Computer-Aided Healthcare Facility Management - Faculty

Computer-Aided Healthcare Facility Management Sarel Lavy, M.ASCE1; and Igal M. Shohet, M.ASCE2 Abstract: The decision-making process in the field of health-care facility management is multifaceted and encompasses many different areas, including maintenance, performance, risk, operations, and development. Information and communications technologies are perceived as the interface that integrates these topics. The main objective of this research is to develop a decision-support system based on core parameters affecting the performance of health-care facilities. This paper presents the preliminary development of a quantitative integrated health-care facility management model, subdivided into the following three interfaces: input, reasoning evaluator and predictor, and output. The model proposes the following five modules: maintenance, performance and risk, energy and operations, business management, and development. It offers projection of maintenance costs, performance, and risk of built facilities in the health-care sector. The model hypotheses are that age, occupancy, and environment affect the maintenance of the facility. These factors are quantitatively developed and analyzed for performance-based maintenance planning, employing an occupancy coefficient and a projection of performance indicator. Simulations of the facility coefficient for different combinations of occupancy and environment reveal that the occupancy level is a major factor that causes an augmentation of more than 18% in the allocation of resources for maintenance compared with standard occupancy. Prediction of the performance score of a building is carried out using a nonlinear pattern for the structural components and linear patterns for the rest of the components. DOI: 10.1061/共ASCE兲0887-3801共2007兲21:5共363兲 CE Database subject headings: Health care facilities; Computer aided operations; Construction management.

Introduction Over the past two decades, facility management 共FM兲 has become one of the most important and central areas in the field of construction management. Today, managing constructed facilities, as well as their support infrastructures, is not only the concern of practitioners, but also includes a wide range of activities and research in academia as well. A facility manager is responsible for making critical strategic, tactical, and operational facilitiesplanning decisions that affect the organization’s business performance. This is particularly true in health-care facilities, which are considered one of the most complicated types of facilities to manage, maintain, and operate. The primary objective of this research is to identify the effect of defined parameters, such as actual service life, level of occupancy, and maintenance expenditure, on the performance of facilities and their systems. Based on these, research efforts have focused on the development of an artificial intelligence model capable of integrating these parameters in the FM decision1

Assistant Professor, Dept. of Construction Science, College of Architecture, Texas A&M Univ., College Station, TX 77843-3137. E-mail: [email protected] 2 Associate Professor, Head, Division of Construction Management, Dept. of Structural Engineering, Ben-Gurion Univ. of the Negev, P.O. Box 653, Beer-Sheva 84105, Israel. E-mail: [email protected] Note. Discussion open until February 1, 2008. Separate discussions must be submitted for individual papers. To extend the closing date by one month, a written request must be filed with the ASCE Managing Editor. The manuscript for this paper was submitted for review and possible publication on October 7, 2005; approved on October 16, 2006. This paper is part of the Journal of Computing in Civil Engineering, Vol. 21, No. 5, September 1, 2007. ©ASCE, ISSN 0887-3801/2007/5-363–372/ $25.00.

making process. This main research objective includes the following goals: • Identifying core parameters for management of health-care facilities throughout their service life; • Determining performance criteria for assessing the core parameters; • Establishing a multidisciplinary 共managerial, financial, and technological兲 knowledge base for an integrated health-care FM model; • Formulating relationships between these parameters, the performance of the facility, and its fitness for use; and • Validating and reassessing the outputs yielded from the developed model and ascertaining the effectiveness and suitability of these approaches for FM. Accomplishment of these objectives will reinforce the existing body of knowledge on built-asset management and provide generic parameters and variables, as well as methods for health-care facility management. The paper introduces the conceptual development of the model and presents its three interfaces: input, reasoning evaluator and predictor, and output. While discussing the reasoning evaluator and predictor interface, the development of two core procedures will be presented, and their rationale and contribution will be discussed.

Background The International Facility Management Association 共IFMA兲 has recently revised its definition of the term “facility management” from “The practice of coordinating the physical workplace with the people and work of the organization” 共IFMA 2002兲 to “A profession that encompasses multiple disciplines to ensure functionality of the built environment by integrating people, place,

