Gillian Gorham

Background/Objectives: The Australasian College of Dermatologists implemented a supervisory rotational model project funded by the Commonwealth Government to build capacity, quality, and sustainability of specialist dermatology training in the Northern Territory. This project aimed to contribute to evidence‐based literature on rural workforce strategies in Australia,; highlight capacity challenges facing dermatology care in the Northern Territory, and identify opportunities to strengthen training and service delivery. Methods: A qualitative evaluation, including semi‐structured interviews and an anonymous online survey, was undertaken with a non‐probabilistic purposive sampling strategy. Fourteen interviews were conducted with visiting consultant dermatologists, trainee dermatologists, and local dermatology staff in Darwin, Northern Territory. Two researchers used inductive thematic coding to analyse interview transcripts and triangulate findings with survey results. Results: This project increased supervisory and training capacity, produced a documented reduction in dermatology waitlists, enabled increased remote outreach visits and access, and enhanced educational opportunities for junior doctors. Five main themes emerged: (1) acceptability of the supervisory rotational model across different domains; (2) impacts of administrative processes on acceptability of the model; (3) recognition of an ‘invisible’ cohort of patients and impact on workload; (4) sustainability; and (5) scalability of the supervisory rotational model. Conclusion: This supervisory rotational model was acceptable for both trainee and visiting consultant dermatologists and enhanced the capacity of dermatology service delivery and training in the Northern Territory. With sustainable funding, this model can support longer‐term efforts towards building a homegrown rural workforce and facilitate continuity to provide culturally safe care.

Background: The burden of chronic conditions is growing in Australia with people in remote areas experiencing high rates of disease, especially kidney disease. Health care in remote areas of the Northern Territory (NT) is complicated by a mobile population, high staff turnover, poor communication between health services and complex comorbid health conditions requiring multidisciplinary care. Aim: This paper aims to describe the collaborative process between research, government and non-government health services to develop an integrated clinical decision support system to improve patient care. Methods: Building on established partnerships in the government and Aboriginal Community-Controlled Health Service (ACCHS) sectors, we developed a novel digital clinical decision support system for people at risk of developing kidney disease (due to hypertension, diabetes, cardiovascular disease) or with kidney disease. A cross-organisational and multidisciplinary Steering Committee has overseen the design, development and implementation stages. Further, the system’s design and functionality were strongly informed by experts (Clinical Reference Group and Technical Working Group), health service providers, and end-user feedback through a formative evaluation. Results: We established data sharing agreements with 11 ACCHS to link patient level data with 56 government primary health services and six hospitals. Electronic Health Record (EHR) data, based on agreed criteria, is automatically and securely transferred from 15 existing EHR platforms. Through clinician-determined algorithms, the system assists clinicians to diagnose, monitor and provide guideline-based care for individuals, as well as service-level risk stratification and alerts for clinically significant events. Conclusion: Disconnected health services and separate EHRs result in information gaps and a health and safety risk, particularly for patients who access multiple health services. However, barriers to clinical data sharing between health services still exist. In this first phase, we report how robust partnerships and effective governance processes can overcome these barriers to support clinical decision making and contribute to holistic care.

Background: Electronic health records can be used for population-wide identification and monitoring of disease. The Territory Kidney Care project developed algorithms to identify individuals with chronic kidney disease (CKD) and several commonly comorbid chronic diseases. This study aims to describe the development and validation of our algorithms for CKD, diabetes, hypertension, and cardiovascular disease. A secondary aim of the study was to describe data completeness of the Territory Kidney Care database.Methods: The Territory Kidney Care database consolidates electronic health records from multiple health services including public hospitals (n = 6) and primary care health services (> 60) across the Northern Territory, Australia. Using the database (n = 48,569) we selected a stratified random sample of patients (n = 288), which included individuals with mild to end-stage CKD. Diagnostic accuracy of the algorithms was tested against blinded manual chart reviews. Data completeness of the database was also described.Results: For CKD defined as CKD stage 1 or higher (eGFR of any level with albuminuria or persistent eGFR < 60 ml/min/1.73², including renal replacement therapy) overall algorithm sensitivity was 93% (95%CI 89 to 96%) and specificity was 73% (95%CI 64 to 82%). For CKD defined as CKD stage 3a or higher (eGFR < 60 ml/min/1.73²) algorithm sensitivity and specificity were 93% and 97% respectively. Among the CKD 1 to 5 staging algorithms, the CKD stage 5 algorithm was most accurate with > 99% sensitivity and specificity. For related comorbidities – algorithm sensitivity and specificity results were 75% and 97% for diabetes; 85% and 88% for hypertension; and 79% and 96% for cardiovascular disease.Conclusions: We developed and validated algorithms to identify CKD and related chronic diseases within electronic health records. Validation results showed that CKD algorithms have a high degree of diagnostic accuracy compared to traditional administrative codes. Our highly accurate algorithms present new opportunities in early kidney disease detection, monitoring, and epidemiological research.

