Volume Flexible Strategies in Health Services: A Research Framework
Jack, Eric P
The prevalence of fluctuating demand is increasingly seen as a serious and ongoing issue facing the health services industry. Volume flexibility in a health care setting represents a means to improve service delivery and it allows organizations to leverage their scarce resources for optimal utilization in response to fluctuations in patient demand. This paper develops a research framework that describes four volume flexible strategies based on literature reviews and structured field interviews of health care administrators at a Carnegie I research and teaching hospital. This prescriptive framework and the propositions that are developed create a foundation to help guide future research on the important relationships between demand uncertainty, volume flexible strategies, and organizational performance.
Key words: services; demand uncertainty; volume flexibility; technology; health care; service operations Submissions and Acceptance: Received June 2003; revision received November 2003; accepted January 2004.
Health care organizations are increasingly facing changing demand patterns, escalating pressures to reduce costs, and consolidation of facilities into a few large health care organizations (Post and Kagan 1998). These challenges are further exacerbated by increasing concerns about access to health care for many Americans (Murray et al. 2003). In health care delivery, the inability to meet demand has more serious consequences than it does in other services such as restaurants, travel providers, beauty salons, retail shops, or hotels. Denying or limiting service to patients can have negative consequences-including patient death. In some other service areas, a limited ability to meet demand can even have a positive impact for the service provider because it creates an image of exclusivity that may enable the organization to raise its fees. The situation is quite different in health care, where organizations must incur high costs for enough slack resources to provide timely care to all patients. At the same time, in a regulated reimbursement system, they are unable to charge higher fees to compensate for these costs. Thus, healthcare organizations are challenged to develop volume flexible strategies that enable them to consistently provide high quality services despite the fluctuation in demand for these services.
The need to deliver high-volume health care services is seen as an important determinant of quality of care as measured by outcomes such as re-admittance rates, morbidity, and mortality rates (Hughes and Lee 1991). Several studies provide evidence that health care organizations that perform high volumes of a particular procedure tend to be more proficient and have better outcomes for patients. For example, Gutierrez (1998) analyzes an elective surgical procedure to estimate the relationship between hospital procedure, specific volume, and average hospital treatment costs. The results of this study suggest that the incremental cost savings associated with increased patient volume depends on the hospital’s current volume level and its size. In a large national study, Birkmeyer (2002) provides evidence that higher-volume hospitals had lower operative mortality rates for six types of cardiovascular procedures and eight types of major cancer resections. Also, a recent Journal of the American Medical Association (JAMA) article (Carey et al. 2003) alerted the health care community to the strong relationship between hospital volume of service and quality of care outcomes and it also suggested minimum volume levels of cardiovascular procedures that hospitals should perform in order to maintain quality standards. This article further concluded that volume flexible strategies are needed, indicating, “although volume is clearly related to outcome, patient-related factors and process variables may be more important.”
Volume flexible strategies that focus on delivery processes offer potential solutions to the significant challenges that health care organizations face when responding to demand variability. Volume flexibility enables an organization to effectively increase or decrease output levels in response to customer demand with minimal disruption to current operations (Hayes and Wheelwright 1984). Volume flexible strategies involve the strategic decisions that organizations make in order to leverage their scarce resources and technological capabilities in response to demand uncertainty. A volume flexible organization is able to maintain a high level of delivery reliability by preventing a situation where services cannot be delivered due to demand exceeding the organization’s service capacity. Conversely, in periods of slow demand, a volume flexible organization is not burdened with surplus capacity that increases its cost position without a corresponding level of revenue to offset those costs (Jack and Raturi 2003).
This paper focuses on how health care organizations develop and leverage their resources to achieve volume flexible responses to demand variability. The research is based on a review of operations strategy literature and on structured field interviews of health care administrators at a Carnegie I research and teaching hospital. The authors present a model that classifies responses to demand uncertainty into four generic strategic response classifications. Propositions are then developed to provide guidelines for future research. This paper contributes to the literature by (I) reviewing the current literature for deploying volume flexible strategies in the health services industry; (2) reporting the results of field interviews of health care administrators; (3) developing a model for investigating appropriate volume flexible responses; and (4) presenting propositions to guide future research that is needed in this area.
This discussion extracts key arguments from the literatures on operations strategy, volume flexibility, and demand uncertainty in the health care environment. By combining these elements with insights from interviews with senior health care administrators, a model of strategic volume flexible responses to demand uncertainty is developed.
2.1. Operations Strategy in Health Care
In the service operations literature there is a broad body of knowledge on service processes and service packages (Kellogg and Nie 1995), workforce management (Sasser 1976), and planning and control (Chase 1978, 1981; Lovelock 1983). The health services industry has a very complex structure where the ownership of these organizations has taken on many forms including: for profit, not-for-profit, and various types of private and public hospitals. Some of the more common service process structures that we find in health care delivery include: primary care clinics, ambulatory care centers, surgical centers, and different types of hospitals (rural, community, regional, children’s, veteran’s, military and teaching). Also, the degree of customization characterizes the service package or the range of services provided by these delivery systems (from complex emergency care, trauma care, elective surgery down to more routine primary care).
Operations strategy takes on a significantly different emphasis in health care than it does in a manufacturing setting. In manufacturing, an organization’s operations strategy is characterized by the level of importance given to cost efficiency, dependability, speed, and flexibility. Also, in manufacturing, demand rates for products are usually predictable and the impact of long waits can result in spoilage and higher inventory cost. However, in the health care industry, organizations are challenged to meet a highly variable rate of demand with a constant rate of high quality service where the consequences of poor service can result in patient death. Also, since the service package is highly customized in health care, the predominant operations strategy generally emphasizes quality of care, timeliness, and flexibility. Health care organizations are asked to provide high quality service to each patient (regardless of the patient’s ability to pay) and this presents a significant challenge in developing effective operations strategies.
