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Integrated healthcare facilities maintenance management model: case studies Sarel Lavy Department of Construction Science, College of Architecture, Texas A&M University, College Station, Texas, USA, and
Healthcare facilities management 107 Received July 2008 Accepted September 2008
Igal M. Shohet Department of Structural Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel Abstract Purpose – Increasing demand for healthcare services world-wide creates continuous requirements to reduce expenditures on “non-core” activities, such as maintenance and operations. At the same time, owners, users, and clients of healthcare expect a high level of built-facilities performance and minimized risks. The objective of this research is to develop an integrated facilities management (FM) model for healthcare facilities. Design/methodology/approach – The paper presents a case study analysis of an Israeli acute care hospital, in which the integrated healthcare facilities management model (IHFMM) was implemented, and the findings were examined and evaluated three years later. The case studies investigated the effectiveness of the developed model in terms of maintenance and performance management. The robustness of the model was also examined by applying sensitivity analyses to its parameters. Findings – Both of the case studies show significant results in predicting FM-related aspects, such as the level of performance and the required maintenance budgets. The findings reveal a high correlation between the two phases of the case studies in terms of financial outcomes and performance predictions. Originality/value – The core of the model is based on the strength of identified effects of certain parameters, such as maintenance expenditure and actual service life, on the performance and maintenance of healthcare facilities. The proposed IHFMM addresses two core topics of FM: maintenance and performance, for strategic FM decision making. Keywords Facilities, Health services, Cost analysis, Modelling, Israel, Resource allocation Paper type Case study
Introduction Increased competitiveness in the business sector puts considerable pressure on companies to reduce expenditures on “non-core” activities, such as maintenance and operations. This encourages buildings’ owners and users to raise their expectations and requirements of facilities. Facility managers are thus expected to attain lower operational costs and risks through effective and efficient design, construction, management, and maintenance of facilities, without compromising their performance. Over the past three decades, the field of facilities management (FM) has witnessed significant development, mainly due to the following five global trends: (1) increased construction costs; (2) greater recognition of the effects of space on productivity;
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(3) increased performance requirements by users and owners; (4) contemporary bureaucratic and statutory restrictions; and (5) recognition that the performance of facilities is highly dependent on their maintenance (Shohet, 2006). As a result, the traditional “maintenance manager” has become a “facility manager,” and FM practices have been enriched with methodological procedures particularly implemented with key performance indicators. The facility manager makes strategic and operational facilities-planning decisions that affect the organization’s business performance. This is particularly true in healthcare facilities, which are considered to be among the most complicated and difficult types of facilities to manage, maintain, and operate. This paper illustrates the implementation of the integrated healthcare facilities management model (IHFMM), as developed in the frame of this research, using a case study. The results and the conclusions of this case study are presented and discussed. Background Facilities Management has traditionally been regarded in the old-fashioned sense of cleaning, repairs and maintenance (Atkin and Brooks, 2000). A decade ago, FM responsibilities broadened to encompass “buying, selling, developing and adapting stock to meet wants of owners regarding finance, space, location, quality and so on” (O’Sullivan and Powell, 1990). Recognition of the effects of space on productivity stimulated the development of the Facilities Management discipline (Alexander, 1996; Brown et al., 2001; Douglas, 1996; Granath and Alexander, 2006; Kweon et al., 2008; Neely, 1998; Then, 1999). From the 1990s onward, there has been a trend toward more open markets, and especially toward gradually increased competition, as a result of globalization (Hamer, 1994). Now, at the beginning of the twenty-first century, it is recognized that property is a cost-center that can contribute to the performance of an organization, and as such, it requires effective management. As stated by the International Facility Management Association (IFMA, 2004), FM is “[a] profession that encompasses multiple disciplines to ensure functionality of the built environment by integrating people, place, process and technology.” Drivers of healthcare facilities management are discussed extensively in the literature. Gallagher (1998), for instance, defines the following six issues as encouraging successful implementation of healthcare FM: strategic planning, customer care, market testing, benchmarking, environmental management, and staff development. Amaratunga et al. (2002) demonstrate a model developed for assessing the impact of organizational FM cultural processes (SPICE-FM) on a hospital facility. The healthcare sector in many countries suffers from an under-investment in the allocation of resources, as reflected in different financial reports (AHA, 2004; British Ministry of Finance, 2003). This trend might adversely affect the non-core activities of healthcare providers, and primarily facilities management aspects, such as maintenance and operations. Ritchie (2002) posits that improving the delivery of healthcare services, as well as the services’ performance and quality, can be achieved by paying similar attention to the quality of service as is paid to financial issues. The reforms made by the UK government in the National Health System (NHS) during the
