Healthcare Buildings Research Articles

Opportunities for improving hypertension control in sub-Saharan Africa.

Hypertension is currently one of the most common risk factors for the development of cardiovascular disease. An estimated 46% of the adult population in sub-Saharan Africa (SSA) has hypertension and its increasing prevalence is associated with factors such as urbanization and adverse lifestyle behavior. The process of managing hypertension in SSA includes increasing screening and diagnosis, and treatment of diagnosed patients. In addition to this process, community-based interventions have also highlighted promotion of educating healthcare providers and building capacity within the health system. Although there seems to be a process in place to control blood pressure of hypertensive individuals in SSA, optimal management of hypertension in this region remains a challenge, and the need for implementation of innovative strategies to achieve optimal BP control in hypertensive individuals remains critical. Various hypertension guidelines recommend the use of out-of-office blood pressure monitoring as a technique to control hypertension. Thus far, several publications have highlighted the potential of implementing the use of home blood pressure telemonitoring (HTM) in general practice, globally. HTM is a telemedicine strategy that is implemented to remotely transmit blood pressure data and additional health information of patients from the comfort of their home to the health practitioner. To date, there is evidence from various controlled trials reporting that prolonged and regular use of HTM is associated with a significant reduction of blood pressure in hypertensive patients compared with usual care. Despite HTM being reported as a potential measure for control of hypertension, there are no known estimates of hypertensive patients being managed using HTM in SSA and there have been few successful HTM programs implemented in SSA. HTM activity is reported in only 4 out of 46 countries in SSA using telephone calls, text messages and mobile phone health applications. In these HTM programs, significant reduction in blood pressure of patients was reported showing the great potential that HTM has in controlling blood pressure of hypertensive patients in SSA.

Building thermal resilience framework (BTRF): A novel framework to address the challenge of extreme thermal events, arising from climate change

Over the past 2 decades, many parts of the world have experienced unprecedented record-breaking temperatures; these extremes fall on both ends of the temperature spectrum ranging from excessively hot to freezing low. Moreover, recently, the rate and the impacts of these extremes have increased, despite all the mitigation efforts, necessitating a resilience-based approach to address these challenges stemming from the accelerated global warming and the advent of climate change. Examples of such extremes include the 2003 and 2022 heatwaves in Europe, claiming approximately 4,000 and 12,000 lives, respectively as well as the 2021 heatwave in the Pacific Northwest region of North America and the deep freeze in Southeast Texas. In this paper, we reflect on previous studies, identifying both internal and external aspects that contribute to a building’s thermal performance. We then incorporate these factors into a proposed framework, covering the important phases of a building’s life cycle, to reflect its thermal resilience. During each phase, an associated Building Thermal Resilience Profile (BTRP), taken from accumulated data of previous phases, provides the needed assessment of the building, and is regularly adapted to changes in the building and its surroundings. BTRP will be a valuable tool for the resilience evaluation of different design options. Furthermore, during the operation phase, it will contribute to real-time monitoring and assessment, facilitating disaster management and response, at both the buildings and city scale, reducing the causalities of extreme events. Thus, the BTRF has the potential to expand into various fields such as healthcare, green and resilient buildings rating systems, and even to improve the municipal regulations. Nevertheless, the prime aim of this paper is to address the challenge of extreme thermal events, arising from climate change, and pave the way for the adoption of effective thermal resilience in building design and operation practices.

Automated Regulatory Compliance towards Quality Assurance in Healthcare Building Projects

Healthcare building projects are severely constrained by their associated regulatory frameworks. In this context, regulatory requirements define a basic outline upon which design is developed, as well as aid designers towards compliance to minimum standards. Automation has been explored by existing research focusing mostly on compliance checking (i.e., quality control). There has been limited developments within this domain related to quality assurance. This paper aims to highlight what are key needed improvements to enable the use of automation to promote quality assurance for regulatory compliance in healthcare building projects. For this purpose, an ongoing revision of a British healthcare design guidance document (HBN 11-01) was analysed according to a requirements’ taxonomy. Key areas of improvement needs were highlighted based on a series of interviews. Our main findings relate to identifying that despite the guidance character of regulatory documents in the UK, they are rarely used for this purpose, revealing the focus on quality control. In this context, the regulatory framework could be repositioned as a catalyst towards automated design quality assurance as long as (i) the regulatory documents are developed and revised to fit automated design processes; and (ii) there are compatible software developments to streamline design processes through automation.

