Abstract

Objectives

The aim of this exploratory study was to investigate to what extent primary care professionals are able to change their systems for delivering care to chronic obstructive pulmonary disease (COPD) patients and what professional and organizational factors are associated with the degree of process implementation.

Design

Quasi-experimental design with 1 year follow-up after intervention.

Setting

Three regional COPD management programmes in the Netherlands, in which general practices cooperated with regional hospitals.

Participants

All participating primary care professionals (n = 52).

Intervention

COPD management programme.

Main Outcome Measures

Professional commitment, organizational context and degree of process implementation.

Results

Professionals significantly changed their systems for delivering care to COPD patients, namely self-management support, decision support, delivery system design and clinical information systems. Associations were found between organizational factors, professional commitment and changes in processes of care. Group culture and professional commitment appeared to be, to a moderate degree, predictors of process implementation.

Conclusions

COPD management was effective; all processes improved significantly. Moreover, theoretically expected associations between organizational context and professional factors with the implementation of COPD management were indeed confirmed to some extent. Group culture and professional commitment are important facilitators.

Introduction

Chronic diseases are the main cause of death and disability worldwide, and as the population ages, prevalence of chronic conditions will increase [1]. Chronic obstructive pulmonary disease (COPD) is a common multicomponent disease that imposes an enormous burden on the patient, medical professionals and society in terms of morbidity, mortality, health care resource utilization and cost [2]. Already highly prevalent, COPD is projected to become the third most common cause of death by 2020 [1]. Efforts to improve quality and efficiency of health care have been given high priority. Disease management, defined as ‘an approach to patient care that emphasizes coordinated, comprehensive care along the continuum of disease and across health care delivery systems’ [3], has emerged as a new strategy to achieve improvement. It has three key features: it uses empowerment strategies (patient oriented) and decision support tools (professional directed), preferably supported by changes in the organizational system [4].

A growing body of literature argues that improving the delivery of primary care is an effective approach to implement disease management [5, 6], as chronic illness care is largely performed within a primary care setting. Primary care professionals are therefore at the forefront of COPD diagnosis and management [2]. Their motivation to redesign care is one of the keys to successful disease management [7]. However, most professionals lack the time, information technology and financial resources to systematically improve the quality of care provided to these patients [6]. There is a gap between what professionals do for patients with chronic diseases (e.g. COPD) and what should be done [8]. This raises questions about professional commitment in changing chronic illness care. There are currently no research reports, however, examining professionals' roles in implementing disease management within primary care.

In general, variations in degree of implementation of changes can be attributed to characteristics of the professionals, but also to the organizational context [9, 10]. The aim of this exploratory study is to investigate to what extent primary care professionals are able to change their processes for delivering care to COPD patients, and what professional and organizational factors are associated with the degree of process implementation.

Theoretical framework

The framework is based on theoretical approaches by Cretin et al. [9] and Lin et al. [7]. Both papers argue that a multilevel approach is needed to improve quality; they focus on professional level as well as organizational context. This study combines the approaches by relating professional and organizational factors to changes in processes of care (Fig. 1). At professional level, professional commitment is approached in terms of motivation, using the expectancy theory [11, 12]. This theory acknowledges the multifaceted nature of professional commitment by distinguishing valence, instrumentality and expectancy. Valence is the attractiveness that professionals assign to the outcomes associated with successful implementation of disease management. These outcomes induce both intrinsic and extrinsic rewards, such as improvement in patient health and satisfaction or higher professional self-esteem. Instrumentality refers to the belief that disease management implementation will lead to these targeted outcomes. Expectancy concerns the association between effort and success in disease management implementation. Thus, the product of an individual's assessment of valence, instrumentality and expectancy is the professional commitment towards implementing disease management [7, 12].

Figure 1

Theoretical model on professional commitment to changing chronic illness care.

Figure 1

Theoretical model on professional commitment to changing chronic illness care.

Culture, organizational commitment to quality improvement and climate are considered to be important conditions in the organizational context. They provide information on the setting and contingencies that professionals are confronted with in their work environment [13]. Culture concerns the norms, values, beliefs and behaviours of an organization, reflecting ‘how we do things around here’. The competing values framework distinguishes four types of culture: group culture (teamwork and participation), developmental culture (risk-taking, innovation and change), hierarchical culture (rules, regulations and bureaucracy) and rational culture (efficiency, goal attainment and achievement) [14, 15]. Previous research on chronic care improvement showed that having a group culture is related to higher commitment from professionals [16]. The core principles of disease management emphasize change and teamwork efforts; hence, group culture may be a facilitator for chronic care improvement. A higher degree of the practice group's commitment to quality improvement is expected to positively influence professional commitment as well, since quality activities are hypothesized to support implementation of disease management. Climate refers to an individual's perception of the environmental and organizational contingencies between individual behaviours related to COPD management and anticipated collegial responses [15]. Professionals are hypothesized to be more committed to implementing COPD management when they perceive a supportive climate [7]. A group culture, a supportive climate and commitment to quality tend to create a positive workplace with dedicated professionals working together effectively [17]. This supports the development and implementation of system changes that improve processes and, ultimately, patient outcomes.

