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Adoption of a health education intervention for family members of breast cancer patients
Research Articles
Adoption of a Health Education Intervention for Family Members of Breast Cancer Patients Paul K. Halverson, DrPH, Glen P. Mays, PhD, Barbara K. Rimer, DrPH, Caryn Lerman, PhD, Janet Audrain, PhD, Arnold D. Kaluzny, PhD Background: Relatives of breast cancer patients often face substantial uncertainty and psychological stress regarding their own health risks and optimal strategies for prevention and early detection. Efficacious educational and counseling interventions are rarely evaluated for their potential adoption and use in medical practice settings. This study evaluates a health education program for first-degree relatives of breast cancer patients based on the program’s potential for being adopted and used by medical practices affiliated with cancer centers. Methods:
A randomized, controlled trial was implemented in four community hospital– based medical practices. After 9 months, clinical and administrative staff at each practice were given self-administered surveys. Of 90 staff members recruited to respond, useable responses were received from 60 (67%), including 13 physicians (31%), 43 nurses (98%), and four program managers (100%). Participants made self-reports of program awareness, program support, perceived program performance, likelihood of program adoption and use, and barriers to adoption.
Results:
A strong majority of respondents (80%) reported that all or most staff agreed with the need for the program. Perceived program performance in meeting goals was generally favorable but varied across sites and across staff types. Overall, 56% of respondents indicated that their practices were likely or highly likely to adopt the program in full. The likelihood of adoption varied substantially across sites and across program components.
Conclusions: Evaluating the potential for program adoption offers insight for tailoring preventive health interventions and their implementation strategies to improve diffusion in the field of practice. Medical Subject Headings (MeSH): breast neoplasms, counseling, family, health education (Am J Prev Med 2000;18(3):189 –198) © 2000 American Journal of Preventive Medicine
Introduction
M
ounting publicity about breast cancer in general, and about genetic susceptibility in particular, leads many women to seek information about disease risks, screening strategies, and primary prevention practices. The need for informaFrom the Department of Health Policy and Administration, School of Public Health, University of North Carolina (Halverson), Chapel Hill, North Carolina; Public Health Practice Program Office, Centers for Disease Control and Prevention (Halverson), Atlanta, Georgia; Department of Health Care Policy, Harvard Medical School (Mays), Boston, Massachusetts; Division of Cancer Control and Population Sciences, National Cancer Institute (Rimer), Rockville, Maryland; Lombardi Cancer Center, Georgetown University Medical Center (Lerman and Audrain), Washington DC; School of Public Health, Cecil G. Sheps Center for Health Services Research and Lineberger Comprehensive Cancer Center, University of North Carolina (Kaluzny), Chapel Hill, North Carolina Address correspondence and reprint requests to: Glen P. Mays, PhD, Department of Health Care Policy, Harvard Medical School, 180 Longwood Drive, Boston, MA 02115. E-mail: [email protected]. harvard.edu.
tion is particularly acute in families of women who have been diagnosed with breast cancer. Relatives of breast cancer patients often face substantial uncertainty and psychological stress regarding their own health risks and the optimal prevention strategies. To address the information and counseling needs confronted by family members of breast cancer patients, a health education intervention was developed for implementation as part of a high-risk education and counseling program. This study examines the intervention’s prospects for adoption and dissemination by specialized medical practice settings for cancer care. The success of any health education intervention must be judged not only by its efficacy in addressing the information and counseling needs of its users but also by its adoption and use in medical practice. Efficacy for the intervention of interest was evaluated previously through a randomized, controlled trial discussed elsewhere.1 A substudy of the larger controlled trial was launched to evaluate the process of adoption once the
Am J Prev Med 2000;18(3) 0749-3797/00/$–see front matter © 2000 American Journal of Preventive Medicine • Published by Elsevier Science Inc. PII S0749-3797(99)00163-4
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intervention was implemented at community hospital– based medical practices affiliated with comprehensive cancer centers.
adoption? and (3) How could adoption of the intervention be enhanced?
Examining Program Adoption Intervention Design Before initiating the study of program adoption, a randomized trial was conducted to assess the effect of a problem-solving intervention to help first-degree relatives of breast cancer patients cope with their relatives’ diagnoses and to increase adherence to recommended cancer surveillance strategies. Eligible participants for the randomized trial were 1128 women aged 20 to 75 who had a first-degree relative with a recent diagnosis of primary breast cancer.1–3 The Problem-Solving Training (PST) intervention was based on Lazarus’s transactional model of stress and coping, and included components designed to encourage women to define problems, identify solutions, and then to try them.4 Through this intervention, participants received information about breast cancer risk factors via in-person and printed communications—including their Gail estimate of individual breast cancer risk5; references to published information on breast cancer; and advice about screening and prevention practices such as diet, physical activity, and smoking cessation. The PST intervention was compared with a control intervention, the General Health Counseling (GHC) intervention, in which participants received information about breast cancer risk factors via traditional in-person and printed communications that did not involve the specialized problem-solving activities. Both interventions were delivered, in sessions of the same length, by a trained health educator who used a scripted protocol and flip chart. The PST intervention was found to reduce cancer-specific distress among women, especially those with fewer formal years of education when compared with the GHC control intervention (see Lerman et al.1 for a more detailed presentation of the study design and findings). During the program adoption phase of the study, study sites combined their treatment (PST) and control (GHC) arms to offer a single intervention comprised of traditional information and counseling strategies as well as problem-solving activities. The combined intervention included components of the GHC control intervention because we hypothesized that medical practices might be more likely to adopt the intervention if it included traditional information and counseling strategies in addition to the more nontraditional PST strategies. The program adoption phase of the study was designed to address three critical policy questions: (1) How likely were the medical practices to adopt the intervention as a permanent program? (2) What characteristics of the intervention and the organizational environment might facilitate or impede 190
The successful introduction of a health education intervention into specialized medical practice settings is far from automatic.6 Such interventions may represent a significant departure from the diagnostic and therapeutic technologies emphasized in these settings. Characteristics of the intervention, the organizational environment, and the process of introduction all may influence adoption.7 A growing body of evidence in organizational sociology suggests that institutions adopt innovations through a staged process involving stakeholders at multiple levels within their organization.8,9 Most models indicate that adoption begins with an awareness stage, during which key stakeholders recognize the existence of a problem or of an unmet need. Stakeholders in medical care organizations may include physicians and other clinicians, administrative and support staff, health care consumers, insurers, employers and other purchasers, and health care regulators and policymakers.10,11 After awareness has been achieved, organizations progress to an identification stage in which stakeholders begin to link the innovation with the recognized need. Organizations may then proceed to an implementation stage in which the innovation is tested within the organization. Finally, organizations may achieve an institutionalization stage during which the innovation is made routine within the operation of the organization. Success at any stage of the adoption process hinges on the achievement of earlier stages. The successful transition from implementation to institutionalization is a critical area of interest for studies of program adoption. This transition is particularly important for studies such as this one in which implementation is explicitly imposed through the study design. We hypothesize that four factors influence program adoption during the transition from implementation to institutionalization: First, adoption depends on the degree to which the earlier stages of awareness and identification have been achieved among the organization’s key stakeholders. Second, adoption is sensitive to stakeholder perceptions about the program’s performance in meeting goals and expectations during implementation. Third, specific program attributes may facilitate or impede an institution’s ability to integrate the program within its existing structure, due to factors such as cost and resource use, administrative complexity, compatibility with existing services (including economies of scope), and consumer demand (including economies of scale).12–15 Finally, characteristics of the organization and its environment may facilitate or impede adoption, including the availability of human and capital resources, competitive pressures for market share, the flow of information
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within and across organizations, and the decisionmaking processes within organizations.8,16 This study uses measures of these four factors to assess the likelihood of program adoption among medical practices that have implemented the program.
Methods Five community hospital– based medical practices specializing in breast cancer treatment were recruited to participate in a demonstration of the health education program. Each practice agreed to implement the program for a 9-month period during calendar year 1996. Each participating practice received payments to cover program costs incurred during the study period. A uniform method was used for introducing each practice to the health education intervention. A nursetrained project coordinator was identified at each practice and trained by study investigators to deliver the intervention. During the last month of the implementation period, we surveyed clinical and managerial staff within each practice to assess the practice’s likelihood of adopting various components of the program upon completion of the implementation period. A self-administered questionnaire was used to collect measures regarding each of the four factors likely to influence program adoption.