Enhancing Healing Environment and Sustainable Finishing Materials in Healthcare Buildings

This research developed an assessment tool for enhancing the healing environment in healthcare buildings, especially interior finishing materials, based on sustainability standards, i.e., Leadership in Energy and Environmental Design (LEED) standards and WELL building standards. The purpose of this tool is to help decision-makers, interior designers, and client committees to identify the compatible interior materials’ specifications with the healing and non-infection environment in order to create a better experience for patient, staff, and visitors. The current study adopts a sustainability-oriented approach to bring more objectivity for assessing the architectural finish in the healing environment at the healthcare projects through a case study of King Faisal University’s (KFU) hospital project with a 182,000 m2 built area. Following the consensus of different experts, the assessment tool integrated two main international standards: LEED healthcare and WELL building standards, in addition to chromotherapy method. This tool assesses interior finishing materials, which affect the healing environment and sustainability in healthcare buildings. Each standard has credits, including items and scores for each item. The developed assessment tool was adopted in the KFU Hospital project, which has gained high acceptance among all stakeholders, including decision-makers. The developed tool is an integrated instrument (based on LEED standards, WELL standards, and chromotherapy method) for assessment; instead of using different tools for assessing finishing materials in healthcare buildings, the assessment tool supports all stakeholders in analyzing interior finishing material to achieve the optimum healing environment and sustainability.

Post-occupancy evaluation and codesign in mental healthcare buildings: User’s input as a driver for functional and technical adaptations in post COVID-19 reality

During the COVID-19 pandemic, social distancing protocols imposed the suspension of many activities in mental healthcare facilities, limiting the use of the facility to people in comprehensive care. With the advancement of vaccination, these buildings are gradually reopening for community use. However, managers and workers now face challenges in accommodating new design demands related to reducing the risk of contamination by COVID-19 within the building. This research adopted a multi-method approach in two phases, combining a post-occupancy evaluation with a cocreation activity to help mental healthcare workers and patients to suggest design changes in their environment. Three Psychosocial Care Centers (CAPS) in São Paulo were selected as case-study buildings. The first phase was the Post-Occupancy Evaluation (POE), in which the lead researcher conducted a walkthrough with the facility administrator, applied questionnaires to 100 employees, and conducted a walking interview with 12 patients. During Phase 02, a codesign activity entitled “Dream CAPS” was conducted with 25 healthcare workers and 12 patients. In this activity, participants were asked to customize a physical model of a mental healthcare facility, choosing the wall and floor finishings, the types of openings (windows, doors), furniture, and ornaments. Afterward, they participated in an interview with illustrated cards. All activities were audio-recorded and textual analysis qualified and categorized the content. Objective and subjective data analysis identified five characteristics of the built environment that prevented or increased risk of COVID-19 spread: Places that support risk mitigation procedures, access and circulation control, extended use of outdoor spaces, natural and artificial air renovation systems, and materials resistant to terminal cleaning. Patients participated in the activities but did not express opinions on the impact of architecture on airborne disease prevention. Healthcare workers claimed that environments that improve mental health must be attractive and prone to social interaction and relaxation without compromising biological safety. The combination of post-occupancy evaluation and codesign proved an excellent tool to identify the demands of mental healthcare buildings and discuss the solutions that must be implemented to deal with contemporary and future crises in a pandemic context.

