Organizational Readiness to Implement System Changes in an Alaskan Tribal Dental Care Organization

Abstract

Introduction:

Tribal health care systems are striving to implement internal changes to improve dental care access and delivery and reduce health inequities for American Indian and Alaska Native children. Within similar systems, organizational readiness to implement change has been associated with adoption of system-level changes and affected by organizational factors, including culture, resources, and structure.

Objectives:

The objectives of this study were to assess organizational readiness to implement changes related to delivery of evidence-based dental care within a tribal health care organization and determine workforce- and perceived work environment–related factors associated with readiness.

Methods:

A 92-item questionnaire was completed online by 78 employees, including dental providers, dental assistants, and support staff (88% response rate). The questionnaire queried readiness for implementation (Organizational Readiness for Implementing Change), organizational context and resources, workforce issues, organizational functioning, and demographics.

Results:

Average scores for the change commitment and change efficacy domains (readiness for implementation) were 3.93 (SD = .75) and 3.85 (SD = .80), respectively, where the maximum best score was 5. Perceived quality of management, a facet of organizational functioning, was the only significant predictor of readiness to implement change (B = .727, SE = .181, P < .0002) when all other variables were accounted for.

Conclusion:

Results suggest that when staff members (including dentists, dental therapists, hygienists, assistants, and support staff) from a tribal health care organization perceive management to be high quality, they are more supportive of organizational changes that promote evidence-based practices. Readiness-for-change scores indicate an organization capable of institutional adoption of new policies and procedures. In this case, use of more effective management strategies may be one of the changes most critical for enhancing institutional behaviors to improve population health and reduce health inequities.

Knowledge Transfer Statement:

The results of this study can be used by clinicians and other leaders implementing changes within dental care organizations. To promote organizational readiness for change and, ultimately, more expedient and efficient adoption of system-level changes by stakeholders, consideration should be given to organizational functioning generally and quality of management practices specifically.

Organizational readiness for change is a factor widely considered to be critical for change implementation in health care settings (Weiner et al. 2008). Organizational readiness for change is a multilevel and multifaceted construct conceptually defined as “a shared psychological state in which organizational members feel committed to implementing an organizational change and confident in their collective abilities to do so” (Weiner 2009). Theoretically, readiness is considered to be a function of contextual factors (e.g., culture, resources, structure) and to encompass both change efficacy (i.e., the perceived capability for change) and change commitment (i.e., steadfast engagement with whatever may be required to attain change; Weiner 2009). High organizational readiness for change has been associated with successful implementation of new initiatives across multiple health care disciplines (e.g., Caldwell et al. 2009; Pare et al. 2011; Hannon et al. 2017); however, very few studies (Reynolds et al. 2016; Cunha-Cruz et al. 2017) have assessed the role of this important construct in dentistry and dental care organizations. Changes in care delivery systems are occurring in dentistry at greater frequency and at faster pace than before, including an increasing number of providers working in large group practices (Vujicic 2017).

Within these large group practices, administrators, clinical supervisors, and other decision makers often implement system-wide changes to policies and practices to respond to the most current evidence, clinical recommendations, and regulations. Rapid and smooth adoption of such changes, including novel approaches to health care management and delivery of evidence-based treatment, is good for patient care and can help to reduce health inequities (Hamilton and Mittman 2018; Yancey et al. 2018). Given how they are organized, tribally controlled health systems—which employ dental teams to address health inequities in the populations that they serve—offer a unique context in which to study the implementation of systems-level change in dental practices and the organizational factors that facilitate the change.

The causes of inequities in American Indian and Alaska Native (AIAN) people’s oral health are complex and include social, biological, economic, cultural, and historical factors (Iralu et al. 2010; Jernigan et al. 2010). Though not sufficient to address all of the underlying factors that have resulted in inequities, a multipronged dental care system that increases emphasis on employing evidence-based practices for population health—like the one that is the focus of this study—can improve organizational effectiveness. For instance, the organization studied here wanted to implement a new protocol, adapted to the Alaska Native context, for prevention and treatment of dental caries in the children and adolescents from the rural, remote villages that it serves, where the caries rate is alarming. The new protocol placed emphasis on a team-based care model to reach out to all children and adolescents, promptly treating decay and providing preventive services based on caries risk. Adoption of such approaches and practices to reduce the impact of the factors contributing to inequities is possible but requires organizational/systems strategies for facilitating change. For example, the Alaskan tribal organization studied here identified collaborative goal setting and planning by providers, with ongoing performance monitoring, as 2 such strategies to facilitate best clinical practices.

