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      Improvement in delivery of type 2 diabetes services differs by mode of care: a retrospective longitudinal analysis in the Aboriginal and Torres Strait Islander Primary Health Care setting

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          Abstract

          Background

          Addressing evidence-practice gaps in primary care remains a significant public health challenge and is likely to require action at different levels of the health system. Whilst Continuous Quality Improvement (CQI) is associated with improvements in overall delivery, little is known about delivery of different types of care processes, and their relative improvement during CQI.

          Methods

          We used data from over 15,000 clinical audit records of clients with Type 2 diabetes collected as part of a wide-scale CQI program implemented between 2005 and 2014 in 162 Aboriginal and Torres Strait Islander health centres. We abstracted data from clinical records on 15 service items recommended in clinical guidelines and categorised these items into five modes of care on the basis of the mechanism through which care is delivered: laboratory tests; generalist-delivered physical checks; specialist-delivered checks; education/counselling for nutrition and physical activity and education/counselling for high risk substance use. We calculated delivery for each patient for each of mode of care by determining the proportion of recommended services delivered for that mode. We used multilevel regression models to quantify variation attributable to health centre or client level factors and to identify factors associated with greater adherence to clinical guidelines for each mode of care.

          Results

          Clients on average received 43 to 60 % of recommended care in 2005/6. Different modes of care showed different patterns of improvement. Generalist-delivered physical checks (delivered by a non-specialist) showed a steady year on year increase, delivery of laboratory tests showed improvement only in the later years of the study, and delivery of counselling/education interventions showed early improvement which then plateaued. Health centres participating in CQI had increased odds of top quartile service delivery for all modes compared to baseline, but effects differed by mode. Health centre factors explained 20–52 % of the variation across jurisdictions and health centres for different modes of care.

          Conclusions

          Levels of adherence to clinical guidelines and patterns of improvement during participation in a CQI program differed for different modes of care. Policy and funding decisions may have had important effects on the level and nature of improvements achieved.

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          Most cited references9

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          Duration of participation in continuous quality improvement: a key factor explaining improved delivery of Type 2 diabetes services

          Background It is generally recognised that continuous quality improvement (CQI) programs support development of high quality primary health care systems. However, there is limited evidence demonstrating their system-wide effectiveness. We examined variation in quality of Type 2 diabetes service delivery in over 100 Aboriginal and Torres Strait Islander primary health care centres participating in a wide-scale CQI project over the past decade, and determined the influence of health centre and patient level factors on quality of care, with specific attention to health centre duration of participation in a CQI program. Methods We analysed over 10,000 clinical audit records to assess quality of Type 2 diabetes care of patients in 132 Aboriginal and Torres Strait Islander community health centres in five states/territories participating in the ABCD project for varying periods between 2005 and 2012. Process indicators of quality of care for each patient were calculated by determining the proportion of recommended guideline scheduled services that were documented as delivered. Multilevel regression models were used to quantify the amount of variation in Type 2 diabetes service delivery attributable to health centre or patient level factors and to identify those factors associated with greater adherence to best practice guidelines. Results Health centre factors that were independently associated with adherence to best practice guidelines included longer participation in the CQI program, remoteness of health centres, and regularity of client attendance. Significantly associated patient level variables included greater age, and number of co-morbidities and disease complications. Health centre factors explained 37% of the differences in level of service delivery between jurisdictions with patient factors explaining only a further 1%. Conclusions At the health centre level, Type 2 diabetes service delivery could be improved through long term commitment to CQI, encouraging regular attendance (for example, through patient reminder systems) and improved recording and coordination of patient care in the complex service provider environments that are characteristic of non-remote areas.
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            Assessing quality of diabetes care and its variation in Aboriginal community health centres in Australia.

