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Correspondence: Address correspondence to Judith A. Lucas, EdD, APN-BC, Rutgers, The State University of New Jersey, Institute for Health, Health Care Policy, and Aging Research, 30 College Avenue, New Brunswick, NJ 08901-1293. E-mail: Jlucas{at}ihhcpar.rutgers.edu
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Key Words: Innovation adoption • NH organization, and environment
In New Jersey, the 1996–2000 Nursing Home Quality Partnership Project (Reinhard et al., 1999) tested NH oversight supplemented with collaboration between quality improvement experts and nursing facilities. The project included education in continuous quality improvement (CQI), electronic feedback of outcome measures (Zimmerman et al., 1995), technical assistance for CQI teams, and regional meetings. The full project is reported elsewhere (Reinhard, Koren, & Morris, 2000). The study we report here took place during the Nursing Home Quality Partnership Project.
Empirical studies have shown specific benefits of CQI efforts, such as reduced costs, employee satisfaction, and better continuity of care, in hospital systems (Shortell et al., 1995). Although the link between CQI adoption and improved quality in NHs is less developed than in hospitals, a few NH studies suggest that CQI strategies, when enacted in specific ways, contribute to improved clinical outcomes (Castle, 2003; Rantz et al., 2001; Ryden et al., 2000). The aim of our study was to identify the best configuration of predictors that provides the maximum discrimination between formal CQI adopters and nonadopters. We aimed to create a diagnostic profile for facility administrators and policy makers who promote CQI in quality initiatives. The question of which facilities adopt formal CQI is now even more important, because the Institute of Medicine (2001) has recommended CQI be used in addition to oversight and enforcement to improve quality in NHs, and there is now a groundswell to include more consumer-oriented approaches for quality measurement and public reporting (e.g., consumer satisfaction); these are basic tenets of CQI.
Formal CQI is a structured program that uses the systematic examination of key clinical and nonclinical processes, including an improvement orientation, customer focus, structured analytical and interdisciplinary team processes, and organization-wide commitment and participation (Berwick, Godfrey, & Roessner, 1990; Deming, 1986; Kaluzny, McLaughlin, & Jaeger, 1993; Shortell et al., 1995). CQI represents the new emphasis on continuous improvement that goes beyond quality assurance. CQI is a strategic tool for process improvements in NHs, because it demonstrates an explicit organization-wide commitment to quality and stresses organizational efforts that meet or exceed customer expectations (Hernandez & Kaluzny, 1994). However, although the hospital industry has substantially embraced the concepts of CQI (Carman et al., 1996), NHs lag behind in adopting the CQI innovation (Zinn, Weech, & Brannon, 1998).
For our study, we define an innovation as any idea, practice, or technology perceived to be new to the unit or organization adopting it (Hernandez & Kaluzny, 1993; Kimberly, 1981). We conceive of CQI adoption as a means of changing an organization, whether as a response to environmental changes or as a preemptive action to influence its environment; it intends to contribute to the effectiveness of the organization (Damanpour, 1988). Formal CQI may be studied as an innovation, because when it is introduced, formal CQI changes the nature, location, quality, or quantity of information that is available in the decision-making process (Kimberly, 1981). CQI is thus expected to have significant effects on the objectives, policies, and processes of health care service organizations (Kaluzny et al., 1993; Zinn et al., 1998), and therefore improve quality (Rantz et al., 2001; Samsa & Matchar, 2000). In the limited research examining the CQI innovation in NHs, we found only two studies on the adoption or impact of CQI as an innovation (Weech-Maldonado, Zinn, & Brannon, 1999; Zinn et al., 1998). This study contributes to our understanding of what stimulates quality improvement in NHs.
Explanations for CQI adoption as an organizational innovation require understanding the leadership, design, and complexity of the health care organization itself, as well as the organization within its larger community (Kaluzny & McLaughlin, 1994). Organizations embark on formal CQI for a number of internal and external reasons, including accreditation requirements, cost control, competition for customers, and pressure from corporate employers and payers (Kaluzny et al., 1993). Previous studies in NHs have primarily described the relationships among market competition, facility characteristics, and CQI adoption. The aim of our study is to identify the configuration of environmental and organizational factors that best discriminates between adopters and nonadopters of formal CQI. To our knowledge, our study is the first to explore how three theoretical perspectives contribute to NH adoption of CQI and to identify a diagnostic profile of predictors useful for policy makers and facility administrators attempting to promote formal CQI for improved NH quality outcomes.