A critical initial step in fuller implementation is to assess and understand organizational readiness for change among those who will play a role in implementation. Thus, with an Alaskan tribal health care organization as a model, the 2 main objectives of this study were: 1) to assess organizational readiness to implement management/procedural changes related to delivery of evidence-based dental care, and 2) to determine personal, workforce-related, and perceived work environment–related factors associated with readiness.

Materials and Methods

Design and Setting

This cross-sectional study presents results from a survey conducted prior to the launch of Oral Health Equity for Alaska, a project involving implementation and evaluation of delivery system changes to improve the reach and effectiveness of care provided by a tribal health care organization. The University of Washington (UW) and the Alaska Area Institutional Review Boards approved the study.

The setting was the dental program at a large health care organization in Alaska controlled by a consortium of 18 Alaska Native tribes. At the time of the survey, the organization had approximately 54 dental providers. Providers include primary dental health aides (who provide preventive care such as fluoride and serve as patient navigators), primary dental health aide therapists (who have a larger scope of practice, which includes caries treatments such as fillings and simple extractions), and dentists and pediatric dentists. These providers are supported by dental hygienists and assistants, sterilization technicians, and administrative staff, and they operate in 3 main dental clinics in Juneau, Sitka, and Haines and itinerant clinics in 15 remote villages. The populations of the villages range from approximately 400 to 900 inhabitants, with 40% to 70% being Alaska Native.

Sample Selection, Participant Recruitment, and Survey Administration

All employees from the tribal health care organization’s dental department, including dental providers and support/administrative staff, were invited to participate (n = 89). To be eligible for participation, individuals had to be a full- or part-time employee of the tribal health care organization and be directly involved with the dental program. The sample pool included the dental director, dentists, dental therapists, dental health aides, dental hygienists, dental assistants, sterilization technicians, and administrative/management staff from the dental department.

One week before the survey start date, the health care organization’s dental director sent an email to potential participants indicating that they would be receiving a study invitation email from researchers at UW and encouraging them to respond. Individuals were invited to participate via a subsequent email message from the UW principal investigator, containing a link to the web-based survey sent from a secure system. The invitation stated that the purpose of the survey was to learn about the organization to direct future initiatives aimed at improving children’s dental care and oral health. Participants accessed a secure website to accept the invitation and were notified of waived written documentation of consent, that completion of the survey was voluntary, and that their individual responses were confidential (i.e., identifying information was not linked to responses) and not going to be shared with their employers. The survey was administered with Online Survey Software (Qualtrics) and in English, as all employees speak and read English. The survey could be accessed on work or personal computers, and the median completion time was 23 min. Participants were thanked for their time but received no monetary incentive for participating. The recruitment period lasted 45 d, and invitations were repeated up to 5 times within that window to encourage participation. The survey was carried out between June 1 and July 15, 2017.

Sample Size Considerations

The main variable of interest was readiness for organizational change among staff members from the dental department. Given a response rate of >80% obtained in a prior survey within this tribal health care organization, we expected a response rate of 70% to 80%. A sample of 62 to 71 participants from a pool of 89 eligible dental staff would provide estimates of readiness for organizational change with a precision of about one-quarter of a point (on a 1 to 5 scale assuming a SD of 1.0) based on one-half width of a 95% CI for a mean. The power would be ≥80% to detect a correlation (association) ≥0.35 between readiness for organizational change and workforce- and work environment–related variables.

Instruments

The 92-item survey questionnaire (available upon request from the corresponding author) consisted of multiple-choice questions from widely used and validated scales as well as questions developed by the investigators for this study based on themes that emerged from an earlier qualitative study (Senturia et al. 2018). The complete questionnaire underwent prelaunch cognitive (i.e., “think aloud”) testing and revision with 2 providers and 1 administrative staff person from the health care organization, chosen by the investigative team to assess understanding of questions and response options. Cronbach’s alpha values, as calculated for all scales per the study data presented, were good to excellent (0.63 to 0.99) for all scales except task demands (0.09).

