital stays post-surgery.
A study by Correia and Waitzberg (2003) found that assessing patients' nutritional status within 72 hours of admission revealed malnutrition as an independent risk factor that adversely affects patient outcomes, prolongs hospital stays, and increases mortality rates.
RESEARCH METHODOLOGY
To describe trends in bed utilisation within selected wards in eight district hospitals in the Cape Town metro district during April 2016 to March 2017
1 To analyse the annual inpatient bed utilisation in hospital wards against the hospital target;
2 To describe the average length of stay in these hospital wards against the hospital target set by the district;
3 To ascertain seasonal trends in the BUR across the hospitals
4 To identify wards where patient stay is well above the ALOS and where beds are under or over utilised
5 To describe the number of deaths in the wards of these hospitals
The study employed a cross-sectional descriptive design, utilizing retrospective record reviews of secondary data from the Western Cape Government’s health information system (HIS) A quantitative approach was adopted to analyze and compare bed occupancy trends across various wards, as the focus was on evaluating performance against established targets and assessing ward-level performance Qualitative methods were excluded from the study to maintain this objective.
The study focused on inpatient wards across eight district hospitals in Cape Town, ensuring a robust sample size of at least 30 subjects by including all wards To facilitate the generation of standardized monthly Inpatient Ward Activity reports, the research utilized Clinicom, an electronic patient information system, as the preferred health information system.
The study period from 1 April 2016 to 31 included a total of 84 wards, excluding virtual wards such as theatres, resus wards, transit lounges, day wards, empty wards, and closed wards, due to the lack of inpatients and beds in these areas.
Approval was secured from the provincial Health Impact Assessment (HIA) directorate to utilize the data Subsequently, each hospital's Chief Executive Officer (CEO) was approached to gain access to the monthly IP Ward activity reports covering the period from April 1, 2016, onward.
As of March 31, 2017, accessing reports did not require registration on the HIS, as hospitals maintained these reports for auditing purposes However, one hospital mandated a presentation to its board prior to granting data access The IP ward activity reports were provided to the researcher in either PDF or MS Excel format.
The IP ward activity reports detail essential metrics for each hospital, including the number of operational and actual beds, inpatient days, admissions, day cases, discharges, deaths, and transfers Additionally, they provide operational and actual occupancy percentages, average length of stay (ALOS), total separations, and patient days The metro district office supplied the final outputs and targets for the 2016/2017 period, outlining ALOS and bed utilization goals and achievements for the eight hospitals, as documented in the annual expenditure review.
Pre-analysis quality control checks were conducted to identify and rectify errors in the data capture process from PDF IP Ward activity reports to MS Excel To minimize potential errors, two independent individuals, an intern and a researcher, performed quality checks The data was then compiled into a single consolidated workbook for analysis.
To ensure confidentiality, each hospital was assigned a pseudonym, and ward names were standardized for clarity, as many were not self-explanatory For instance, one hospital used a numbering system for its wards, such as 1A, 1B, and 2A To clarify the types of wards, information management officers at the respective hospitals were contacted.
Standardisation of ward names was also required to group similar wards In appendix A,
Hospital B combined its male and female medical wards into a single unit, while Hospital E merged its two surgical wards, surgical ward 1 and surgical ward 2, for standardization and simplification As a result, the total number of wards included in the study decreased from 84.
Input data was created and consolidated in MS Excel and Stata 11.0 was then used for prevalence data analysis
For this study the hospitals were divided into small and large district hospitals From appendix
Small and large hospitals differ in their range of wards, services, specialties, and complexities To categorize these hospitals, inpatient bed numbers were utilized, with those having fewer than 166.5 beds classified as small district hospitals, while those with more beds are considered large district hospitals.
Table 4: Small and Large district hospitals
Table 4 lists both small and large district hospitals, highlighting that Hospitals H, B, A, and C are smaller facilities with bed capacities of 50, 70, 120, and 152, respectively In contrast, the larger hospitals, G, D, E, and F, have bed sizes of 181, 298, 300, and 361.
The validity of data refers to the extent to which measurements measure what it is intended to measure (Ehrlich and Joubert, 2008)
The quality of the data collected was satisfactory, thanks to effective programmatic and governance processes in hospitals and district management Hospital IP ward activity reports were archived monthly for easy access by the auditor general, eliminating the need for on-demand report generation and minimizing the risk of data alteration.
Two independent data clerks performed quality checks during the transfer of data from PDF to MS Excel, minimizing the risk of errors The sample size calculation was unnecessary as all wards in the eight district hospitals were included, except for those that did not meet the inclusion and exclusion criteria.