Parametric-Based Multi-Objective Optimization Workflow: Daylight and Energy Performance Study of Hospital Building in Algeria

Daylight is an important factor that significantly contributes to patients’ healing, with a reduction in the length of stay in the hospital. It can strongly affect energy consumption negatively or positively through lighting control strategies. Therefore, the case of healthcare buildings is very particular and sensitive, especially under extreme climate conditions as in hot and arid regions. The present study aims to determine a balance between daylight use and energy consumption through a parametric-based optimization of the external shading system in a typical hospital room in Biskra. This paper demonstrates how the implementation of parametric design with evolutionary algorithms is considered a reliable strategy to reach optimum solutions in building performance problems. The daylight performance is investigated based on multi-objective optimization to minimize the Energy Use Intensity “EUI”, while maximizing Spatial Daylight Autonomy “sDA” and Useful Daylight Illuminance “UDI”. A simulation model was developed via Grasshopper, which was employed with the use of Ladybug, Honeybee, and Octopus plug-ins. The results revealed that the adaptive facade system can improve indoor daylight levels and energy performance simultaneously compared to the conventional shading system. The presented framework may be used as a reference model, which can enhance opportunities to solve complex design problems in the early design stages and suggest recommendations for sustainable building design.

Indoor environmental quality-based space categorization framework: the case of health-care facilities

PurposeIndoor environmental quality (IEQ) in buildings has an impact on people’s health, productivity and comfort. Maintaining the highest possible IEQ level in complex buildings, such as health care, is difficult due to economic and organizational constraints. This study aims to categorize the vicinities in a typical health-care facility in terms of importance and criticality in relation to the various IEQ factors, as well as to develop an IEQ assessment procedure.Design/methodology/approachA comprehensive literature review, established standards and structured interviews with industrial hygiene professionals in health-care settings were used in this study. To test the applicability of the developed IEQ assessment procedure, a pilot study was conducted in an existing health-care facility.FindingsThis study categorized health-care facilities into various vicinities and discovered three respondents group had varying IEQ perceptions (facility managers, environmental health specialists and nurses). According to the findings, indoor air quality is the most important and dominant factor influencing overall IEQ in health-care facilities. The trial application of the framework shows that much work is needed to improve the level of response and readiness of facility management and occupants to allow for the effective use of the developed procedure.Originality/valuePrevious research did not include a detailed categorization of vicinities in health-care buildings based on IEQ requirements. The findings of this study will help to close this knowledge gap and guide facility managers and operators in recognizing the relative importance of different IEQ factors, maintaining functional requirements and identifying priorities when developing maintenance and operational procedures and allocating resources.

Building cooling energy consumption prediction with a hybrid simulation Approach: Generalization beyond the training range

Greenhouse gas emissions associated with energy consumption in buildings contribute significantly to climate change. In subtropical regions, a well-designed building envelope can effectively reduce the energy demand for space cooling. This study proposes a hybrid simulation approach that can be applied to diverse building types to estimate the annual cooling energy consumption. The proposed model, which is based on Bayesian regularization, demonstrates good generalization ability to predict energy consumption for residential as well as healthcare buildings. In the study, a genetic algorithm was employed to optimize the model parameters for obtaining the minimum or maximum envelope heat gain. It is recommended through this study that by adopting a combination, namely, (i) design parameters resulting in minimum envelope heat gain, (ii) coupling of an air change rate of 9 h−1 and recirculation ratio of 50 %, (iii) lowering lighting power density from 13 W/m2 to 7.3 W/m2, would prove to be an efficient strategy that balances energy and infection control for a general inpatient ward. The proposed generalized hybrid simulation approach can be a helpful tool for building systems engineers to formulate design guidelines or renovation plans to reduce energy consumption and thus greenhouse gas emissions.

Evaluating Modular Healthcare Facilities for COVID-19 Emergency Response—A Case of Hong Kong

In response to the COVID-19 pandemic, modular construction has been adopted for rapidly delivering healthcare facilities, but few have systematically explored the impacts of the pandemic and the contributions of modular construction. This paper aims to evaluate modular construction for delivering healthcare facilities in response to COVID-19, through the exploration of the challenges, strategies, and performance of using modular construction for emergency healthcare building project delivery. The study was conducted using 12 real-life healthcare building projects in Hong Kong with both within- and cross-case analyses. The results of the within-case study reveal critical challenges such as tight program but limited resources available and the corresponding strategies such as implementation of smart technologies. The results of the cross-case analysis indicate 106% improved time efficiency and 203% enhanced cost efficiency of using modular construction compared with conventional practices. Based on the multi-case studies, the paper develops an innovative framework which illustrates the roles of stakeholders, goals, engineering challenges, and management principles of using modular construction. Practically, the paper should assist both policymakers and industry stakeholders in addressing the critical challenges of delivering healthcare facilities under COVID-19 in an efficient and collaborative manner. Theoretically, it should set an exemplar of linking the building construction industry with emergency management and healthcare service systems to facilitate efficient response to pandemics.