Background: Clinical decision support (CDS) is increasingly used to facilitate chronic disease care. Despite increased availability of electronic health records and the ongoing development of new CDS technologies, uptake of CDS into routine clinical settings is inconsistent. This qualitative systematic review seeks to synthesise healthcare provider experiences of CDS—exploring the barriers and enablers to implementing, using, evaluating, and sustaining chronic disease CDS systems.Methods: A search was conducted in Medline, CINAHL, APA PsychInfo, EconLit, and Web of Science from 2011 to 2021. Primary research studies incorporating qualitative findings were included if they targeted healthcare providers and studied a relevant chronic disease CDS intervention. Relevant CDS interventions were electronic health record-based and addressed one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolaemia. Qualitative findings were synthesised using a meta-aggregative approach.Results: Thirty-three primary research articles were included in this qualitative systematic review. Meta-aggregation of qualitative data revealed 177 findings and 29 categories, which were aggregated into 8 synthesised findings. The synthesised findings related to clinical context, user, external context, and technical factors affecting CDS uptake. Key barriers to uptake included CDS systems that were simplistic, had limited clinical applicability in multimorbidity, and integrated poorly into existing workflows. Enablers to successful CDS interventions included perceived usefulness in providing relevant clinical knowledge and structured chronic disease care; user confidence gained through training and post training follow-up; external contexts comprised of strong clinical champions, allocated personnel, and technical support; and CDS technical features that are both highly functional, and attractive.Conclusion: This systematic review explored healthcare provider experiences, focussing on barriers and enablers to CDS use for chronic diseases. The results provide an evidence-base for designing, implementing, and sustaining future CDS systems. Based on the findings from this review, we highlight actionable steps for practice and future research.Trial registration: PROSPEROCRD42020203716

Background: Aboriginal people in the Northern Territory (NT) suffer the heaviest burden of kidney failure in Australia with most living in remote areas at time of dialysis commencement. As there are few dialysis services in remote areas, many Aboriginal people are required to relocate often permanently, to access treatment. Missing dialysis treatments is not uncommon amongst Aboriginal patients but the relationship between location of dialysis service and dialysis attendance (and subsequent hospital use) has not been explored to date.Aim: To examine the relationships between location of dialysis service, dialysis attendance patterns and downstream health service use (overnight hospital admissions, emergency department presentations) among Aboriginal patients in the NT.Methods: Using linked hospital and dialysis registry datasets we analysed health service activity for 896 Aboriginal maintenance dialysis patients in the NT between 2008 and 2014. Multivariate linear regression and negative binomial regression analyses explored the associations between dialysis location, dialysis attendance and health service use.Results: We found missing two or more dialysis treatments per month was more likely for Aboriginal people attending urban services and this was associated with a two-fold increase in the rate of hospital admissions and more than three-fold increase in ED presentations. However, we found higher dialysis attendance and lower health service utilisation for those receiving care in rural and remote settings. When adjusted for age, time on dialysis, region, comorbidities and residence pre-treatment, among Aboriginal people from remote areas, those dialysing in remote areas had lower rates of hospitalisations (IRR 0.56;P < 0.001) when compared to those who relocated and dialysed in urban areas.Conclusion: There is a clear relationship between the provision and uptake of dialysis services in urban, rural and remote areas in the NT and subsequent broader health service utilisation. Our study suggests that the low dialysis attendance associated with relocation and care in urban models for Aboriginal people can potentially be ameliorated by access to rural and remote models and this warrants a rethinking of service delivery policy. If providers are to deliver effective and equitable services, the full range of intendedand unintendedconsequences of a dialysis location should be incorporated into planning decisions.

Background: Rates of end-stage kidney disease in Australia are highest in the Northern Territory (NT), with the burden of disease heaviest in remote areas. However, the high cost of delivering dialysis services in remote areas has resulted in centralisation, requiring many people to relocate for treatment. Patients argue that treatment closer to home improves health outcomes and reduces downstream healthcare use. Existing dialysis cost studies have not compared total health care costs associated with treatment in different locations. Objective: To estimate and compare, from a payer perspective, the observed health service costs (all cause hospital admissions, emergency department presentations and maintenance dialysis) associated with different dialysis models in urban, rural and remote locations. Methods: Using cost weights attributed to diagnostic codes in the NT Department of Health’s hospital admission data set (2008–2014), we calculated the mean (SD) total annual health service costs by dialysis model for 995 dialysis patients. Generalized linear modeling with bootstrapping tested the marginal cost differences between different explanatory variables to estimate ‘best casemix’/‘worst casemix’ cost scenarios. Results: The mean annual patient hospital expenditure was highest for urban models at $97 928 (SD $21 261) and $43 440 (SD $5 048) and lowest for remote at $19 584 (SD $4 394). When combined with the observed maintenance dialysis costs, expenditure was the highest for urban models at $148 510 (SD $19 774). The incremental cost increase of dialysing in an urban area, compared with a rural area, for a relocated person from a remote area, was $5 648 more and increased further for those from remote and very remote areas to $10 785 and $15 118 respectively. Conclusions: This study demonstrates that dialysis treatment in urban areas for relocated people has health and cost implications that maybe greater than the cost of remote service delivery. The study emphasises the importance of considering all health service costs and cost consequences of service delivery models. Key points for decision makers: Relocation for dialysis treatment has serious health and economic consequences. Relocated people have low dialysis attendance and high hospital costs in urban areas. While remote dialysis service models are more expensive than urban models, the comparative cost differences are significantly reduced when all health service costs are included. The delivery of equitable and accessible dialysis service models requires a holistic approach that incorporates the needs of the patient; hence dialysis cost studies must consider the full range of cost impacts beyond the dialysis treatments alone.