The Organizational Readiness for Implementing Change (ORIC) questionnaire (Shea et al. 2014) was used to assess the primary outcome. This 10-item instrument contains 2 scales: one that measures employees’ perception of the degree to which people in the organization are committed to proposed change (change commitment) and one that measures employees’ perception of the degree to which the organization can handle the adjustments needed for smooth and effective implementation (change efficacy). Participants rated level of agreement with items by using a 5-point Likert scale with anchors ranging from strongly disagree to strongly agree (1 to 5 points), where a higher score means greater change commitment or efficacy.

Personnel characteristics were assessed with an 11-item study-specific demographic questionnaire querying age, sex, race/ethnicity, and tribe as well as job role and activities, tenure, part- versus full-time status, and office/practice location.

Organizational context and resources were assessed, in part, with 14 study-specific questions written or modified to tap perceived resources (5 questions), skills and training (3 questions), task demands (3 questions), and external context (3 questions). Questions on external context were selected from the Determinants of Implementation Behavior Questionnaire (Huijg et al. 2014). Response options for these questions were on a 4-point Likert-type scale, with higher scores indicative of perceiving availability of more resources or higher-quality work context.

Ten questions, forming 2 constructs, were written for this study to assess perceptions about the receipt of dental care: confidence that children and adolescents in the villages served by the organization have access to dental care (2 items) and receive specific types of preventive and restorative dental services (8 items). These questions were answered with a 5-point Likert scale, with higher ratings suggestive of greater confidence.

Workforce issues were assessed with a single item on burnout (Rohland et al. 2004; 5-point rating scale, with lower scores assigned to less pronounced feelings of burnout), a single item on likelihood to leave practice (Linzer et al. 2005; 5-point scale, with higher scores indicating greater likelihood of leaving), the 4-item Perceived Job Stress Scale (Motowidlo et al. 1986; 5-point scale with greater scores suggestive of higher stress), and the 5-item Job Satisfaction Scale (Williams et al. 1999; 5-point scale with higher scores indicative of greater satisfaction).

Organizational climate was assessed by a 33-item organizational climate instrument and a single item related to office/practice chaos. The questions were taken from the Minimizing Errors/Maximizing Outcomes questionnaire (Linzer et al. 2005). The instrument taps 5 constructs with a 4-point Likert-type scale: workplace emphasis on quality, workplace cohesiveness, trust in the organization, leadership and governance alignment, and workplace emphasis on information and communication. For each construct, a higher score means more positive and supportive organizational climate.

Perceived quality of management of the organization was assessed with 8 items designed for this study querying important management strategies/behaviors, such as having organizational goals, guidelines, monitoring, action planning, and supervision. Items were assessed with a 5-point rating scale, where higher scores indicated higher-quality management and greater use of effective management strategies.

Guided by implementation science literature, each of the predictor constructs and variables assessed was assigned to 1 of 4 conceptual domains: demographic variables to personnel characteristics; resources, skills and training, task demands, external context, and perceptions about receipt of dental care by stakeholders to organizational context and resources; burnout, likelihood to leave practice, job stress, and job satisfaction to workforce issues; and organizational climate and quality of management to organizational functioning.

Statistical Analysis

Descriptive statistics were generated for demographics, organizational readiness for change, and the scales in each of the 4 domains for all participants and by job role. Linear regression was used to assess the relation between organizational readiness to implement change and each scale (results not presented). Backward stepwise regression was then performed within each domain for change commitment and, separately, for change efficacy. Given the relatively small sample size, a liberal criterion for retaining variables in the backward selection was used (i.e., P < 0.2). Scales that were retained in the backward stepwise regressions were then entered into linear regression models sequentially (hierarchical linear regression) by domain according to the theory-informed conceptualization described here (i.e., personnel characteristics, organizational context and resources, workforce issues, and organizational functioning; Neter et al. 1985). Robust standard errors were used with all linear regressions in case of heteroscedasticity (White 1980).

Nine participants were missing responses to 1) the dental care access and delivery scales in the context and resources domain and 2) the management scale in the organizational climate domain as a result of an incorrect skip pattern in the survey. Multiple imputation procedures were used to account for the missing items via 10 imputed data sets generated through Markov chain Monte Carlo estimation. The regression results obtained for each data set were combined with Rubin rules to adjust the standard errors for the uncertainty about imputed values (Schafer 1997; Rubin 2004).