Measures of Program Adoption Potential Four sets of measures were used, each one corresponding to one of the four types of factors hypothesized to influence program adoption. An initial set of 6 measures assessed program awareness and program identification as a needed intervention. Respondents used a Likert-type scale to rate staff awareness of the program, patient awareness of the program, family awareness of the program, staff agreement with the need for the program, staff desire to continue the program, and staff encouragement of patients and family members to use the program. Additionally, respondents were asked to indicate the specific types of staff who were not aware of the program and who did not identify the program as a needed intervention. A second set of measures assessed staff perceptions about the program’s performance in meeting selected expectations and goals. These measures were adapted from established assessment instruments developed for measuring perceived organizational performance and innovation in health care organizations.17,18 First, respondents answered a question about whether the program fulfilled all, most, some, few, or none of the goals established for the program. Additionally, five goal-specific performance measures were constructed by asking staff to rate the program’s potential for encouraging the use of preventive and screening services, enabling earlier cancer treatment, improving
patient satisfaction, improving family member satisfaction, and attracting additional patients. A third set of measures assessed attributes of the health education program that might facilitate or impede adoption. Respondents were asked to indicate their perceptions about the likelihood of adopting specific program components and about the perceived barriers to adopting these components. Six specific program components were examined, and respondents used a 5-point Likert-type scale to indicate the perceived likelihood of adopting each component. Additionally, respondents reported perceived barriers to adoption for each of the program components by selecting from among a predetermined list of potential barriers. The list of potential barriers used for this analysis was not exhaustive, but it nonetheless encompassed key dimensions found to be important in previous studies.9 Respondents were also asked to report other major barriers to implementation not included on the list, but fewer than 5% of respondents listed additional barriers. Finally, a fourth set of measures assessed attributes of the program environment that might influence adoption of the intervention. Although a broad range of organizational and environmental characteristics comprises the program environment, we examined only a subset of these characteristics because a full exploration was beyond the scope of the study. First, we assessed the aggregate effects of the program environment by comparing site-specific measures of program adoption among the participating study sites. Significant acrosssite variation in program awareness, perceived performance, and likelihood of program adoption provided evidence that adoption was sensitive to attributes of the program environment. Second, we examined two specific measures of the program environment that have been found highly predictive of adoption in previous empirical studies: (1) the degree to which competitive pressure and “peer pressure” for adoption was perceived to exist in an organization’s service area and (2) the degree to which innovators or role models for program adoption were perceived to exist outside the service area.9,19 –21
Data Collection and Analysis Methods Each project coordinator solicited responses from a predetermined group of staff in each medical practice who carry out primary clinical or managerial responsibilities within the practice’s breast cancer program. This group included the chief administrator of the practice’s breast cancer program, all oncologists and hematologists affiliated with the breast cancer program, and all full-time registered nurses who spend at least 50% of their time staffing the breast cancer program. Although not inclusive of all program staff, the groups selected for study have considerable influence over the Am J Prev Med 2000;18(3)
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Table 1. Descriptive characteristics of the study sites Characteristic I. Metropolitan Statistical Area Characteristics Region Population Percent of population nonwhite Median age (in years) Per capita income Active nonfederal physicians per 100,000 population Oncologists per 100,000 population Acute care hospital beds per 100,000 population Number of acute care hospitals Percent providing genetic counseling Percent providing mammography screening services Percent providing oncology services Percent providing radiation therapy Percent providing women’s health services Percent of population with commercial health insurance enrolled in HMOs Percent of population with commercial health insurance enrolled in PPOs II. Hospital and Practice Characteristics Number of staffed beds Number of designated oncology beds Number of designated oncology nurses Number of oncologist/hematologist physicians
Site A
Site B
Site C
Site D
Northeast 5,956,296 9% 31 $23,900 353 2.0 347 90 19% 84% 86% 28% 48% 61%
Mid-Atlantic 4,360,349 32% 30 $26,800 329 1.5 304 44 11% 80% 80% 39% 32% 44%
Southeast 1,212,393 24% 30 $19,900 166 1.0 267 10 10% 80% 60% 40% 40% 28%
Midwest 7,560,720 29% 30 $23,800 282 1.8 386 92 22% 84% 77% 41% 43% 36%
15%
34%
46%
43%
80 14 22 3
296 28 25 7
593 not reported 12 12
270 0 9 11
Data on metropolitan statistical area characteristics were obtained from the U.S. Department of Health and Human Services 1996 Area Resource File and were measured at end of year 1992, except data on health maintenance organization (HMO) and preferred provided organization (PPO) enrollment, which were obtained from the American Association of Health Plans and were measured at end of year 1996. Data on hospital and practice characteristics were obtained directly from the responding organizations and were measured at end of year 1996.
program adoption process because of their direct responsibilities in program implementation and medical practice management. Responses were solicited from these staff during the last month of the 9-month program-evaluation period. Project coordinators were offered a small monetary incentive for achieving 90% response rates among their sites’ nurses and physicians. Project coordinators at four of the five implementation sites obtained responses to the questionnaires within 3 months. The nonresponding site was a small breast cancer clinic staffed by a single physician and nurse practitioner. All of the remaining four sites obtained responses from program managers and nurses, and three of the four sites obtained responses from their affiliated physicians. Overall, we received responses from 67% of the staff targeted in the four responding sites, including 31% of the physicians, 98% of the nurses, and 100% of the program managers. These response rates yielded 60 staff responses, including 43 nurses, 13 physicians, and 4 program managers. Survey responses were summarized overall and by site. Across-site differences were assessed for statistical significance using Fisher exact tests because some comparisons involve small cell sizes that threaten the validity of standard chi-square tests.22 We also examined the statistical correlates of program adoption by classifying each respondent into one of two groups—probable adopters and probable nonadopters— based on whether or not the respondent reported 192
the likelihood of adopting all program components as “likely” or “highly likely” using the 5-point Likert scale. This classification scheme allowed comparisons between respondents who are relatively confident that the program will be adopted by their practice and those who are not confident about adoption. Based on previous theoretical and empirical studies of organizational innovation, potential adopters were expected to report higher levels of program awareness, program identification, and perceived program performance than were potential nonadopters, because these factors were hypothesized to increase the probability of program adoption.8,9,15 Fisher exact tests were used to assess differences between groups.
Results The four participating study sites showed moderate diversity in their practice environments and operational characteristics (Table 1). All sites were located in relatively large metropolitan areas with at least 1 million residents. Compared with the other sites, Site C was located in an area with substantially lower per capita income, physician availability, hospital-bed capacity, and health maintenance organization (HMO) penetration. Site A was located in a community hospital having fewer staffed beds and fewer affiliated oncologists and hematologists than did other sites.
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Table 2. Awareness of the women’s health education program Percent of staff reporting itema Survey Response Item All or most staff are aware of the program. Some physicians are not aware.* Some nurses are not aware. Some managers are not aware.** Some support staff are not aware.* All or most breast cancer patients are aware of the program. All or most family members of breast cancer patients are aware of the program.
All (N ⴝ 60)
Site A (n ⴝ 18)
Site B (n ⴝ 14)
Site C (n ⴝ 13)
Site D (n ⴝ 15)
32 32 32 25 57 31
13 12 18 41 75 47
46 29 36 21 50 36
39 39 23 0 69 23
33 53 53 33 33 13
25
41
21
31
7
a Numbers reflect the proportion of staff that reported the survey response item listed in the first column. Fisher exact tests are used to test the null hypothesis that the proportions across all 4 sites are equal. **p ⬍ 0.05 *p ⬍ 0.10
Program Awareness and Identification The health education program achieved relatively modest levels of awareness among the medical practices’ major stakeholders, according to survey respondents. Only one third of the respondents reported that all or most staff within their practices were aware of the program (Table 2). One third of the respondents reported that both physicians and nurses were unaware of the program. By comparison, one fourth of the respondents indicated that some managers in their practice were not aware of the program, and more than half reported that some support staff personnel lacked program awareness. Respondents reported similarly low levels of awareness among breast cancer patients and family members. Despite modest levels of awareness, there appeared
to be relatively strong support for the program among staff who were aware of the program (Table 3). More than 80% of respondents indicated that all or most staff agreed with the need for the program. Similarly, two thirds of respondents reported that all or most staff wanted the program to continue beyond the study period. Finally, roughly half of all respondents indicated that all or most staff encouraged family members to use the program. Perhaps not surprisingly, respondents rated the nonclinical staff as least supportive of the program.
