Discriminant analysis

The discriminant analysis is a multivariate technique. The discriminant analysis

identifies the relative importance of variables that may differ across the job types of the survey respondents according to Table 5.2.

The other purpose of this analysis is to examine whether significant differences exist among the respondents’ job types in relation to predictor variables. The analysis uses the multiple discriminant analysis technique because the criterion variable has more than two categories.

The question of interest is whether or not the ‘job type’ categories can be differentiated in terms of ACIC variables. In this analysis, the five job type categories are the

grouping variables and ACIC variables of the first six sub-scales are the independent variables.

Considering the five categories, the analysis extracts a total of four functions. Table 5.17 shows that the eigenvalue associated with the first function (57.768) accounts for 75.9% of the explained variance. Because the eigenvalue is fairly large, this first function has superiority. The second function has a relatively smaller eigenvalue (13.876) and accounts for only 18.2% of the explained variance.

Table 5.17: Canonical discriminant functions by eigenvalues Function

Eigenvalue % of Variance Cumulative %

Canonical Correlation 1 57.768a 75.9 75.9 .991 2 13.876a 18.2 94.1 .966 3 2.846a 3.7 97.9 .860 4 1.625a 2.1 100.0 .787

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Table 5.18 shows that the four functions together significantly discriminate among five groups. The value of Wilk’s Lambda indicates that the group means appear to differ. However, when the first function is removed, the second function is not significant at the 5% level. So the second function does not contribute significantly to the group differences.

Table 5.18: Wilk’s Lambda values and level of significance Test of

Function(s)

Wilks'

Lambda Chi-square Df Sig.

1 through 4 .000 136.283 108 .034

2 through 4 .007 75.179 78 .569

3 through 4 .099 34.683 50 .951

4 .381 14.477 24 .935

Table 5.19 indicates that the first function has large coefficients for variables like organisational leadership, self-management support, address concerns of patients and families, effective behaviour change interventions, practice team leadership, practice team functioning and reminder to provider.

Figure 5.5 shows the group means according to respondents’ job types by two functions. This is a scattergram plot of all the job types on first and second functions.

The non-clinician manager job type category has the highest value for the first function whereas the non-clinician job type has the lowest. Therefore the variables with larger coefficients are likely to contribute for the variances.

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Table 5.19: Standardised Canonical Discriminant Function Coefficients

Variables Function

1 2 3 4

Organisational leadership 9.533 -4.032 3.268 -.830

Organisational goals 1.283 -.582 -1.694 .664

Improvement strategy 1.136 2.406 -.945 1.502

Incentives and regulations -1.070 1.566 -.599 -.620

Senior leaders -5.345 1.654 .712 -.181

Benefits 2.046 -1.899 -.445 .446

Linking to outside resources -3.667 3.153 -.210 1.068

Partnership with community organisations 2.318 -2.112 -.429 -1.156

Regional health plans -5.411 2.581 .280 .082

Assessment and documentation of self-management needs

5.134 3.928 .671 1.376

Self-management support 12.425 -11.426 1.468 -2.729

Address concerns of patients and families 9.763 3.140 -.820 1.694 Effective behaviour change interventions -12.579 .830 .047 -.755 Evidence-based guidelines and peer support 4.590 -14.497 3.376 -1.769 Involvement of specialists in improving primary care -6.240 4.271 -2.122 .296 Provider education for chronic illness care -5.973 -2.652 -1.601 1.027 Informing patients about guidelines -6.098 12.838 -.978 -.276

Practice team functioning -17.435 8.352 -3.556 2.784

Practice team leadership 15.124 -10.823 4.398 -1.917

Appointment system 4.669 -6.111 2.939 .100 Follow-up -4.285 11.364 -3.288 .706 Planned visits -2.544 .096 -.241 -.268 Continuity of care 4.905 1.424 -.129 -.175 Registry -2.829 2.278 .665 .179 Reminder to providers 7.724 -3.496 .205 -.032 Feedback -2.098 1.043 -2.534 -.557