Previous research [9, 18] suggests that it is the interplay between professional level factors and the organizational context that explains the success or failure of the implementation of disease management programmes. This study explores associations between professional commitment, organizational context and perceived degree of implementation of changes within current practice groups of COPD (Fig. 1).

Methods

Setting and design

This study evaluates professionals' commitment within three regional pilots of COPD management programmes in the Netherlands. The pilot studies were supported by Partners in Care Solutions for COPD, a national programme that aims to optimize COPD care by supporting the implementation of disease management. The programmes were carried out in 21 general practices in central Netherlands: the ‘Gelderse Vallei’ region, Doetinchem and Nijmegen. The practices coordinated with the regional hospital(s) and were supported by their own practice nurses or practice nurses of a regional home care organization.

The COPD management programme consisted of patient education, protocolized assessment and treatment of COPD, and coordination of care. Practice nurses systematically educated patients on smoking behaviour, medication usage, nutrition and physical activity so as to increase their understanding of COPD and self-management skills. Next to that, professionals were educated on the guidelines and programme. These interventions were supported by organizational interventions. A multidisciplinary team (general practitioner, practice nurse and a lung specialist) coordinated diagnostic procedures, treatment and ongoing patient management. Team members' tasks and responsibilities were described in a guideline-based protocol. Practice nurses performed lung function tests, assessed patients' conditions, provided disease-related education and advice, coordinated care and organized follow-up meetings. They acted in conjunction with the general practitioner who consulted the specialist when needed. Figure 2 shows the specific characteristics of these programmes and specific differences between the regions. It shows that the programmes are rather similar.

Figure 2

Disease management interventions in the disease management programmes.

Figure 2

Disease management interventions in the disease management programmes.

An exploratory study was performed as part of a larger evaluation study on COPD management programmes in the Netherlands. Data were collected by means of postal questionnaires at baseline and 1 year after intervention. Both the researcher and the programme manager invited the professionals involved in these programmes to complete the questionnaire. The characteristics and psychometric properties of the measures are described in Table 1.