Perceived Program Performance Staff perceptions of the health education program’s performance varied but were generally favorable. Nearly two thirds of the respondents reported that the
Table 3. Staff identification with and support for the women’s health education program Percent of staff reporting itema Survey Response Item
All (N ⴝ 60)
Site A (n ⴝ 18)
Site B (n ⴝ 14)
Site C (n ⴝ 13)
Site D (n ⴝ 15)
All or most staff agree with the need for the program. Physicians agree with tne need for the program.* Nurses agree with the need for the program.** Managers agree with the need for the program.* Support staff agree with the need for the program. All or most staff desire to continue the program. Physicians desire to continue the program. Nurses desire to continue the program. Managers desire to continue the program. Support staff desire to continue the program. All or most staff encourage family members to use the program. Physicians encourage program use. Nurses encourage program use. Managers encourage program use. Support staff encourage program use.
81 81 100 81 44 67 71 90 47 16 49 69 90 33 14
88 81 100 81 44 69 81 94 44 19 47 88 94 31 19
86 43 86 57 21 79 64 93 57 29 50 57 86 50 21
85 85 100 62 15 69 77 100 54 8 54 69 100 31 8
67 80 67 33 20 53 60 73 33 7 47 60 80 20 7
a
Numbers reflect the proportion of staff that reported the survey response items listed in the first column. Fisher exact tests are used to test the null hypothesis that the proportions across all 4 sites are equal. *p ⬍ 0.10 **p ⬍ 0.01
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Table 4. Staff perceptions of program performance Percent of staff reporting elementa Performance Element Fulfilled all or most goalsb* High potential for encouraging use of preventive/screening servicesb High potential for enabling earlier treatment for breast cancerb High potential for improving patient satisfaction with practiceb High potential for improving family member satisfaction with practiceb* High potential for attracting greater number of patients to practiceb*
All (N ⴝ 60)
Site A (n ⴝ 18)
Site B (n ⴝ 14)
Site C (n ⴝ 13)
Site D (n ⴝ 15)
66 85
83 94
62 85
92 85
33 73
74
82
85
62
67
83
88
85
92
67
79
82
92
100
47
57
53
85
54
40
a Numbers reflect the proportion of staff that reported the performance element listed in the first column. Fisher exact tests are used to test the null hypothesis that the proportions across all 4 sites are equal. b Response options for “high potential” and “very high potential” are collapsed in computing proportions for this performance element. *p ⬍ 0.01
program fulfilled all or most of the goals that their practices had for it (Table 4). The proportion of staff members reporting goal fulfillment varied significantly by site, ranging from 33% of Site D staff to 92% of Site C staff (p ⬍ 0.01). Eighty-five percent of respondents felt that the program had a high or very high potential for encouraging appropriate use of preventive and screening services for breast cancer. Respondents reported least frequently the ability to attract greater volumes of patients as a program benefit. For most potential benefits listed on the survey, staff members from Site D were more pessimistic about the potential for realization compared with staff members from the other sites.
Perceived Likelihood of Adopting Program Components Approximately half of all respondents perceived that their practices were likely or highly likely to adopt all components of the health education intervention (Table 5). Respondents appeared most optimistic about adopting the program component of providing family members with information on how to decrease breast cancer risk. Respondents were least optimistic about adopting the component of establishing convenient hours of operation outside normal business hours. Contrary to expectations, substantial numbers of respondents felt that their practices were likely to adopt
Table 5. Staff perceptions of the likelihood of adopting program components Percent of staff reporting adoption as likelya Program Component All components** Providing information about how to decrease breast cancer risk Providing follow-up telephone calls* Providing one-on-one counseling with a trained health educator*** Establishing special hours convenient to family members** Providing services free of charge Providing other services to family members of breast cancer patients
All (N ⴝ 60)
Site A (n ⴝ 18)
Site B (n ⴝ 14)
Site C (n ⴝ 13)
Site D (n ⴝ 15)
56 82
41 72
50 86
92 100
50 73
63 62
56 56
64 50
92 100
47 47
47
39
36
85
33
60 41
44 28
50 36
77 67
73 40
a Numbers reflect the proportion of staff that reported adoption as “likely” or “highly likely.” Fisher exact tests are used to test the null hypothesis that the proportions across all four sites are equal. ***p ⬍ 0.01 **p ⬍ 0.05 *p ⬍ 0.10
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Table 6. Perceived barriers to adoption of program components Percent of staff reporting selected perceived barriers
Program component
Cost required
Time required
Lack of client demand
Lack of client benefit
Lack of trained personnel
1. 2. 3. 4. 5.
38 29 66 57 70
38 64 48 66 38b
19 9 21 21 14
3 3 7 2 3
24a 45 43 43 17c
Providing information about how to decrease breast cancer risk Providing follow-up telephone calls Providing one-on-one counseling with a trained health educator Establishing special hours convenient to family members Providing services free of charge
Across-site differences were not statistically significant except for the following: a Staff at site D were more likely than staff at other sites to report lack of trained personnel as a barrier to adopting component #1 (p ⬍ 0.05). b Staff at site D were more likely than other staff to report time required as a barrier to adopting component #5 (p ⬍ 0.01). c Staff at site D were more likely than other staff to report lack of trained personnel as a barrier to adopting component #5 (p ⬍ 0.10).
the component of providing program services free of charge. Interestingly, physicians and administrators, when taken together, appeared more optimistic than nurses about adopting free-of-charge services (88% vs 49%, p ⫽ 0.01) and special operating hours (65% vs 40%, p ⫽ 0.09). These results may indicate that nurses took a more realistic or even pessimistic view of program adoption possibilities than do physicians and administrators, perhaps because nurses were more directly involved in program implementation.
Perceived Barriers to Adopting Program Components When respondents were queried about potential factors that might inhibit their practices from adopting specific components, the cost and time required to provide the service were the most frequently cited barriers for many program components (Table 6). Consistent with the positive perceptions of program performance, most respondents did not perceive that program adoption would be inhibited by a lack of demand for services or by a lack of benefit to clients. There was a general lack of consensus among respondents regarding the potential inhibiting effects of personnel needs. Taken together, these results indicate that the greatest perceived barriers to program adoption relate to resource constraints rather than to patient demand or program effectiveness. Perceived barriers appeared relatively consistent across the four study sites.
Organizational and Environmental Correlates of Program Adoption Simple bivariate tests of association revealed several statistically significant relationships between perceptions of adoption likelihood and perceptions of program awareness, support, and performance. For these tests, respondents were classified into one of two groups—those who reported their practices as “highly likely” or “likely” to adopt all program components
(potential adopters) and those who did not (potential nonadopters). Overall, potential adopters were more likely to report high levels of program awareness and program support within their practices, compared with nonadopters (Table 7). Two of these differences achieved statistical significance at the 10% level. Adopters were also more likely than nonadopters to report high levels of program performance, and most of these differences achieved statistical significance. Adopters were somewhat less likely than nonadopters to report barriers to program adoption, but only one of these differences (cost) achieved statistical significance. Finally, adopters were slightly more likely than nonadopters to report the availability of other information and counseling sources for family members of breast cancer patients. Most of these differences were small and failed to achieve statistical significance. These results are consistent with the expectation that program awareness, program identification, and perceived program performance increase the likelihood of program adoption.