In a large health care organization that has many specialties, each service line (e.g. emergency department, cardiology, neurology, oncology, etc.) may operate in an autonomous fashion. Administrators in these service lines deploy their resources tactically in response to the level of demand uncertainty that may be unique to their departmental circumstances. This decentralized response to the unique set of demand uncertainties can be likened to the concept of a ‘focused factory’ (Skinner 1974) where organizational performance can be improved if managers focus their resources and streamline their delivery processes in order to serve the specific needs of their individual market segments. In the health care industry, the idea of a focused factory has received increased attention as a means of providing improved health care delivery at a lower cost (Herzlinger 2000; Casalino, Devers, and Brewster 2003). For example, Herzlinger (2000) suggests that in Healthcare, focused factories can offer care for chronic diseases, frequently performed procedures, and primary and diagnostic care. Such organizations will be able to present clear outcome data, charge lower prices, and enhance customer satisfaction simultaneously. On the other hand, Casalino et al. (2003) documents the rapid increase in physicianowned specialty hospitals and ambulatory surgery centers and suggest that these facilities could lead to excess capacity, provision of unnecessary services, and perhaps lower quality because of decreased volume of some services.
To deliver high quality services, health care organizations rely on a broad portfolio of resources such as facilities, manpower, money, materials, machinery, planning and control systems, and time. These organizations leverage their resources by relying on two key enablers: information technology and advances in medical science and technology. Bodenheimer and Grumbach (2003) suggest that information technology has enormous potential to improve primary care in several areas such as: (a) medical records, (b) communication between physicians and patients, (c) information sharing among health care providers, and (d) rapid access to reliable information for both physicians and patients. While a full discussion of technological advances in medical science is beyond the scope of this paper, we are aware of the tremendous impact that these advances (e.g., radiography, CTscanning, mammography, automated drug dispensing systems, and telemedicine) have in increasing patient throughput and improving quality of care. Effective health care organizations use these technological enablers to develop capabilities and core competencies that allow them to successfully compete in the marketplace for such services as organ transplants, cancer treatment, neurosurgery, neonatology, trauma care, and gerontology. Each of these core competencies is sustained by the strategic deployment of the organization’s resources. Heskett et al. (1990) suggest the deployment of several operational tactics that can improve service delivery such as: using overtime and temporary workers; cross training workers; developing complementary product and service offerings based on seasonal demand; improving forecasting and planning systems; and creating and maintaining slack resources.
Research on operations strategy in health care goes back several years, indicating the long-standing nature of the issues (Smith-Daniels et al. 1988; Li and Benton 1995; Heineke 1995; and Ei et al. 2002). Many of the early efforts focused on capacity management and demand management issues in health care. For example, Smith-Daniels et al. (1988) provide a research agenda and suggests that responses to demand and capacity management in health care involves decisions related to the allocation of three types of resources: facilities, equipment, and workforce. More recent research efforts have focused on a variety of operational issues such as: standard performance measures (Li and Benton 1995); the impact of operational decisions on clinical performance (Heineke 1995); and the impact of strategic operations management decisions on hospital performance (Li et al. 2002). While these researchers have investigated different aspects of capacity and demand management, none of these efforts have focused on the specific need for volume flexibility in health care. In this present paper, we add to the current body of knowledge in service operations by embedding theories on volume flexibility from the manufacturing strategy literature.
2.2. Volume Flexibility
Volume flexibility is concerned primarily with an organization’s ability to efficiently manage its output level in response to fluctuations in demand for its current products or services (Upton 1994). The concept of volume flexibility has its roots in operations management and manufacturing strategy literatures where the strategic value of volume flexibility is well documented (Sethi and Sethi 1990; de Groote 1994; Koste and Malhotra 1999). For example, Koste and Malhotra (1999) define volume flexibility as “the extent of change and the degree of fluctuation in aggregate output level, which the system can accommodate without incurring high transition penalties or large changes in performance outcomes.” This definition suggests that there are tradeoffs (transition penalties and changes in performance outcomes) involved in how organizations choose to respond to varying levels of demand. Perhaps de Groote (1994) offers the most convincing linkage between the deployment of resources or technological capabilities and the concept of volume flexibility. De Groote (1994) makes key distinctions between technology, uncertainty in the environment, and flexibility. Technology is defined broadly to include any aspect of the organization’s production resources, control procedures, and overall strategy. Uncertainty in the environment is defined as the variability, variety or complexity related to the proliferation of customized products and service offerings in a given industry. Flexibility is defined as a characteristic of the technological capabilities that provide a hedge against uncertainty in the environment. De Groote (1994) suggests that any flexibility strategy must incorporate three important aspects: technology, uncertainty, and performance.
2.3. Demand Uncertainty in Health Care
There are several factors that lead to demand uncertainty in health care delivery. Vissers et al. (2001) suggest four underlying dimensions of demand uncertainty in health care to include: (1) size of the population in the catchment area from which the potential pool of patients are drawn; (2) demographic changes in the population such as the increasing proportion of elderly patients; and (3) changing professional standards that are influenced by higher patient expectations and technological developments that increase our knowledge and ability to provide more health care services. In our study, we are concerned with the appropriate strategies and tactics that managers use in response to fluctuations in the volume of demand (hereafter referred to as demand uncertainty). If demand for health care services fluctuates significantly over a given time period (e.g., more than 10% per year), administrators may define this level of demand uncertainty as being high.
There are two common managerial misperceptions about demand uncertainty as it relates to the gap between actual demand and the capacity of the organization to respond to that demand. First, in a case where capacity exceeds demand, it is common for managers to perceive a high level of demand uncertainty as the demand fluctuates within the area of excess capacity. For example, in an academic health center or an acute care center, where sufficient capacity is in place to be ready for the highest potential demand for a specialized service, administrators may be overly concerned with demand uncertainty because of the effect that this uncertainty has on capacity utilization and on the ability to cover high fixed costs. second, in the opposite situation where demand exceeds capacity, actual demand may still fluctuate to a great extent. But, since it is above the capacity of the organization to handle in the immediate term, demand uncertainty may be wrongly perceived as virtually stable for that organization. We argue that demand uncertainty is highest in a capacity constrained environment where managers must adopt appropriate strategies and acquire resources in response to this demand uncertainty.