            Examining variation in diabetes care across regions/organizations provides insight into underlying factors related to quality of care. The aims of this study were to assess quality of diabetes care and its variation among Aboriginal community health centres in Australia, and to estimate partitioning of variation attributable to health centre and individual patient characteristics. During 2005-2009, clinical medical audits were conducted in 62 Aboriginal community health centres from four states/territories. Main outcome measures include adherence to guidelines-scheduled processes of diabetes care, treatment and medication adjustment, and control of HbA(1c), blood pressure, total cholesterol and albumin/creatinine ratio (ACR). Wide variation was observed across different categories of diabetes care measures and across centres: (1) overall adherence to delivery of services averaged 57% (range 22-83% across centres); (2) medication adjustment rates after elevated HbA(1c): 26% (0-72%); and (3) proportions of patients with HbA(1c) < 7%:27% (0-55%); with blood pressure < 130/80 mmHg: 36% (0-59%). Health centre level characteristics accounted for 36% of the total variation in adherence to process measures, and 3-11% of the total variation in patient intermediate outcomes; the remaining, substantial amount of variation in each measure was attributable to patient level characteristics. Deficiencies in a range of quality of care measures provide multiple opportunities for improvement. The majority of variation in quality of diabetes care appears to be attributable to patient level characteristics. Further understanding of factors affecting variation in the care of individuals should assist clinicians, managers and policy makers to develop strategies to improve quality of diabetes care in Aboriginal communities. 2010 John Wiley & Sons, Ltd.
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              Explaining the variation in the management of lifestyle risk factors in primary health care: A multilevel cross sectional study

              Background Despite evidence for the effectiveness of interventions to modify lifestyle behaviours in the primary health care (PHC) setting, assessment and intervention for these behaviours remains low in routine practice. Little is known about the relative importance of various determinants of practice. This study aimed to examine the relative importance of provider characteristics and attitudes, patient characteristics and consultation factors in determining the rate of assessment and intervention for lifestyle risk factors in PHC. Methods A prospective audit of assessment and intervention for lifestyle risk factors was undertaken by PHC nurses and allied health providers (n = 57) for all patients seen (n = 732) over a two week period. Providers completed a survey to assess key attitudes related to addressing lifestyle issues. Multi-level logistic regression analysis of patient audit records was undertaken. Associations between variables from both data sources were examined, together with the variance explained by patient and consultation (level 1) and provider (level 2) factors. Results There was significant variance between providers in the assessment and intervention for lifestyle risk factors. The consultation type and reason for the visit were the most important in explaining the variation in assessment practices, however these factors along with patient and provider variables accounted for less than 20% of the variance. In contrast, multi-level models showed that provider factors were most important in explaining the variance in intervention practices, in particular, the location of the team in which providers worked (urban or rural) and provider perceptions of their effectiveness and accessibility of support services. After controlling for provider variables, patients' socio-economic status, the reason for the visit and providers' perceptions of the 'appropriateness' of addressing risk factors in the consultation were all significantly associated with providing optimal intervention. Together, measured patient consultation and provider variables accounted for most (80%) of the variation in intervention practices between providers. Conclusion The findings highlight the importance of provider factors such as beliefs and attitudes, team location and work context in understanding variations in the provision of lifestyle intervention in PHC. Further studies of this type are required to identify variables that improve the proportion of variance explained in assessment practices.
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                Author and article information

                Contributors
                gschierhout@kirby.unsw.edu.au
                veronica.matthews@menzies.edu.au
                christine.connors@nt.gov.au
                sandra.thompson@uwa.edu.au
                ru.kwedza@health.nsw.gov.au
                cath.kennedy@maarima.com.au
                ross.bailie@sydney.edu.au
                Journal
                BMC Health Serv Res
                BMC Health Serv Res
                BMC Health Services Research
                BioMed Central (London )
                1472-6963
                7 October 2016
                7 October 2016
                2016
                : 16
                : 560
                Affiliations
                [1 ]Menzies School of Health Research, Brisbane, Australia
                [2 ]The Kirby Institute, University of New South Wales, Sydney, Australia
                [3 ]Department of Health, Darwin, NT Australia
                [4 ]Western Australian Centre for Rural Health, University of Western Australia, Geraldton, WA Australia
                [5 ]Queensland Health, Cairns, QLD Australia
                [6 ]Maari Ma Health Aboriginal Corporation, Broken Hill, Far West New South Wales Australia
                [7 ]University Centre for Rural Health, University of Sydney, Lismore, NSW Australia
                Article
                1812
                10.1186/s12913-016-1812-9
                5055704
                27717351
                203e926a-2d6d-4575-a2df-f86c514b20f4
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 12 August 2016
                : 30 September 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Award ID: #545267
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2016

                Health & Social care
                health systems,quality improvement,primary care,impact,clinical guidelines
                Health & Social care
                health systems, quality improvement, primary care, impact, clinical guidelines

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