Discussion Results from this analysis provide some useful insight about the adoption of a health education intervention in community hospital settings. Program adoption was far from certain within the four study sites, despite the attitude and behavior changes that might have taken place among staff and other key stakeholders during the intervention trial and implementation periods. Low levels of program awareness emerged as one likely barrier to program adoption. Substantial proportions of staff, patients, and family members were reported to be unaware of the intervention. Thus, the medical practices were likely to face only marginal pressures from their internal and external stakeholders to continue operating the intervention beyond the study period. This finding suggests that adoption could be enhanced with marketing activities designed to elevate awareness about the program and its purpose. MarketAm J Prev Med 2000;18(3)
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Table 7. Program perceptions of potential adopters compared with potential nonadopters† % of adopters who agree (n ⴝ 32)
% of non-adopters who agree (n ⴝ 25)
44 34 34 94 56
19* 29 43 65** 39
84
44***
84 81 71 97 97 81
37*** 53* 38** 63*** 54*** 63
90
75
III. Barriers to adoption Cost of providing services Time required to provide services Lack of demand for services Lack of adequate benefit for clients Lack of trained personnel to provide services
71 87 39 10 48
92* 92 33 8 63
IV. Other sources of specialized information and counseling Available from other hospitals in the service area Available from other medical practices in the service area Available from other health organizations in the service area Available from other US hospitals with cancer programs Available from other US medical practices with cancer programs
39 16 23 45 29
20 4 4* 28 8*
Program characteristic I. Program awareness and identification All/most staff are aware of the program All/most patients are aware of the program All/most family members are aware of the program All/most staff agree with the need for program All/most staff encourage patients and family members to use the program All/most staff desire to continue the program II. Program performance Fulfilled all/most goals Provided other benefits beyond goals High potential for attracting patients to practice High potential for improving patient satisfaction High potential for improving family member satisfaction High potential for allowing cancer treatment activities to be initiated at earlier stages of disease High potential for encouraging the use of preventive and screening services among family members
†
Potential adopters are defined to be respondents who described their practice as “highly likely” or “likely” to adopt all components of the intervention. Potential nonadopters are defined to be respondents who did not describe their practice in this way. *p ⬍ 0.10 **p ⬍ 0.05 ***p ⬍ 0.01
ing was not a formal part of the intervention as tested in this study. Adoption also appeared to be hampered by relatively low levels of program support among nonclinical staff. Although not directly involved in program implementation, these staff often play critical roles in organizational decision making and in the administrative routinization of a practice’s programs and services. Given the relatively high levels of program performance reported by staff at most sites, it appears likely that low levels of program support stem principally from gaps in awareness rather than from perceived gaps in performance. Special training efforts may be needed to educate nonclinical staff about high-priority interventions. Program awareness and support appear higher among physicians and nurses than among nonclinical staff, but these results must be interpreted carefully in light of the study’s low response rate among physicians. Responding physicians were not significantly different 196
from responding nurses in their perceptions of program awareness and support, but it is possible that the low response rate among physicians (31%) was due in part to unmeasured gaps in physician awareness and support for the program. The low physician response rate may stem in part from the fact that the program required relatively little investment of physician time and resources during the study period. If so, health education interventions may confront a double-edged sword in securing adoption by medical practices: Programs that require a high investment of physician time and effort may fail to be implemented due to competing physician priorities and resource constraints, whereas programs that require low investment of physician time and effort may fail to secure the visibility necessary for institutionalization within the practice. Perceived performance emerged as one of the health education program’s strongest assets in achieving internal support for adoption. Most staff rated the program
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highly in terms of accomplishing organizational goals, promoting patient satisfaction, and encouraging screening and risk-reduction behaviors. It is important to note that high program performance could eventually lead to heightened physician awareness and support for the program, assuming that clinicians eventually experience the program’s effects through such outcomes as increased patient demand for screening services and increased patient compliance with prevention practices. Nonetheless, the substantial across-site variation observed in the perceived performance measures revealed that gaps in perceived performance were possible, and that such gaps could limit the prospects for program adoption. This point was underscored by the relatively strong statistical associations found between measures of perceived performance and measures of perceived likelihood of adoption. Results also highlight the importance of program characteristics in the process of program adoption. Staff perceptions of adoption varied markedly across specific program components. One implication of this finding is that program adoption might be enhanced by unbundling individual program components and encouraging medical practices to select the most feasible components for immediate adoption. Under this strategy, additional program components could be adopted through a staged process as practices increase their capacity, expertise, and efficiency in operating the program.23 Resource-intensive components such as inperson counseling and follow-up might require tailored incentives and financing strategies, such as the use of client fees, to encourage adoption. Finally, this analysis revealed substantial organization-level variation in staff perceptions of program adoption. The limited scope of this study, and particularly the small number of study sites involved, precluded a thorough examination of the many specific organizational and environmental characteristics that may influence program adoption, including organization and market structure, clinical capacity and workflow processes, and managerial decision-making authority and leadership. A descriptive review of each study site suggested that some of these characteristics may have played important roles in the process of program adoption (Table 1). For example, the study site with the highest levels of perceived likelihood of program adoption (Site C) experienced the lowest levels of competition from other hospitals and physicians and the lowest levels of HMO market penetration among the four study sites. Additionally, Site C and its affiliated cancer center maintained more labor and capital resources than the other sites. In view of these environmental and organizational conditions, the optimistic perceptions about program adoption among staff at Site C might be explained in part by the site’s favorable market conditions and resource base. Characteristics that influence the flow of information
both within and between organizations may also play a role in program-adoption decisions. Cross-disciplinary communication and information exchange are likely to be particularly important in facilitating the adoption of health education interventions such as the one studied here, because key organizational decision makers have little direct involvement in implementing the intervention. Such communication can enhance the flow of information about program performance from the health educators and nurses that implement the intervention to the physicians and administrators that make decisions regarding adoption. Without such internal information flow, new programs may never achieve the visibility and support necessary to be institutionalized. Frequently, new preventive health interventions are developed and shown to be efficacious through rigorous research designs, but little attention is paid to the prospects and processes of program adoption.23 Consequently, effective programs may fail to be implemented in practice, and the benefits of substantial investments in prevention research may fail to be realized fully. By exploring program adoption issues as part of the evaluation metric, researchers, practitioners, and policymakers can gain valuable insight for enhancing the diffusion of preventive health innovations. We wish to acknowledge the contributions made to this study by Sherry Bargoil, Rose Baylies, Aileen Lachine, Deborah Barnes, Patti Wilcox, Caryn Brunatti, and Bernie Sarifan, who served as health counselors and project coordinators at the study sites. We also wish to thank the members of the High Risk Breast Cancer Consortium for their participation, which includes Lombardi Cancer Center (Washington, DC), Rush Cancer Institute (Chicago, IL), Dana Farber Cancer Institute (Boston, MA), Duke Comprehensive Cancer Center (Durham, NC), Johns Hopkins Oncology Center (Baltimore, MD), and the University of Texas MD Anderson Cancer Center (Houston, TX). This research was supported by a grant from the National Cancer Institute.
References 1. Lerman C, Schwartz MD, Miller SM, Daly M, Sands C, Rimer BK. A randomized trial of breast cancer risk counseling: interacting effects of counseling, educational level, and coping style. Health Psychol 1996;15:75– 83. 2. Audrain J, Lerman C, Rimer B, Cella D, Steffens R, Gomez-Caminero A. Awareness of heightened breast cancer risk among first-degree relatives of recently diagnosed breast cancer patients. The High Risk Breast Cancer Consortium. Cancer Epidemiol Biomarkers Prev 1995;4:561–5. 3. Rimer BK, Schildkraut JM, Lerman C, Lin TH, Audrain J. Participation in a women’s breast cancer risk counseling trial. Who participates? Who declines? High Risk Breast Cancer Consortium. Cancer 1996;77:2348 –55. 4. Lazarus RS, Folkman S. Transactional theory and research on emotions and coping. Eur J Pers 1987;1(suppl):141– 69. 5. Gail MH, Brinton LA, Byar DP, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 1989;81:1879 – 86. 6. Botelho RJ, Richmond R. Secondary prevention of excessive alcohol use: assessing the prospects of implementation. Fam Pract 1996;13:182–93. 7. Rogers EM. Diffusion of Innovations, 5th ed. New York: Free Press, 1995.