Table 1

Descriptive statistics of theoretical constructs and instruments per variable

Construct Variable(s) Instrument No. of items Range Cronbach's α T0, mean (SD) T1, mean (SD) Change (95% CI) P-value 
Organizational factors Culture CVF 20 0–100      
 Group     40.8 (17.0) 40.6 (18.6) −0.3 (−3.7 to 3.1) 0.88 
Developmental     27.7 (14.3) 26.2 (12.2) −1.4 (−5.6 to 2.8) 0.49 
 Hierarchical     16.3 (9.8) 19.0 (11.1) 2.7 (−0.3 to 5.6) 0.07 
 Rational     15.1 (10.9) 14.0 ( 9.7) −1.1 (−4.1 to 1.8) 0.44 
QI commitment EFQM    3.8 (0.5) 3.8 (0.6) 0.0 (−0.2 to 0.2) 0.89 
 Policy and strategy  1–5 0.69 3.9 (0.6) 4.0 (0.7) 0.1 (−0.2 to 0.3) 0.77 
 Quality development  1–5 0.78 3.8 (0.6) 4.0 (0.6) 0.1 (−0.1 to 0.3) 0.23 
 Employee involvement  1–5 0.78 3.9 (0.6) 3.8 (0.7) −0.1 (−0.4 to 0.1) 0.23 
 Customer satisfaction  1–5 0.76 3.4 (0.6) 3.4 (0.6) 0.0 (−0.2 to 0.2) 0.97 
Climate ICIC 1–4 0.48 3.0 (0.3) 3.5 (0.5) 0.5 (0.4 to 0.7) <0.001 
Professional commitment Professional commitment     124.8 (28.0) 135.7 (33.8) 10.9 (1.6 to 20.3) 0.02 
 Valence ICIC 1–5 0.87 3.6 (0.5) 3.8 (0.5) 0.2 (0.1 to 0.3) 0.009 
Instrumentality ICIC 1–7 0.78 5.7 (0.5) 5.8 (0.5) 0.1 (0.0 to 0.2) 0.16 
 Expectancy ICIC 1–7  6.1 (0.8) 6.2 (0.8) 0.1 (−0.2 to 0.4) 0.58 
Changes in care processes Self-management support ACIC 0–11 0.94 4.8 (2.5) 7.2 (1.7) 2.4 (1.7 to 3.1) <0.001 
Decision support ACIC 0–11 0.85 5.8 (1.9) 7.5 (1.3) 1.7 (1.1 to 2.3) <0.001 
Delivery system design ACIC 0–11 0.94 4.8 (2.3) 7.7 (1.3) 3.0 (2.3 to 3.6) <0.001 
Clinical information systems ACIC 0–11 0.91 4.5 (2.3) 6.7 (1.6) 2.3 (1.7 to 2.9) <0.001 
Construct Variable(s) Instrument No. of items Range Cronbach's α T0, mean (SD) T1, mean (SD) Change (95% CI) P-value 
Organizational factors Culture CVF 20 0–100      
 Group     40.8 (17.0) 40.6 (18.6) −0.3 (−3.7 to 3.1) 0.88 
Developmental     27.7 (14.3) 26.2 (12.2) −1.4 (−5.6 to 2.8) 0.49 
 Hierarchical     16.3 (9.8) 19.0 (11.1) 2.7 (−0.3 to 5.6) 0.07 
 Rational     15.1 (10.9) 14.0 ( 9.7) −1.1 (−4.1 to 1.8) 0.44 
QI commitment EFQM    3.8 (0.5) 3.8 (0.6) 0.0 (−0.2 to 0.2) 0.89 
 Policy and strategy  1–5 0.69 3.9 (0.6) 4.0 (0.7) 0.1 (−0.2 to 0.3) 0.77 
 Quality development  1–5 0.78 3.8 (0.6) 4.0 (0.6) 0.1 (−0.1 to 0.3) 0.23 
 Employee involvement  1–5 0.78 3.9 (0.6) 3.8 (0.7) −0.1 (−0.4 to 0.1) 0.23 
 Customer satisfaction  1–5 0.76 3.4 (0.6) 3.4 (0.6) 0.0 (−0.2 to 0.2) 0.97 
Climate ICIC 1–4 0.48 3.0 (0.3) 3.5 (0.5) 0.5 (0.4 to 0.7) <0.001 
Professional commitment Professional commitment     124.8 (28.0) 135.7 (33.8) 10.9 (1.6 to 20.3) 0.02 
 Valence ICIC 1–5 0.87 3.6 (0.5) 3.8 (0.5) 0.2 (0.1 to 0.3) 0.009 
Instrumentality ICIC 1–7 0.78 5.7 (0.5) 5.8 (0.5) 0.1 (0.0 to 0.2) 0.16 
 Expectancy ICIC 1–7  6.1 (0.8) 6.2 (0.8) 0.1 (−0.2 to 0.4) 0.58 
Changes in care processes Self-management support ACIC 0–11 0.94 4.8 (2.5) 7.2 (1.7) 2.4 (1.7 to 3.1) <0.001 
Decision support ACIC 0–11 0.85 5.8 (1.9) 7.5 (1.3) 1.7 (1.1 to 2.3) <0.001 
Delivery system design ACIC 0–11 0.94 4.8 (2.3) 7.7 (1.3) 3.0 (2.3 to 3.6) <0.001 
Clinical information systems ACIC 0–11 0.91 4.5 (2.3) 6.7 (1.6) 2.3 (1.7 to 2.9) <0.001 

QI, quality improvement; CVF, competing values framework; EFQM, European foundation for quality management; ICIC, improving chronic illness care and ACIC, assessing chronic illness care.

Questionnaire

Professionals completed a questionnaire based on the improving chronic illness care questionnaire [7]. Areas covered were (i) general professional characteristics, (ii) COPD disease management implementation and (iii) factors associated with changing chronic COPD care. The first part related to background characteristics, such as duration of professional activity. Second, the assessing chronic illness care survey [19] aimed to determine the perceived degree of actual implementation of the programme. Only the elements reflecting the interventions within the disease management programme were included: self-management support (patient-related intervention), decision support (professional-directed intervention), delivery system design and clinical information systems (both evaluating implementation of the organizational intervention) (Fig. 2). The four to six items per element were rated on a 0–11 response scale, with higher scores indicating more complete implementation. Mean scores for each element were obtained. To validate self-reports by professionals, we compared changes in assessing chronic illness care elements to available data on structural changes in care practices and to available process measures, namely application of patient education for changes in self-management support, provision of inhalation instruction and application of spirometry for changes in decision support and regularity of follow-up for changes in delivery system design. The process measures were expressed as percentages of patients within practices who received a specific intervention.

The third part of the questionnaire measured professional commitment in implementing COPD management (professional level: valence, instrumentality and expectancy) and identified relevant contextual factors (organizational level: culture, commitment and climate). Valence is measured on a 9-item scale; professionals are asked to report the importance of each of the described outcomes, for example, improving continuity of care. Response categories range from 1 (not important) to 5 (extremely important). Instrumentality is made up of nine items; response categories range from 1 (strongly disagree) to 7 (strongly agree). Expectancy is a single item to be rated from 1 (strongly disagree) to 7 (strongly agree) in response to a statement on the association between effort and success in disease management implementation. Consistent with Vroom's conceptualization of the expectancy framework [12], these scales add up to one professional commitment score.