Results

In total, 78 employees from the health care organization’s dental department participated (response rate = 88%). To account for potential influence of degree of patient contact, participants were assigned to 1 of 3 groups based on job role: providers (n = 35, 45%; i.e., those who carry out treatment planning and/or procedures, such as dentists, dental health aides, and dental hygienists), dental assistants (n = 28, 36%), or support staff (n = 15, 19%; e.g., administrators, clerical staff, sterilization techs). Demographic and workforce-related characteristics of the respondents are summarized in Table 1. Sex, race/ethnicity, employment status/tenure, and urban-versus-rural location of employment were generally consistent with the overall makeup of the dental department’s employees. Across staff types, the majority of participants had never or only occasionally experienced burnout; 40% to 50% of staff indicated having had some intention to leave.

Table 1.

Demographic and Workforce-Related Characteristics of Dental Department Staff from the Tribal Health Care Organization Responding to the Study Survey (N = 78).

	Provider		Dental Assistant		Support Staff		Total
	n	%	n	%	n	%	n	%
Personnel Characteristics
Age, y	34	35.9 (10.4)a	25	30.7 (10.4)a	14	43.2 (16.1)a	73	35.6 (12.4)a
Sex
Male	9	25.7			2	13.3	11	14.1
Female	25	71.4	27	96.4	13	86.7	65	83.3
Race
White	21	60.0	5	17.9	6	40.0	32	41.0
American Indian /   Alaska Native	3	8.6	11	39.3	7	46.7	21	26.9
Native Hawaiian /   Pacific Islander	1	2.9			1	6.7	2	2.6
Asian	1	2.9	1	3.6			2	2.6
>1 race	6	17.1	9	32.1			15	19.2
Other	2	5.7	1	3.6			3	3.8
Ethnicity
Hispanic/Latino	9	25.7			2	13.3	11	14.1
Non-Hispanic/Latino	25	71.4	27	96.4	13	86.7	65	83.3
Employment status
Full-time	30	85.7	26	92.9	15	100	71	91.0
Part-time	5	14.3	2	7.1			7	9.0
Employment tenure, y
<1	8	22.9	8	28.6	7	46.7	23	29.5
1 to 5	12	34.3	12	42.9	1	6.7	25	32.1
>5	15	42.9	8	28.6	6	40.0	29	37.2
Primary employment location
Urban	24	68.6	20	71.4	8	53.3	52	66.7
Rural	10	28.6	7	25.0	7	46.7	24	30.8
Workforce Issues
Burnout
None/occasionally	27	77.1	21	75.0	14	93.3	62	79.5
Definite/completely	8	22.9	7	25.0	1	6.7	16	20.5
Turnover intentions
Not at all	19	54.3	14	50.0	9	60.0	42	53.8
Slightly to definitely	16	45.7	14	50.0	6	40.0	36	46.2

Full-time employment status is defined as 40 h/wk; part-time employment status, <40 h/wk. Age data are missing for 5 participants.

^aMean (SD).

For the total sample, mean scores for the change commitment and change efficacy scales of the ORIC were 3.93 (SD = 0.75) and 3.85 (SD = 0.80), respectively, with higher scores indicating greater commitment and efficacy and participants’ moderately high readiness for change, on average. Mean scores for the change commitment scale were 3.86 for providers, 4.05 for dental assistants, and 3.89 for support staff; mean scores for the change efficacy scale were 3.71 for providers, 4.06 for dental assistants, and 3.77 for support staff. Additional descriptive statistics for organizational readiness for change and work environment–related variables are presented in Table 2 for the total sample.

Table 2.

Organizational Readiness for Change, Organizational Context and Resources, Workforce Issues, and Organizational Functioning Reported by Dental Department Staff from the Tribal Health Care Organization (N = 78).

	n	M	SD
Readiness for change: ORICa
Commitment	78	3.9	0.8
Efficacy	78	3.9	0.8
Organizational context and resources
Resourcesb	78	3.0	0.6
Skills and trainingb	78	3.2	0.6
Task demandsb	78	2.9	0.5
External contextb	76	2.9	0.5
Dental care accessc	69	3.7	0.9
Dental care deliveryc	69	3.0	0.8
Workforce issuesd
Job stress	78	3.1	0.9
Job satisfaction	78	4.0	0.7
Organizational functioninge
Organizational climate
Emphasis on quality	78	3.2	0.4
Cohesiveness	78	2.9	0.7
Trust	78	3.1	0.5
Leadership/governance	78	2.7	0.6
Information/communication	78	3.4	0.4
Management	69	4.0	0.6

ORIC, Organizational Readiness for Implementing Change.