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8. Kaluzny AD, Hernandez SR. Managing organizational change and innovation. In: Shortell S, Kaluzny AD, eds. Health care management: a text in organizational theory and behavior. New York: Wiley and Sons, 1988. 9. Scott WR. Innovation in medical care organizations: a synthetic review. Med Care Rev 1990;47:165–92. 10. Blair JD, Fottler MD, Lazarus SS, Paolino AR, Rotarius TR. Strategic stakeholder management: first round results from ‘facing the uncertain future.’ Med Group Manage J 1995;42:16 –21. 11. Blair JD, Rock TT, Rotarius TM, Fottler MD, Bosse GC, Driskill JM. The problematic fit of diagnosis and strategy for medical group stakeholders— including IDS/Ns. Health Care Manage Rev 1996;21:7–28. 12. Zaltman G, Duncan R, Holbek J. Innovations and organizations. New York: John Wiley and Sons, 1973. 13. Delbecq AL, Mills PK. Managerial practices that enhance innovation. Organ Dyn 1985;14:24 –34. 14. Barnsley J, Hynes D, Warnecke R. Ensuring access to quality care. In: Kaluzny A, Warnecke R, eds. Managing a health care alliance:improving community cancer care. San Francisco, CA: Jossey-Bass, 1996. 15. Kaluzny AD, Lacey L, Warnecke R, Morrissey J, Sondik E, Ford L. Using a community cancer treatment trials network for cancer prevention and control research: challenges and opportunities. Cancer Epidemiol Biomarkers Prev 1994;3:261–9.
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16. Fischer LR, Solberg LI, Kottke TE. Quality improvement in primary care clinics. Jt Comm J Qual Improv 1998;24:361–70. 17. Kazandjian VA, Lied TR. Healthcare performance measurement: systems design and evaluation. Chicago: American Society for Quality, 1999. 18. Dowling WL. Strategic alliances as structure for integrated delivery systems. In: Kaluzny AD, Zuckerman HS, Ricketts TC, eds. Partners for the dance: forming strategic alliances in health care. Ann Arbor, MI: Health Administration Press, 1995. 19. Ruef M, Mendel P, Scott WR. An organizational field approach to resource environments in healthcare: comparing entries of hospitals and home health agencies in the San Francisco Bay region. Health Serv Res 1998; 32:775– 803. 20. Scott WR. The organization of medical care services: toward an integrated theoretical model. Med Care Rev 1993;50:271–303. 21. Kaluzny AD, Lacey L, Warnecke R, et al. Predicting the performance of a strategic alliance: an analysis of the Community Clinical Oncology Program. Health Serv Res 1993;28:159 – 82. 22. Woolson RF. Statistical methods for the analysis of biomedical data. New York: Wiley, 1987. 23. Solberg LI, Kottke TE, Brekke ML, Calomeni CA, Conn SA, Davidson G. Using continuous quality improvement to increase preventive services in clinical practice— going beyond guidelines. Prev Med 1996;25:259 – 67.
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Adoption of a Health Education Intervention for Family Members of Breast Cancer Patients Paul K. Halverson, DrPH, Glen P. Mays, PhD, Barbara K. Rimer, DrPH, Caryn Lerman, PhD, Janet Audrain, PhD, Arnold D. Kaluzny, PhD Background: Relatives of breast cancer patients often face substantial uncertainty and psychological stress regarding their own health risks and optimal strategies for prevention and early detection. Efficacious educational and counseling interventions are rarely evaluated for their potential adoption and use in medical practice settings. This study evaluates a health education program for first-degree relatives of breast cancer patients based on the program’s potential for being adopted and used by medical practices affiliated with cancer centers. Methods:
A randomized, controlled trial was implemented in four community hospital– based medical practices. After 9 months, clinical and administrative staff at each practice were given self-administered surveys. Of 90 staff members recruited to respond, useable responses were received from 60 (67%), including 13 physicians (31%), 43 nurses (98%), and four program managers (100%). Participants made self-reports of program awareness, program support, perceived program performance, likelihood of program adoption and use, and barriers to adoption.
Results:
A strong majority of respondents (80%) reported that all or most staff agreed with the need for the program. Perceived program performance in meeting goals was generally favorable but varied across sites and across staff types. Overall, 56% of respondents indicated that their practices were likely or highly likely to adopt the program in full. The likelihood of adoption varied substantially across sites and across program components.
Conclusions: Evaluating the potential for program adoption offers insight for tailoring preventive health interventions and their implementation strategies to improve diffusion in the field of practice. Medical Subject Headings (MeSH): breast neoplasms, counseling, family, health education (Am J Prev Med 2000;18(3):189 –198) © 2000 American Journal of Preventive Medicine
Introduction
M
ounting publicity about breast cancer in general, and about genetic susceptibility in particular, leads many women to seek information about disease risks, screening strategies, and primary prevention practices. The need for informaFrom the Department of Health Policy and Administration, School of Public Health, University of North Carolina (Halverson), Chapel Hill, North Carolina; Public Health Practice Program Office, Centers for Disease Control and Prevention (Halverson), Atlanta, Georgia; Department of Health Care Policy, Harvard Medical School (Mays), Boston, Massachusetts; Division of Cancer Control and Population Sciences, National Cancer Institute (Rimer), Rockville, Maryland; Lombardi Cancer Center, Georgetown University Medical Center (Lerman and Audrain), Washington DC; School of Public Health, Cecil G. Sheps Center for Health Services Research and Lineberger Comprehensive Cancer Center, University of North Carolina (Kaluzny), Chapel Hill, North Carolina Address correspondence and reprint requests to: Glen P. Mays, PhD, Department of Health Care Policy, Harvard Medical School, 180 Longwood Drive, Boston, MA 02115. E-mail: [email protected]. harvard.edu.
tion is particularly acute in families of women who have been diagnosed with breast cancer. Relatives of breast cancer patients often face substantial uncertainty and psychological stress regarding their own health risks and the optimal prevention strategies. To address the information and counseling needs confronted by family members of breast cancer patients, a health education intervention was developed for implementation as part of a high-risk education and counseling program. This study examines the intervention’s prospects for adoption and dissemination by specialized medical practice settings for cancer care. The success of any health education intervention must be judged not only by its efficacy in addressing the information and counseling needs of its users but also by its adoption and use in medical practice. Efficacy for the intervention of interest was evaluated previously through a randomized, controlled trial discussed elsewhere.1 A substudy of the larger controlled trial was launched to evaluate the process of adoption once the
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intervention was implemented at community hospital– based medical practices affiliated with comprehensive cancer centers.
adoption? and (3) How could adoption of the intervention be enhanced?
Examining Program Adoption Intervention Design Before initiating the study of program adoption, a randomized trial was conducted to assess the effect of a problem-solving intervention to help first-degree relatives of breast cancer patients cope with their relatives’ diagnoses and to increase adherence to recommended cancer surveillance strategies. Eligible participants for the randomized trial were 1128 women aged 20 to 75 who had a first-degree relative with a recent diagnosis of primary breast cancer.1–3 The Problem-Solving Training (PST) intervention was based on Lazarus’s transactional model of stress and coping, and included components designed to encourage women to define problems, identify solutions, and then to try them.4 Through this intervention, participants received information about breast cancer risk factors via in-person and printed communications—including their Gail estimate of individual breast cancer risk5; references to published information on breast cancer; and advice about screening and prevention practices such as diet, physical activity, and smoking cessation. The PST intervention was compared with a control intervention, the General Health Counseling (GHC) intervention, in which participants received information about breast cancer risk factors via traditional in-person and printed communications that did not involve the specialized problem-solving activities. Both interventions were delivered, in sessions of the same length, by a trained health educator who used a scripted protocol and flip chart. The PST intervention was found to reduce cancer-specific distress among women, especially those with fewer formal years of education when compared with the GHC control intervention (see Lerman et al.1 for a more detailed presentation of the study design and findings). During the program adoption phase of the study, study sites combined their treatment (PST) and control (GHC) arms to offer a single intervention comprised of traditional information and counseling strategies as well as problem-solving activities. The combined intervention included components of the GHC control intervention because we hypothesized that medical practices might be more likely to adopt the intervention if it included traditional information and counseling strategies in addition to the more nontraditional PST strategies. The program adoption phase of the study was designed to address three critical policy questions: (1) How likely were the medical practices to adopt the intervention as a permanent program? (2) What characteristics of the intervention and the organizational environment might facilitate or impede 190
The successful introduction of a health education intervention into specialized medical practice settings is far from automatic.6 Such interventions may represent a significant departure from the diagnostic and therapeutic technologies emphasized in these settings. Characteristics of the intervention, the organizational environment, and the process of introduction all may influence adoption.7 A growing body of evidence in organizational sociology suggests that institutions adopt innovations through a staged process involving stakeholders at multiple levels within their organization.8,9 Most models indicate that adoption begins with an awareness stage, during which key stakeholders recognize the existence of a problem or of an unmet need. Stakeholders in medical care organizations may include physicians and other clinicians, administrative and support staff, health care consumers, insurers, employers and other purchasers, and health care regulators and policymakers.10,11 After awareness has been achieved, organizations progress to an identification stage in which stakeholders begin to link the innovation with the recognized need. Organizations may then proceed to an implementation stage in which the innovation is tested within the organization. Finally, organizations may achieve an institutionalization stage during which the innovation is made routine within the operation of the organization. Success at any stage of the adoption process hinges on the achievement of earlier stages. The successful transition from implementation to institutionalization is a critical area of interest for studies of program adoption. This transition is particularly important for studies such as this one in which implementation is explicitly imposed through the study design. We hypothesize that four factors influence program adoption during the transition from implementation to institutionalization: First, adoption depends on the degree to which the earlier stages of awareness and identification have been achieved among the organization’s key stakeholders. Second, adoption is sensitive to stakeholder perceptions about the program’s performance in meeting goals and expectations during implementation. Third, specific program attributes may facilitate or impede an institution’s ability to integrate the program within its existing structure, due to factors such as cost and resource use, administrative complexity, compatibility with existing services (including economies of scope), and consumer demand (including economies of scale).12–15 Finally, characteristics of the organization and its environment may facilitate or impede adoption, including the availability of human and capital resources, competitive pressures for market share, the flow of information
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within and across organizations, and the decisionmaking processes within organizations.8,16 This study uses measures of these four factors to assess the likelihood of program adoption among medical practices that have implemented the program.