The UWC research ethics committee reviewed the study proposal for ethical soundness and approved it with minor amendments, granting the necessary ethics clearance Access to the data was authorized by the Western Cape Department of Health (DOH), with approval obtained through the provincial HIA directorate Additionally, permission was secured from the individual hospital CEOs via the HIA directorate.
To ensure confidentiality, each hospital was assigned a pseudonym, and data from the PDF IP ward activity reports was collected on a password-protected computer All hard copies utilized for quality checks were securely stored in a locked cupboard Following the quality checks, data analysis was performed using the assigned pseudonyms, with no patient information included in the study.
Results
This chapter presents the study's findings, emphasizing bed utilization and the average length of stay in individual wards across the eight district hospitals in the Cape Town Metro district.
During the data analysis, wards were reorganized to enhance the description of similar units For instance, instead of categorizing surgical wards as separate male and female wards, they were collectively described Initially, the eight district hospitals had 84 wards, which were reduced to 55 after regrouping A summary of the original versus regrouped wards for each district hospital is presented in Table 5.
Table 5: Number of original wards vs regrouped wards
Hospital Name Original Wards Regrouped wards
Hospital A's general wards were classified as medical wards, according to the information management clerk As shown in Appendix A, the eight hospitals varied in their ward offerings While all hospitals included medical, surgical, casualty, overnight, and pediatric wards, larger facilities featured additional specialized wards, including psychiatry, maternity, obstetrics, gynecology, and high care.
To achieve the first objective of this study, a detailed description of the number and type of beds in each hospital was essential The study aimed to focus on usable beds rather than actual beds for a more accurate representation of hospital usage; however, this was hindered by the lack of routine recording of usable beds in many hospitals Consequently, actual beds were utilized for the analysis The findings revealed significant variability in the number of inpatient beds across hospitals and ward types, with Hospital G having the smallest ward type consisting of only three high care beds, while District Hospital F featured the largest with 116 medical beds distributed among three medical wards.
Table 6 presents the descriptive statistics for inpatient beds across various hospitals Smaller district hospitals, such as Hospital H, B, A, and C, exhibit the lowest average number of inpatient beds per ward type In contrast, larger hospitals, including Hospital G, D, E, and F, show a higher average of inpatient beds per ward type.
Table 6: Inpatient beds per hospital ward type
Min Max STDEV Median Mean
Table 7 summarizes the distribution of beds across various ward types in hospitals Notably, only 50% of hospitals provided inpatient beds in the Casualty Overnight Ward and Acute beds in the Emergency Care (EC) unit Additionally, separate gynaecology inpatient beds were available in just 37% of hospitals, while only 20% offered high care beds In terms of maternity care, 75% of hospitals had maternity and obstetric inpatient beds, and 62% had psychiatry inpatient beds Most hospitals were equipped with medical, paediatrics, and surgical inpatient beds, but only one hospital featured dedicated neonatal inpatient beds.
Table 7: Beds per ward type within each hospital
Figure 3: Proportion of bed distribution across hospital wards in eight district hospitals
Figure 3 illustrates the distribution of inpatient beds across eight district hospitals, revealing that medical beds constitute the largest share at 31% Surgical beds follow as the second largest category, accounting for 20% of the total Maternity, Obstetrics, and Paediatric beds each represent 11%, while Psychiatry, Casualty overnight ward, EC acute, KMC, Nursery ward, Gynaecology, Neonatal, and high care beds make up smaller proportions, ranging from 6% to less than 1%.
PROPORTION OF BED DISTRIBUTION IN HOSPITAL WARD FOR EIGHT
Table 8: 2016-2017 Fin year -Targets for district hospitals
Hospital Target Achievement Target Achievement
Table 8 outlines the overall hospital targets for Average Length of Stay (ALOS) and bed utilization established by district management for the 2016/17 financial year The actual achievements were reported in the annual district health expenditure review at the end of the financial year These targets, based on the previous year's performance, may not represent ideal figures and do not account for individual ward performances or the number and types of wards Alarmingly, 50% of hospitals have bed occupancy targets exceeding the recommended 80-90% optimal rate, as noted by Olukoga (2007) Notably, ALOS achievements for Hospitals A, D, E, and F surpassed the established targets for the year 2016/17.
B, C and H were below the target and Hospital G achieved the target ALOS
Bed Utilisation in the Wards
To address the third objective of this study, the following section will analyze trends in bed occupancy rates (BUR) across various hospitals Figures 4 and 5 illustrate the monthly bed occupancy for each ward type in the eight district hospitals, categorized into small (hospitals A, B, C, and H) and large (hospitals D, E, F, and G) facilities The data, covering the period from April 2016 to March 2017, includes hospital targets, enabling a comparison of occupancy percentages by ward type.