1980s and 1990s improved efficiency by increasing the responsibilities given to the management level (Procter and Brown, 1997). From this review of literature the authors of this paper conclude that the effectiveness of healthcare services will increase with the growth and development of the facilities management profession. This in turn will lead to a change in the position of FM in healthcare organizations to being a more central part of the organization – a position that will help shape organizational decisions and processes. The integrated healthcare facilities management model The integrated healthcare facilities management model (IHFMM) was developed to establish a deductive mechanism capable of identifying how maintenance and performance of healthcare FM can be related to each other and synergize each other. The model provides insight into the parameters that affect maintenance and performance in healthcare facilities, e.g. level of occupancy, age of buildings, annual maintenance expenditure, and level of performance. The proposed model consists of three main interfaces: input interface, reasoning evaluator and predictor phase, and output interface. These interfaces are divided into five phases (A to E), which are further subdivided into 12 levels (1 to 12). Lavy and Shohet (2007b) present a detailed description of the structure of the IHFMM. The Input Interface requires the user to provide parameters related to the facility, while the output interface provides the user with a review of the main topics analyzed by the reasoning interface. The reasoning evaluator and predictor phase implements 15 procedures used by the model for computing the key performance indicators (KPI’s) for the facility in question. Two main principles outline the design of the IHFMM, as follows: (1) The structure of the database is object-oriented, enabling the database’s adaptability to diverse healthcare buildings. (2) The model links topics that combine the core issues of healthcare FM. It could be expanded to include operations and energy, business management, and development aspects in addition to the existing modules focused on maintenance and performance of facilities. The following paragraphs depict the rationale, reasoning, and functions of the major procedures, as developed in the IHFMM. These represent five out of the 15 developed procedures, and they were selected as the core of the model. The discussion will also assist in understanding the case study, presented in the following sections. Building performance indicator (BPI) The building performance indicator (BPI) aims to compute the actual physical performance score for each system in a given building, for each building and for the entire facility (Shohet, 2003), by providing a physical performance indicator, measured on a 100-point rating scale. Weighting the performance indicator in a building level is based on a life cycle cost (LCC) calculation of all the components in that building. This means that the BPI is a combination of the physical performance of components and their life cycle costs implications. The BPI for building i is calculated by using equation (1):
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BPIi ¼
10 X j¼1
LCCi;j APi;j £ LCCi
ð1Þ
where BPI is the building performance indicator, APi, j is the actual physical performance score for system j in building i measured on a 100-point rating scale, LCCi, j is life cycle costs for system j in building i, and LCCi is total life cycle costs of the building. This procedure acts as a physical assessment mechanism that monitors the building and its systems and components. Nevertheless, instead of being a tool used only to assess the physical condition of a building, it also incorporates an engineering-economic aspect that supports the weighting of the different systems in a building while taking their LCC into consideration. It provides the facility manager with a novel perspective that creates a simultaneous link between physical performance and the economic aspects of building systems. As an economic-performance indicator, it is used in a later stage of the analysis to assess the efficiency with which the actual performance is achieved. Other qualitative approaches may also be considered for assessing the physical condition of a building; however, the authors believe that these approaches are subjective to the evaluator, and may not completely reflect the actual performance and functionality of the building and its systems. As a result, weighting the building systems by their contribution to the total life cycle costs of the entire building was selected as the approach used for the purpose of this study. Facility coefficient The facility coefficient procedure computes the adjusting coefficient for the annual maintenance expenditure (AME). This coefficient is affected by the type of environment in which the facility is located (whether marine or in-land environment), its occupancy (low, standard, or high), the actual age of the buildings in the facility, and the individual configuration of the buildings in terms of the amount, type, and quality of the components (Lavy and Shohet, 2007a). The coefficient expresses the maintenance resources required for implementing a policy of preventive and breakdown maintenance. Each building is then compared with a normative hospital building, with the characteristics of location in an in-land