Relevant Literature
Much of the current literature on CQI has come from studies in hospitals and has focused on best practices rather than adoption (Boerstler et al., 1996; Carman et al., 1996). However, research from hospital settings may not be relevant to the NH industry, as it has a different economic structure and faces heavy regulation, diminishing some of the incentives created by competition (Castle & Banaszak-Holl, 1997).
Most quality improvement studies in LTC settings have been small and anecdotal: describing quality improvement techniques used to improve one problem such as restraints (Schnelle et al., 1992), single-home quality improvements (Morley, Kraenzle, Jensen, Gettman, & Tetter, 1994), and programs for achieving quality standards or best practices (Miller, Coe, Romeis, & Morley, 1995). Three recent studies testing quality improvement strategies (Castle, 2003; Rantz et al., 2001; Ryden et al., 2000) demonstrated that on-site consultation by nurse specialists, comparative performance feedback alone, or that with nurse specialist consultation helps staff to implement care protocols and conduct CQI activities that improved clinical processes and outcomes. These studies provide evidence that effectiveness of CQI strategies on select clinical outcomes depends on the way CQI is implemented and may require added features (e.g., nurse specialists).
Research on innovation adoption in NHs has primarily focused on the influence of market pressures and facility characteristics. Results have shown inconsistent associations among facility size, proprietary status, competition, and adoption of various innovations. For example, NHs in markets with high HMO penetration operated more subacute units, whereas facilities with fewer Medicare patients operated more Alzheimer's special care units; furthermore, competition among NHs was not an incentive to innovate, but larger size and proprietary status were enablers (Banaszak-Holl, Zinn, & Mor, 1996). Participation in a provider network, an increased proportion of Medicare residents, and for-profit status increased the likelihood of participating in managed care, whereas a more competitive market and facility size were unrelated (Zinn, Mor, Castle, Intrator, & Brannon, 1999). However, in a subsequent study, larger bed size, chain membership, and high levels of private-pay residents, as well as market factors, increased the likelihood of early adoption of special care units (Castle, 2001). Minimum Data Set computerization was contingent on managers' characteristics of education, tenure, and professional involvement, except for chain-affiliated homes, in which corporate directives had the strongest effect (Castle & Banaszak-Holl, 1997).
We identified only two studies of formal CQI adoption in NHs in the current literature. Zinn and colleagues (1998) reported that perceived competition, Medicare market penetration, and the proportion of residents with Medicare coverage were significantly associated with adoption of formal CQI; however, NH size, market share concentration and excess bed capacity, and location were not. Weech-Maldonado and associates (1999) reported that board involvement in CQI was not associated with the extent of quality improvement activities, but perceived market competition was.
Conceptual Framework
In our study, variance innovation theory, resource dependence, and institutional theories of organizational behavior guide the inclusion of organizational and environmental predictors of CQI adoption. Our rationale for these approaches is that organizations and management, although exercising strategic choices, must do so within the constraints of resource capabilities, the market, and the institutional environment (Oliver, 1991; Zinn et al., 1998).
Because in this study we conceptualize formal CQI as an innovation, we utilize the variance theory of innovation adoption (Mohr, 1982), which considers the explanation for adoption at a particular point in time and in which all contributing factors are viewed as occurring simultaneously. The theory proposes that internal and external factors predict adoption; the unit of analysis is the organization as a whole; and organizational characteristics, the values orientation of leaders, community relations, and the innovation itself may be critical predictors of CQI adoption (Kaluzny et al., 1993). Organizations vary in their ability to respond to their environment, with larger ones having more flexibility to free up slack resources to facilitate CQI adoption (Kaluzny et al., 1993). Organizational leadership values must be congruent with the innovation to be adopted (Kaluzny et al.). Leadership that is customer oriented and values CQI will demonstrate this through allocation of resources (Carman et al., 1996; Kaluzny et al., 1993). It is conventional wisdom that the values of the organization's leaders are critical to the adoption of CQI. The importance of senior leadership commitment to CQI and the creation of strategies that facilitate adoption have been demonstrated in hospital studies (Carman et al., 1996; Shortell et al., 1995). In NHs, board involvement with CQI activities has been associated with outcomes, but not with formal CQI adoption (Weech-Maldonado et al., 1999).