In District Hospital A, the overnight casualty ward beds consistently exceeded the hospital's target throughout the entire period Meanwhile, the bed occupancy rates for surgical and medical inpatients aligned with the established targets, while pediatric inpatient bed occupancy fell below the hospital's expectations.
In District Hospital B, bed occupancy rates were generally below target for most wards throughout the year, with the exception of the medical ward, which surpassed the target three times in August, January, and February In contrast, District Hospital A maintained relatively stable bed occupancy rates The paediatric and maternity units experienced particularly low occupancy, often falling below 50%, with the paediatric ward dropping to as low as 20% by the end of the year.
District Hospital C aimed for a bed occupancy target exceeding 120% The psychiatric ward surpassed this target three times, while the KMC ward experienced significantly low occupancy, occasionally falling below 20% Similarly, the nursery ward's occupancy dropped below 50% at times In contrast, inpatient bed occupancy in maternity, obstetrics, gynecology, medical, surgical, high care, and pediatrics remained stable, ranging between 80% and 100%.
At District Hospital H, the bed occupancy target was set at nearly 120%, with most wards falling below this target throughout the year, except for the EC acute ward, which surpassed it in February The surgical ward experienced significant fluctuations, reaching a low of 0% in May and a peak of over 90% in March Meanwhile, bed occupancy in the maternity, obstetrics, and medical wards remained stable, while the paediatric ward saw a rise in occupancy from August to November.
Figure 5 illustrates bed occupancy rates in large district hospitals, highlighting that District Hospital D aimed for a target of 110% Notably, the EC acute ward consistently exceeded this target, with occupancy rates ranging from 270% to 370% In contrast, other wards maintained stable occupancy levels below the hospital's target throughout the period, while the paediatric ward occasionally fell below 50%.
At District Hospital E, the target for bed occupancy was established at 90% Throughout the period, the inpatient bed occupancy rates for the gynaecology and EC acute wards consistently surpassed this target In contrast, while most wards maintained stable occupancy levels, the casualty overnight and paediatric wards experienced fluctuations, with occupancy dropping below 50% at various times.
In District Hospital F the bed occupancy target was set at 77% The surgical, medical and psychiatry inpatient bed occupancy remained above the target throughout period
DISCUSSION
This study aimed to analyze bed utilization trends in eight district hospitals within the Cape Town metro area by examining bed occupancy and average length of stay (ALOS) The hospitals ranged in size from 50 inpatient beds in Hospital H to 361 inpatient beds in Hospital F, allowing for a classification into small and large district hospitals The research also sought to highlight trends in bed occupancy across various wards and identify areas of over or underutilization.
Bed occupancy rates frequently exceeded 100%, with the Bed Utilization Rate (BUR) calculated by adding inpatient days to half the number of day patients and dividing by usable bed days (Massyn et al., 2016) High BUR can result from prolonged Average Length of Stay (ALOS) and an influx of day patients, despite their shorter hospital stays Each hospital set its occupancy targets based on the previous year's performance, with an ideal rate of 80-90% for optimal patient care (Keegan, 2010; Olukoga, 2007) While bed occupancy remained consistent across wards, certain areas, such as the EC acute, casualty overnight, and psychiatry wards, experienced excessively high rates.
The Average Length of Stay (ALOS) in hospitals varied from 2.3 to 4.6 days, while at the ward level, it ranged from under one day to over 20 days in a psychiatric ward Throughout the study period, there were no significant seasonal trends observed in bed occupancy or ALOS Additionally, the analysis of deaths in the wards revealed that the majority occurred in medical wards, where ALOS consistently exceeded the target.
In five out of eight hospitals (A, D, E, G, H), bed occupancy rates in the emergency and casualty acute wards consistently exceeded the target for the majority of the year, reaching levels as high as 850% in certain months.
Efficient management of emergency care (EC) and casualty beds, which account for only 4% and 5% of hospital beds respectively, is crucial to prevent overcrowding and ensure access to necessary medical care (Hoot & Aronsky, 2008) These wards serve as vital entry points in the healthcare system, where patients often require urgent or life-threatening assistance High bed occupancy in ECs poses significant risks, including overcrowding that can lead to increased mortality rates (Blum et al., 2008).
In May 2016, two of the four psychiatry wards in Hospital F experienced bed occupancy rates as high as 350%, with psychiatry beds constituting 6% of all inpatient beds across four of the eight district hospitals This alarming situation necessitates further investigation, as it jeopardizes the quality of care and access to health services in an already strained healthcare system Key factors contributing to this high bed occupancy include inappropriate admissions, patient flow issues, increased demand for beds, changing population epidemiology, and delayed discharges Additionally, the Western Cape government identified a significant shortage of beds in step-down facilities and referral hospitals as a major contributor to the overcrowding in these district hospitals (MDHS, 2016).