Culture was assessed using the competing values framework [13], which asks respondents to distribute 100 points across series of four statements on the culture of their practice group. These statements reflect the four culture types. Commitment to quality improvement is measured by an overall average score on quality improvement questions by the European foundation for quality management scale adapted for primary care [13, 20]. Response categories range from 1 (strongly disagree) to 5 (strongly agree). The scale covers four subjects: policy and strategy, quality development, employee involvement and customer satisfaction. Examples of items are: ‘the practice group uses data from patients to improve services’ and ‘professionals are recognized for improving quality’. Climate is measured as the average of four questions assessing expected collegial responses to initiating behaviours and performing tasks related to COPD management [7]. Response categories range from 1 (admonition or disapproval) to 4 (reward or approval).

Statistical analyses

For all study variables, comparisons were made between baseline (T0) and post-intervention (T1) data using paired-sampled t-tests and Wilcoxon signed rank tests (one sided; α = 0.05) where appropriate. Process changes in assessing chronic illness care were validated by comparing them to the process measures using Pearson's correlation coefficients. Differences between groups determined by professional characteristics were explored with Mann–Whitney U tests. To determine whether the theoretical model propositions are consistent with the data, regression analyses were applied in two steps. The associations between organizational context, professional commitment and degree of process changes were explored using professional commitment as a mediator variable (Fig. 1) [21]. Baseline as well as change scores were entered into the model to account for the individual baseline levels and to control for regression to the mean effects [22]. Data were analysed using SPSS 13 for Windows. A prior significance level of 0.05 was used for all statistical tests.

Results

Response and characteristics

The eligible study population consisted of 60 professionals. Initial responses were received from 54 professionals; as 2 were lost to follow-up, the final response rate was 87%. Professional characteristics at baseline are presented in Table 2. We explored the differences between general practitioners and practice nurses. Overall, the latter tended to score more positively; differing significantly on instrumentality and the implementation of information systems. Furthermore, professionals with specific COPD education had significantly higher scores on COPD management implementation, except for decision support. After the implementation of the disease management intervention, these differences dissipated.

Table 2

Professional characteristics at baseline

Characteristic % (n
Sex 
 Male 50 (27) 
 Female 50 (27) 
Profession 
 General practitioner 61 (33) 
 Practice nurse 31 (17) 
 Practice assistant 4 (2) 
 Physiotherapist 4 (2) 
Years in practice 
 0–1 year 6 (3) 
 1–5 years 26 (14) 
 5–10 years 19 (10) 
 Longer than 10 years 50 (27) 
Specific COPD education 
 Yes 63 (34) 
 No 37 (20) 
Number of hours a week working with COPD patients 
 <8 h 68 (35) 
 8–15 h 12 (6) 
 16–22 h 12 (6) 
 >22 h 8 (4) 
Participating in regional multidisciplinary COPD meetings 
Yes 64 (35) 
No 36 (19) 
Characteristic % (n
Sex 
 Male 50 (27) 
 Female 50 (27) 
Profession 
 General practitioner 61 (33) 
 Practice nurse 31 (17) 
 Practice assistant 4 (2) 
 Physiotherapist 4 (2) 
Years in practice 
 0–1 year 6 (3) 
 1–5 years 26 (14) 
 5–10 years 19 (10) 
 Longer than 10 years 50 (27) 
Specific COPD education 
 Yes 63 (34) 
 No 37 (20) 
Number of hours a week working with COPD patients 
 <8 h 68 (35) 
 8–15 h 12 (6) 
 16–22 h 12 (6) 
 >22 h 8 (4) 
Participating in regional multidisciplinary COPD meetings 
Yes 64 (35) 
No 36 (19) 

Professional commitment

At baseline, attractiveness assigned to the outcomes associated with successful implementation of COPD management (valence) was rated a mean 3.57 (±0.44) on a 1–5 scale. This indicates that professionals attach importance to these outcomes. The statement that implementation of disease management leads to the targeted outcomes was rated a mean of 5.69 (±0.51) (1–7 scale), denoting that professionals believe that they can successfully implement COPD management. Expectancy was rated a mean of 6.10 (±0.75) (1–7 scale), which implies that disease management is seen as an effective tool in improving outcomes. These measures add up to an overall professional commitment score of 124.8 (±28.0). Comparing baseline and post-intervention scores showed that professional commitment (P = 0.02) and valence (P = 0.009) had improved significantly after intervention (Table 1).

