Development of a Conceptual Benchmarking Framework for Healthcare Facilities Management: Case Study of Shanghai Municipal Hospitals

Li, Yongkui; Cao, Lingyan; Han, Yilong; Wu, Jianjun

doi:10.1061/(asce)co.1943-7862.0001731

Cited by 25 publications

(20 citation statements)

References 61 publications

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“…However, there is a lack of theoretical and empirical justification for implementing a value-based approach in healthcare building maintenance [20,21]. Past research in hospital building maintenance was fragmented and diversified in maintenance efficiency [32], benchmarking [33], cost [34,35], audit assessment [19], defects [36], strategy [37], and effectiveness [2]. Recent research trends focus on energy-saving [38], green hospitals [39], service quality in hospital FM [6,40], lean six sigma [41], performance [42], and fire safety [12].…”

Section: Introductionmentioning

confidence: 99%

Importance and Performance of Value-Based Maintenance Practices in Hospital Buildings

2021

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After two decades of privatization of building maintenance service in government hospitals in Malaysia, evidence of under-maintained hospital buildings suggests a need to raise the level of hospital maintenance service delivery. This study identified the critical success factors to enhance the value outcomes of hospital maintenance service. A total of 66 questionnaire survey responses from maintenance personnel in public hospitals were analyzed using the Importance-Performance Matrix Analysis (IPMA) in the SmartPLS3.0 software. The Importance versus the Performance of value-based practices was mapped to identify the critical areas that require greater considerations to improve maintenance service delivery. The findings revealed four critical success factors: Responsive to Needs, Integrated Service Solutions, Innovative Improved Practices, and Value for Money. These practices were found to be the impetus that can bring significant enhancement to hospital building maintenance service delivery. Although the findings are based on data derived from public hospitals in Malaysia, the outcomes are applicable to private hospitals both in and outside of Malaysia.

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Section: Introductionmentioning

confidence: 99%

Importance and Performance of Value-Based Maintenance Practices in Hospital Buildings

2021

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“…The ABC approach and analytical accounting confirm an approach already developed for some time which, despite its diffusion, has not had a large impact in the purely health scientific community (Bilcke et al, 2011;Brescia et al, 2019;Biancone et al, 2018). Others analyze possible benchmarks applicable to health services through the delphi method (Li et al, 2020). There are several mathematical approaches that recall previous systems already considered (Bayer et al, 2020;Yakutcan et al, 2020).…”

Section: Discussionmentioning

confidence: 80%

“…The study helps researchers to define future research strands and develop new evidence starting from previous calculation methodologies. Bibliometric analysis highlights among the major research lines articles based on analytical accounting and activity-based costing associated with specialist care activities or home care projects (Sulo et al, 2020;Malanga et al, 2020;Liu et al, 2020a), involvement of patients in the treatment process for remodeling the path of administration and management of drugs (Gerlach et al, 2020), delphi method for the analysis of common hospital benchmarks using the delphi method between specialists and managers (Li et al, 2020), and recalls mathematical models for forecasting expenditure based on population and incidence of diseases (Bayer et al, 2020;Yakutcan et al, 2020). At the same time, the study contributes to the technical knowledge of the professionals who have the opportunity to determine the most applied methods by type of topic.…”

Section: Discussionmentioning

confidence: 99%

Ordering Cost-Effectiveness Management Studies in Healthcare: A PRISMA-Compliant Systematic Literature Review and Bibliometric Analysis

Campra¹,

Orlandini²,

Amelio³

et al. 2021

IJBA

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The issue of effectiveness in healthcare plays a role in international debates. The search for adequate tools allowing management to evaluate the correct allocation of resources becomes increasingly necessary. Cost-effectiveness analysis responds to this need, but the variety of tools and solutions proposed makes their application and replicability complex. The aim of this study was to create a starting approach model useful to researchers and professionals to cost-effectiveness problem solving. The study integrates two approaches by unifying the PRISMA-Compliant Systematic Literature Review and Bibliometric Analysis. The results obtained from the analysis are manifold. Scientific production related to cost-effectiveness in healthcare has increased in the last ten years and is mainly concentrated in three sources. Researchers insert multiple keywords into the articles, but the main ones are cost effectiveness analysis, human, health care cost. The topics covered can be divided into two clusters, which can be further divided into several subgroups. PRISMA analysis reinforces and confirms what has been identified through bibliometric analysis: in cost-effectiveness analysis different methodological bases are applicable to specific individual topics; in particular, the most used approaches to evaluate cost-effectiveness are DES (discrete event simulation) and Mathematical-statistical analysis methodologies, whose applications need the highlighted data. The study also underlines the literature absence of some specific topics such as spillover from primary and secondary health prevention activities, organization of services, rehabilitation activities, centralization of services related to contracts and PPPs, evaluation of infra-hospital care pathways.

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“…Therefore, the occupancy of hospitals mainly consists of three types of patients, i.e., outpatients, emergency patients, and inpatients. In order to ensure careful control of the indoor climate, healthcare buildings consume more energy than other types of commercial buildings, to achieve a comfortable indoor environment since occupants in hospitals are more sensitive to the physical environment ( Li et al, 2020 ). Occupancy fluctuations complicate decisions concerning the need for a variety of auxiliary facilities, including physical therapy, laboratory tests, surgical services, pharmacy, and housekeeping, and may ultimately impact the energy consumption ( Littig and Isken, 2007 ).…”

Section: Experimental Settingsmentioning

confidence: 99%

“…In this experiment, the building electrical load data were acquired every hour from intelligent–Building Energy Support System (i-BESS) developed by the Shanghai Hospital Development Center (SHDC) ( Li et al, 2020 ), the administrative agency for the investment, management, and operation of 38 municipal public hospitals in Shanghai, including STPH. Building electrical load data from January 1, 2017, to December 31, 2018 (a total of 730 days) were collected and used for training and testing.…”

Section: Experimental Settingsmentioning

confidence: 99%

Electrical load prediction of healthcare buildings through single and ensemble learning

Cao

Zhang

et al. 2020

Energy Reports

Self Cite

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Healthcare buildings are characterized by complex energy systems and high energy usage, therefore serving as the key areas for achieving energy conservation goals in the building sector. An accurate load prediction of hospital energy consumption is of paramount importance to a successful healthcare building energy management. In this study, eight machine learning models of single learning and ensemble learning were developed for predicting healthcare facilities’ energy consumption. To validate the performance of the proposed model, an experiment was conducted on a general hospital in Shanghai, China. It was found that the two ensemble models, Extreme Gradient Boosting (XGBoost) model and Random Forest (RF) model, outperformed single models in daily electrical load prediction. A further comparison between models trained with daily and weekly temporal resolution electrical data shows that it is more likely to achieve higher accuracy with finer time granularity. Through feature importance analysis, the most influential features under the daily and weekly electrical load prediction were identified. Based on the prediction results, it is expected that hospital facility managers will be able to conveniently assess the expected energy usage of their hospitals with the machine learning models.

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Development of a Conceptual Benchmarking Framework for Healthcare Facilities Management: Case Study of Shanghai Municipal Hospitals

Cited by 25 publications

References 61 publications

Importance and Performance of Value-Based Maintenance Practices in Hospital Buildings

Importance and Performance of Value-Based Maintenance Practices in Hospital Buildings

Ordering Cost-Effectiveness Management Studies in Healthcare: A PRISMA-Compliant Systematic Literature Review and Bibliometric Analysis

Electrical load prediction of healthcare buildings through single and ensemble learning

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