For a variety of strategic and tactical reasons, organizations choose different strategic responses to demand uncertainty. In this study, we focus on processes used to develop a volume flexible response to one element of demand uncertainty, i.e., fluctuations in the level of demand for services. To support their chosen healthcare delivery strategy, organizations deploy their resources strategically by utilizing internal planning and control systems and other technological capabilities. Clearly, there are many choices for the organization; therefore, the key question that requires clarification and investigation is how does the organization choose to deploy its portfolio of resources and technological capabilities to handle demand uncertainty? In the next section, we discuss each volume flexible strategy as well as specific tactics used to deploy each of these volume flexible strategies.
3. Volume Flexible Strategies and Methods
Several inquiries have used qualitative and quantitative approaches in a health care setting to determine relevant constructs at play in the phenomenon under research (Heineke 1995; Li and Benton 1995; Li et al. 2002). In this study, we used the case study methodology to collect and analyze data from a Carnegie I research and teaching hospital. This organization is a major regional health care facility that differentiates itself based on the level of acuteness, transplantation, and multi-organ diseases that span a wide range of several specialty services such as a Level I trauma care, cardiology, gastroenterology, urology, oncology, primary care, and ambulatory services. We interviewed 10 health care administrators in order to understand why and how these administrators deploy volume flexible strategies in response to demand uncertainty. Prior research incorporating interviews as a preliminary stage to survey development has used 10 interviews (Garver 1998). Other sources indicate that eight detailed interviews should be enough for efficient, effective research (McCracken 1988). The administrators who participated in our interviews held titles such as chief operating officer, chief information officer, chief strategy and planning officer, chief nursing officer, and chief of staff. A summary of the hospital’s performance data along with the list of interviewees is shown in Table 1. In our discussion of the respondents’ remarks, respondents are referred to by the letter that corresponds to the respondent as seen in Table 1.
Interviews took place separately at each of the administrators’ offices over a 4-week period. Two researchers conducted the interviews jointly using a structured interview process. The interviews were documented by taking notes during the session and by coordinating a summary of the meetings with each of the respondents. The interviews revealed how the respondents deployed different volume flexible strategies in response to varying levels of demand uncertainty. We were particularly interested in the administrators’ perceptions of the relationship between the range of volume flexibility desired, the level of uncertainty encountered and the chosen volume flexible strategy. We probed the respondents in order to understand how administrators chose to deploy their resources to achieve at least four possible volume flexible strategies.
The unit of analysis in this research is the medical service line within the hospital. A hospital is a complex organization that is comprised of a number of medical service lines. These service lines also serve as quasi-strategic business units for a hospital. Numerous service lines exist in a research and teaching hospital and these include services such as transplantation, cardiology, open-heart surgery, oncology, neurology, obstetrics and gynecology, acute care, and pediatrics. As part of the research effort to define and understand the possible strategies that are used within each service line, hospital administrators provided valuable insights on the tradeoffs involved in responding to different levels of demand uncertainty.
Based on these field interviews and on our literature review, we suggest that there are four volume flexible strategies that health care organizations can use to become volume flexible in response to demand uncertainty. The four strategies for creating a volume flexible response are outlined in Figure 1. The x-axis plots the level of uncertainty from low to high while the y-axis plots the range of flexibility provided by a given strategy. The four strategies are introduced by the following propositions:
P^sub 1^ Low range of flexibility in combination with a high level of demand uncertainty will be associated with a Shielding strategy.
P^sub 2^ Low range of flexibility in combination with low level of demand uncertainty will be associated with an Absorbing strategy.
P^sub 3^ High range of flexibility in combination with low level of demand uncertainty will be associated with a Containing strategy.
P^sub 4^ High range of flexibility in combination with high level of demand uncertainty will be associated with a Mitigating strategy.
This volume flexible framework suggests that organizations will selectively choose a volume flexible strategy based on the level of internal flexibility desired in response to a given level of demand uncertainty. First, when the organization faces a high degree of uncertainty and does not have the internal or external capability to respond, then the organization may choose a shielding strategy that attempts to protect the organization from the negative effects of high fluctuations in demand. second, when an organization is facing a low uncertainty level and desires a low range of flexibility, then an absorbing strategy is appropriate because it will allow the organization to use existing internal buffers to achieve this level of flexibility. Third, if a high degree of flexibility is desired in response to a low level of demand uncertainty, a containing strategy is appropriate because it allows the organization to leverage its workforce, technology and internal planning and control systems to achieve the desired level of performance. While at face value, the choice of a containing strategy may seem suboptimal, organizations may select this strategy because of competitive factors driven by the market segment or portfolio of services that they offer. In addition, some organizations may be faced with situations where the level of demand exceeds the capacity of the organization to deliver some health care services. So although the organization may see no unusual fluctuations in demand, they are nevertheless driven to be more flexible because they may be operating at or near full capacity levels. Finally, a mitigating strategy is most appropriate when the organization faces a high level of uncertainty and chooses a high-volume flexibility response because it enables the organization to leverage both internal and external sources of volume flexibility (Jack and Raturi 2002). We next explain how we developed this framework using a comprehensive literature review supported by insights from the interviews with health care administrators.
3.1. Shielding from Uncertainty
In an environment characterized by high levels of demand uncertainty, some organizations may not be able to deploy a high level of volume flexible response. In this case, when faced with a high level of demand uncertainty and the organization chooses a low level of volume flexible response, management may choose to respond with a strategy designed to shield the organization from the negative effects of demand uncertainty. This alternative prescribes identifying the sources that exaggerate demand uncertainty and removing those sources from the value added chain that provides goods and services to the consumer. Klassen and Rohleder (2001) suggest that many service organizations place more emphasis on demand management strategies and less on capacity management strategies. For example, in the retailing sector, it is well known that price promotions, couponing and other sales incentives artificially drive up demand uncertainty through the phenomenon commonly referred to as the “bullwhip effect” (Lee et al. 1997). Similarly, in the health care arena, organizations may use three basic approaches to remove demand uncertainty: pricing and rationing strategies; demand management models; and manage care control strategies.