environment (more than 1,000 meters off the Mediterranean coastline), facing a standard level of occupancy (a yearly average of ten occupied patient beds per 1,000 m2 of floor area), and high quality of components to be installed. A facility coefficient of 1.15, for example, represents a requirement to invest 15 percent more in maintenance activities than in a standard hospital building, under standard service conditions. In this research, six simulations were conducted to examine the total maintenance requirements during the designed lifespan of a hospital building under different service conditions (Lavy and Shohet, 2007a). The conclusions drawn from these simulations reveal that the AME in extreme conditions may vary from 9.0 percent lower (in-land environment and low level of occupancy) to 18.6 percent higher (marine environment and high level of occupancy) than standard conditions. This observation is significant, since it means that the AME in built facilities depends significantly on factors such as the type of environment in which the facility is located, and even more, it depends on the level of occupancy in the facility and on its actual age. Consequently,
the implementation of this coefficient elucidates an uneven allocation of resources in healthcare facilities; it also explains that the particular conditions of each facility should be taken into account. Annual maintenance expenditure (AME) and normalized annual maintenance Expenditure (NAME) Annual maintenance expenditure (AME), measured in $US per square meter, expresses the amount of resources spent on maintenance and replacement (also known as capital renewal) activities during a fiscal year, and combines expenditures on in-house personnel, outsourcing contractors, and materials and spare parts (Shohet et al., 2003). Any activity intended to prevent a failure or deterioration of building components, to repair a component that failed, or to replace a component as it reached the end of its service life is included in the AME. This indicator may be used to normalize the expenditures in a facility from one year to another, as well as to compare maintenance expenditures between different facilities. The normalized annual maintenance expenditure (NAME) is defined as the AME divided by the facility coefficient. This eliminates the effects of building age, level of occupancy, category of environment, and configuration of building components by normalizing the annual maintenance expenditure into a value that can be compared to other facilities of different ages and under different service conditions. This parameter can be combined with the BPI as an indicator for the building performance to cost ratio. Projected performance Similar to the BPI, projected performance computes performance scores of the building, systems, and components on a 100-point scale. This procedure, however, aims to project the future level of performance for the different systems in a building (Lavy and Shohet, 2007b). In order to predict the performance of each component, it is assumed that its deterioration pattern is either linear or non-linear (Moubray, 1997). Then, each building system is weighted according to its share in the LCC of the entire building. The projection of a building’s performance aims at forecasting the future level of its functional condition based on actual monitoring of its performance. 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 that is composed of several buildings. This study proposes the use of different patterns of deterioration not only to predict the performance of a single element or system in a building, but to project the performance score for the entire building and even of the entire facility, using LCC as the weighting principle for the building’s various systems. Moreover, it allows FM decision-makers to break each building down into its individual systems, and to analyze it at a great 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 particular component is exponential, changes in the databases can be effected with no significant effort. Likewise, the projected performance mechanism does not consider renovation or capital improvement projects that may be conducted in a building. Since these types of projects depend on the mission of the building, as well as on available resources, it is
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very difficult to plan for and incorporate them into the prediction model. Thus, the authors suggest that further research is required in this area. Maintenance efficiency indicator The maintenance efficiency indicator (MEI) indicates the efficiency with which maintenance activities are implemented. The MEI calculation requires three other indicators: the annual maintenance expenditure (AME), the building performance indicator (BPI), and the facility coefficient (FAC), using equation (2): MEI ¼
AME BPI £ FACð yÞ
ð2Þ
Shohet et al. (2003) surveyed a sample of 25 public acute-care hospitals in Israel, and defined the normative range of MEI values for healthcare facilities as: . lower than 0.37, representing a high level of efficiency and/or scarce resources; . 