Information about relevant sub-groups of patients needing services

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Figure 5.5: Combined group plot

5.4 Discussion

The ACIC survey analysis quantitatively draws results in relation to the second research objective ‘to assess the organisation’s capacity and state of readiness to implement a CDM register to facilitate cooperation between services in the provision of patient care and in support of patient self-management’ and associated research question. The ACIC sub-scales scores reveal the status of chronic care in ACT Health region according to Wagner’s CCM components. The data analysis explores the strengths and opportunities of ACT Health’s chronic care elements. Although the sample size is small, it covered a wide range of participants and perspectives (diseases, conditions, service areas and so on) to confirm the survey results.

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The ACIC sub-scales scores, on a scale of 0 (limited support) to 11 (full support for chronic care), range from 4.1 to 5.8. The clinical information system score (4.1) and the decision support score (5.3) are at the basic level of support. These two sub-scale scores contribute to lowering the overall program score to 5.3 (basic support for chronic care range). The pattern of ACIC sub-scales score and the overall program score is similar to other Australian health care organisations that have used the ACIC instrument (Si et al. 2005; Beer and Forster 2010).

ACT Health has a reasonably good policy environment and organisational leadership in chronic care. Within the policy contexts, the policy on providers’ incentives for chronic care requires further strengthening to support them to achieve patient care goals. The clinical information system is least developed and a real disease register is not in place. The low score of the clinical information system sub-scale has direct influences on the integration of ACT Health’s chronic care interventions according to the CCM

components. Examining individual ACIC surveys, diabetes and cancer services have functional service specific databases and systems to remind providers.

The result suggests that a strengthened clinical information system can support evidence-based management of chronic conditions, establish systems to implement a CDM register, and remind providers (Bodenheimer et al. 2002a). ACIC data in this research shows that these roles of the clinical information system are the least developed.

Of the variables grouped under the original six ACIC sub-scales, the factor analysis identified four intelligible factors. I have given these four factors names based on the respective correlated ACIC variables of which they are composed. Among these four factors, the organisational support, information system and chronic care delivery factor correspond closely to the ACIC’s organisation of health care delivery system, clinical

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information system; and delivery system design sub-scale respectively. The patient empowerment factor does not correspond to any of the specific ACIC sub-scales. This patient empowerment factor can be considered as a validated and new sub-scale within ACIC to track progress toward the CCM. This patient empowerment factor derives from three different ACIC sub-scales variables as below:

Self-management support sub-scale:

 Assessment and documentation of self-management needs  Self-management support

 Effective behaviour change interventions

Decision support sub-scale:

 Informing patient about guidelines

Clinical information system sub-scale:

 Patient treatment plans

The patient empowerment factor becomes the fundamental concept in this analysis. This is important because it drives the achievement of chronic disease management goals by encouraging patients to be active and informed. The composition of the variables and their descriptions also suggest that a strengthened clinical information system can enable linkage between the clinical management processes and self- management supports to achieve CDM goals.

The ACIC survey results explore the opportunity to implement evidence-based chronic care in this region. This is achievable through strengthening the clinical information system and implementing a CDM register. Despite upgrading isolated CCM

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components, the effectiveness of ACT Health’s chronic care initiatives requires comprehensiveness and integration according to the CCM framework. The

improvement of the capacity of the current clinical information system can connect service delivery units for better organisational planning for chronic care. On the other hand, the introduction of a CDM register will enable the system to improve chronic care performance management and remind the providers.

The ACIC survey provides the baseline data about ACT Health’s chronic care interventions. This ACIC instrument can be further used to track improvements in chronic care and integration of chronic care elements in relation to the CCM framework in this regional health system.

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6

Chapter Six

Potential impacts of a CDM register on chronic illness

Chapter outline

6.1 Introduction

6.2 Background characteristics of the respondents