In large district hospitals, paediatric wards experienced the lowest bed occupancy rates, with the average length of stay (ALOS) meeting or falling below hospital targets for most months Bed occupancy occasionally peaked at 80%, which aligns with the ideal range of 80% to 90%, suggesting that relocating beds from these wards could lead to underutilization Notably, paediatric beds represented 11% of the total inpatient beds across the eight district hospitals.
The study found that the average length of stay (ALOS) in medical and psychiatry wards exceeded hospital targets and was notably higher than in most other wards Specifically, one psychiatry ward (Hospital E) recorded an ALOS of 20 days, while the medical ward with the highest ALOS (Hospital A) had an average of 12 days.
A consistently high Average Length of Stay (ALOS) suggests that patients are remaining in wards longer than necessary Literature indicates that potential factors for extended stays may include discharge delays, unavailability of referrals to the next level of care, or suboptimal quality of medical care While this study did not explore the specific reasons for prolonged hospital stays, it highlights the need for further investigation, particularly in psychiatric wards, which also exhibit elevated bed occupancy rates (Massyn et al., 2016).
Shorter ALOS, which ranged between 1 and 2 days, was observed in the maternity and obstetrics, paediatrics and EC acute wards According to Clarke (1996) this does not
A shorter average length of stay (ALOS) does not necessarily reflect optimal care, as it may lead to patients leaving without adequate treatment or being prematurely referred to community health centers Additionally, a reduced ALOS can increase the expenditure per patient day equivalent (PDE) since the initial higher daily costs outweigh the lower costs of the final days in the hospital Maternity and obstetrics, pediatrics, and emergency care acute wards represented 11%, 11%, and 4% of total inpatient beds, respectively, with no significant seasonal trends in ALOS observed over the year.
During the study period, it was found that 61% of deaths occurred in medical wards, with 14% in surgical wards, and 10% and 9% in the EC acute and casualty wards, respectively The remaining 5% of deaths were distributed across various other wards, including psychiatry, KMC, medical/psychiatry, neonatal, pediatrics, maternity and obstetrics, nursery, high care, and gynecology Medical wards exhibited a high average length of stay (ALOS), while the EC acute and casualty wards, which accounted for 19% of total deaths, showed elevated bed occupancy rates These findings raise concerns, as ALOS and bed occupancy are critical indicators of care quality and access (Hoot and Aronsky, 2008) Further investigation by health managers is necessary to explore the causes of prolonged stays and overcrowding in these wards, as there may be a link between poor bed management and mortality rates in the medical, EC acute, and casualty overnight wards, which was not addressed in this study.
In larger hospitals, the medical wards account for 65% of deaths, which is higher than the overall rate of 61% In contrast, smaller hospitals report a slightly lower death rate of 52% This discrepancy may be attributed to the varying specialties available in small versus large hospitals, warranting further investigation.
This study utilized secondary datasets, providing benefits like speed and cost-efficiency, while also facing challenges such as incomplete information The researcher aimed to compare usable beds with actual beds, recognizing that these figures may differ; however, not all wards collected both data elements Additionally, the inpatient ward reports lacked expenditure data, which could have enhanced the analysis by allowing a comparison of resource allocation across wards.
CONCLUSION & RECOMMENDATIONS
The bed occupancy and average length of stay (ALOS) were analyzed in eight district hospitals within the Cape Town metro area, comparing them to the targets for the financial year 2016-2017 While no specific seasonal trends were identified in this study, a longer-term review over five years could provide valuable insights Most wards maintained a consistent ALOS and bed occupancy in line with targets; however, concerning findings included high occupancy rates in psychiatry, emergency care, and casualty overnight wards, along with elevated ALOS in medical and psychiatry wards Further investigations are necessary to understand the underlying causes of the high bed occupancy and ALOS in these hospitals.
The study identified significant concerns regarding Bed Utilization Rate (BUR) and Average Length of Stay (ALOS) in specific wards across eight district hospitals Additionally, a potential link was observed between inadequate bed management and increased mortality rates in medical, emergency care acute, and casualty overnight wards Consequently, the study recommends addressing these issues to improve patient outcomes.