Organizational context

At baseline, most professionals (62%) indicated that their practice is best characterized by a group culture; 23% indicated their practice culture as developmental. A rational culture and a hierarchical culture each were perceived by only 8%. Questions related to commitment to quality improvement were rated with a mean of 3.79 (±0.47) on a 1–5 scale; scores for the four items ranged from 3.40 (±0.57) to 3.91 (±0.57). These scores indicate a relatively high commitment to quality, involving staff members and supported by strategy. With regard to climate, professionals gave a rating of 2.96 (±0.29) on a 1–4 scale for the expectation of a somewhat positive collegial response when they would initiate behaviours and perform tasks related to COPD management. After the intervention, the climate had improved significantly (P < 0.001). No significant changes were found on culture and commitment to quality improvement (Table 1).

Programme implementation

Table 1 also summarizes the extent to which the professionals felt that the disease management programme was actually implemented (process changes). Overall, subscale scores at baseline ranged from 4.45 (information systems) to 5.80 (decision support). This indicates that basic support for COPD management was present, for example, facilitating access to evidence-based guidelines or providing educational materials to patients [19]. Scores for all elements—self-management support, decision support, delivery system design and clinical information systems—improved significantly after intervention, with delivery system design to the highest degree. Changes in self-reported, self-management support and actual application of patient education in practice correlated significantly (r = 0.38, P < 0.01). Changes in decision support correlated significantly with provision of inhalation instruction (r = 0.52, P < 0.01) and changes in delivery system design correlated significantly with the proportions of patients regularly followed up (r = 0.42, P < 0.01). In addition, the process measures showed that all patients underwent spirometry as indicated by guidelines, validating reported improvements in decision support.

Professional commitment to changing chronic illness care

Table 3 presents zero-order correlations between all variables of the model (Fig. 1). It shows that climate baseline (r = 0.24, P < 0.05) and climate change scores (r = 0.31, P < 0.05) correlate significantly with professional commitment at baseline. Changes in commitment to quality improvement and changes in professional commitment correlate marginally and significantly with each other (r = 0.23, P < 0.10). No significant correlations were found between group culture and professional commitment. Furthermore, (marginal) significant correlations are found between organizational or professional factors and process changes, except for decision support.

Table 3

Zero-order correlations between independent and dependent variables (n = 52)

 1. Group culture T0 2. QI commitment T0 3. Climate T0 4. Δ Group culture 5. Δ QI commitment 6. Δ Climate 7. Professional commitment T0 8. Δ Professional commitment 9. Δ Self-management support 10. Δ Decision support 11. Δ Delivery system design 12. Δ Clinical information systems 
2. QI commitment T0 −0.17            
3. Climate T0 0.01 0.15           
4. Δ Group culture −0.20*** −0.07 0.26**          
5. Δ QI commitment −0.02 −0.48* −0.03 0.14         
6. Δ Climate 0.22*** −0.07 −0.25** −0.17 0.08        
7. Professional commitment T0 −0.06 0.07 0.24** −0.18 −0.04 0.31**       
8. Δ Professional commitment −0.00 0.18 −0.14 0.02 0.23*** −0.04 −0.41      
9. Δ Self-management support 0.35* −0.10 0.22*** 0.05 0.04 0.07 0.18*** −0.03     
10. Δ Decision support 0.21*** 0.04 0.22*** −0.12 0.14 0.03 0.13 0.19*** 0.43*    
11. Δ Delivery system design 0.46* −0.09 0.18 0.07 0.07 0.06 0.13 0.14 0.79* 0.66*   
12. Δ Clinical information systems 0.21*** 0.08 0.18 −0.17 −0.02 0.11 0.35** −0.02 0.72* 0.57* 0.77*  
13. Δ Disease management 0.36* −0.02 0.23*** −0.04 0.06 0.06 0.22*** 0.08 0.87* 0.76* 0.94* 0.89* 
 1. Group culture T0 2. QI commitment T0 3. Climate T0 4. Δ Group culture 5. Δ QI commitment 6. Δ Climate 7. Professional commitment T0 8. Δ Professional commitment 9. Δ Self-management support 10. Δ Decision support 11. Δ Delivery system design 12. Δ Clinical information systems 
2. QI commitment T0 −0.17            
3. Climate T0 0.01 0.15           
4. Δ Group culture −0.20*** −0.07 0.26**          
5. Δ QI commitment −0.02 −0.48* −0.03 0.14         
6. Δ Climate 0.22*** −0.07 −0.25** −0.17 0.08        
7. Professional commitment T0 −0.06 0.07 0.24** −0.18 −0.04 0.31**       
8. Δ Professional commitment −0.00 0.18 −0.14 0.02 0.23*** −0.04 −0.41      
9. Δ Self-management support 0.35* −0.10 0.22*** 0.05 0.04 0.07 0.18*** −0.03     
10. Δ Decision support 0.21*** 0.04 0.22*** −0.12 0.14 0.03 0.13 0.19*** 0.43*    
11. Δ Delivery system design 0.46* −0.09 0.18 0.07 0.07 0.06 0.13 0.14 0.79* 0.66*   
12. Δ Clinical information systems 0.21*** 0.08 0.18 −0.17 −0.02 0.11 0.35** −0.02 0.72* 0.57* 0.77*  
13. Δ Disease management 0.36* −0.02 0.23*** −0.04 0.06 0.06 0.22*** 0.08 0.87* 0.76* 0.94* 0.89* 

Correlations are significant at the *0.01, **0.05 and ***0.10 levels (one tailed).