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process and technology” 共IFMA 2004兲. Comparing these two definitions reveals that facility management has matured from a “practice” to a multidisciplinary “profession.” This terminology is reinforced by adding that the facility management 共FM兲 discipline covers an extremely wide range of activities, which include physical issues of the built environment, human and business aspects of facility performance and use, financial issues of property investment, environmental health issues, management of structures, and management of construction 共Atkin and Brooks 2000; BIFM 2004; Hinks and McNay 1999; Nutt 1999兲. Langston and Lauge-Kristensen 共2002兲 identify three levels of FM: operational, tactical, and strategic. While operational-level activities deal with the short-term management of the facility 共maintenance and repairs, security, gardening, etc.兲, tactical-level activities deal with adding value to the organization 共planning, support services, management of processes, etc.兲, and strategiclevel activities deal with guiding the organization toward meeting its objectives. When examining FM in the health-care sector, an underinvestment in the allocation of resources is observed. This might adversely affect the noncore activities of health-care providers and primarily facility management aspects, such as maintenance activities and operations. The American Hospital Association 共AHA兲 states in its 2003 annual report that “Hospitals have been under financial pressure in the last five years, both from public and private payers. Since 1999, up to one third of hospitals have had negative total margins” 共AHA 2004兲. A similar state of affairs is presented in the 2003 annual report of the British Ministry of Finance, which states that “Over the past 30 years the U.K. has consistently invested a smaller share of its national income in healthcare . . . Historical underinvestment has resulted in poorer health outcomes than the EU average” 共British Ministry 2003兲. The natural population growth, aging of the population, and consumer revolution have all increased the demand for health services in public hospitals 共Hosking and Jarvis 2003兲. Consequently, the total number of in-patient admissions has increased as well. To deal effectively with this increase, and as a result of their limited resources, hospitals tend to reduce the patients’ average length of stay 共AHA 2004; Federal Statistical Office Germany 2003兲. These trends have also led to an increasing investigation of the structure of health-care systems 共Melin and Granath 2004; Waring and Wainwright 2002; Payne and Rees 1999; Probert et al. 1999兲. Drivers of health-care facility management 共FM兲 are also discussed extensively in the literature. Gallagher 共1998兲, for instance, defines the following six issues as encouraging successful implementation of health-care FM: strategic planning, customer care, market testing, benchmarking, environmental management, and staff development. Amaratunga et al. 共2002兲 conclude with a definition of the following attributes as key processes for successful implementation of FM: service requirements management, service planning, service performance monitoring, supplier and contractor management, health and safety processes, risk management, and service coordination. Shohet and Lavy 共2004b兲 identify the following set of five core domains of health-care FM: maintenance management, performance management, risk management, supply services management, and development, claiming also that information and communication technology 共ICT兲 is the domain that integrates the other five. Artificial intelligence aims to create applications that perform as well as humans on tasks involving learning, vision, language, and robotic motion 共Gallant 1993兲. This research suggests a

health-care FM decision-support system, using a case-based reasoning 共CBR兲 approach. The CBR technique is based on the human problem-solving mechanism, which recalls similar dilemmas, analyzes how they were solved in the past, and assesses how good the outcomes were 共Kim and Han 2001; Watson 1999兲. The fundamental steps in the problem-solving procedure are known as the five Rs: retrieve, reuse, revise, review, and retain 共Aha 1998兲. CBR solving techniques are well developed, especially in the field of medicine 共Abidi and Manickam 2002; Ozturk and Aamodt 1998兲; Nevertheless, in recent years they have also been used in civil engineering 共Ceccaroni et al. 2004; Sadek et al. 2003; Brandon and Ribeiro 1998; Yau and Yang 1998兲, as well as in construction engineering and management 共Dzeng and Tommelein 2004; Kim et al. 2004; Ng 2001; Burke et al. 2000; Arditi and Tokdemir 1999兲. Research efforts were also expended in developing computer applications in the field of FM. Examples of this are the development of a framework of a maintenance management system for a municipal infrastructure at the Fort Gordon Army facility in Augusta, Georgia 共Ganeshan et al. 2001兲 and the implementation of case-based reasoning techniques to predict the deterioration of infrastructure facilities 共Morcous et al. 2002兲. Yet current applications are still limited; Vanier 共2001兲, for instance, discusses the challenges faced by the construction industry in applying decision-support tools for maintenance, repair, and renewal of municipal infrastructure projects. These studies emphasize the IFMA perception of FM and stress Clark and Rees’s 共2000兲 argument that the professional body of knowledge of FM, particularly in the health-care sector, is growing steadily. However, the current situation may still be improved significantly by involving the board-level personnel of the facility in the various facility management decision-making processes. Scarponcini 共1996兲 argues that “it is about time” to encourage the use of computer applications for an FM integrated approach. This approach is further strengthened by Yu et al. 共2000兲, who observed that “future FM software must be more integrated so that facilities can be managed in a more comprehensive manner during their life cycle.” This may be established by using case-based reasoning as an approach that suits the characteristics of the problem presented in this paper.