1 Review similar trends over a longer period, e.g over 3 to 5 years in order to determine if there is any consistency
2 Conduct further research to investigate the possible relationship between high bed occupancy and mortality in the ward
3 Investigate the relationship between high ALOS and mortality in the wards
4 For district management to start reviewing ward level data to gain insight into the ward pressures
5 Investigate and address the causes of the high bed occupancy at the psychiatry, EC and casualty overnight wards This can be done by the district management through operational research
6 Conduct a follow-on study that will consider the resources allocated to the various wards to determine efficiency and effectiveness in the bed management of individual wards
A study by Al-Kandari and Thomas (2009) published in the Journal of Clinical Nursing examines the relationship between nurses' workload and perceived adverse patient outcomes in medical and surgical wards of selected hospitals in Kuwait The research highlights the significant impact of nursing workload on patient care quality, emphasizing the need for effective workload management to enhance patient safety and outcomes The findings underscore the importance of addressing nursing staffing levels to mitigate risks associated with high workloads.
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I don't know!
In the article "Hospital bed occupancy: more than queuing for a bed," Keegan (2010) discusses the complexities surrounding hospital bed occupancy, emphasizing that it involves more than just the wait for a bed The study, published in The Medical Journal of Australia, highlights the various factors influencing bed occupancy rates and the implications for patient care and hospital management The full text is available for further insights into the topic.
Kumar and Mo (2010) presented models for managing bed occupancy in a Singapore hospital, highlighting strategies to optimize resource allocation Their research was published in the Proceedings of the 2010 International Conference on Industrial Engineering and Operations Management, providing valuable insights for healthcare management The full paper is accessible online for further reference.
21 KZN Dept of Health (2001) Definitions of Health Facilities Available at: http://www.kznhealth.gov.za/definitions.htm (Accessed: 18 August 2018)
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24 Majeed, M U et al (2012) ‘Delay in discharge and its impact on unnecessary hospital bed occupancy’, BMC Health Services Research, 12(1) doi: 10.1186/1472-6963-12-
25 Massyn N, Peer N, English R, Padarath A, Barron P, D C (2016) District Health Barometer 2015_16
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30 MDHS (2017) ‘District Health Expendicture Review City of Cape Town 2016/2017’, p 2016
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Rissanen, Aro, and Paavolainen (1996) explored the factors influencing the length of hospital stays for patients undergoing hip and knee replacements Their study, published in the International Journal of Technology Assessment in Health Care, highlights the significance of both hospital and patient-related characteristics in determining recovery times The findings emphasize the need for tailored approaches in managing patient care to optimize hospital stay durations.
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In their 2009 study published in the American Journal of Medical Quality, Soria-Aledo et al investigate the factors associated with inappropriate hospital admissions and extended stays in a second-level hospital The research highlights the significant costs incurred due to these admissions, emphasizing the need for improved hospital management and patient assessment to enhance healthcare quality and efficiency The findings underscore the importance of addressing the underlying issues contributing to unnecessary hospitalizations.
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43 WCGH (2017) Annual Performance Plan 2017/18 Cape Town
44 Western Cape Government (2013) ‘Healthcare 2030’, pp 1–160
45 WHO (2017) ‘From MDGs to SDGs’ Available at: http://www.who.int/mediacentre/events/meetings/2015/MDGs-SDGs-
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APPENDIX A: Standardisation of ward names per hospital
Original Ward Name Regrouped Ward Name Hospital Name
Casualty Overnight Casualty Overnight Ward Hospital A
Overnight Ward Casualty Overnight Ward Hospital B
Maternity Ward Maternity and Obstetrics
Kangaroo Mother Care Ward Kangaroo Mother Care Ward
M Maternity and Obstetrics Maternity and Obstetrics
E General Medicine Ward Medical Ward
EC Acute Ward EC Acute Hospital D
6A-Kangaroo Care (&Gynae) Kangaroo Mother Care Ward
3A-Hospital Maternity Antenatal Maternity and Obstetrics
1A-Medical/Psychiatry Female Medical /Psychiatry
2A-Intensive Medical/Surgical Medical Ward
Overnight Ward EMC Casualty Overnight Ward Hospital E
EC Majors Adults EC Acute
Kangaroo Care Kangaroo Mother Care Ward
Antenatal Ward Maternity and Obstetrics
Female Psychiatric Ward Psychiatry Ward
EC Overnight Adult ICD Casualty Overnight Ward Hospital F
EC Overnight Medical Adult Ward
KMC Kangaroo Mother Care Ward
Antenatal Ward Maternity and Obstetrics
Mental Health Female Psychiatry Ward
Emergency Ward EC Acute Hospital G
High Care Unit High Care
Sandes Medical Female Total Medical Ward
Currie Surgical Female Surgical Ward
Emergency Centre EC Acute Hospital H
Maternity Ward Maternity and Obstetrics