Table 4 shows the results of the regression analyses that explored whether organizational context and professional commitment could be predictors of changes in processes of care. In order to compare the magnitude of the effects of various independent variables, we presented the standardized regression coefficients from the models. In the final model, group culture at baseline appeared to be a significant predictor of process changes in self-management support, delivery system design and disease management (total score), with standardized regression coefficients of 0.38, 0.52 and 0.40, respectively. In the multiple regression models for process changes in delivery system design, clinical information systems and disease management, (marginally) significant effects of professional commitment at baseline were found in the final model as well, with standardized regression coefficients of 0.34, 0.38 and 0.34, respectively. Total explained variance after adjustment was small; except for the delivery system design (22%). Professional commitment was not identified as a mediator of organizational factors in influencing process changes.

Table 4

Determinants of process changes from hierarchical regression analyses (standardized regression coefficients)

Variables Self-management support Decision support Delivery system design Clinical information systems Disease management 
Group culture T0 0.38** 0.21 0.52* 0.22 0.40** 
QI commitment T0 −0.11 0.03 −0.10 0.03 −0.05 
Climate T0 0.15 0.23 0.08 0.12 0.16 
Δ Group culture 0.12 −0.14 0.19 −0.09 0.03 
Δ QI commitment −0.03 0.13 −0.05 0.00 0.01 
Δ Climate −0.03 −0.04 −0.10 −0.03 −0.07 
Professional commitment T0 0.26 0.19 0.34** 0.38** 0.34*** 
Δ Professional commitment 0.12 0.27 0.31*** 0.15 0.25 
Adjusted R2 0.07 0.04 0.22 0.05 0.12 
Variables Self-management support Decision support Delivery system design Clinical information systems Disease management 
Group culture T0 0.38** 0.21 0.52* 0.22 0.40** 
QI commitment T0 −0.11 0.03 −0.10 0.03 −0.05 
Climate T0 0.15 0.23 0.08 0.12 0.16 
Δ Group culture 0.12 −0.14 0.19 −0.09 0.03 
Δ QI commitment −0.03 0.13 −0.05 0.00 0.01 
Δ Climate −0.03 −0.04 −0.10 −0.03 −0.07 
Professional commitment T0 0.26 0.19 0.34** 0.38** 0.34*** 
Δ Professional commitment 0.12 0.27 0.31*** 0.15 0.25 
Adjusted R2 0.07 0.04 0.22 0.05 0.12 

*P < 0.01; **P < 0.05 and ***P < 0.10 (one tailed) pairwise.

Discussion

The aim of this study was to explore factors that contribute to success or failure of the implementation of disease management programmes. COPD management was effective; all processes changed significantly. Furthermore, these exploratory results provided some support for the theoretically proposed associations between organizational, professional factors and the degree of process changes. The presence of a group culture and professional commitment were indeed associated with positive changes in processes of care. Moreover, these factors were found to be significant predictors of process changes to a moderate degree. The significant effect of group culture on process changes can be explained by a more autonomous way of working within a group culture; fewer rules and agreements enhance implementation of improvements. Health care cultures that emphasize group affiliation, teamwork and coordination have been associated with more complete implementation of quality improvement [17, 23]. Accordingly, professional commitment was associated with more complete disease management implementation. As this association was not confirmed for all process changes, it would seem that the professionals' commitment to change is determined by particular characteristics of the interventions (self-management, guidelines, information technology), which may promote or hamper their actual adoption. This is widely discussed in various theories on involving professionals in implementation of innovations [24].

Previous research identified a number of barriers to professionals' commitment in changing chronic illness care [25]. The present study finds some evidence that a supportive climate is associated with professional commitment, implying that peer support is an important motivator of professional commitment. In contrast to culture, climate is an individual perception of the day-to-day practice group environment. It is therefore interesting that a supportive climate stimulates professionals to engage in improving chronic illness care. The finding that change in commitment to quality improvement is associated with change in professional commitment confirms our initial hypothesis; quality activities within a practice group do support implementation of disease management. Various associations were found between organizational factors and professional commitment on the one hand and changes in processes of care on the other, providing support for our theoretical model. No associations were found between professional and organizational factors and changes in decision support, probably due to only small changes in this parameter. A high baseline score left little room for improvement. General practices apparently are rather used to working with guidelines, as a major tool for making clinical decisions and improving patient care [26].