3.1. Pricing and Rationing
Efforts to shield the organization from demand uncertainty focus on numerous strategies that control pricing and rationing such as: differential pricing strategies, product and service rationing, creation of dual markets (one of which guarantees service and product availability), reservation systems with penalties for absenteeism or a transition to specialized services with unspecified delivery times, denial of admission to nonemergency patients, relocation or discharge of some patients, and forfeiture of revenues through transfer of patients to another hospital (Ellwood 1996). Karatzas et al. (1992) also suggest different pricing strategies to manage demand for services. Rice and Morison (1994) suggest that patient cost sharing (at least in fee-for-service) can result in a reduction in the over-utilization of services, and better control of health care costs. Paul (2000) echoes similar sentiments and suggests that demand management programs are growing in prevalence throughout the health care benefits arena. Finally, Glazer (1999) emphasizes rationing and suggests denial of service strategies that reduce demand in order to limit the provisions of care.
During our interviews, we found that administrators were quite concerned about their inability to effectively use pricing or rationing to stabilize demand. In fact, several of the administrators stressed that as the Level I trauma center and major regional hospital, they have additional responsibility for indigent care and charity cases. Therefore, quality of care takes precedence over any concern for the ability of the patient to pay. Respondent A provides some insight into the dilemma, stating “pricing and rationing strategies are of limited value to us in stabilizing patient demand. Instead, our objective is to have a large patient population with a balanced payor mix in order to remain profitable. However, there is growing concern with increasing bad debt (last year we experienced approx 1% of annual revenues in bad debt). Yet, our operating margins remain favorable and they are influenced primarily by: (a) increasing volume; (b) operational efficiency (c) a strong physician referral base; and (d) attracting physicians who bring along their practice.” Furthermore, Respondent E emphasized the regulatory restrictions in using pricing or any cost shifting strategies to stabilize demand. He stated “any attempts to shift costs between the hospital and our clinics are prohibited under the anti-kickback laws.”
3.1.1. Demand Management Models. Organizations can also rely on more sophisticated technological capabilities such as demand management models to help shield themselves from demand uncertainty (Sasser 1976). Demand for health care services is influenced by several factors including size of the population, demographic changes in the population that drive disease incidences, technological advances, and changing professional standards (Vissers 2001). In some ways, demand management in a service environment resembles planning and control responses in manufacturing firms. Demand management involves the guidelines for controlling demand and managing the flow of patients through the service system (Heineke 1995).
Several researchers highlight the actions organizations take to implement a shielding strategy. First, to reduce demand, Fries (1998) suggests that administrators use a broadened definition of health promotion including chronic disease self-management, risk reduction, and increased self-efficacy. Second, Baker (2000) evaluates several health benefits programs that use interventions at specific ages in order to distribute services to a given population. Baker’s findings suggest the appropriate use of expected discounted costs and benefits in order to optimize the underlying tradeoffs between the financial viability of such health promotion programs and the cost of alternatives used to streamline the delivery of such services. Finally, Thompson (1991) suggests there are a number of marketing tools that can be used to manage demand and reduce scarce situations by more equitably matching supply with demand, including consumer analysis, communication programs, product development, and pricing strategies.
The interviewees underscored the importance of demand management models to help stabilize demand. For example, Respondent B stated that deployment of forecasting systems and real-time tracking systems would be beneficial in managing bed-spaces. Respondent I stated, “investments in forecasting systems technology and a longer planning horizon will help our responsiveness in scheduling our operating room facilities.” Respondent E advocated that more investments in forecasting systems can help predict changes in demand but this respondent cautions that internal capacity constraints will only allow them to accommodate a realistic maximum increase of only 2% to 5% annual change in demand.
3.1.2. Managed Care Control. Organizations can also use several managed care strategies to remove demand uncertainty. For example, Spetz (1999) suggests that in the health care industry, regulatory organizations, managed care entities, insurance firms, and group purchasing organizations all play a key role in stabilizing demand through pricing. Organizations use HMOs or insurance plans to guarantee a stable patient population, whereby HMOs assume risk of comprehensive provider and risk management entity. To manage excess demand, organizations may use demographic factors such as geographic distance, HMO membership, competitor location, and the availability of different service offerings in a given location (Chernew 1995). Fleming (1995) examines the relationship between primary care, potentially avoidable hospitalizations, and outcomes of care. Fleming’s work classified patients’ visits to physicians on the basis of type of care, illness and linkage to hospital episodes. Fleming’s overall findings suggest that managed-care strategies that focus on primary care can be used to remove demand uncertainty and reduce costs.
Several of the interviewees acknowledged that managed care has the potential to stabilize demand, but that it depends on the level of HMO penetration in the area. For example, Respondent F explained that the hospital system has limited exposure to HMOs and PPOs. Most of the patients are insured through Medicare, Medicaid, Blue Cross, and Blue Shield. Therefore, in their geographic location and market area, managed care offered little opportunity to stabilize demand. Respondent J also underscored the limited potential of any leverage to be gained from an alliance with HMOs or PPOs stating that “while each department has some ability to raise their own prices on an annual basis, actual reimbursements are limited by several factors including medical insurers, high bad debt rates, capitation programs, support for the teaching and research mission, and charity and indigent care services.”
3.2. Absorbing Uncertainty
In an environment characterized by low levels of demand uncertainty, some organizations may choose to deploy a correspondingly low level of volume flexible response. An examination of the literature suggests that health care organizations deploy two prevalent methods in order to absorb fluctuations in demand: time buffers, and slack capacity [Klassen and Rohleder (2001), and Gallivan (2002)].
3.2.1. Time Buffers. Time buffers such as appointment reservation systems are used extensively in service delivery (Klassen and Rohleder 2001). Medical practices absorb demand for physician services by relying on appointment reservation systems (Gallivan 2002). These systems range from relatively simple paper and pencil lists to elaborate computer spreadsheets. Computer assisted appointment systems have been designed in ways that allow for increasing complexity as the types of appointments have exploded. Today, different amounts of time can be automatically allotted for new patient visits, rechecks, and physical examinations.