Conceptual Development and Architecture of the Model The conceptual frame of the Integrated Healthcare Facilities Management Model 共IHFMM兲 was developed and discussed in Shohet and Lavy 共2004a兲. The IHFMM is composed of three interfaces: input interface, reasoning evaluator and predictor, and output interface, as will be described shortly. Both the input and output interfaces embrace the five core modules of health-care facilities management: performance and risk management, maintenance management, development, energy and operations, and business management 共Fig. 1兲. The first two modules 共maintenance management and performance and risk management兲 were investigated and the relations between their parameters were studied. Although significant research was performed on the different aspects of development, rehabilitation, and renovation of facilities 共Shohet and Perelstein 2004; Shen and Lo 1999; Shen and Spedding 1998兲, this module, as well as the other two 共energy and operations and business management兲, will be developed at the next stage of the research effort. In the reasoning evaluator and predictor interface, two methods are implemented: a statistical

364 / JOURNAL OF COMPUTING IN CIVIL ENGINEERING © ASCE / SEPTEMBER/OCTOBER 2007

Fig. 1. Outline of the conceptual architecture of the model

data analysis, based on the statistical development of key performance indicators 共KPIs兲, and a heuristic data analysis, based on cases stored in the system’s databases. The IHFMM model is vertically divided into the three interfaces, as described earlier, and into five phases. The input interface is subdivided into two phases: 共A兲 facility phase and 共B兲 buildings, systems, and components phase, in which a variety of input parameters are required; the output interface is also subdivided into two phases: 共D兲 analyses and 共E兲 policy setting, in which the main topics according to which the facility is analyzed are reviewed; and the reasoning evaluator and predictor interface includes one phase: 共C兲 key performance indicators, which is the core of the system, since in this phase the different procedures used by the model are implemented. Two principles outline the design of the proposed IHFMM, as follows: 1. The structure of the database is object-oriented, enabling adaptability of the database to a diversity of health-care facilities and buildings. This means that the model is flexible and capable of receiving information about different types of health-care buildings, according to their particular configurations; and 2. The model links topics that among them combine the core issues of health-care facility management. The proposed model deals simultaneously with aspects related to the maintenance, performance, and risk of health-care facilities. Input Interface The input interface uses general data about the facility 共such as type of facility, environment, availability of labor, and designation of areas within the facility兲 as well as specific data for each particular building and system in the facility 共such as actual and required service life of buildings, actual and required performance for systems and components, actual levels of risk, and maintenance policies兲. In addition, a list of building components for each building surveyed, for which the reinstatement value per square meter 共m2兲 of floor area, designed life cycle, replacement cost per sq-m, and annual maintenance costs are required. The system employs several databases, such as a cost of building components database and a database of deterioration patterns for each of the building’s main components 共Construction Audit 1999; BPG 1999; Allweil 1989兲. Although the system uses built-in

Fig. 2. Hierarchical architecture of the input interface

default databases, users can change the parameters in these databases according to their requirements and with regard to the characteristics of the facility in question. The input interface is subdivided into two separate phases 共Fig. 2兲: The first 共Phase A兲 deals with general data on the facility, while the second 共Phase B兲 deals with particular data on each building surveyed. The input interface is structured in a hierarchical manner in which these two phases are further subdivided into four layers. Phase A includes Layer 1—Facility, into which the general data are input. The rest of this interface is Phase B and is subdivided into three layers that represent the input of the particular data for each building surveyed. Each one of these layers refers to a different aspect of the facility: Layer 2—Building— deals with aspects related to the buildings surveyed, Layer 3—System—deals with aspects related to the 10 defined building systems, and Layer 4—Component—the base of the pyramid, deals with aspects related to the components of the different building systems. The input interface is designed according to a deductive reasoning approach, i.e., from the general facility level to the specific system and components level. It begins by acquiring facility data, then buildings and systems, and finally specific data about the system components. Reasoning Evaluator and Predictor Interface The second part of the model, the reasoning evaluator and predictor interface, is composed of 15 procedures, measuring key performance indicators 共KPIs兲 of the maintenance, performance, and risk of the built asset. This part of the model is the core of the developed model, since it includes the entire computing processes of calculating, analyzing, and deducing the outputs. Basically this interface uses the data provided by the user in the previous parts of the input interface, as well as the data stored in its databases. Then the process of analyzing the data is implemented using KPIs for the facility in question. Thus, a set of outcomes and recommendations is produced, as will be described in the output interface. The scheme of the reasoning evaluator and predictor interface is presented in Fig. 3. This interface includes a single phase: Phase C, which is subdivided into three layers 共5 to 7兲 that together hold the 15 developed procedures. This interface is hierarchic, i.e., the procedures are implemented and computed from Facility parameters 共Layer 5兲, through Actual indicators 共Layer 6兲, to Prediction indicators 共Layer 7兲. The main outcomes