One of the critical factors for better chronic disease management is the effective use of specialized practice nurses [27]. This study shows that practice nurses strongly believe that one can really achieve the desired change in general practice, an attitude that may act as a catalyst to adapting disease management in primary care. Previous research showed that lack of outcome expectancy was a possible barrier in implementing the changes in health care. There was great variation, however, among patient groups, with positive results in the case of alcohol abuse prevention but not for clinical breast screening [28]. The present study shows that disease management is seen as an effective tool in improving outcomes in COPD care, as can be derived from high scores on expectancy and valence. Furthermore, significantly improved professional commitment and valence suggest that professionals' and their exposure to disease management programmes may positively influence the behavioural intention of professionals to implement disease management, which is confirmed by previous findings [29].

To date, most health care improvements have been targeted at factors related to individual professionals [30]. Still, achieving changes in health care typically will require interplay between a range of factors at different levels, i.e. professional level, organizational context and the economic and political context [24]. To determine whether and to what extent change is achieved, we will need information on all levels [9]. This study provides a first empirical test of a theoretical model on professional and organizational aspects important in changing chronic illness care. By combining two models, this multilevel approach was effectuated. Yet, more research is needed, including research on internal and external incentives, such as financial support or quality targets.

Findings of this study must be interpreted in the light of several limitations. First, this study was an exploratory analysis as part of a research project on COPD disease management programmes. Consequently, the sample of the study was small, leaving limited predictive power of organizational and professional factors in process changes. Furthermore, mass significance in the regression analyses may have led to too positive results. However, we explicitly tested associations derived from a theoretical model. To avoid type one errors, we set the significance level at 0.05 (one tailed). Second, self-reported instruments were used. The assessing chronic illness care measures professionals' perceptions; these perceptions may have been influenced by the professionals' expectations in addition to the actual implementation. However, professionals' perceptions were validated by available process measures. Even though these process measures only reflect part of the change in practice, we found associations between these measures and reported changes in related assessing chronic illness care domains: self-management support, decision support and delivery system design. Whereas assessing chronic illness care is a multidimensional survey, process measures only measure fragments of changes. In addition, structural changes within the programmes were also apparent, such as COPD consulting hours, specialized nurses and a COPD registry within practices, which supports the changes reported by professionals by pointing to actual implementation of the programme. Third, this study concerned pilot programmes. This may have biased the results, since professionals participating in pilot programmes may be assumed to have a fair degree of commitment right from the start. Yet, despite high baseline scores significant improvements in these pilots were found, which suggests improvement possibilities for practices which are less far ahead in disease management implementation. Hence, large-scale follow-up research is needed.

In conclusion, the present exploratory study showed that professionals significantly changed their systems for delivering care to COPD patients. Organizational context and professional factors proved to be instrumental to these changes. Group culture and professional commitment are important facilitators for implementation of COPD management in primary care.

Funding

The research project was supported by an unrestricted grant from PICASSO for COPD, an initiative of Pfizer BV and Boehringer Ingelheim BV in cooperation with research institute Caphri (Care and Public Health Research Institute) of Maastricht University.