During our interviews, several administrators underscored the importance of an effective appointment reservation system in health care delivery. For example, Respondent J stated, “My staff has overall responsibility for registering patients admitted to the hospital system. However, even in this closed health care delivery system there are 16 other decentralized offices where patients are also registered and admitted into the system. This decentralization makes it difficult for us to respond effectively to changes in the volume of demand.” Respondent C expressed some frustration with the lack of a centralized appointment reservation system, stating “sadly we do not have a central reservation system for ambulatory care at our clinic. Although there are approximately 675 physicians who provide primary care and other services, most of these providers are actually assigned to other medical departments and they manage their own appointment schedules. While this practice provides flexibility for individual doctors, it leads to inefficiencies when you view the entire health system as a whole.” Respondent H stated, “our hospital appointment reservation systems are not centralized. Therefore, it is difficult for managers to have full visibility of demand fluctuations. Each physician has the ability to admit patients and they sometimes do not coordinate their plans effectively with the administrative functions. Approximately 25% of patients flow to the hospital through the emergency department. Approximately 60% of patients flow to the hospital through the ambulatory clinics. The remaining 15% of patients flow through hospital transfers.”
3.2.2. Slack Capacity Buffers. Health care organizations also use several capacity buffering capabilities to absorb demand uncertainty in basic processes such as admissions scheduling, or in scheduling of operating rooms (Egger 2000). One key way to absorb uncertainty is by deploying surplus resources (lieberman 1987). Capacity buffering systems must be put in place to handle uncertainties caused by factors such as high no-show rates, early and late arrivals of patients, confusion and crowding in waiting areas, late arrival of physicians, inadequate equipment for treatment, or for emergency admissions (McLendon 2001).
Several respondents explained the importance of slack capacity in being able to respond to different volumes of demand. For example, Respondent B stated that “the average capacity utilization is around 85% (based on an average daily census of staffed beds) and this limits our ability to use slack capacity buffers to support unforeseen demand. However, bed-space availability depends on day of the week (highest on Tuesdays and Wednesdays) and also on time of the day with peak demand occurring around noon (based on check-ins and check-outs).” Respondent I underscored the importance of slack capacity in managing the operating rooms stating that “the current utilization rate is approximately 80% which leaves little flexibility in scheduling and as a result, patients may have to wait several hours before their surgical procedure can be done.” Respondent F explained how slack capacity impacts patient throughput, stating, “patient throughput is influenced by many dynamic constraints and bottlenecks (such as admissions, bed capacity, OR’s, ICUs, and specialty areas such as radiology) within the hospital system. Other bottlenecks are caused by time delays when physicians make their daily rounds and also by patients (50% come from more than 50 miles away) who have not made arrangements for their discharge. Some of our metrics used to monitor throughput include bed occupancy based on average daily census data, patient days, and discharges.”
3.3. Containing Uncertainty
In an environment characterized by low levels of demand uncertainty, some organizations may choose to deploy a correspondingly high level of volume flexible response. This situation may occur where the level of demand exceeds the capacity of the organization to deliver some health care services and the organization is effectively operating at or near its capacity level. In this situation, organizations can achieve high levels of volume flexibility through three approaches: (1) by proactively deploying flexible workforce strategies; (2) by relying on technological capabilities that enhance efficiency; and (3) by investing in information technologies that enhance organizational performance (Bowen et al. 1990; Barnett 1990).
3.3.1. Workforce Flexibility. Organizations may use flexible workforce policies that make innovative use of their human resources such as: the deployment of overtime and temporary employees to increase capacity; innovative shift schedules, and creative use of existing employees through cross-training (Bloom 1997). Although limited by specialty licensing requirements, health care organizations can increase their flexibility by relying on multi-skilled employees such as physician extenders, nurse practitioners and physician assistants. For example, Grandinetti (2000) recommends, “care teams” to promote continuous workflow and improve staff flexibility where reengineering efforts involving teams and staff cross training (nonphysician practitioners) can improve flexibility. Also, some organizations are turning to Hospitalists to guide patients through the complex delivery system where one team or individual coordinates a single patient’s overall clinical experience from admission to discharge (Singer 2003). Perhaps the biggest challenge in developing an effective containment strategy is workforce scheduling. Since the nursing work force constitutes the single largest operating cost in most hospitals, most workforce scheduling studies deal with inpatient nursing resources (Bloom 1997, and Cayirli and Veral 2003).
During our interviews, respondent A explained that the hospital system employs some 1,800 registered nurses and has about 200 vacancies at any one time. This respondent further explained how this hospital is deploying a wide array of measures to improve the flexibility and responsiveness of the nursing staff such as: (a) overtime; (b) internal stat nurses and nurse pools; (c) vacancy management with special efforts to streamline the hiring process for nurses (limited by the university hiring system); (d) staff reallocations between departments (limited by turf battles and dissatisfaction with the reallocation process); (e) voluntary nursing staff internship and reassignment programs; and (f) nursing staff recognition programs that emphasize “cheerfulness factor” to improve quality of care. This respondent stated, “the key to improving workforce flexibility is the empowerment of the nursing staff.” Several other interviewees also underscored the importance of workforce flexibility measures focused on the nursing staff. For example, respondent E explained that it is primarily the shortage of nurses, not the availability of doctors that has impeded them from implementing several new patient care services. This respondent also stated “nursing shortages, resulting from a 25% vacancy level, are an on-going challenge and a significant bottleneck in any attempt to increase patient throughput.” Respondent A explained that the local nursing shortage is not unique because “national trends show that, from 2002 to 2020 there will be a 36% increase in demand for nursing services and only a 3% increase in the supply of nurses-if nothing changes.” This respondent further explained that the hospital has “done a pretty good job holding our own” in that the nursing turnover rate for their 2002 fiscal year was 21.4%, down from 22.9% in the prior fiscal year.