Methods Five community hospital– based medical practices specializing in breast cancer treatment were recruited to participate in a demonstration of the health education program. Each practice agreed to implement the program for a 9-month period during calendar year 1996. Each participating practice received payments to cover program costs incurred during the study period. A uniform method was used for introducing each practice to the health education intervention. A nursetrained project coordinator was identified at each practice and trained by study investigators to deliver the intervention. During the last month of the implementation period, we surveyed clinical and managerial staff within each practice to assess the practice’s likelihood of adopting various components of the program upon completion of the implementation period. A self-administered questionnaire was used to collect measures regarding each of the four factors likely to influence program adoption.
Measures of Program Adoption Potential Four sets of measures were used, each one corresponding to one of the four types of factors hypothesized to influence program adoption. An initial set of 6 measures assessed program awareness and program identification as a needed intervention. Respondents used a Likert-type scale to rate staff awareness of the program, patient awareness of the program, family awareness of the program, staff agreement with the need for the program, staff desire to continue the program, and staff encouragement of patients and family members to use the program. Additionally, respondents were asked to indicate the specific types of staff who were not aware of the program and who did not identify the program as a needed intervention. A second set of measures assessed staff perceptions about the program’s performance in meeting selected expectations and goals. These measures were adapted from established assessment instruments developed for measuring perceived organizational performance and innovation in health care organizations.17,18 First, respondents answered a question about whether the program fulfilled all, most, some, few, or none of the goals established for the program. Additionally, five goal-specific performance measures were constructed by asking staff to rate the program’s potential for encouraging the use of preventive and screening services, enabling earlier cancer treatment, improving
patient satisfaction, improving family member satisfaction, and attracting additional patients. A third set of measures assessed attributes of the health education program that might facilitate or impede adoption. Respondents were asked to indicate their perceptions about the likelihood of adopting specific program components and about the perceived barriers to adopting these components. Six specific program components were examined, and respondents used a 5-point Likert-type scale to indicate the perceived likelihood of adopting each component. Additionally, respondents reported perceived barriers to adoption for each of the program components by selecting from among a predetermined list of potential barriers. The list of potential barriers used for this analysis was not exhaustive, but it nonetheless encompassed key dimensions found to be important in previous studies.9 Respondents were also asked to report other major barriers to implementation not included on the list, but fewer than 5% of respondents listed additional barriers. Finally, a fourth set of measures assessed attributes of the program environment that might influence adoption of the intervention. Although a broad range of organizational and environmental characteristics comprises the program environment, we examined only a subset of these characteristics because a full exploration was beyond the scope of the study. First, we assessed the aggregate effects of the program environment by comparing site-specific measures of program adoption among the participating study sites. Significant acrosssite variation in program awareness, perceived performance, and likelihood of program adoption provided evidence that adoption was sensitive to attributes of the program environment. Second, we examined two specific measures of the program environment that have been found highly predictive of adoption in previous empirical studies: (1) the degree to which competitive pressure and “peer pressure” for adoption was perceived to exist in an organization’s service area and (2) the degree to which innovators or role models for program adoption were perceived to exist outside the service area.9,19 –21
Data Collection and Analysis Methods Each project coordinator solicited responses from a predetermined group of staff in each medical practice who carry out primary clinical or managerial responsibilities within the practice’s breast cancer program. This group included the chief administrator of the practice’s breast cancer program, all oncologists and hematologists affiliated with the breast cancer program, and all full-time registered nurses who spend at least 50% of their time staffing the breast cancer program. Although not inclusive of all program staff, the groups selected for study have considerable influence over the Am J Prev Med 2000;18(3)
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Table 1. Descriptive characteristics of the study sites Characteristic I. Metropolitan Statistical Area Characteristics Region Population Percent of population nonwhite Median age (in years) Per capita income Active nonfederal physicians per 100,000 population Oncologists per 100,000 population Acute care hospital beds per 100,000 population Number of acute care hospitals Percent providing genetic counseling Percent providing mammography screening services Percent providing oncology services Percent providing radiation therapy Percent providing women’s health services Percent of population with commercial health insurance enrolled in HMOs Percent of population with commercial health insurance enrolled in PPOs II. Hospital and Practice Characteristics Number of staffed beds Number of designated oncology beds Number of designated oncology nurses Number of oncologist/hematologist physicians
Site A
Site B
Site C
Site D
Northeast 5,956,296 9% 31 $23,900 353 2.0 347 90 19% 84% 86% 28% 48% 61%
Mid-Atlantic 4,360,349 32% 30 $26,800 329 1.5 304 44 11% 80% 80% 39% 32% 44%
Southeast 1,212,393 24% 30 $19,900 166 1.0 267 10 10% 80% 60% 40% 40% 28%
Midwest 7,560,720 29% 30 $23,800 282 1.8 386 92 22% 84% 77% 41% 43% 36%
15%
34%
46%
43%
80 14 22 3
296 28 25 7
593 not reported 12 12
270 0 9 11
Data on metropolitan statistical area characteristics were obtained from the U.S. Department of Health and Human Services 1996 Area Resource File and were measured at end of year 1992, except data on health maintenance organization (HMO) and preferred provided organization (PPO) enrollment, which were obtained from the American Association of Health Plans and were measured at end of year 1996. Data on hospital and practice characteristics were obtained directly from the responding organizations and were measured at end of year 1996.
program adoption process because of their direct responsibilities in program implementation and medical practice management. Responses were solicited from these staff during the last month of the 9-month program-evaluation period. Project coordinators were offered a small monetary incentive for achieving 90% response rates among their sites’ nurses and physicians. Project coordinators at four of the five implementation sites obtained responses to the questionnaires within 3 months. The nonresponding site was a small breast cancer clinic staffed by a single physician and nurse practitioner. All of the remaining four sites obtained responses from program managers and nurses, and three of the four sites obtained responses from their affiliated physicians. Overall, we received responses from 67% of the staff targeted in the four responding sites, including 31% of the physicians, 98% of the nurses, and 100% of the program managers. These response rates yielded 60 staff responses, including 43 nurses, 13 physicians, and 4 program managers. Survey responses were summarized overall and by site. Across-site differences were assessed for statistical significance using Fisher exact tests because some comparisons involve small cell sizes that threaten the validity of standard chi-square tests.22 We also examined the statistical correlates of program adoption by classifying each respondent into one of two groups—probable adopters and probable nonadopters— based on whether or not the respondent reported 192
the likelihood of adopting all program components as “likely” or “highly likely” using the 5-point Likert scale. This classification scheme allowed comparisons between respondents who are relatively confident that the program will be adopted by their practice and those who are not confident about adoption. Based on previous theoretical and empirical studies of organizational innovation, potential adopters were expected to report higher levels of program awareness, program identification, and perceived program performance than were potential nonadopters, because these factors were hypothesized to increase the probability of program adoption.8,9,15 Fisher exact tests were used to assess differences between groups.