Impact of IoT on Achieving Smart Primary Healthcare Building Facilities in Gauteng, South Africa.

Processes and services undertaken in smart primary healthcare building facilities capture operational data through advanced monitoring and enable experts to use these building facilities for efficient healthcare service delivery. This study assessed the impact of Internet of Things (IoT) services on achieving efficient primary healthcare in the rural areas of South Africa. The study identified three (3) basic constructs of IoT services. They include IoT location recognition and tracking services, the application of the IoT high-speed communication network-based services, and the application of IoT-based services. The study is quantitative, and a questionnaire was used to collect data from the project managers and healthcare practitioners working with the primary healthcare agency in South Africa. The study found a variable degree of impact between the three (3) IoT constructs and the successful development of primary healthcare building facility services in South Africa. The study recommends adopting IoT essential services for achieving efficient primary healthcare services in the rural areas of South Africa and other developing countries facing similar primary healthcare delivery challenges.

Investigating the performance of machine learning models combined with different feature selection methods to estimate the energy consumption of buildings

Machine learning is considered a promising method for developing building energy-benchmarking models. However, the high dimensionality of building energy datasets can reduce model accuracy and generalization but increase the computational cost. Meanwhile, the poor interpretability of machine learning models limits the understanding of insights and, in turn, hinders policymaking. Therefore, the first objective of this study was to investigate the benefits of feature selection on the performance of machine learning-based energy usage models. Three typical feature selection methods (filter, wrapper, and embedded) were selected, and the effect of each method was evaluated based on three tree-ensemble learning algorithms. Another objective was to analyze the interpretability of the machine learning model using the Shapley additive explanation method. The results were obtained using a city-scale energy consumption dataset consisting of 478 healthcare buildings in Chongqing, China. It was found that the wrapper method generally improved the accuracy of the machine learning models compared to that of the other two methods. In addition, the model developed using extreme gradient boosting combined with the wrapper method achieved the best accuracy. Moreover, the model interpretability analysis demonstrated important features and revealed how these features influence energy use for individual buildings.

Sustainable Building Standards, Codes and Certification Systems: The Status Quo and Future Directions in Saudi Arabia

Sustainable building practices are a response to environmental issues. Businesses and industries are assessing how their activities affect the environment. The architecture, engineering and construction (AEC) industries have significant impacts on the environment and economy, while the industry is considered one of the largest contributors to greenhouse gas (GHG) emissions and has, therefore, been highlighted by researchers as a key area of intervention with a great potential to reduce environmental impacts. This paper critically reviews and evaluates the current state of sustainable building certification systems with the purpose of having a good understanding of the status quo and possibilities for future directions in Saudi Arabia. It reviews the academic literature on Saudi Arabia’s green/sustainable building codes, standards, certification systems, methods and tools. It starts by addressing sustainability in the broadest sense. Then, it investigates sustainability strategies and evaluates the building certification systems in Saudi Arabia, followed by an introduction to the new practice of sustainable healthcare building assessment. Life cycle assessment (LCA) and building information modelling (BIM) techniques have also been investigated. The paper introduces the updated Saudi Building Code (SBC) with further evaluation of the Saudi Green Building Code (SBC 1001-CR). Finally, the paper clearly highlights the key role of sustainable building practices and the need to develop a certification system that considers the new trends and the local context.