3.3.2. Efficiency Measures. Organizations can also enhance their flexibility by focusing on processes that enhance their efficiency and improve their internal planning and control systems (Chirikos 2000). Some of these efficiency measures include utilization reviews, standard costing, and a focus on improving labor productivity. For example, McCallion (1999) suggests that larger hospitals displayed higher cost efficiency, higher allocative efficiency and higher technical efficiency than their smaller counterparts. Also, Rhyne (1988) suggests that in an effort to gain more control over operations and cost, many hospital boards of directors are implementing the creation of physician-approved standard medical protocols for each diagnosis related group (DRG), tracking staff physician DRG compliance, product line management to determine which services offer the best financial potential, development of standard cost systems, and proliferation of labor productivity improvement systems.
During our interviews, respondent G explained the importance of efficiency in providing a high-volume services, suggesting that competing objectives (e.g., teaching, research, and level I trauma service) generally lead to some inefficiencies. This respondent underscored these dynamics stating “while the management system was designed to support the entrepreneurial spirit of the service providers, this decentralization makes it difficult to properly allocate fixed costs to the departments and this sometimes acts as a disincentive for providers to increase patient throughput. For example, the medical department derives approximately $130M in annual revenues from research grants versus $20M from patient services. So, the leadership has a significant challenge in trying to align the incentive systems to support increased patient throughput. This hospital also needs to increase the volume of patient services in order to cover the increasing fixed costs represented by the new hospital facility currently under construction.”
3.3.3. Information Technology. Information technology offers several opportunities for health care organizations to enhance their volume flexibility and contain uncertainty (Lynch 2001). For example, the deployment of new information technology, online patient records, electronic billing systems, and highspeed telecommunication systems can significantly enhance the organizations ability to respond flexibly to demand uncertainty. Deployment of these systems enable the organization to adopt quick response systems to contain demand uncertainty levels through artificial means such as more effective rescheduling while minimizing the disruptions in work flow and preventing severe degradation of resource utilization levels. Hersh (2002) documented the adoption and use of a wide variety of clinical information technology systems that includes electronic patient records that track the majority of physician progress notes, prescribed medications, medication decision support systems that guard against drug interactions, and the tracking of laboratory and radiology results. An example of innovative use of technology is provided by Moynihan (1997) suggesting that information technology contributes positively to the production of services in the health care industry where achieving targeted savings will require widespread adoption of electronic data interchange (EDI) applications throughout the healthcare industry. Dashi (2001) suggests that new information technology, such as application service provider software, may help hospitals better cope with overcrowding in emergency rooms by using this technology to replace fax transmissions between emergency rooms and county EMS headquarters.
Respondent E provided interesting insights into why this health care organization chose to invest heavily in information technology. This respondent stated, “we have made significant investments in technology in order to improve patient throughput and quality of care.” The respondent indicated that this commitment is also evidenced by the elevation of the information officer to the corporate officer level that now administers information management resources for both the hospital and the clinics. “The new patient identification information management system is five years old and it has been adopted by over 90% of the departments. Patient records are created using a transcription system. Some of the metrics used to manage patient records in the system include time benchmarks for dictation, transcription, and validation by the providers. While some physical records may be kept within the departments, an electronic longitudinal patient record is centrally available to authorized providers throughout the system. There are several benefits to this system including timeliness, patient throughput, epidemiology and quality of care in general.”
3.4. Mitigating Uncertainty
In an environment characterized by high levels of demand uncertainty, some organizations may choose to deploy a correspondingly high level of volume flexible response. Strategies that mitigate uncertainty underscore the need for organizations to improve coordination at each echelon of its supply chain, especially in the face of increasing demand (Cooper et al. 1997). In order to become volume flexible, organizations may selectively choose from a variety of resource options involving both internal resources and external outsourcing arrangements. To develop these high levels of volume flexible responses, organizations must make significant changes to their internal functions and must also align themselves more closely with their external network of vendors and suppliers. Common approaches here include: major restructuring and reallocation of facilities, risk pooling, outsourcing and strategic alliances.
3.4.1. Restructuring. Organizations can use restructuring to mitigate uncertainty of demand volume. Mullaney (1989) describe how one hospital used restructuring and downsizing to respond to decreasing demand by examining current staffing patterns, corresponding work volume, and development of the layoff plan. It is noteworthy that although workforce changes can occur in both mitigating and in containing strategies, a mitigating strategy requires substantially more structural changes within the organization to deliver a higher level of flexibility. Green and Nguyen (2001) suggest other techniques including resource sharing during high demand periods or by taking beds out of service (reduce staffing) during low demand periods. In addition to internal restructuring of current operations, organizations can also choose to acquire additional facilities. For example, there is evidence of growth in freestanding ambulatory care facilities such as urgent care clinics and surgical centers that offer a limited range of services at a low cost and at convenient location for the patient (Branas 2000). These large hospital systems offer the potential for significant operational savings and downsizing opportunities through the regionalization of services.
During our interviews, the administrators in this university hospital system suggested that restructuring has the potential to increase their responsiveness in delivering high volumes of patient services. However, the main obstacle cited is that the administrators viewed the university hospital system as a closed system. While the hospital has tried to expand facilities into local communities to reach more patients, these efforts are limited to primary care and a few noncapital-intensive ambulatory care services. Respondent G indicated that the on-going construction of a new hospital and internal restructuring will result in the acquisition of additional bed spaces and additional operating rooms. In this regard, this respondent noted that this restructuring and acquisition of additional capacity will enhance their ability to service a higher volume of patients.
3.4.2. Risk Pooling. Organizations can also mitigate uncertainty by relying on risk pooling strategies that address the product and service offerings (Gaynor and Anderson 1995). For example, an organization with inversely correlated complementary portfolio of services hedges its demand uncertainty by pooling the product/service lines under one roof. Thus, when demand for one service offering is high, the demand for the other is low, allowing the organization to use its resources effectively at all times. Similarly, organizations with a network of facilities can shift capacity from slow economy locations to areas where demand is aggressive thereby pooling their risks across different geographical locations. Egger (2000) suggests that hospitals can save $2,000 to $3,000 per bed by managing their real estate more efficiently through multiple facility use strategies. Castle and Mor (1996) review the literature on hospitalization of nursing home residents and suggest that significant cost savings can be derived from a small reduction in transfers from nursing homes to hospitals.