Assumptions

Fig. 3. Scheme of the reasoning evaluator and predictor interface

of this interface are shown in Layer 7, which constitutes four procedures for computing the following predictions for FM planning of a facility: • Projection of the annual maintenance expenditure 共AME兲 per built sq-m of floor area in the facility for predicted and required levels of performance; • Projection of the performance indicator for the different components, systems, buildings, and the entire facility; • Projection of risk levels involved in the maintenance of the surveyed buildings; and • Policy setting to compare the surveyed facility with other similar facilities, based on best practice cases. Layer 6 includes the following four procedures for which their outputs are used by Layer 7: • Maintenance efficiency indicator 共MEI兲 indicates the efficiency with which maintenance activities are implemented; • Annual maintenance expenditure 共AME兲 calculates the annual resources allocated for maintenance activities; • Building performance indicator 共BPI兲 indicates the actual performance of the surveyed buildings, weighted according to their systems’ and components’ percentage in the building’s life cycle costs; and • Actual risk calculates the levels of risk faced by the facility managers with regard to each of the systems in the surveyed buildings. The following paragraphs will describe in depth the development of two key procedures: prediction of the performance indicator, located in Layer 7, and facility coefficient, located in Layer 5.

Prediction of Performance Indicator Objective To predict the performance score for each component, system, and building and for the entire facility. This procedure provides a projection of physical performance of the building’s components and systems, measured on a 100-point scale, based on their actual physical performance.

The following hypotheses were employed in the development of this procedure: • The deterioration pattern of each component in the structural system is taken to be nonlinear; • The deterioration pattern of each component in all systems other than the structural system was taken to be linear. The linear pattern of deterioration assumes standard service conditions that yield time-dependent linear deterioration of the building components, based on previous research that found that linear patterns of deterioration are appropriate and valid for interior and exterior claddings 共Shohet and Paciuk 2004; Shohet et al. 2002; Moubray 1997兲. Although this was not proved for all building components, and since this research does not aim to investigate the precise pattern of deterioration for all components, this hypothesis was made in order to simplify the calculation process; • For all building systems other than structural systems 共for which the pattern of deterioration is not linear兲, the performance decreases from 100 points 共representing a new component兲 to 60 points 共representing a component that should be replaced兲 within its designed life cycle, which means a total decrease of 40 points during the component’s designed life cycle; • The weight of each system in the building’s performance indicator is calculated according to the ratio between the system’s life cycle costs 共LCC兲 and the building’s LCC. This ratio weighs the systems based on their LCC relative to the total LCC of the building, and therefore represents a combination of physical and financial performance; and • The performance indicator of each system in the facility and the total performance indicator of the facility are weighed according to the floor area of the surveyed buildings. This means that the larger the building, the greater the effect of the building on the performance indicator of the facility. The components included in the structural system are all made of reinforced concrete and include columns, beams, and ceilings. These components are almost always irreplaceable. Thus the age of these components is identical to that of the entire building, making them unlike any other building components, whose age must be ascertained using the maintenance history and replacement documentation for the building. Consequently, this research examined the pattern of deterioration for the structure building system in greater detail in 53 buildings in acute-care and psychiatric health-care facilities. This building sample is located in inland environments, and its actual service life ranges from 0 to 72 years. The following paragraphs present the statistical analyses that led to the formula developed for the deterioration patterns of these components. Fig. 4 delineates the actual performance, over the building’s service life, of the structure system in buildings located in an inland environment. This figure shows that the buildings sample population can be subdivided into two main subgroups, where the boundary between them is defined as a building’s age of 15 years. Statistical t-test analysis assuming unequal variances shows at a very high significance level that these two subgroups differ from one another 共the Pvalue was calculated to be 5.93· 10−10兲. These findings are supported by the results presented by Bentur et al. 共1997, Fig. 10.2兲, which studied the chloride content due to corrosion of steel in reinforced concrete structures and found they are changed from linear to nonlinear after approximately 10 to 15 years, as depicted in Fig. 5. Although that research did not exam-


t 3

y = 100 −

兩0 ⱕ t ⱕ 15

共1兲

• Between years 15 and 72, the performance decreases exponentially from 95 points to 60 points, as described in Eq. 共2兲, where y⫽projected performance score for year t. The correlation coefficient of this equation was found to be R2 = 0.59 y = 28.67 * exp共2.7619 * t−0.3084兲兩15 ⱕ t ⱕ 72兩