0.37 to 0.52, representing a standard-normative efficiency; and . higher than 0.52, representing a level at which the available resources are not efficiently utilized. This procedure provides strategic level FM decision-makers with valuable information regarding the effectiveness of maintenance implementation in different buildings and facilities. This indicator can also be used as a decision-making criterion for the allocation of maintenance resources in cases where limited resources are available, e.g. public sector facilities. Illustration of the model – a case study Method The IHFMM was evaluated by conducting two case studies in Israeli acute-care hospital facilities. The case studies investigated the effectiveness of the developed model in terms of maintenance and performance management. The following paragraphs describe one of the two case studies, as well as its results and conclusions, and how these conclusions may be transferred into operational recommendations. Since a hospital facility is multifaceted and consists of a large variety of complex buildings, and in order to explain the implementation of the model in a clear manner, one case study is discussed in detail. The conclusions drawn from implementing the IHFMM on the second case study will be summarized at the end of this chapter. The case study was subdivided into three main phases, as follows: (1) An initial field survey conducted in 2001. (2) Recording of all non-regular replacement and maintenance activities implemented between 2001 and 2004. (3) A second field survey conducted in 2004, similar to that carried out in 2001. The reason for these phases was to investigate and to compare the results, obtained in the same hospital, across a time span of three years. The case study is a peripheral hospital located in the north part of Israel (Plate 1). Its total floor area in 2001 was approximately 39,000 m2, with 301 patient-beds. The total number of buildings was 24, five of which, constituting 29,070 m2, were selected for the
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Plate 1. Aerial view of the hospital used as case study
facility’s performance survey. The following paragraphs will elucidate the built-environment parameters of this hospital, the results of applying the model to the 2001 data, including the model’s policy setting, and the results from applying the model to the 2004 data, including a comparison between 2001 projected values of performance and the corresponding values observed in the 2004 survey. It should also be mentioned that the financial analyses are based on the assumption of an annual interest rate of 4 percent. Results and analyses – 2001 field survey The main parameters and key performance indicators obtained from the 2001 vs 2004 surveys are introduced in Table I. Parameter/KPI Floor area (m2) No. of patient beds No. of buildings No. of buildings surveyed Percentage of floor area surveyed (%) AME ($US/m2) BPI Facility coefficient MEI
2001
2004
39,000 301 24 5 74.5 25.6 78.2 0.6293 0.521
42,000 301 24 5 69.2 25.8 74.7 0.7564 0.457
Table I. Parameters and KPIs for 2001 vs 2004 surveys
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From these figures it can be seen that almost three-quarters of the built floor area was surveyed in 2001, and the average BPI in the surveyed areas was found to be satisfactory (78.2 points on a 100-point scale). The low facility coefficient (0.6293) reflects the relatively new portfolio of buildings, in-land environment, and very low level of occupancy. As a result, the maintenance efficiency indicator was deduced to be in the range of values that reflect high maintenance expenditure in comparison with actual performance, although the actual performance is itself relatively high. Figure 1 demonstrates this point by comparing the case study hospital to the population of all other hospitals participating in this study, with reference to the normative range of efficiency. This figure elucidates that the BPI vs NAME of the case study hospital in 2001 places it on the marginal line, representing low efficiency of maintenance implementation (MEI ¼ 0:52). This finding implies that changes in the maintenance work methods, such as considering the distribution of sources of labor, and investigating the maintenance policies of the hospital (preventive versus corrective), were needed. Furthermore, the major recommendation for the decision-makers in this facility is to shift toward the MEI ¼ 0:45 line. This objective can be achieved by improving the performance of the facility as well as reducing the expenditure for maintenance. Actual performance may also be broken down for each particular building, as shown in Table II. Here, we can see that building 1 performed at a good level, building 4 at a satisfactory-marginal level, and three buildings at a deteriorating level (buildings 2, 3, and 5). The model projected that by 2004, these buildings would be found at the
Figure 1. BPI vs NAME of the case study hospital
Building # Actual performance – 2001 Projected performance – 2004 Actual performance – 2004 Table II. Comparison of performance of buildings between 2001 and 2004 surveys
1 2 3 4 5 Total
88.3 66.2 64.9 75.1 65.1 78.2
82.9 60.4 59.2 69.7 59.4 72.7
81.1 62.4 60.1 81.2 63.0 74.7
bottom range of this performance category (building 2), or even in a run-down condition (buildings 3 and 5), unless substantial corrective maintenance was carried out. These results were further broken down and analyzed from a system perspective, as well. Analyses of the 2001 field survey showed an actual performance of 78.2 points, an annual maintenance expenditure of $25.6 per m2 of floor area, and a maintenance efficiency indicator of 0.521. Assuming that between 2001 and 2004, no large replacement or major capital renewal would be carried out, other than implementing periodical maintenance activities, the predicted performance for 2004 was 72.7 points. Assuming improved efficiency in the implementation of maintenance (MEI ranging from 0.45 to 0.52), a predicted annual maintenance expenditure ranging from $24.7 to $28.6 per m2 is required. This means that the annual maintenance expenditure will vary from 3.5 percent lower to 11.5 percent higher than its value in 2001. Field survey results and analyses – 2004 survey The main parameters and key performance indicators obtained from the 2004 survey are introduced in Tables I and II and in Figure 1, and can be compared to the observations from the 2001 survey. The FM department invested moderately in replacement and capital