^aScored 1 to 5, where a higher score means greater change commitment and perception of efficacy to enact change.

^bScored 1 to 4, where higher scores mean perceiving availability of more resources or higher-quality work context. External context data were missing for 2 participants because they skipped >1 item on the scale.

^cScored 1 to 5, with higher ratings suggestive of greater confidence. Data were missing for 9 participants due to an inadvertent incorrect skip pattern in the survey.

^dScored 1 to 5, with higher scores suggestive of greater stress/burnout or satisfaction.

^eScored 1 to 5, where higher scores mean more positive/supportive organizational climate or higher quality of management. Data were missing for 9 participants for the management scale due to an inadvertent incorrect skip pattern in the survey.

The domains and constructs associated with change commitment in the backward stepwise regression (P < 0.20) were as follows: organizational context and resources (R² = 17.5%; composed of the perceptions about receipt of dental care and skills and training constructs), job satisfaction (R² = 8.1%), and organizational functioning (R² = 43.7%; composed of the emphasis on quality and management constructs). The domains and constructs associated with change efficacy (P < 0.20) were as follows: job role (R² = 4.0%), organizational context and resources (R² = 26.7%; composed of the task demands, perceptions about receipt of dental care, and skills and training constructs), job satisfaction (R² = 3.8%), and organizational functioning (R² = 50.2%; composed of the emphasis on quality and management constructs).

In the hierarchical models, organizational context and resources explained 17.5% of the variation in change commitment, and adding workforce issues resulted in a small increase in the R² value. When organizational functioning was added, the R² value increased to 45.5%, with management as the only statistically significant construct (see Table 3). Personnel characteristics explained only 4% of the variation in change efficacy. Sequentially adding organizational context and resources and workforce issues slightly increased the R² value. When organizational functioning was added, the model explained 54.6% of the variation in change efficacy, with, again, management as the only statistically significant construct (see Table 4). Each unit increase in the scale measuring perceived quality of management predicted 0.697- and 0.781-unit increases in change commitment and change efficacy, respectively. Results were similar when sex and age were included in the regression models.

Table 3.

Hierarchical Linear Regression Predicting Change Commitment from Workforce- and Work Environment–Related Variables in Dental Department Staff from the Tribal Health Care Organization (N = 78).

	Model 1		Model 2		Model 3
	B (SE)	P Value	B (SE)	P Value	B (SE)	P Value
Organizational context and resources		<.0001a		<.0001a		.396a
Skills and training	0.470 (0.127)	.0004	0.410 (0.162)	.0137	−0.028 (0.187)	.882
Dental care delivery	0.164 (0.121)	.180	0.178 (0.124)	.156	0.107 (0.086)	.219
Workforce issues
Job satisfaction			0.148 (0.140)	.293a	0.002 (0.091)	.986a
Organizational functioning						.0003a
Management					0.727 (0.181)	.0002
Emphasis on quality					0.222 (0.247)	.372
R 2	17.5%		19.1%		45.5%

The outcome variable, change commitment, is measured with the ORIC change commitment subscale. A higher score means greater change commitment. Scales that were retained in the backward stepwise regressions were entered into linear regression models sequentially by domain according to the theory-informed conceptualization (organizational context and resources, workforce issues, and organizational functioning).

^aP value for the domain. The P value for the last domain entered in the model is, equivalently, that for the change in the R² from the prior model.

Table 4.

Hierarchical Linear Regression Predicting Change Efficacy from Workforce- and Work Environment–Related Variables in Dental Department Staff from the Tribal Health Care Organization (N = 78).