Variance innovation theory predicts that nursing facilities with values that are congruent with CQI are more likely to be adopters. Because one of the principles of formal CQI is to meet or exceed customer expectations, we expect CQI adopters to value customer-focused objectives (Shortell et al., 1995; Zinn et al., 1992). The commitment to CQI rather than error correction is key and involves teams of employees from various levels of the organization to study data and act on improvements. Using variance theory, we expect the importance of customer-focused objectives for doing CQI, leadership commitment to CQI principles, use of CQI facilitating strategies, and size of the facility to contribute to explaining formal CQI adoption.
We also use two theories of health care organizational behavior, resource dependence and institutional theory, to identify environmental and organizational predictors of CQI adoption. Resource dependence theory emphasizes an organization's intentional strategy to enhance survival in a changing environment. Accommodating the needs of key resource-providing constituents is critical in more competitive markets, where there are higher demands on limited resources (Pfeffer & Salancik, 1978). Facilities may gain a competitive advantage by having a CQI program that meets the needs of external constituents such as hospitals and managed care contractors, and that projects an image of quality to potential private-pay residents and purchasers of care.
A NH's decision to adopt formal CQI is viewed as an adaptive response to environmental changes. In more competitive environments, organizations enter into cooperative relationships with other organizations to secure and stabilize resource flows (Oliver, 1991). For example, hospitals looking to benefit from early discharge strategies seek NHs that have the capacity to meet more stringent Medicare service guidelines and demonstrate a short-stay customer approach. NHs in a more competitive environment are likely to develop these capabilities (e.g., staffing levels, designated beds) and maintain closer ties with hospitals to attract discharged Medicare patients (Zinn, Brannon, & Weech, 1997). NHs try to position themselves favorably with hospitals for referrals by designating Medicare beds and maintaining bed availability to demonstrate their capacity for postacute care and secure a regular flow of non-Medicaid resources. We expect a higher proportion of Medicare beds for CQI adopters.
Institutional theory, in contrast, conceptualizes the environment as characterized by external norms and requirements to which organizations must conform to receive legitimacy and ensure survival (Meyer & Scott, 1992). In some of their later research, Meyer and Scott (1992) specifically state that NHs fit well the model of an institutionalized organization: "These organizations survive by concentrating their energies not on the effective and efficient performance of their technical work but on conforming to the requirements of regulatory agencies" (p. 125). Facilities that respond to new consumer-oriented norms such as accreditation requirements for CQI and OBRA standards are recognized with accreditation status and may be rewarded with increased managed care and self-paying clients. Meeting the standards projects an image of quality and of being more consumer oriented, which are hallmarks of CQI (Shortell & Kaluzny, 1994). The institutional theory incentive for adopting CQI is thus to avoid negative perceptions of external groups by meeting standards to remain deficiency free and project an image of quality.
These two theories of organizational response to the environment have been viewed as complementary and have been used in combination in prior studies of innovation in health care (Oliver, 1991; Zinn et al., 1998). Both of these approaches recognize organizations and management exercise strategic choices within the constraints of resource capabilities, the market, and interest groups in the environment (Oliver; Zinn et al.). Resource dependence emphasizes the organization's active negotiations to control resources, whereas institutional theory emphasizes conformance with external expectations (Oliver; Tolbert, 1985). Using both, we expect environmental predictors of competition, Medicare program capacity, and the objective of conformance with new OBRA and Joint Commission on Accreditation of Healthcare Organizations (JCAHO) standards to explain formal CQI adoption.
Using these three theoretical perspectives, that is, resource dependence theory, variance theory, and institutional theory, we asked this research question: What is the unique contribution of the environmental predictors (competition, Medicare program capacity, and objectives of conformance with new standards) and the organizational predictors (internal customer-focused objectives, leadership commitment, CQI facilitating strategies, and facility size) to the function that maximizes the differences between adopters and nonadopters?