共2兲

Rationale Fig. 4. Actual performance over building’s age for buildings located in an inland environment

ine the pattern of deterioration in the performance of structural components, their graph seems to have similar trends; consequently, this research assumes that the pattern of deterioration for the structural components in an inland environment is subdivided into the following two categories, as presented in Fig. 6: • Throughout years 0 to 15, the performance deteriorates linearly from 100 points to 95 points, as described in Eq. 共1兲, where y⫽projected performance score for year t. The correlation coefficient of this equation was found to be R2 = 0.29

The projected performance of each component whose pattern of deterioration was assumed to be nonlinear is calculated based on Eqs. 共1兲 and 共2兲. For all other building components, the projected performance is calculated based on the research hypotheses, using Eq. 共3兲 PPi,j,k = APi,j,k −

40 dlc j,k

共3兲

where PPi,j,k⫽projected performance score for component k of system j in building i; APi,j,k⫽actual performance score for component k of system j in building i; and dlc j,k⫽ designed life cycle for component k of system j. Then the projected performance score of each system in each surveyed building is the mean of the components in this system. This is followed by the calculation of the predicted performance indicator for each surveyed building using Eq. 共4兲 10

BPIPPi =

兺 j=1

冉

PPi,j ·

LCCi,j LCCi

冊

共4兲

where BPIPPi is projected performance indicator for building i; LCCi,j⫽life-cycle costs for system j in building i 共$US per square meter兲; and LCCi is life-cycle costs for building i 共$US per square mile兲. Finally, the performance indicators for each system in the facility and for the entire facility are calculated using Eqs. 共5兲 and 共6兲, respectively NBS

PP j =

PPi,j · TOAi 兺 i=1 NBS

共5兲

TOAi 兺 i=1

Fig. 5. Deterioration due to corrosion of steel in reinforced concrete structures 共Bentur et al. 1997, with permission兲 NBS

BPIPP =

BPIPPi · TOAi 兺 i=1 NBS

共6兲

TOAi 兺 i=1

where PP j⫽predicted performance indicator for system j; NBS ⫽ number of buildings surveyed; TOAi⫽total floor area of building i 关sq-m兴; and BPIPP⫽predicted performance indicator for the facility. Contribution Fig. 6. Predicted performance for structural components in an inland environment

The projection of a building’s performance indicator aims at forecasting the future level of its performance based on actual moni-


toring of its performance and on other hypotheses, as detailed above. In this research, patterns of performance projection were developed for 51 main building components. Based on this, future performance can be projected for each system in the building, for the building as a whole, and for the entire facility, which is composed of several buildings. The process of performance projection includes two patterns of deterioration: nonlinear deterioration 共used for all structural components in the building兲, and linear deterioration 共used for all the other nine building systems兲. Although the concept of different patterns of deterioration is discussed in the literature, this research takes it one step further; it proposes the use of these patterns of deterioration not only to predict the performance of a single element or system in a building, but also to predict the performance indicator for the entire building and even of the entire facility, using life-cycle costs as the weighing principle for the various building systems. Furthermore, it allows decision-makers to break each building down into its separate systems and to analyze it at a greater level of detail, down to its components. In addition, the model is flexible and able to accommodate any change in the patterns of deterioration. This means that if future research reveals that the deterioration pattern of a specific component is exponential, changes in the databases can be effected with no significant effort.

“Facility Coefficient” „Layer 5… Objective To compute the annual maintenance expenditure adjusting coefficient for each of the surveyed buildings in the facility, and for the entire facility, in comparison with a standard hospitalization building 共whose features will be described hereafter兲. This financial coefficient expresses the maintenance resources required for implementing a preventive maintenance policy based on the facility’s level of occupancy, type of environment, age of buildings, and the components included in the buildings. Since the facility coefficient is compared with a standard hospitalization building, a facility coefficient value of 1.25, for example, represents an increase of 25% in maintenance activities with reference to the standard hospitalization building, under standard service conditions 共occupancy and environment兲.

• •

•

•

outdoor components exposed to severe environmental conditions 共BPG 1999; Construction 1999兲; Life-cycle costs 共reinstatement, maintenance, and replacement兲 are all translated into an annual equivalent value; Each component is replaced at the end of its life cycle, unless the residual service life of the building is less than half of the component’s designed life cycle. In that case, the component remains in the building and is maintained until the end of the building’s life cycle 共Allweil 1989; Shohet and Paciuk 2004兲; Due to the high costs involved in the replacement of building components, these are distributed over a period of 10 years, using a 10-year moving average 共Shohet et al. 2003兲, except for boundary years. This assumption was made in order to flatten the fluctuations in the facility coefficient, as well as to prevent drastic changes from occurring between sequential years; however, it may be modified separately for each case; and The facility coefficient is calculated according to the floor area of the surveyed buildings. This means that the larger the building, the greater its effect on the facility coefficient.