Results The four participating study sites showed moderate diversity in their practice environments and operational characteristics (Table 1). All sites were located in relatively large metropolitan areas with at least 1 million residents. Compared with the other sites, Site C was located in an area with substantially lower per capita income, physician availability, hospital-bed capacity, and health maintenance organization (HMO) penetration. Site A was located in a community hospital having fewer staffed beds and fewer affiliated oncologists and hematologists than did other sites.
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Table 2. Awareness of the women’s health education program Percent of staff reporting itema Survey Response Item All or most staff are aware of the program. Some physicians are not aware.* Some nurses are not aware. Some managers are not aware.** Some support staff are not aware.* All or most breast cancer patients are aware of the program. All or most family members of breast cancer patients are aware of the program.
All (N ⴝ 60)
Site A (n ⴝ 18)
Site B (n ⴝ 14)
Site C (n ⴝ 13)
Site D (n ⴝ 15)
32 32 32 25 57 31
13 12 18 41 75 47
46 29 36 21 50 36
39 39 23 0 69 23
33 53 53 33 33 13
25
41
21
31
7
a Numbers reflect the proportion of staff that reported the survey response item listed in the first column. Fisher exact tests are used to test the null hypothesis that the proportions across all 4 sites are equal. **p ⬍ 0.05 *p ⬍ 0.10
Program Awareness and Identification The health education program achieved relatively modest levels of awareness among the medical practices’ major stakeholders, according to survey respondents. Only one third of the respondents reported that all or most staff within their practices were aware of the program (Table 2). One third of the respondents reported that both physicians and nurses were unaware of the program. By comparison, one fourth of the respondents indicated that some managers in their practice were not aware of the program, and more than half reported that some support staff personnel lacked program awareness. Respondents reported similarly low levels of awareness among breast cancer patients and family members. Despite modest levels of awareness, there appeared
to be relatively strong support for the program among staff who were aware of the program (Table 3). More than 80% of respondents indicated that all or most staff agreed with the need for the program. Similarly, two thirds of respondents reported that all or most staff wanted the program to continue beyond the study period. Finally, roughly half of all respondents indicated that all or most staff encouraged family members to use the program. Perhaps not surprisingly, respondents rated the nonclinical staff as least supportive of the program.
Perceived Program Performance Staff perceptions of the health education program’s performance varied but were generally favorable. Nearly two thirds of the respondents reported that the
Table 3. Staff identification with and support for the women’s health education program Percent of staff reporting itema Survey Response Item
All (N ⴝ 60)
Site A (n ⴝ 18)
Site B (n ⴝ 14)
Site C (n ⴝ 13)
Site D (n ⴝ 15)
All or most staff agree with the need for the program. Physicians agree with tne need for the program.* Nurses agree with the need for the program.** Managers agree with the need for the program.* Support staff agree with the need for the program. All or most staff desire to continue the program. Physicians desire to continue the program. Nurses desire to continue the program. Managers desire to continue the program. Support staff desire to continue the program. All or most staff encourage family members to use the program. Physicians encourage program use. Nurses encourage program use. Managers encourage program use. Support staff encourage program use.
81 81 100 81 44 67 71 90 47 16 49 69 90 33 14
88 81 100 81 44 69 81 94 44 19 47 88 94 31 19
86 43 86 57 21 79 64 93 57 29 50 57 86 50 21
85 85 100 62 15 69 77 100 54 8 54 69 100 31 8
67 80 67 33 20 53 60 73 33 7 47 60 80 20 7
a
Numbers reflect the proportion of staff that reported the survey response items listed in the first column. Fisher exact tests are used to test the null hypothesis that the proportions across all 4 sites are equal. *p ⬍ 0.10 **p ⬍ 0.01
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Table 4. Staff perceptions of program performance Percent of staff reporting elementa Performance Element Fulfilled all or most goalsb* High potential for encouraging use of preventive/screening servicesb High potential for enabling earlier treatment for breast cancerb High potential for improving patient satisfaction with practiceb High potential for improving family member satisfaction with practiceb* High potential for attracting greater number of patients to practiceb*
All (N ⴝ 60)
Site A (n ⴝ 18)
Site B (n ⴝ 14)
Site C (n ⴝ 13)
Site D (n ⴝ 15)
66 85
83 94
62 85
92 85
33 73
74
82
85
62
67
83
88
85
92
67
79
82
92
100
47
57
53
85
54
40
a Numbers reflect the proportion of staff that reported the performance element listed in the first column. Fisher exact tests are used to test the null hypothesis that the proportions across all 4 sites are equal. b Response options for “high potential” and “very high potential” are collapsed in computing proportions for this performance element. *p ⬍ 0.01
program fulfilled all or most of the goals that their practices had for it (Table 4). The proportion of staff members reporting goal fulfillment varied significantly by site, ranging from 33% of Site D staff to 92% of Site C staff (p ⬍ 0.01). Eighty-five percent of respondents felt that the program had a high or very high potential for encouraging appropriate use of preventive and screening services for breast cancer. Respondents reported least frequently the ability to attract greater volumes of patients as a program benefit. For most potential benefits listed on the survey, staff members from Site D were more pessimistic about the potential for realization compared with staff members from the other sites.
Perceived Likelihood of Adopting Program Components Approximately half of all respondents perceived that their practices were likely or highly likely to adopt all components of the health education intervention (Table 5). Respondents appeared most optimistic about adopting the program component of providing family members with information on how to decrease breast cancer risk. Respondents were least optimistic about adopting the component of establishing convenient hours of operation outside normal business hours. Contrary to expectations, substantial numbers of respondents felt that their practices were likely to adopt
Table 5. Staff perceptions of the likelihood of adopting program components Percent of staff reporting adoption as likelya Program Component All components** Providing information about how to decrease breast cancer risk Providing follow-up telephone calls* Providing one-on-one counseling with a trained health educator*** Establishing special hours convenient to family members** Providing services free of charge Providing other services to family members of breast cancer patients
All (N ⴝ 60)
Site A (n ⴝ 18)
Site B (n ⴝ 14)
Site C (n ⴝ 13)
Site D (n ⴝ 15)
56 82
41 72
50 86
92 100
50 73
63 62
56 56
64 50
92 100
47 47
47
39
36
85
33
60 41
44 28
50 36
77 67
73 40
a Numbers reflect the proportion of staff that reported adoption as “likely” or “highly likely.” Fisher exact tests are used to test the null hypothesis that the proportions across all four sites are equal. ***p ⬍ 0.01 **p ⬍ 0.05 *p ⬍ 0.10
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Table 6. Perceived barriers to adoption of program components Percent of staff reporting selected perceived barriers
Program component
Cost required
Time required
Lack of client demand
Lack of client benefit
Lack of trained personnel
1. 2. 3. 4. 5.
38 29 66 57 70
38 64 48 66 38b
19 9 21 21 14
3 3 7 2 3
24a 45 43 43 17c
Providing information about how to decrease breast cancer risk Providing follow-up telephone calls Providing one-on-one counseling with a trained health educator Establishing special hours convenient to family members Providing services free of charge
Across-site differences were not statistically significant except for the following: a Staff at site D were more likely than staff at other sites to report lack of trained personnel as a barrier to adopting component #1 (p ⬍ 0.05). b Staff at site D were more likely than other staff to report time required as a barrier to adopting component #5 (p ⬍ 0.01). c Staff at site D were more likely than other staff to report lack of trained personnel as a barrier to adopting component #5 (p ⬍ 0.10).
the component of providing program services free of charge. Interestingly, physicians and administrators, when taken together, appeared more optimistic than nurses about adopting free-of-charge services (88% vs 49%, p ⫽ 0.01) and special operating hours (65% vs 40%, p ⫽ 0.09). These results may indicate that nurses took a more realistic or even pessimistic view of program adoption possibilities than do physicians and administrators, perhaps because nurses were more directly involved in program implementation.
Perceived Barriers to Adopting Program Components When respondents were queried about potential factors that might inhibit their practices from adopting specific components, the cost and time required to provide the service were the most frequently cited barriers for many program components (Table 6). Consistent with the positive perceptions of program performance, most respondents did not perceive that program adoption would be inhibited by a lack of demand for services or by a lack of benefit to clients. There was a general lack of consensus among respondents regarding the potential inhibiting effects of personnel needs. Taken together, these results indicate that the greatest perceived barriers to program adoption relate to resource constraints rather than to patient demand or program effectiveness. Perceived barriers appeared relatively consistent across the four study sites.