renewal during the years 2002 to 2004. In these three years, the total floor area of the hospital expanded by approximately 7.7 percent in comparison with the reference floor area observed in 2001. However, no change was observed in the total number of patient beds. In order to be consistent with the performance comparisons, the same five buildings were surveyed in 2004 as in 2001, with a built floor area constituting 69.2 percent of the hospital’s total floor area. The annual maintenance expenditure in 2004 was similar to the value found in 2001. The actual performance score in the facility was 74.7 points, which indicates a marginal level of performance. The facility coefficient in 2004 shows an increase of more than 20 percent in comparison with the coefficient computed in 2001, indicating growing needs for maintenance due to ageing of the existing portfolio of buildings. Consequently, the maintenance efficiency indicator in 2004 reflects improved efficiency, which falls into the range of values that indicate a reasonable use of maintenance resources (Figure 1). The actual performance score of the hospital for the 2004 field survey is higher by 2.0 points in comparison with the predicted performance, yet, 2004 actual performance is lower by 3.5 points than the actual performance found three years earlier. With performance scores broken down for particular buildings, it can be seen that for buildings 1, 2, and 3, the performance measured in 2004 is comparable to the values projected in the 2001 survey. Substantial differences between the predicted performance for 2004 and the actual scores were found in buildings 4 and 5. These differences were caused by a large renovation project that took place in Building 4, in which $5,500,000 was invested in most of the building systems, and by an improvement of the electricity system and its components in building 5. Case study 2 – summary and conclusions The second hospital has a total floor area of 57,186 m2 occupied with 444 patient beds. The total number of buildings is 23, five of which, constituting 40,246 m2, were selected for the facility’s performance survey. The total annual maintenance expenditure in 2001 was found to be $2,025,000, which means $35.4 per m2, or $4,561 per patient-bed.
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The actual performance score in the facility was found to be 82.8 points, indicating a good level of performance. The facility coefficient in 2001 was calculated as 0.8327 (in-land environment and a very low level of occupancy), and as a result, the maintenance efficiency indicator was 0.514 – close to 0.52, meaning that the maintenance expenditure is high in comparison with the actual performance. The total actual performance in this hospital was predicted to decrease by 5.4 points during the three years between 2001 and 2004, causing deterioration in the condition of the facility from good (82.8 points) down to a satisfactory level of performance (77.4 points). However, a substantial investment in installation, replacement and renovation projects of more than $8,700,000 in the five buildings (excluding all regular maintenance activities), which equals more than $215 per m2 of these buildings, led to a 2004 actual performance (82.7 points) similar to the value found in 2001. One building (out of the five surveyed) was found to have a 2004 performance score similar to the predicted performance score for this year. This is the only building in which regular maintenance activities (no major replacement and renovation activities) were solely implemented during the three-year period. This value strengthens the validity of the model, and reinforces its results. The annual maintenance expenditure was predicted to be in the range of $30.6 to $35.0 per m2. However, the annual maintenance expenditure in 2004, also affected by the high level of performance, was found to be $42.9 per m2, which means a lower level of maintenance efficiency. Therefore, the maintenance efficiency indicator (MEI) in 2004 was found to be higher by more than 14 percent in comparison with the 2001 value, meaning that the department did not improve its efficiency, but actually lowered it, particularly due to the high maintenance expenditure observed in 2004 that reflects poorer efficiency. Robustness of the model The robustness of the model was examined by applying sensitivity analyses to its parameters. Within these analyses, two principal parameters were studied by examining the sensitivity of the core outcomes to inaccuracies in the performance scores used as input, as well as to the hypotheses of the components’ patterns of deterioration used to project future performance and the AME. Analyzing the sensitivity of the outcomes to inaccuracies in the performance rating revealed that the output of the model is slightly sensitive to inaccuracies in the performance ratings of four systems. This finding coincides with the central limit theorem (CLT): as the distribution of a variable composed of a sum of multiple variables is normal, its variance equals the variance of the parent variables divided by the sample size. This is attributed to the large number of components sampled in the survey. The second sensitivity analysis studied the effect of different patterns of deterioration of two building components. One assumption of this research states that the performance of all building components, apart from the components in the Structure system, deteriorates in a linear pattern during the designed life cycle. This analysis examines this hypothesis for two out of 51 components existing in the ten building systems. Conclusions of this analysis may be summarized as follows: . The sensitivity of the predicted performance to diverse patterns of deterioration was found to be low for a single component and very low for the total BPI.