	Model 1		Model 2		Model 3		Model 4
	B (SE)	P Value	B (SE)	P Value	B (SE)	P Value	B (SE)	P Value
Personnel characteristics
Job role		.146a		.0842a		.179a		.494a
Support staff	Reference		Reference		Reference		Reference
Dental assistant	0.284(0.201)	.683	0.350(0.155)	.0271	0.293(0.165)	.0811	0.149(0.157)	.346
Provider	−0.059(0.237)	.412	0.240(0.203)	.242	0.136(0.235)	.565	0.029(0.192)	.880
Organizational context and resources				<.0001a		<.0001a		.0779a
Task demands			0.263(0.182)	.151	0.262(0.175)	.139	0.228(0.129)	.0806
Skills and training			0.514(0.117)	<.0001	0.433(0.160)	.0083	0.001(0.181)	.995
Dental care delivery			0.230(0.114)	.0481	0.232(0.110)	.0384	0.159(0.076)	.0405
Workforce issues: job satisfaction					0.189(0.159)	.237	0.062(0.104)	.550
Organizational functioning								.0003a
Emphasis on quality							0.168(0.244)	.492
Management							0.754(0.184)	.0001
R 2	4.0%		29.1%		31.3%		54.6%

The outcome variable, change efficacy, is measured with the ORIC change efficacy subscale. A higher score means greater change efficacy. Scales that were retained in the backward stepwise regressions were entered into linear regression models sequentially by domain according to the theory-informed conceptualization (personnel characteristics, organizational context and resources, workforce issues, and organizational functioning).

^aP value for the domain. The P value for the last domain entered in the model is, equivalently, that for the change in the R² from the prior model.

Discussion

This study aimed to describe the organizational readiness of an Alaskan tribal health care organization to change delivery of pediatric dental care by implementing system-level management/procedural changes, such as goal setting and performance monitoring, to increase use of evidence-based dental practices for achieving population health through a new protocol to prevent and stop tooth decay early. In the dental department, change commitment and change efficacy—both domains of organizational readiness to implement change—were moderately high. A second aim of this study sought to identify personal, workforce-related, and perceived work environment–related variables predictive of organizational readiness to implement change. When all measured constructs were accounted for, the only one that predicted organizational readiness to implement change was perceived quality of management strategies used by the organization, such as having organizational goals and provision of feedback. This finding provides reassurance that a focus on organizational functioning, especially effective management strategies, is important when changes are implemented in dental care delivery systems.

Organizational readiness to implement change was moderately high across job roles, with providers, dental assistants, and support staff endorsing, on average, moderate to high levels of change commitment and change efficacy. We suggest that one reason for this finding is that, historically, this health care organization has implemented innovative workforce models. Thus, employees already were familiar with major implementation changes and so were more likely to be comfortable with them. Moreover, those working to treat dental disease in the area may be more receptive to changes in dental care delivery, expecting that those changes will lead to improved population oral health and a change in the status quo. The disproportionately high rates of dental caries experience, including early childhood, in the AIAN population is severe. A large proportion (26.9% to 46.1%) of the employees of the health care organization, which serves a nearly entirely AIAN population, identified as AIAN themselves. Results from this study then suggest that organizational readiness to implement change is moderately high in a tribal health care organization for which many native people work.

Perceived quality of management was the only significant predictor of change commitment and change efficacy after adjusting for other factors, which suggests that it is a key influencer of organizational readiness to implement change. This finding is consistent with that of another recent study demonstrating that among other organizational functioning variables, organizational readiness to implement change in a health care setting is predicted by transformational leadership (von Treuer et al. 2018). Likewise, this result is consistent with recent work suggesting that more instrumental constructs, such as planning and support, may be more important for implementation than attitudinal ones (Helfrich et al. 2018). In our study, management-related variables included the presence of measurable goals that are reviewed routinely, the routine use of evidence-based guidelines/protocols that are matched to the community being served, adequate training in the new guidelines, the regular monitoring and tracking of progress, clear plans for how guidelines will be used, and/or receipt of regular feedback on progress. Good management strategies may provide a context perceived as supportive and empowering, which may buffer, to some extent, excessive time/task demands, suboptimal training, and higher levels of job stress. Thus, when organizations are considering implementing large-scale changes, it may be important to ensure high-quality management to promote readiness for change. The structure and support facilitated by good organizational functioning (including, as in this study, positive organizational climate and quality management) may equip employees with change commitment and efficacy.