Organizational and Environmental Correlates of Program Adoption Simple bivariate tests of association revealed several statistically significant relationships between perceptions of adoption likelihood and perceptions of program awareness, support, and performance. For these tests, respondents were classified into one of two groups—those who reported their practices as “highly likely” or “likely” to adopt all program components
(potential adopters) and those who did not (potential nonadopters). Overall, potential adopters were more likely to report high levels of program awareness and program support within their practices, compared with nonadopters (Table 7). Two of these differences achieved statistical significance at the 10% level. Adopters were also more likely than nonadopters to report high levels of program performance, and most of these differences achieved statistical significance. Adopters were somewhat less likely than nonadopters to report barriers to program adoption, but only one of these differences (cost) achieved statistical significance. Finally, adopters were slightly more likely than nonadopters to report the availability of other information and counseling sources for family members of breast cancer patients. Most of these differences were small and failed to achieve statistical significance. These results are consistent with the expectation that program awareness, program identification, and perceived program performance increase the likelihood of program adoption.
Discussion Results from this analysis provide some useful insight about the adoption of a health education intervention in community hospital settings. Program adoption was far from certain within the four study sites, despite the attitude and behavior changes that might have taken place among staff and other key stakeholders during the intervention trial and implementation periods. Low levels of program awareness emerged as one likely barrier to program adoption. Substantial proportions of staff, patients, and family members were reported to be unaware of the intervention. Thus, the medical practices were likely to face only marginal pressures from their internal and external stakeholders to continue operating the intervention beyond the study period. This finding suggests that adoption could be enhanced with marketing activities designed to elevate awareness about the program and its purpose. MarketAm J Prev Med 2000;18(3)
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Table 7. Program perceptions of potential adopters compared with potential nonadopters† % of adopters who agree (n ⴝ 32)
% of non-adopters who agree (n ⴝ 25)
44 34 34 94 56
19* 29 43 65** 39
84
44***
84 81 71 97 97 81
37*** 53* 38** 63*** 54*** 63
90
75
III. Barriers to adoption Cost of providing services Time required to provide services Lack of demand for services Lack of adequate benefit for clients Lack of trained personnel to provide services
71 87 39 10 48
92* 92 33 8 63
IV. Other sources of specialized information and counseling Available from other hospitals in the service area Available from other medical practices in the service area Available from other health organizations in the service area Available from other US hospitals with cancer programs Available from other US medical practices with cancer programs
39 16 23 45 29
20 4 4* 28 8*
Program characteristic I. Program awareness and identification All/most staff are aware of the program All/most patients are aware of the program All/most family members are aware of the program All/most staff agree with the need for program All/most staff encourage patients and family members to use the program All/most staff desire to continue the program II. Program performance Fulfilled all/most goals Provided other benefits beyond goals High potential for attracting patients to practice High potential for improving patient satisfaction High potential for improving family member satisfaction High potential for allowing cancer treatment activities to be initiated at earlier stages of disease High potential for encouraging the use of preventive and screening services among family members
†
Potential adopters are defined to be respondents who described their practice as “highly likely” or “likely” to adopt all components of the intervention. Potential nonadopters are defined to be respondents who did not describe their practice in this way. *p ⬍ 0.10 **p ⬍ 0.05 ***p ⬍ 0.01
ing was not a formal part of the intervention as tested in this study. Adoption also appeared to be hampered by relatively low levels of program support among nonclinical staff. Although not directly involved in program implementation, these staff often play critical roles in organizational decision making and in the administrative routinization of a practice’s programs and services. Given the relatively high levels of program performance reported by staff at most sites, it appears likely that low levels of program support stem principally from gaps in awareness rather than from perceived gaps in performance. Special training efforts may be needed to educate nonclinical staff about high-priority interventions. Program awareness and support appear higher among physicians and nurses than among nonclinical staff, but these results must be interpreted carefully in light of the study’s low response rate among physicians. Responding physicians were not significantly different 196
from responding nurses in their perceptions of program awareness and support, but it is possible that the low response rate among physicians (31%) was due in part to unmeasured gaps in physician awareness and support for the program. The low physician response rate may stem in part from the fact that the program required relatively little investment of physician time and resources during the study period. If so, health education interventions may confront a double-edged sword in securing adoption by medical practices: Programs that require a high investment of physician time and effort may fail to be implemented due to competing physician priorities and resource constraints, whereas programs that require low investment of physician time and effort may fail to secure the visibility necessary for institutionalization within the practice. Perceived performance emerged as one of the health education program’s strongest assets in achieving internal support for adoption. Most staff rated the program
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highly in terms of accomplishing organizational goals, promoting patient satisfaction, and encouraging screening and risk-reduction behaviors. It is important to note that high program performance could eventually lead to heightened physician awareness and support for the program, assuming that clinicians eventually experience the program’s effects through such outcomes as increased patient demand for screening services and increased patient compliance with prevention practices. Nonetheless, the substantial across-site variation observed in the perceived performance measures revealed that gaps in perceived performance were possible, and that such gaps could limit the prospects for program adoption. This point was underscored by the relatively strong statistical associations found between measures of perceived performance and measures of perceived likelihood of adoption. Results also highlight the importance of program characteristics in the process of program adoption. Staff perceptions of adoption varied markedly across specific program components. One implication of this finding is that program adoption might be enhanced by unbundling individual program components and encouraging medical practices to select the most feasible components for immediate adoption. Under this strategy, additional program components could be adopted through a staged process as practices increase their capacity, expertise, and efficiency in operating the program.23 Resource-intensive components such as inperson counseling and follow-up might require tailored incentives and financing strategies, such as the use of client fees, to encourage adoption. Finally, this analysis revealed substantial organization-level variation in staff perceptions of program adoption. The limited scope of this study, and particularly the small number of study sites involved, precluded a thorough examination of the many specific organizational and environmental characteristics that may influence program adoption, including organization and market structure, clinical capacity and workflow processes, and managerial decision-making authority and leadership. A descriptive review of each study site suggested that some of these characteristics may have played important roles in the process of program adoption (Table 1). For example, the study site with the highest levels of perceived likelihood of program adoption (Site C) experienced the lowest levels of competition from other hospitals and physicians and the lowest levels of HMO market penetration among the four study sites. Additionally, Site C and its affiliated cancer center maintained more labor and capital resources than the other sites. In view of these environmental and organizational conditions, the optimistic perceptions about program adoption among staff at Site C might be explained in part by the site’s favorable market conditions and resource base. Characteristics that influence the flow of information
both within and between organizations may also play a role in program-adoption decisions. Cross-disciplinary communication and information exchange are likely to be particularly important in facilitating the adoption of health education interventions such as the one studied here, because key organizational decision makers have little direct involvement in implementing the intervention. Such communication can enhance the flow of information about program performance from the health educators and nurses that implement the intervention to the physicians and administrators that make decisions regarding adoption. Without such internal information flow, new programs may never achieve the visibility and support necessary to be institutionalized. Frequently, new preventive health interventions are developed and shown to be efficacious through rigorous research designs, but little attention is paid to the prospects and processes of program adoption.23 Consequently, effective programs may fail to be implemented in practice, and the benefits of substantial investments in prevention research may fail to be realized fully. By exploring program adoption issues as part of the evaluation metric, researchers, practitioners, and policymakers can gain valuable insight for enhancing the diffusion of preventive health innovations. We wish to acknowledge the contributions made to this study by Sherry Bargoil, Rose Baylies, Aileen Lachine, Deborah Barnes, Patti Wilcox, Caryn Brunatti, and Bernie Sarifan, who served as health counselors and project coordinators at the study sites. We also wish to thank the members of the High Risk Breast Cancer Consortium for their participation, which includes Lombardi Cancer Center (Washington, DC), Rush Cancer Institute (Chicago, IL), Dana Farber Cancer Institute (Boston, MA), Duke Comprehensive Cancer Center (Durham, NC), Johns Hopkins Oncology Center (Baltimore, MD), and the University of Texas MD Anderson Cancer Center (Houston, TX). This research was supported by a grant from the National Cancer Institute.
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