.
Replacing the linear pattern of deterioration with either intensive or moderating patterns of deterioration revealed that for the two examined components, a maximum effect of 3.2 points in the predicted performance was found within a period of three years. Furthermore, it was found that the maximum effect on the facility’s total predicted performance is less than 0.11 points, which represents less than 0.2 percent of its performance score. The sensitivity of the predicted annual maintenance expenditure to diverse patterns of deterioration was found to be very low. Substituting the linear pattern of deterioration with either intensive or moderating patterns of deterioration revealed that for the two examined components, a maximum effect of 3.5¢ per m2 in the predicted maintenance expenditure was observed. This result represents less than 0.15 percent of the total predicted Annual Maintenance Expenditure, and as a result, is negligible.
Conclusions Existing methods for facilities management decision-making are limited, particularly at the strategic level of facilities management. This research focused on identifying principal parameters that affect the performance and maintenance of facilities throughout their service life. An integrated healthcare facilities management model has been developed, which proposes simultaneous analysis of the complexities involved in the field, such as resource allocation and setting of maintenance policy for a given level of performance, or improving efficiency with which the implementation of maintenance activities are carried out. These complexities are dealt with by almost all facility managers of public as well as private facilities; nevertheless, this point is even more crucial and significant in healthcare facilities that operate 24 hours a day, seven days a week, provide care and treatment services, and support critical infrastructures of healthcare such as medical gas and power for operating theatres. The model developed in the research includes 15 procedures, out of which five core procedures were discussed in the frame of this paper: building performance indicator, facility coefficient, annual maintenance expenditure, projected performance, and maintenance efficiency indicator. The implementation of the methodology was illustrated by two case studies that confirmed the viability of the model. Both of these case studies show high correlations and significant results, by predicting different FM-related aspects, such as the level of performance and the required maintenance budgets. The model’s robustness was examined using sensitivity analyses. Two principal factors were considered: inaccuracies in the performance scores, and sensitivity to the hypothesized deterioration patterns of building components. Robustness of the predictions of the model is achieved primarily due to the central limit theorem. The present research enables an analytical hierarchical process for facilities maintenance strategic and operational decision making by simultaneous analysis of facilities maintenance core parameters. The core procedures are illustrated in this research with the building performance indicator, facility coefficient for the adjustment of the maintenance resources to prevailing building environment and occupancy, and maintenance efficiency, as expressed by the ratio between expenditure on maintenance and performance.