Rationale In order to calculate the facility coefficient, counters must be defined that represent the years in the building’s service life for which maintenance and replacement costs are calculated. This is done using Eqs. 共7兲 and 共8兲, both calculate dummy variables: Eq. 共7兲 represents the building’s age for which these costs are attributed 共m1 for the first year, m2 for the last year, and m3 for boundary cases兲, while Eq. 共8兲 stands for the year counters 共n1 for the first year, n2 for the last year, and n3 for number of years between兲

共7兲

0;m1 = 0 1;Else 0;m2 = DLCi + 1 n2 = 1;Else ASLi + 4;ASLi ⱕ 5 n3 = 9;6 ⱕ ASLi ⱕ DLCi − 6 DLCi + 5 − ASLi ;ASLi ⱖ DLCi − 5

共8兲

n1 = Assumptions The following assumptions were used in the development of this procedure: • The facility coefficient is affected by four main parameters: 共1兲 age of the building; 共2兲 type of environment 共marine or inland兲; 共3兲 average occupancy level of the facility; and 共4兲 list of components included in each building; • The occupancy level affects the life cycle of a component or its annual maintenance costs, or both, particularly in the case of indoor components exposed to intensive or moderate service conditions. Nevertheless, this is a parametric assumption that may be modified in each case separately; • The level of occupancy is defined as the product of the following two parameters: 共1兲 ratio between the total equivalent number of patient beds in the facility and the standard designed value 共defined as 10 patient beds per 1,000 m2兲; and 共2兲 average annual occupancy 共percentage兲 of the facility; • Marine environments affect the life cycle of a component or its annual maintenance costs, or both, particularly in the case of

再再再再再

0;ASLi ⱕ 5 ASLi − 5;ASLi ⱖ 6 ASLi + 5;ASLi ⱕ DLCi − 6 m2 = DLCi + 1;DLCi − 5 ⱕ ASLi ⱕ DLCi 1;ASLi ⱕ 5 m3 = ASLi − 4;ASLi ⱖ 6

m1 =

冦

where m1, m2, m3 are dummy variables that denote the building’s ages for which the costs are calculated; ASLi⫽actual age of building i; DLCi⫽designed life cycle for building i; and n1, n2, and n3 are dummy variables that denote year counters. Based on the research assumptions, Eq. 共9兲 expresses the annual replacement costs for each year in which the component should be replaced, while Eq. 共10兲 expresses the total annual replacement and maintenance costs for that component

冦

y = 1,2, . . . ,NRi,j,k dlc Ry,i,j,k = i,j,k 0;Else Ri,j,k ;


共9兲

共10兲

AnCy,i,j,k = Ry,i,j,k + M i,j,k

where Ry,i,j,k is replacement cost in year y for component k of system j in building i 共$US per square meter兲; Ri,j,k is replacement cost for component k of system j in building i 共$US per square meter兲; dlci,j,k is designed life cycle of component k of system j in building i; NRi,j,k is number of replacements during design life cycle of component k of system j in building i; AnCy,i,j,k is total annual replacement and maintenance costs in year y for component k of system jin building i 共$US per square meter兲; and M i,j,k is annual maintenance cost for component k of system j in building i 共$US per square meter兲. This process is repeated for each of the building components and systems, and is represented by Eq. 共11兲, which expresses the total building’s annual replacement and maintenance costs for the surveyed year AnCy,i =

兺j 兺k 共AnCy,i,j,k兲

共11兲

where AnCy,i is total annual replacement and maintenance costs for year y in building i 共$US per square meter兲. Eq. 共12兲 calculates the average replacement and maintenance costs 共distributed over 10 years兲 for the year in question. It can be seen that this equation uses the dummy variables calculated by Eqs. 共7兲 and 共8兲 1 共AnCm1,i + AnCm2,i兲 + 2 AAnCy,i =

NBS

FAC共y兲 =

兺

x=m3

共12兲

共13兲

where ACy,i is annual coefficient for year y in building i; and Const is constant value that represents the average annual equivalent value of replacement and maintenance costs 共distributed over 10 years兲 for a standard hospitalization building 共$US per square meter兲. Fig. 7 depicts the results of a simulation of ACy for a standard hospital building along a designed service life of 75 years. The dependent variable represents the age coefficient, and the independent variable denotes the building age in years. The results depict that the age coefficient is less than 1.00 in the first 16 years of service life of the building. During the service life of the building, three peaks of maintenance activities exist: 25, 40, and 60 years of service life. These maximum points delineate the extensive renovation of the building’s electromechanical systems and interior finishing components. Toward the end of the facility service life, the age coefficient decreases below the level of 1, as

