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      Describing and analysing primary health care system support for chronic illness care in Indigenous communities in Australia's Northern Territory – use of the Chronic Care Model

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          Abstract

          Background

          Indigenous Australians experience disproportionately high prevalence of, and morbidity and mortality from chronic illness such as diabetes, renal disease and cardiovascular disease. Improving the understanding of how Indigenous primary care systems are organised to deliver chronic illness care will inform efforts to improve the quality of care for Indigenous people.

          Methods

          This cross-sectional study was conducted in 12 Indigenous communities in Australia's Northern Territory. Using the Chronic Care Model as a framework, we carried out a mail-out survey to collect information on material, financial and human resources relating to chronic illness care in participating health centres. Follow up face-to-face interviews with health centre staff were conducted to identify successes and difficulties in the systems in relation to providing chronic illness care to community members.

          Results

          Participating health centres had distinct areas of strength and weakness in each component of systems: 1) organisational influence – strengthened by inclusion of chronic illness goals in business plans, appointment of designated chronic disease coordinators and introduction of external clinical audits, but weakened by lack of training in disease prevention and health promotion and limited access to Medicare funding; 2) community linkages – facilitated by working together with community organisations (e.g. local stores) and running community-based programs (e.g. "health week"), but detracted by a shortage of staff especially of Aboriginal health workers working in the community; 3) self management – promoted through patient education and goal setting with clients, but impeded by limited focus on family and community-based activities due to understaffing; 4) decision support – facilitated by distribution of clinical guidelines and their integration with daily care, but limited by inadequate access to and support from specialists; 5) delivery system design – strengthened by provision of transport for clients to health centres, separate men's and women's clinic rooms, specific roles of primary care team members in relation to chronic illness care, effective teamwork, and functional pathology and pharmacy systems, but weakened by staff shortage (particularly doctors and Aboriginal health workers) and high staff turnover; and 6) clinical information systems – facilitated by wide adoption of computerised information systems, but weakened by the systems' complexity and lack of IT maintenance and upgrade support.

          Conclusion

          Using concrete examples, this study translates the concept of the Chronic Care Model (and associated systems view) into practical application in Australian Indigenous primary care settings. This approach proved to be useful in understanding the quality of primary care systems for prevention and management of chronic illness. Further refinement of the systems should focus on both increasing human and financial resources and improving management practice.

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          Most cited references37

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          Assessment of chronic illness care (ACIC): a practical tool to measure quality improvement.

          To describe initial testing of the Assessment of Chronic Illness Care (ACIC), a practical quality-improvement tool to help organizations evaluate the strengths and weaknesses of their delivery of care for chronic illness in six areas: community linkages, self-management support, decision support, delivery system design, information systems, and organization of care. (1) Pre-post, self-report ACIC data from organizational teams enrolled in 13-month quality-improvement collaboratives focused on care for chronic illness; (2) independent faculty ratings of team progress at the end of collaborative. Teams completed the ACIC at the beginning and end of the collaborative using a consensus format that produced average ratings of their system's approach to delivering care for the targeted chronic condition. Average ACIC subscale scores (ranging from 0 to 11, with 11 representing optimal care) for teams across all four collaboratives were obtained to indicate how teams rated their care for chronic illness before beginning improvement work. Paired t-tests were used to evaluate the sensitivity. of the ACIC to detect system improvements for teams in two (of four) collaboratives focused on care for diabetes and congestive heart failure (CHF). Pearson correlations between the ACIC subscale scores and a faculty rating of team performance were also obtained. Average baseline scores across all teams enrolled at the beginning of the collaboratives ranged from 4.36 (information systems) to 6.42 (organization of care), indicating basic to good care for chronic illness. All six ACIC subscale scores were responsive to system improvements diabetes and CHF teams made over the course of the collaboratives. The most substantial improvements were seen in decision support, delivery system design, and information systems. CHF teams had particularly high scores in self-management support at the completion of the collaborative. Pearson correlations between the ACIC subscales and the faculty rating ranged from .28 to .52. These results and feedback from teams suggest that the ACIC is responsive to health care quality-improvement efforts and may be a useful tool to guide quality improvement in chronic illness care and to track progress over time.
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            Complexity, leadership, and management in healthcare organisations.

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              Organizational interventions employing principles of complexity science have improved outcomes for patients with Type II diabetes

              Background Despite the development of several models of care delivery for patients with chronic illness, consistent improvements in outcomes have not been achieved. These inconsistent results may be less related to the content of the models themselves, but to their underlying conceptualization of clinical settings as linear, predictable systems. The science of complex adaptive systems (CAS), suggests that clinical settings are non-linear, and increasingly has been used as a framework for describing and understanding clinical systems. The purpose of this study is to broaden the conceptualization by examining the relationship between interventions that leverage CAS characteristics in intervention design and implementation, and effectiveness of reported outcomes for patients with Type II diabetes. Methods We conducted a systematic review of the literature on organizational interventions to improve care of Type II diabetes. For each study we recorded measured process and clinical outcomes of diabetic patients. Two independent reviewers gave each study a score that reflected whether organizational interventions reflected one or more characteristics of a complex adaptive system. The effectiveness of the intervention was assessed by standardizing the scoring of the results of each study as 0 (no effect), 0.5 (mixed effect), or 1.0 (effective). Results Out of 157 potentially eligible studies, 32 met our eligibility criteria. Most studies were felt to utilize at least one CAS characteristic in their intervention designs, and ninety-one percent were scored as either "mixed effect" or "effective." The number of CAS characteristics present in each intervention was associated with effectiveness (p = 0.002). Two individual CAS characteristics were associated with effectiveness: interconnections between participants and co-evolution. Conclusion The significant association between CAS characteristics and effectiveness of reported outcomes for patients with Type II diabetes suggests that complexity science may provide an effective framework for designing and implementing interventions that lead to improved patient outcomes.
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                Author and article information

                Journal
                BMC Health Serv Res
                BMC Health Services Research
                BioMed Central
                1472-6963
                2008
                28 May 2008
                : 8
                : 112
                Affiliations
                [1 ]Menzies School of Health Research, Institute of Advanced Studies, Charles Darwin University, Darwin, NT, Australia
                [2 ]School for Social and Policy Research, Institute of Advanced Studies, Charles Darwin University, Darwin, NT, Australia
                [3 ]Northern Territory Department of Health and Community Services, Darwin, NT, Australia
                Article
                1472-6963-8-112
                10.1186/1472-6963-8-112
                2430955
                18505591
                925d1355-c952-41f1-b6b2-c0a02dad5d79
                Copyright © 2008 Si et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 14 November 2007
                : 28 May 2008
                Categories
                Research Article

                Health & Social care
                Health & Social care

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