User Awareness on Fire Escape in Hospital Building in Perak

A healthcare building is a place for the patient’s treatment. Hospital resilience is defined as the medical facility's capacity to withstand a disaster, maintain and expand their medical capacity, and respond to a sudden increase in patient demand. There has been a global outcry from fire outbreaks with hospital building being affected. From 2010 – 2019, 14 cases of deathliest fire happened in the hospital building around the globe. All of these fatalities occurred as a result of the failure of fire escape routes, locked exits, lack of staff evacuation training, lack of awareness, and lack of safety measures, particularly on the fire escape itself. The leading causes of fire outbreak are human factors such as carelessness, negligence and lack of fire safety awareness. Therefore, this research intended to evaluate the level of user’s awareness towards fire escape in hospital. Questionnaire survey were distributed to 90 respondents who is user of the hospital. The results were analysed using SPSS descriptive analysis and presented in the table form. Based on the analysis, 90% of the respondents are aware and strongly aware. The three top ranking of user’s awareness are using a staircase is much safer instead of using lifts, people will be warned if a fire is discovered, there is a person responsible in case of fire and the existence of fire escape in the hospital. It is recommended that the hospital management share and provide information for the users regarding the nearest fire escape available to the user. As this paper only considers the level of awareness on fire escape in hospital, further study is needed to identify in detail the design criteria of fire escape in hospital building.

Factors That Affect Legionella Positivity in Healthcare Building Water Systems from a Large, National Environmental Surveillance Initiative.

Legionella growth in healthcare building water systems can result in legionellosis, making water management programs (WMPs) important for patient safety. However, knowledge is limited on Legionella prevalence in healthcare buildings. A dataset of quarterly water testing in Veterans Health Administration (VHA) healthcare buildings was used to examine national environmental Legionella prevalence from 2015 to 2018. Bayesian hierarchical logistic regression modeling assessed factors influencing Legionella positivity. The master dataset included 201,146 water samples from 814 buildings at 168 VHA campuses. Overall Legionella positivity over the 4 years decreased from 7.2 to 5.1%, with the odds of a Legionella-positive sample being 0.94 (0.90-0.97) times the odds of a positive sample in the previous quarter for the 16 quarters of the 4 year period. Positivity varied considerably more at the medical center campus level compared to regional levels or to the building level where controls are typically applied. We found higher odds of Legionella detection in older buildings (OR 0.92 [0.86-0.98] for each more recent decade of construction), in taller buildings (OR 1.20 [1.13-1.27] for each additional floor), in hot water samples (O.R. 1.21 [1.16-1.27]), and in samples with lower residual biocide concentrations. This comprehensive healthcare building review showed reduced Legionella detection in the VHA healthcare system over time. Insights into factors associated with Legionella positivity provide information for healthcare systems implementing WMPs and for organizations setting standards and regulations.

Energy Analysis and Forecast of a Major Modern Hospital

Healthcare buildings often have high energy use intensity, which is potentially influenced by a few factors, such as occupancy and climate. A suite of data analysis methods, including principal component analysis and regressions, is applied to analyse monthly electricity data of a modern major hospital in subtropical Australia. The analysis shows that occupancy is not highly correlated with the hospital’s electricity use, nor is it important for building energy modelling. However, outdoor environment temperature is highly correlated with the hospital’s electricity use. Then, the hospital’s electricity uses in 2030 to 2090 scenarios are forecast with future climate files. The impacts are analysed in terms of bill increases and renewable capacity needed to offset the increased electricity use. This study has established a process to predict future hospital energy use using data-driven energy modelling. This succinct article provides vital evidence to support the healthcare sector to continuously improve energy efficiency for health buildings, which is a major asset to adapt to the changing climate.

Model of monthly electricity consumption of healthcare buildings based on climatological variables using PCA and linear regression

At this time, due to the global pandemic that has occurred, public administrations want to optimize resources and reduce greenhouse gases with more interest than before. It is the case of the Energy Regional Entity of the Junta de Castilla y León (Spain) that pursues the optimization of the energy consumption in particular of healthcare sector buildings. For this purpose, this work focuses on estimating electricity consumption for each month, for which different scenarios will be generated and the corresponding model is obtained for each scenario. This model has been developed considering the historical monthly data of consumption and climatic variables for the last 3 years. Electricity consumption in public sanitary buildings is related to their climatology, due to the use of air conditioning to adjust the indoor temperature. Subsequently, from the models obtained, the results will be analyzed. Significant differences have been observed in the estimation of electricity consumption with respect to the real data provided by the Junta de Castilla y León. The results obtained show how the availability of climatic variables increases the accuracy of the model obtained by about 30%.