ACy,i * TOAi 兺 i=1 NBS

共14兲

TOAi 兺 i=1

AnCx,i

where AAnCy,i is average annual replacement 共distributed over 10 years兲 and maintenance costs for year y in building i 共$US per square meter兲; and AnCm1,i, AnCm2,i, AnCx,i is annual replacement and maintenance costs for years m1, m2, and x, respectively, in building i 共$US per square meter兲. Then, the annual coefficient is calculated for said building using Eq. 共13兲. This equation uses a constant value that represents a standard set of components in a standard hospitalization building, under standard environmental conditions, with a standard level of occupancy AAnCy,i Const

replacement of components is carried out only if the residual life cycle of the facility is greater than half of the component/system designed life cycle. Finally, the facility coefficient is calculated using Eq. 共14兲 for the year in which the survey is carried out

m3+n3−1

1 n3 + 共n1 + n2兲 2

ACy,i =

Fig. 7. Age coefficient 共ACy兲 along a hospital building designed life cycle of 75 years in standard occupancy

where FAC共y兲 is facility annual coefficient for year y; NBS is number of buildings surveyed; and TOAi is total floor area of building i 关sq-m兴. Contribution The facility coefficient developed in this research is used in the projection of annual maintenance resources required by healthcare facilities. The coefficient enables delineation of the resources required for replacement and maintenance activities; based on this outline, an annual maintenance plan can be drawn up. In addition, this coefficient is used to evaluate the actual efficiency with which maintenance activities are implemented. Most of the assumptions used for development of the facility coefficient procedure are parametric; they can be modified and adapted to other types of buildings and conditions as well. The facility coefficient employs life-cycle cost techniques and develops them for different environmental and occupancy conditions with which healthcare facility managers deal on a regular basis. The facility coefficient is calculated separately for each building according to its unique conditions. Six typical combinations of occupancy and environmental conditions involving inland or marine environment, and low, standard, and high occupancy were simulated to quantify and study the effects of the various conditions of occupancy and environment on facility maintenance. Fig. 8 delineates the accumulated facility coefficient for a hospital facility under the six configurations of occupancy and environment. The dependent variable is the annual maintenance expressed in percentage of the reinstatement value of a hospital building; the value 1 represents the reference percentage required to maintain the building, which was calculated to be 3.25% of the reinstatement value of a hospital building. The results of this analysis reveal the following findings:


6.

increased effect of the marine environment on the maintenance of exterior components. The combination of marine environment and a high level of occupancy represents the most intensive service conditions analyzed. The total area below this graph is 88.9—18.6% higher than under the standard configuration. Since this is the extreme case among all six configurations examined, in which interior, exterior, and electromechanical components have shorter life cycles and require higher annual maintenance expenditure, this is also the highest building coefficient computed.

Output Interface

Fig. 8. Accumulative facility coefficient of hospital facility under six configurations of service

1.

2.

3.

4.

5.

Standard configuration was defined as inland environment and standard occupancy. The results obtained from this simulation were used as the reference case. The area below the solid line represents the sum of resources required for maintenance and replacement activities throughout the building’s designed life cycle of 75 years. The total area below the graph was found to be 75.0. This result corresponds to the building’s service life 共75 years兲 since it represents the average maintenance expenditure throughout the designed life cycle. For the configuration of low level of occupancy and inland environment, the total area below the graph is 68.3 points, which is 9.0% lower than in the case of the standard configuration. Low occupancy affects the interior finishing components, leading to longer life cycles and lower annual maintenance expenditures, and as a result, this is the lowest value among all six main combinations examined. The configuration of inland environment and a high level of occupancy accumulates to 86.1, which is 14.8% higher than under standard conditions. High occupancy leads to an intensive degradation and shorter life cycle for building components, which results in more replacements of these components. Marine environment and a standard level of occupancy—the total area below the graph of this configuration is 77.9, 3.8% higher than under the standard configuration. This finding emphasizes the moderate effect of the marine environment on the facility coefficient, which is explained by the small number of components 共external envelope兲 affected by this aggressive environment, assuming that the electromechanical components, such as cooling towers, are protected from the effects of corrosion. Marine environment and a low level of occupancy—the total area below this graph is 71.1 points, 5.1% lower than under the standard configuration. The moderating effect of low occupancy on the interior finishing components exceeds the