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References Alexander, K. (1996), Facilities Management: Theory and Practice, E&FN Spon, London. Amaratunga, D., Haigh, R., Sarshar, M. and Baldry, D. (2002), “Assessment of facilities management process capability: a NHS facilities case study”, International Journal of Health Care Quality Assurance, Vol. 15 No. 6, pp. 277-88. American Hospital Association (AHA) (2004), “TrendWatch Chartbook 2004: Trends affecting hospitals and health systems – September 2004”, available at: www.hospitalconnect.com/ ahapolicyforum/trendwatch/chartbook2004.html (accessed June 2004). Atkin, B. and Brooks, A. (2000), Total Facilities Management, Blackwell Science, Oxford. British Ministry of Finance (2003), “Budget 2003: report – chapter 6: delivering high quality public services”, available at: www.hm-treasury.gov.uk/budget/bud_bud03/ budget_report/bud_bud03_repchap6.cfm (accessed February 2005). Brown, A., Hinks, J. and Sneddon, J. (2001), “The facilities management role in new building procurement”, Facilities, Vol. 19 Nos 3/4, pp. 119-30. Douglas, J. (1996), “Building performance and its relevance to facilities management”, Facilities, Vol. 14 Nos 3/4, pp. 23-32. Gallagher, M. (1998), “Evolution of facilities management in the health care sector”, in Harlow, P. (Eds), Construction Papers, No. 86, The Chartered Institute of Building, Ascot. Granath, J.A. and Alexander, K. (2006), “A theoretical reflection on the practice of designing for usability”, Proceedings of the 2006 European Facility Management Conference, Frankfurt, March 2006, pp. 379-89. Hamer, J.M. (1994), “Facility management system”, in Wrennell, W. and Lee, Q. (Eds), Handbook of Commercial and Industrial Facilities Management, McGraw-Hill, New York, NY, pp. 525-32. International Facility Management Association (IFMA) (2004), “FM definitions”, available at: www.ifma.org/what_is_fm/fm_definitions.cfm (accessed January 2004). Kweon, B.S., Ulrich, R.S., Walker, V.D. and Tassinary, L.G. (2008), “Anger and stress: the role of landscape posters in an office setting”, Environment and Behavior, Vol. 40 No. 3, pp. 355-81. Lavy, S. and Shohet, I.M. (2007a), “On the effect of service life conditions on the maintenance costs of healthcare facilities”, Construction Management and Economics, Vol. 25 No. 10, pp. 1087-98. Lavy, S. and Shohet, I.M. (2007b), “Computer-aided healthcare facility management”, ASCE Journal of Computing in Civil Engineering, Vol. 21 No. 5, pp. 363-72. Moubray, J. (1997), Reliability-centred Maintenance, 2nd ed., Butterworth-Heinemann, Oxford. Neely, A. (1998), Measuring Business Performance, Economist Books, London. O’Sullivan, P.E. and Powell, G.C. (1990), “Facilities management: growth and consequences”, Proceedings of the International Symposium on Property Maintenance Management and Modernization, CIB International Council for Building Research Studies and Documentation Working Commission 70, Singapore, Vol. 1, pp. 156-61. Procter, S. and Brown, A.D. (1997), “Computer-integrated operations: the introduction of a hospital information support system”, International Journal of Operations & Production Management, Vol. 17 No. 8, pp. 746-56. Ritchie, L. (2002), “Driving quality – clinical governance in the National Health Service”, Managing Service Quality, Vol. 12 No. 2, pp. 117-28.
Shohet, I.M. (2003), “Building evaluation methodology for setting maintenance priorities in hospital buildings”, Construction Management and Economics, Vol. 21 No. 7, pp. 681-92. Shohet, I.M. (2006), “Key performance indicators for strategic healthcare facilities maintenance”, ASCE Journal of Construction Engineering and Management, Vol. 132 No. 4, pp. 345-52. Shohet, I.M., Lavy-Leibovich, S. and Bar-on, D. (2003), “Integrated maintenance monitoring of hospital buildings”, Construction Management and Economics, Vol. 21 No. 2, pp. 219-28. Then, D.S.S. (1999), “An integrated resource management view of facilities management”, Facilities, Vol. 17 Nos 12/13, pp. 462-9. About the authors Sarel Lavy is a faculty member in the Department of Construction Science, which is one of four departments in the College of Architecture at Texas A&M University. He also serves as the Associate Director of the CRS Center for Leadership and Management in the Design and Construction Industry, as well as a fellow of the CRS Center and of the Center for Health Systems and Design in the College of Architecture at Texas A&M University. Dr Lavy’s principal research interests are: facilities management in the healthcare sector, maintenance management, and performance and condition assessments of buildings. Sarel Lavy is the corresponding author and can be contacted at:
[email protected] Igal M. Shohet has the principal research interests of: maintenance and performance management of complex infrastructures such as healthcare, laboratories, and transportation facilities; extreme events engineering and management in the built environment; procurement methods; and construction safety. Dr Shohet’s maintenance and performance management models are implemented in the last decade in healthcare facilities in Israel, and in civil infrastructures in Israel and in the US. Prior to joining Ben-Gurion University in 2004, Dr Shohet served as a faculty member and senior researcher in the faculty of Civil and Environmental Engineering and the National Building Research Institute in the Technion for eight years.
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