During our interviews, the administrators in this university hospital system viewed risk pooling as a potentially effective tool that can be used to increase their responsiveness in delivering high volumes of patient services. However, the main obstacle cited is that the senior administrators again viewed the university hospital as a closed system where there are limited opportunities to use risk pooling. For example, respondent F explained that arrangements are in place to share emergency supplies and some bed-spaces (through local hospital transfers). Respondent F further explained that some risk pooling strategies are in place because there are some standing arrangements with other hospitals to care for patients. This respondent noted that at present, 10% to 15% of the hospital patients flow through hospital transfers.
3.4.3. Outsourcing and Strategic Alliances. Organizations are increasingly using outsourcing and strategic alliances to mitigate demand uncertainty (Anonymous 2002). The motivations for outsourcing include: lower costs, ability to offer broader service mix and the opportunity for market share gains with well-established providers (Roberts 2001). Roberts suggests that outsourcing requires an understanding of outsourcing strategy, the benefits and risks of outsourcing, the evaluation process, and the methods to managing strategically. Roberts further suggests that with appropriate management, strategic outsourcing should provide health care executives with a viable strategy for controlling costs and maintaining quality patient care. Two other researchers (Renner 1999) and Gustafson (1999) suggests that health care organizations experience improved performance through tighter control of outsourced facility services and that outsourcing partnerships can improve performance and control service costs. Brennan (1998) provides additional evidence by suggesting that today’s integrated health care delivery systems require efficient supply chain processes to speed products to users at the lowest possible cost. Brennan also states that most excess costs within the supply chain are a result of inefficient and redundant processes involved in the transport and delivery of supplies from suppliers to healthcare providers. Cooper et al. (1997) indicates that external networks and strategic alliances enable organizations to manage the source of demand and/or to enhance control of the inputs through ownership of supply and distribution channels. Other researchers, Ermann (1990) and Savage (1992), suggest outsourcing as a strategic response to uncertainty that is used by some organizations such as rural hospitals that are forming alliances, forging affiliations, or seeking some other means of collective action in an effort to survive and provide benefits to both the multihospital systems and the rural hospitals.
During our interviews, several of the senior administrators expressed concern that more emphasis needs to be placed on outsourcing as a means to improve their organization’s responsiveness. However, when we probed into the reasons why outsourcing is used so little, the administrators explained a reluctance to outsource any function that directly impacts the quality of patient care. Therefore, only a few non-clinical services (laundry, waste disposal, and parking) are currently outsourced. Respondent F indicated that “2% defects in quality makes a big difference in quality of care and this is difficult to control when key services are outsourced.” Respondent F also explained the history of poor quality with outsourced services by discussing the outsourcing of laundry services that he terminated after a short time period in the contract. Even though there was a clear reluctance by the senior administrators interviewed to outsource services that impact quality of care, respondent G indicated, “although there is much built-up resistance to outsourcing, this organization will have to address this issue in the near future as our fixed costs increases significantly with the completion of the new hospital.”
3.5. Volume Flexible Strategies and Hospital Size Since our field interviews were conducted at a large and complex research and teaching hospital, the administrators provided evidence of how the four volume flexible strategies are deployed at the delivery system level. Based on the results of the interviews, Figure 2 provides examples of how selected service lines deploy these volume flexibility strategies.
As seen in Figure 2, in service lines such as primary care, geriatric services, elective plastic surgery, and health education/prevention services, administrators may use a shielding strategy to adjust their delivery capacity by using rationing strategies, demand management models and by relying on Health Maintenance Organizations (HMOs) or Preferred Provider Organizations (PPOs). In an ambulatory care service line, administrators may use an absorbing strategy that focuses on time buffers and appointment reservation systems. However, in acute care such as emergency care, intensive care and neonatology departments administrators may favor an absorbing strategy because the organization must have enough slack resources to respond to emergencies (Ridge 1998). In the cardiology, open-heart surgery, neurosurgery, surgical oncology and orthopedics service lines, administrators rely on containing strategies in order to leverage the skills and flexibility of their providers and surgical teams that deliver these services. Finally, administrators indicated that at this university hospital, mitigating strategies are used primarily for non-clinical services (food services, house keeping, environmental and biomedical engineering services). However, Respondent F indicated that many hospitals outsource clinical specialty services (e.g., radiology and anesthesiology) and a few medical service lines such rehabilitation, mental health and pediatrics.
A related issue that requires investigation is the effect of the size of the hospital on the deployment and use of these volume flexible strategies. For example, administrators suggested that the larger the hospital system, the more we can expect to find decentralized deployment of these strategies. Future research is needed to investigate to what extent these four strategies are more prevalent in larger hospitals than in the smaller rural or community hospitals.
Hence, we offer the following propositions:
P^sub 5^ Volume flexible strategies may be deployed tactically at the service line or departmental delivery system level within the organization.
P^sub 6^ Size of the health care organization may be positively related to the extent of deployment and use of the four volume flexible strategies.
3.6. Volume Flexible Strategies and Performance Closely related to the issue of where a volume flexible strategy is deployed is the performance result of that strategy. Performance is defined as improvements in service level, financial performance, market share, and revenue growth. Although the respondents’ comments substantiated the four overall strategies, they indicated that the use of the underlying tactics may not be restricted to each of the individual strategies. In addition, respondents were unable to consistently suggest what tactics actually resulted in the highest performance for a given strategy. The underlying challenge for administrators is to determine when and how to deploy the appropriate tactics within each strategy in order to achieve the highest level of performance. The framework that is developed in this paper indicates that in the case of a given level of flexibility in combination with a given level of demand uncertainty one of the four volume flexible strategies may be most appropriate. It follows that for a chosen strategy (for a given combination of desired flexibility and demand uncertainty), the deployment of suggested tactics within that strategy will result in higher performance outcomes than will occur if these strategies are used in the other three volume flexible strategies. Hence, we offer the following research propositions:
P^sub 7^ When an organization has a low range of flexibility in combination with a high level of demand uncertainty (resulting in a shielding strategy), the use of pricing and rationing, demand management models, and managed care control will result in greater performance outcomes than in other strategies.