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Our second data source was the 1998 NH Profiles Chart in the consumer guide Selecting a Long-Term Care Setting (New Jersey Department of Health and Senior Services [NJDHSS], 1998). These facility-specific data (e.g., size, Medicare beds, and census) are reported annually by facilities and validated through the annual state recertification process.
Respondents
The Nursing Care Quality Improvement Survey (Zinn et al., 1998) was mailed in 1999 to the licensed NH administrator of all 350 New Jersey licensed LTC facilities listed in the 1998 NH Profiles Chart (NJDHSS, 1998). Reminder letters and calls resulted in 159 completed questionnaires (46%). This response rate is well within the range (27–73%) reported in other mail surveys of NH administrators (Coburn, Fralich, McGuire, & Fortinsky, 1996; Kisor, 1996). Although the facilities were not randomly selected, a statistical examination indicated facilities were generally comparable with licensed NHs in New Jersey with regard to size, Medicare census, ownership, and location. A chi-square test showed no significant difference between proprietary and nonprofit facilities across adopters and nonadopters, and therefore precluded examination of ownership status in the discriminant analysis.
Measures
The Dependent Variable
See Table 1 for definitions and sources of all variables. We defined adoption of formal CQI as a facility having met all of the following five criteria: (a) written statement of philosophy or commitment to CQI; (b) use of structured problem-solving approaches incorporating data collection and measurement to identify quality problems and monitor improvements; (c) use of employee teams from multiple departments and organizational levels as the major mechanism for analyzing and improving processes; (d) systematic assessment of satisfaction data from residents and care providers; and (e) empowerment of employees to identify and take action on quality improvement problems and opportunities. The five criteria we used are defining characteristics and essential components of formal CQI and are consistent with those used in hospitals and industry (Berwick, Godfrey, & Roessner, 1990; Deming, 1986; Shortell et al., 1995), and they are relevant to the NH industry (Zinn et al., 1998). Previous studies of NH innovation adoption have used formal CQI adoption versus the practice of any CQI activities (Zinn et al). Our aim was to examine adoption (indicated by reporting all five criteria of formal CQI) as a single event occurring at one point in time (according to the variance theory).
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We defined Medicare capacity as the readiness to attract Medicare resources by participation in the Medicare program (i.e., meeting more stringent Medicare standards, staffing, and orientation to needs of short-stay residents) and measured it as proportion of Medicare beds in the facility for 1998. Utilizing institutional theory, we defined external customer-focused objectives of CQI as conformance with new requirements (OBRA) and accrediting standards, and we measured these as the extent of the importance of meeting these new standards. Facilities are not required to receive Medicare and Medicaid certification or JCAHO accreditation to operate, but most find it in their own best interest because participation indicates that the facility is presumptively endorsed by the authorities. NHs pride themselves on being "deficiency free"—a commonly held measure of NH quality (Angelelli, Mor, Intrator, Feng, & Zinn, 2003). Thus, facilities might initiate CQI to meet new standards to remain deficiency free and project an image of quality (Shortell & Kaluzny, 1994).
From variance innovation theory, we included four organizational predictors: importance of internal customer-focused objectives of CQI, extent of leadership commitment to CQI, extent of CQI facilitating strategies, and facility size. We defined internal customer-focused objectives of CQI as financial, service quality, and human resource objectives. Respondents indicated the extent of importance of each type of CQI objective. We defined leadership commitment to CQI as the extent the facility's board or corporate directors required reports and CQI projects within the past 12 months to improve quality. We measured leadership commitment by using the sum of 10 dichotomous items from the Nursing Care Quality Improvement Survey.
We defined organizational CQI facilitators as helpful strategies including structural, senior management, implementation commitment, time, and training managers in CQI. According to variance theory, larger organizations have more flexibility to free up slack resources to facilitate CQI adoption (Kaluzny et al., 1993). We defined facility size as the number of LTC beds (see Table 1).