The output interface provides the user with the analyses and results for the facility in question on a variety of topics, e.g., financial, performance, risk, maintenance policy setting, and sources of labor. This interface implements inductive reasoning, i.e., the output parameters are deduced from the component to system layers, and then the latter layers are incorporated into the analysis of the building and facility. The output interface is subdivided in two phases: The first 共Phase D兲 deals with particular data for the facility, which includes analyses of financial, performance, and risk aspects into the following four layers: component, system, building, and facility 共layers 8–11兲, while the second phase 共Phase E兲 provides policy setting recommendations for maintenance and sources of labor for each of the systems and buildings in the facility 共Layer 12兲. The recommendations given by the model are usually neither tactical nor operational, but they are on a strategic level. Based on these recommendations, a facility manager may set short- as well as long-term maintenance plans and embed them into the buildings, systems, and components in the facility.

Conclusions The paper presents the development of a prototype of a decisionsupport system for healthcare facility management. It delineates the model architecture and demonstrates 2 procedures out of 15 developed in the reasoning evaluator and predictor interface. Managing a health-care facility is a complicated, difficult, and confusing process. On the one hand, there is a commitment to provide buildings that perform at a level that satisfies the users’ requirements 共medical staff, patients, visitors, etc.兲, while on the other hand, it should address strict budgetary constraints. Moreover, a health-care facility manager’s responsibility is to select the best combination of sources of labor from both professional and financial aspects, to select the best combination of maintenance policies for each of the building’s components in the campus, and for make many more decisions as well. When a health-care facility manager finally reaches a decision, he or she has to consider a variety of variables and parameters, as well as take into account large amounts of data that sometimes are even implicit. Recognizing that the person who is responsible for making all these decisions is not a computer, the proposed model contributes to this process of decision making, turning it into a structured, quantitative, performance-based, and computerized process. To analyze a health-care facility according to the proposed model, and with the purpose of producing viable results, the model is object-oriented. This means that examining a health-care facility requires the facility manager to first collect information regarding the parameters of the facility, then about the different


buildings in it, as well as their systems, and finally, concerning their components. Although the IHFMM addresses a variety of FM aspects, including maintenance, performance, and risk, there are still missing aspects pertaining to the basic occupation of a health-care facility manager, as suggested in the “pentagon of health-care FM.” The IHFMM is a performance-based model for the allocation of resources in maintenance of facilities and for the planning of long-term maintenance and development of a healthcare facility portfolio. The research model incorporates age, performance, occupancy, and environment as key drivers in the FM decision making. A facility coefficient was developed that quantifies the effect of occupancy, environment, age, and the particular design of a hospital building on the maintenance of the facility. Simulations of the facility coefficient for six configurations combining different occupancy levels and environmental categories along the building designed life cycle reveal that occupancy is a major factor to be considered in the planning of maintenance activities in health-care facilities. The effect of high occupancy is as high as 18.6% compared with the maintenance of a hospital building at a standard level of occupancy. The effect of a marine environment, on the other hand, is limited to 3.8% compared with the facility coefficient in a standard environment. The facility coefficient and predicted performance procedures in the developed model provide the decision-makers with quantitative means for the allocation of resources in preventive maintenance. The employment of this coefficient can make the allocation of resources more rationally distributed between facilities and buildings, according to both their independent parameters 共type of environment, level of occupancy, and actual service life兲 and dependent parameters 共actual and predicted levels of performance and risk兲. Although the developed prototype is far from being completed, it may already be used to assess the maintenance efficiency, performance, and risk in health-care facilities, to observe, monitor, and revise annual maintenance resources, to point out the maintenance policy of buildings and systems, and additional issues concerning the usual work of the health-care facility manager. A later stage of the development may also suggest energy and operations, administrative management, and renovation and development aspects of a health-care facility. The proposed model may be adjusted for the analysis of different types of buildings, such as offices, educational campuses, public buildings, etc., according to their exclusive and unique parameters. This research expands the existing body of knowledge in the area of FM by proposing a model that analyzes facilities in a quantitative performance-based manner using integrated KPIs. The IHFMM addresses key drawbacks in contemporary FM by integrating its core issues: risk management, maintenance, performance, development, and operations. Actual decisions and resource allocation can be carried out with a clear projection of the performance, risks, and resources for the coming years. Clear quantitative decision making can be carried out to implement the most effective allocation of resources using minimization of risks, minimization of resources, or maximization of the performance. The proposed architecture outlines the methods for quantitative performance-based integrated FM. Furthermore, the benefits of applying the proposed model may be significant to FM understanding, as it provides new concepts for measuring the effectiveness and efficiency of maintenance and performance of facilities.

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