References

WHO
Chronic Diseases and their Common Risk Factors
 
Geneva
World Health Organization
 
Bellamy
D
Smith
J
Role of primary care in early diagnosis and effective management of COPD
Int J Clin Pract
 , 
2007
, vol. 
61
 (pg. 
1380
-
9
)
Ellrodt
G
Cook
D
Lee
J
, et al.  . 
Evidence-based disease management
J Am Med Assoc
 , 
1997
, vol. 
278
 (pg. 
1687
-
92
)
Lemmens
KM
Nieboer
AP
van Schayck
CP
, et al.  . 
A model to evaluate quality and effectiveness of disease management
Qual Saf Health Care
 , 
2008
, vol. 
17
 (pg. 
447
-
53
)
Bodenheimer
T
Wagner
EH
Grumbach
K
Improving primary care for patients with chronic disease
J Am Med Assoc
 , 
2002
, vol. 
288
 (pg. 
1775
-
9
)
Casalino
LP
Disease management and the organization of physician practice
J Am Med Assoc
 , 
2005
, vol. 
293
 (pg. 
485
-
8
)
Lin
MK
Marsteller
JA
Shortell
SM
, et al.  . 
Motivation to change chronic illness care: results from a national evaluation of quality improvement collaboratives
Health Care Manage Rev
 , 
2005
, vol. 
30
 (pg. 
139
-
56
)
McGlynn
EA
Asch
SM
Adams
J
, et al.  . 
The quality of health care delivered to adults in the United States
N Engl J Med
 , 
2003
, vol. 
348
 (pg. 
2635
-
45
)
Cretin
S
Shortell
SM
Keeler
EB
An evaluation of collaborative interventions to improve chronic illness care. Framework and study design
Eval Rev
 , 
2004
, vol. 
28
 (pg. 
28
-
51
)
Shortell
SM
Bennett
CL
Byck
GR
Assessing the impact of continuous quality improvement on clinical practice: what it will take to accelerate progress
Milbank Q
 , 
1998
, vol. 
76
 (pg. 
593
-
624
510
Donaldson
L
The Contingency Theory of Organizations
 , 
2001
Thousand Oaks, London, New Delhi
Sage
Vroom
VH
Work and Motivation
 , 
1995
San Francisco
Jossey-Bass, Co
Shortell
SM
O'Brien
JL
Carman
JM
, et al.  . 
Assessing the impact of continuous quality improvement/total quality management: concept versus implementation
Health Serv Res
 , 
1995
, vol. 
30
 (pg. 
377
-
401
)
Shortell
SM
Marsteller
JA
Lin
M
, et al.  . 
The role of perceived team effectiveness in improving chronic illness care
Med Care
 , 
2004
, vol. 
42
 (pg. 
1040
-
8
)
Zammuto
RF
Gifford
G
Goodman
EA
Ashkanasy
N
Wilderom
C
Peterson
M
Managerial ideologies, organisation culture and the outcomes of innovation: a competing values perspective
The Handbook of Organisational Culture and Climate
 , 
2000
Thousand Oaks, CA
Sage
Waters
TM
Budetti
PP
Reynolds
KS
, et al.  . 
Factors associated with physician involvement in care management
Med Care
 , 
2001
, vol. 
39
 (pg. 
I79
-
91
)
Ferlie
EB
Shortell
SM
Improving the quality of health care in the United Kingdom and the United States: a framework for change
Milbank Q
 , 
2001
, vol. 
79
 (pg. 
281
-
315
)
Hroscikoski
MC
Solberg
LI
Sperl-Hillen
JM
, et al.  . 
Challenges of change: a qualitative study of chronic care model implementation
Ann Fam Med
 , 
2006
, vol. 
4
 (pg. 
317
-
26
)
Bonomi
AE
Wagner
EH
Glasgow
RE
, et al.  . 
Assessment of chronic illness care (ACIC): a practical tool to measure quality improvement
Health Serv Res
 , 
2002
, vol. 
37
 (pg. 
791
-
820
)
Geboers
H
Grol
R
van den Bosch
W
, et al.  . 
A model for continuous quality improvement in small scale practices
Qual Health Care
 , 
1999
, vol. 
8
 (pg. 
43
-
8
)
Baron
RM
Kenny
DA
The moderator-mediator variable distinction in social psychological research: conceptual, strategic and statistical considerations
J Pers Soc Psychol
 , 
1986
, vol. 
51
 (pg. 
1173
-
82
)
Glymour
MM
Weuve
J
Berkman
LF
, et al.  . 
When is baseline adjustment useful in analyses of change? An example with education and cognitive change
Am J Epidemiol
 , 
2005
, vol. 
162
 (pg. 
267
-
78
)
Scott
T
Mannion
R
Davies
HT
, et al.  . 
Implementing culture change in health care: theory and practice
Int J Qual Health Care
 , 
2003
, vol. 
15
 (pg. 
111
-
8
)
Grol
RP
Bosch
MC
Hulscher
ME
, et al.  . 
Planning and studying improvement in patient care: the use of theoretical perspectives
Milbank Q
 , 
2007
, vol. 
85
 (pg. 
93
-
138
)
Newton
PJ
Halcomb
EJ
Davidson
PM
, et al.  . 
Barriers and facilitators to the implementation of the collaborative method: reflections from a single site
Qual Saf Health Care
 , 
2007
, vol. 
16
 (pg. 
409
-
14
)
Farquhar
CM
Kofa
EW
Slutsky
JR
Clinicians' attitudes to clinical practice guidelines: a systematic review
Med J Aust
 , 
2002
, vol. 
177
 (pg. 
502
-
6
)
Harrison
S
Dowswell
G
Wright
J
Practice nurses and clinical guidelines in a changing primary care context: an empirical study
J Adv Nurs
 , 
2002
, vol. 
39
 (pg. 
299
-
307
)
Cabana
MD
Rand
CS
Powe
NR
, et al.  . 
Why don't physicians follow clinical practice guidelines? A framework for improvement
J Am Med Assoc
 , 
1999
, vol. 
282
 (pg. 
1458
-
65
)
Fernandez
A
Grumbach
K
Vranizan
K
, et al.  . 
Primary care physicians' experience with disease management programs
J Gen Intern Med
 , 
2001
, vol. 
16
 (pg. 
163
-
7
)
Grimshaw
JM
Thomas
RE
MacLennan
G
, et al.  . 
Effectiveness and efficiency of guideline dissemination and implementation strategies
Health Technol Assess
 , 
2004
, vol. 
8
 (pg. 
ii
-
iv
1–72

Supplementary data