P^sub 8^ When an organization has a low range of flexibility in combination with a low level of demand uncertainty (resulting in an absorbing strategy), the use of time buffers and slack capacity will result in greater performance outcomes than in other strategies.
P^sub 9^ When an organization has a high range of flexibility in combination with a low level of demand uncertainty (resulting in a containing strategy), the use of workforce flexibility, efficiency measures, and information technology will result in greater performance outcomes than in other strategies.
P^sub 10^ When an organization has a high range of flexibility in combination with a high level of demand uncertainty (resulting in a mitigating strategy), the use of restructuring, risk pooling, and outsourcing will result in greater performance outcomes than in other strategies.
3.7. Time and Source Dimensions of Volume Flexible Strategies
Volume flexible strategies can also be categorized using the dimensions of time (short-term vs. long-term) and the sources (internal vs. external) of the resources and methods used to implement these strategies as shown in Figure 3. Structural decisions involving the location and restructuring of facilities, the acquisition of capacity and investments in information technology are normally regarded as having more significant implications in the long term (Hayes and Wheelwright 1984). On the other hand, operational decisions related to workforce management and production planning and control are generally tactical in nature and can be adjusted in the short-term. Since the methods that underlie each of the volume flexible strategies fall into different time and source dimensions, it is not appropriate to simply classify the actual four volume flexible strategies on these dimensions. Hence, we focus on the individual tactics that underlie these strategies. Tactics that rely on long-term structural decisions include restructuring, risk pooling, outsourcing and strategic alliances, investments in information technology, and acquisition of additional slack capacity. In the short-term, the organization will rely on the tactical deployment of existing resources such as time buffers, workforce flexibility, inventory buffers, efficiency measures, demand management models, pricing and rationing, and managed care controls.
The origin or source of the resource used to deploy a given strategy can be either internal or external. Jack and Raturi (2002) have identified several sources of volume flexibility. The external sources identified are outsourcing and strategic alliances, risk pooling, managed care controls, and arguably pricing and rationing strategies. The internal sources are essentially based on the resources, processes and capabilities that the organization currently owns such as time buffers, workforce flexibility, inventory buffers, efficiency measures, information technology, and existing slack capacity. The issues of time and sources result in the following propositions:
P^sub 11^ Methods used to deploy volume flexible strategies can be classified along two time dimensions: short-term and long-term.
P^sub 12^ The sources of volume flexible strategies can be classified along two dimensions: internal or external.
The volume flexible framework developed in this paper offers an opportunity for researchers to understand the underlying dynamics of how and why health care organizations may select a given strategy. We argue that the deployment and use of the four volume flexible strategies are primarily contingent on two factors: (1) the level of flexibility desired; and (2) the varying levels of demand uncertainty encountered. In addition, we acknowledge that the deployment of each of the four volume flexible strategies may depend on the departmental or service level and also on managerial perceptions about performance outcomes. We also acknowledge that these four strategies involve tradeoffs that may potentially result in negative performance consequences for the organization. For example, while it may be possible to absorb demand uncertainty, this strategy requires time buffers and slack resources that may ultimately have a negative impact on the organizations profitability. Similarly, while a shielding strategy may protect the productive resources of an organization from the negative consequences of high levels of demand fluctuations, one questions whether reliance on a shielding strategy can be sustained over time.
4. Summary and Directions for Future Research
Volume flexibility addresses the strategic choices that organizations make in order to respond to demand uncertainty and more broadly to gain and sustain competitive advantage. Both the evidence presented from the literature and the interviews of key health care administrators support the volume flexibility framework and the strategies and tactics that are contained within it. This prescriptive model of volume flexibility provides an initial assessment of this complex set of parameters, strategies, and tactics. In addition, the propositions contribute to important future research in this area as they identify many key relationships that require investigation.
Although future research is needed, there are several aspects of the present work that may have immediate application to the management of volume flexibility in healthcare. There are several steps that administrators can take to assess their volume flexibility needs in response to demand uncertainty. First, administrators can assess their competitive environment for key factors that drive organizations to adopt a volume flexible strategy. second, administrators may use benchmarking of key performance indicators to determine their actual performance and identify their volume flexibility gap. Third, once the gap between desired and actual volume flexibility has been identified, administrators can begin to develop a volume flexible strategy by selecting the appropriate strategy and tactics based on the framework. Fourth, there are significant potential payoffs from efforts to mitigate uncertainty by leveraging their external sources of volume flexibility through outsourcing and strategic alliances.
There are four important issues that require additional study as part of the research framework presented in this paper. First, the specific tactics chosen by management in each quadrant of the volume flexible strategies framework require further description and understanding. This will require empirical research that identifies by service line, the range of flexibility, the level of uncertainty, and the corresponding strategies that are used. Second, the nature of the underlying tactics and how they may be used in other than their primary quadrant in the volume flexibility model should also be investigated to deepen our understanding of these relationships. Third, researchers should investigate the linkages between the strategies, tactics, and their impact on organizational performance. Fourth, it is necessary to determine how these strategies are deployed in different types of health care organizations. Our research framework may serve as a guide to these needed investigations.
This research was funded partially by the Title Vt Mini-grant Program at the University of Alabama at Birmingham (UAB). We would like to thank Amitabh Raturi at the University of Cincinnati for the initial insights that ultimately led to the development of this volume flexibility framework in a health care setting. We would also like to thank the reviewers and the special issue editors for the many suggestions that have significantly improved the content and readability of this paper.
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Eric P. Jack * Thomas L. Powers
Graduate School of Management, School of Business, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA
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