Performance evaluation in healthcare buildings: a systematic literature review

Abstract Healthcare buildings are complex as their occupants may have different health conditions. In this context, building performance evaluations can help to achieve better performance perceived by occupants. Thus, this paper has developed a systematic literature review (SLR) on performance evaluation in healthcare buildings to understand their intrinsic characteristics, in addition to developing an overview of the subject. The objective was to identify the purpose of the evaluations, the criteria evaluated, the evaluation methods, the type of healthcare facilities evaluated, as well as the temporal and spatial distribution of papers. The research was conducted using three electronic databases, and eighty-three papers were examined according to the 5W1H tool. As a result, the buildings assessed covered health services at all stages of life, physical and mental issues. Six groups of criteria were identified, highlighting: spatial, lighting, acoustic comfort, energy issues, and the materials and finishes used. Moreover, relationships were established between the types of buildings, evaluated criteria, and tools used. Finally, the SLR collaborated with the understanding of performance in healthcare buildings, identified that these buildings are being evaluated, contributing to the health, well-being, and satisfaction of occupants as buildings that perform better tend to be better places of healing and work.

A Review of Physical and Digital Mock-Up Applications in Healthcare Building Development

Mock-up simulation is a design or human factor research method to help designers identify key design issues and factors of a product or environment. This paper discusses physical mock-up (PMU) and digital mock-up (DMU) applications in healthcare building development through a narrative literature review. The following questions are addressed in this paper: what would the purposes of using PMU or DMU simulations be? At which phase of a hospital design would a PMU or DMU simulation be used? What methods can be used to conduct PMU and DMU simulations? The paper discusses the advantages and disadvantages of these two mock-up methods and highlights the importance of clinical staff’s involvement in mock-up simulations. It gives recommendations for the design practitioners or project managers of healthcare building development recommendations to implement these two mock-up methods in healthcare building development projects.

Evaluating building performance in healthcare facilities using entropy and graph heuristic theories

Designing a healthcare facility is one of the most challenging tasks due to the complexity associated with these facilities. The primary goal of healthcare facilities is to provide high-quality care; consequently, the design of healthcare facilities and their environments directly affects the facility's productivity, the organization's economic performance, the experienced clinical outcomes in the hospital, as well as patient and staff satisfaction. The redesign of a healthcare facility is essential for ensuring a serene healing environment for the patients and thus influences their healing rates, reduces the amount of time spent in the facilities, and impacts their level of satisfaction with the care provided. The evaluation methodology is a step in the redesign process that measures the performance of healthcare buildings according to international standards. In this study, the collected standards were weighted using an entropy algorithm to evaluate different departments in various hospitals. In addition, the layout score was measured using the adjacent algorithm as one of the graph heuristic methods to determine whether the department or the whole hospital can be redesigned to meet international standards. According to the results of our methodologies being used in one selected hospital in Egypt, the average of the satisfied standards was 43%, standards that could be satisfied were 24%, not applicable standards were 34%, and the average layout score was 25.

Multi-Step Hourly Power Consumption Forecasting in a Healthcare Building with Recurrent Neural Networks and Empirical Mode Decomposition.

Short-term forecasting of electric energy consumption has become a critical issue for companies selling and buying electricity because of the fluctuating and rising trend of its price. Forecasting tools based on Artificial Intelligence have proved to provide accurate and reliable prediction, especially Neural Networks, which have been widely used and have become one of the preferred ones. In this work, two of them, Long Short-Term Memories and Gated Recurrent Units, have been used along with a preprocessing algorithm, the Empirical Mode Decomposition, to make up a hybrid model to predict the following 24 hourly consumptions (a whole day ahead) of a hospital. Two different datasets have been used to forecast them: a univariate one in which only consumptions are used and a multivariate one in which other three variables (reactive consumption, temperature, and humidity) have been also used. The results achieved show that the best performances were obtained with the multivariate dataset. In this scenario, the hybrid models (neural network with preprocessing) clearly outperformed the simple ones (only the neural network). Both neural models provided similar performances in all cases. The best results (Mean Absolute Percentage Error: 3.51% and Root Mean Square Error: 55.06) were obtained with the Long Short-Term Memory with preprocessing with the multivariate dataset.