Analysis
Of the 159 responding facilities, we classified 66 (42%) as adopters (met all five criteria) and 70 (44%) as nonadopters (met four or fewer criteria); 23 (14%) had missing data and we omitted them from further analysis. Table 2 presents means and standard deviations of the predictors. Cronbach's alpha coefficients ranged from.61 to.87 for the scales from the Nursing Care Quality Improvement Survey. A preliminary analysis indicated that data satisfied the requirements for performing a discriminant analysis. Our ultimate aim was to create a diagnostic profile of what promotes quality improvement for facility administrators and policy makers. We express discriminant function coefficients as standardized coefficients similar to beta weights in a regression analysis, but the relative contribution of each predictor to the discriminant function is clear and easy to interpret—thus, it's a more useful diagnostic tool than estimates of the likelihood as in logistic regression. We performed a discriminant analysis to identify the linear discriminant function of organizational and environmental predictors that provided the best discrimination between adopters and nonadopters of CQI, and to estimate the standardized canonical coefficients of this function. We established the level of significance at p =.05. We conservatively set the minimum cutoff for interpretation at coefficient loadings of at least.40 (Tabachnick & Fidell, 1996). We reviewed centroids and performed a classification routine.
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Table 4 summarizes group membership results of the classification routine. Approximately 71% of adopters and 54% of nonadopters were correctly classified. An accurate prediction of adopters versus nonadopters took place for approximately 63% of the homes.
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Several findings are important to the industry. The CQI goal of conformance with new requirements (OBRA) and accrediting standards (JCAHO) was significant in distinguishing CQI adopters. Formally adopting NHs may respond to new criteria for NH quality measurement and new public reporting to avoid negative perceptions and to increase managed care or private-pay client base. Policies that set CQI as an expectation of Medicare and Medicaid recertification or accreditation and provide rewards of accredited or deficiency-free status are likely to stimulate CQI adoption in NHs. Recently the IOM (2001) recommended that policies must go beyond just enforcement of standards to improve quality in LTC. Future attempts to improve quality in LTC through CQI should be sensitive to the environmental and organizational factors that facilitate formal CQI.
Although they report a higher number of nursing facilities in local competition for residents, adopters perceive less intense market competition. At first this may appear contradictory; however, it may be that even though there is a good supply of NHs or beds available, NH administrators report they are not engaging in aggressive marketing practices reflective of perceived intense competition. This contrasts with findings by Zinn and colleagues (1998) and by Weech-Maldonado and colleagues (1999) that adoption of CQI was directly and significantly related to the level of perceived competition. This is likely due to the differences in operationalizing the dependent variables. However, this may also indicate less perceived market pressure in New Jersey, with less penetration of managed care in the NH industry, the lack of full service substitutes from the assisted living industry, and a history of high occupancy rates (Harrington, Carrillo, Thollaug, Summers, & Wellin, 2000) that may have reduced a competitive incentive for CQI adoption during our study (Banaszak-Holl et al., 1996).
Noteworthy for administrators are our findings that adopters reported that structural strategies such as information systems, flexible use of key personnel or consultants, and supports for team processes were important facilitators of formal CQI. These represent the basic infrastructure needed for the day-to-day practical work of CQI. Adopters also reported that training activities for both senior and middle managers in CQI methods facilitated adoption, yet board level commitment did not. Management may best demonstrate CQI values by supplying the infrastructure and providing training to facilitate formal CQI adoption rather than by requiring reports and projects. Both infrastructure and a core of local expertise may be critical to improve NH outcomes through formal CQI. Rantz and associates (2001) reported difficulty with their feedback and nurse consultation intervention when NHs did not have well-developed CQI teams or training in data interpretation. Structural facilitators (e.g., information systems, supports for teams, and training) are all within the control of NH management. Facility leaders can thus develop their own core of local expertise and infrastructure to promote CQI.
Neither facility size nor proportion of Medicare skilled beds was significant in discriminating adopters from nonadopters. Although this is consistent with the findings of Zinn and colleagues (1998) that NH size was not associated with CQI adoption, this does not confirm their finding that a high proportion of Medicare residents was associated with CQI adoption. Our measure of Medicare bed proportion represents the facility's capacity to accommodate the needs of a key resource provider—hospitals rather than Medicare dependence. It may be that facilities striving to attract Medicare-reimbursed referrals and accommodate hospital expectations shift internal resources and reduce slack resources available to embark on a formal CQI program. Because the Medicare prepaid provider system of reimbursement was just being initiated for NHs, many smaller and nonchain homes may have perceived adding Medicare beds as being burdensome rather than conferring a competitive advantage. Although caution should be taken not to generalize these findings beyond nursing facilities in New Jersey, our finding that facility size is not meaningful to formal CQI adoption is, however, noteworthy. It suggests that even small homes are capable of formal CQI adoption, regardless of size, if the structural facilitators and training are in place.