The finding that perceived quality of management was the only significant predictor of organizational readiness for change in this sample differs from results of a similar study that we completed with another dental care organization, which serves a low-income population in largely rural communities in the state of Oregon (Cunha-Cruz et al. 2017). That organization is large, complex, and privately held, with both fixed clinics and mobile care—much of which is in primary schools—and employs expanded practice dental hygienists and outreach workers. As in this investigation, we used the ORIC questionnaire (Shea et al. 2014) in the previous study to measure change commitment and change efficacy. Generally, providers and staff demonstrated moderately high organizational readiness for change. Median ORIC change commitment was 3.8 (interquartile range, 3.3 to 4.3), and median ORIC change efficacy was 3.8 (interquartile range, 3.0 to 4.2). In an adjusted regression model, change commitment was associated with organizational climate and support for methods to arrest tooth decay and was inversely related to office chaos. Change efficacy was associated with organizational climate and support for the organization’s mission and was inversely related to burnout. Thus, in that study, a different facet of organizational functioning—organizational climate—was predictive of readiness for change. Taken together, results of these studies suggest that organizational functioning, broadly defined, may be a critical driver of organizational readiness to implement change and the associated efficient uptake and implementation of new protocols for dental caries control. Differences in results of the 2 studies are probably explained, at least partly, by differences in the organizations studied, highlighting the need to consider unique qualities of organizations in implementation research and change efforts.

Most simply, Weiner’s (2009) theory of organizational readiness for change posits that implementation is a function of change-related effort, which is a function of organizational readiness to implement change, comprising 2 affective states: change commitment and change efficacy. According to Weiner’s theory, context drives change commitment and change efficacy. A goal of this study was to understand the influencing role of context on organizational readiness to implement change and to identify the contextual constructs that exert the most important effects. The framework for dissemination, built from the best available implementation research, suggests that context is complex, including variables that fall into 6 domains: norms and attitudes, structure and process, resources, policies and incentives, networks and linkages, and media and change agents (Mendel et al. 2008). Although we did not explicitly measure all context domains outlined by the framework for dissemination, it is worthwhile to note that quality of management, as assessed in our study and considered an organizational functioning construct, conceptually bridges multiple context domains (i.e., structure and process, resources, policies, and incentives). Future research on organizational readiness to implement change in dental care delivery systems should draw on the theory- and evidence-informed framework for dissemination to more comprehensively assess how context influences readiness, as a complete understanding could identify contextual targets for implementation interventions.

Limitations

This study is limited in the generalizability of its results. Data were collected from a single health care organization that predominately serves a small subset of AIAN people, and results from only dental department employees were included. Moreover, the health care organization operates in a geographically isolated area that experiences unique oral health inequities. Another limitation of this study is its cross-sectional nature; causal associations between variables should be interpreted with caution. Planned future work with this same study cohort may provide important longitudinal data. Additionally, relatively small sample sizes due to a restricted pool of potential participants limit comparisons that can be made by job role. Last, the scope of organizational factors assessed was necessarily limited to prevent respondent burden. Nevertheless, we included those variables that we considered most likely to explain organizational readiness to implement change and/or for which validated assessment instruments were available.

Conclusions

Adoption of new implementation strategies to increase the efficacy of a dental care delivery system rolling out a new protocol for preventing and treating caries is an example of a large and disruptive change that has the potential to affect long-standing oral health inequities experienced by AIAN communities. These implementation strategies may include goal setting, action planning, performance monitoring, and problem solving to overcome barriers. Efficient adoption of and adherence to new protocols and strategies rest on organizational factors that drive readiness to implement change and, subsequently, actual change (Weiner et al. 2008; Damschroder et al. 2009). Here, we demonstrate in a second study that it is possible to measure and describe organizational readiness to implement change within the dental care system and, in this study, that perceived quality of management is the primary predictor of readiness. Longitudinal studies should address the effectiveness of interventions that target managerial quality and its impact. Developing approaches that can influence organizational readiness to implement change can increase the efficiency of health care system improvements, thus positively affecting population health.

Author Contributions

C.L. Randall, contributed to design, data analysis, and interpretation, drafted and critically revised the manuscript; K. Hort, E. Mallott, contributed to data acquisition, critically revised the manuscript; C.E. Huebner, P. Milgrom, contributed to conception, design, and data acquisition, critically revised the manuscript; L. Mancl, J. Cunha-Cruz, contributed to conception, design, data acquisition, analysis, and interpretation, drafted and critically revised the manuscript; L. Nelson, contributed to design, critically revised the manuscript; K. Senturia, contributed to design and data acquisition, critically revised the manuscript; B.J. Weiner, contributed to data interpretation, critically revised the manuscript. All authors gave final approval and agree to be accountable for all aspects of the work.