It is important that the discriminant function was effective in correctly classifying CQI adopters (71.4%), because policies that seek to encourage collaborative projects between government and industry or to reward performance improvement have to correctly identify CQI adopters. The lower total cases classification (63%) reflects the lower effectiveness of classifying nonadopters. We speculate that this may be due to nonadopters' initiating some CQI activities yet still not meeting all five criteria necessary to be defined as formal CQI adopters in our study. Nonadopters most often did not conduct systematic assessment of satisfaction data (68%) or have a written CQI philosophy (55%). The Nursing Home Quality Partnership site visits indicated that some NHs show evidence of CQI activities while not yet carrying out all formal CQI practices (Reinhard et al., 2000). It is important to recognize formal CQI adoption from nonadoption, especially if future policies provide public reports or incentive reimbursement. Facilities could, for the purposes of prestige, superficially adopt the innovation and not have the highest degree of commitment to CQI (Downs & Mohr, 1975).
Conclusions and Implications
Although the cross-sectional nature of this study precludes determination of causality, this study contributes to our understanding of what may stimulate adoption of formal CQI. This analysis is important in that it shows six organizational and environmental predictors representing all three theoretical perspectives that best distinguish formal CQI adopters from nonadopters. Our profile of adopters suggests ways that might assist facilities in adopting CQI principles and methods. Facility leaders can demonstrate commitment, develop their own local expertise, and provide the basic infrastructure and staff training that facilitate CQI and team building. The diagnostic profile of CQI adopters also suggests that new policies requiring NHs to demonstrate adoption of CQI to improve quality outcomes are likely to work, regardless of facility size. However, such policies must be sensitive to factors that facilitate formal CQI program adoption, such as training, consultation, and incentives. In addition, initiatives can expose all facilities to CQI principles and can provide training and incentives directed at the set of internal and external factors contributing to formal CQI adoption and possibly persistence of use, although what influences persistence requires further study.
For example, development of information systems and training in data analysis, leadership, and team building all facilitated CQI adoption. These have since been incorporated into the Nursing Home Quality Partnership project (Reinhard et al., 1999). The frequent changes in ownership and high administrator turnover experienced during the project indicate that education will be needed to maintain a core of CQI expertise among NH administrators, directors of nursing, and staff members. Experts agree that continuing education and formal coursework designed to help physicians and registered nurses use computer information systems and apply quality improvement methods are needed to improve LTC (Kovner, Mezey, & Harrington, 2002; Lucas & Fulmer, 2003).
These findings contribute to our understanding of what may stimulate formal adoption of quality improvement and to the body of literature on innovation in NHs. However, several intriguing questions result from this study. Do characteristics of NH leaders and organizational culture influence adoption, persistence, and impact of CQI on NH outcomes? Do specific policies such as the new minimum staffing levels and new sources of competition from home- and community-based services stimulate CQI adoption? Finally, the impact of specific CQI strategies on resident, employee, and process outcomes should be tested.
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1 Institute for Health, Health Care Policy, and Aging Research, Rutgers, The State University of New Jersey, New Brunswick.
2 School of Allied Health and Life Sciences, New York Institute of Technology, Old Westbury.
3 College of Nursing, Rutgers, The State University of New Jersey, Newark.
4 New Jersey Department of Health and Senior Services, Trenton.
5 The Commonwealth Fund, New York, NY.
6 Center for State Health Policy, Rutgers, The State University of New Jersey, New Brunswick.
Decision Editor: Linda S. Noelker, PhD
Received for publication May 2, 2003. Accepted for publication July 9, 2004.
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1819 [42 USC 1395i–3]. Signed by President, Washington, DC. December 22.This article has been cited by other articles:
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