BackgroundOne prominent barrier faced by healthcare consumers when accessing health services is a common requirement to complete repetitive, inefficient paper-based documentation at multiple registration sites. Digital innovation has a potential role to reduce the burden in this area, through the collection and sharing of data between healthcare providers. While there is growing evidence for digital innovations to potentially improve the effectiveness and efficiency of health systems, there is less information on the willingness of healthcare consumers to embrace and utilise technology to provide data. AimThe study aims to improve understanding of consumers' preference for utilising a digital health administration mobile app. OPEN ACCESSCitation: Lim D, Norman R, Robinson S (2020) Consumer preference to utilise a mobile health app: A stated preference experiment. PLoS ONE 15(2): e0229546. https://doi.org/10.

Objective To provide a review of prediction models that have been used to measure clinical or pathological progression of chronic kidney disease (CKD). Design Scoping review. Data sources Medline, EMBASE, CINAHL and Scopus from the year 2011 to 17th February 2022. Study selection All English written studies that are published in peer-reviewed journals in any country, that developed at least a statistical or computational model that predicted the risk of CKD progression. Data extraction Eligible studies for full text review were assessed on the methods that were used to predict the progression of CKD. The type of information extracted included: the author(s), title of article, year of publication, study dates, study location, number of participants, study design, predicted outcomes, type of prediction model, prediction variables used, validation assessment, limitations and implications. Results From 516 studies, 33 were included for full-text review. A qualitative analysis of the articles was compared following the extracted information. The study populations across the studies were heterogenous and data acquired by the studies were sourced from different levels and locations of healthcare systems. 31 studies implemented supervised models, and 2 studies included unsupervised models. Regardless of the model used, the predicted outcome included measurement of risk of progression towards end-stage kidney disease (ESKD) of related definitions, over given time intervals. However, there is a lack of reporting consistency on details of the development of their prediction models. Conclusions Researchers are working towards producing an effective model to provide key insights into the progression of CKD. This review found that cox regression modelling was predominantly used among the small number of studies in the review. This made it difficult to perform a comparison between ML algorithms, more so when different validation methods were used in different cohort types. There needs to be increased investment in a more consistent and reproducible approach for future studies looking to develop risk prediction models for CKD progression.

Background Chronic kidney disease (CKD) is a major global health problem that affects approximately one in 10 adults. Up to 90% of individuals with CKD go undetected until its progression to advanced stages, invariably leading to death in the absence of treatment. The project aims to fill information gaps around the burden of CKD in the Western Australian (WA) population, including incidence, prevalence, rate of progression, and economic cost to the health system. Methods Given the sensitivity of the information involved, the project employed a privacy preserving record linkage methodology to link data from four major pathology providers in WA to hospital records, to establish a CKD registry with continuous medical record for individuals with biochemical specification for CKD. This method uses encrypted personal identifying information in a probability-based linkage framework (Bloom filters) to help mitigate risk while maximizing linkage quality. Results The project developed interoperable technology to create a transparent CKD data catalogue which is linkable to other datasets. This technology has been designed to support the aspirations of the research program to provide linked de-identified pathology, morbidity, and mortality data that can be used to derive insights to enable better CKD patient outcomes. The cohort includes over 1 million individuals with creatinine results over the period 2002 to 2021. Conclusion Using linked data from across the care continuum, researchers are able to evaluate the effectiveness of service delivery and provide evidence for policy and program development. The CKD registry will enable an innovative review of the epidemiology of CKD in WA. Linking pathology records can identify cases of CKD that are missed in the early stages due to disaggregation of results, enabling identification of at-risk populations that represent targets for early intervention and management.

Aim: In 2020, the European Kidney Function Consortium (EKFC) published a new creatinine-based equation to estimate glomerular filtration rate (eGFR) to overcome known limitations in existing equations. The aim of this study is to model the potential impact on service referral and health expenditure of routine reporting of eGFR using the EKFC equation as compared to the CKD-EPI equation in a Western Australian population. Methods: eGFR was calculated for 760 614 patients with 2 368 234 creatinine results using the CKD-EPI and EKFC formulas. Patients were grouped into a CKD cohort if they had at least two eGFR results of <60 ml/min/1.73 m 2 from tests at least 90 days apart. The impact of each equation on the reclassification of CKD stages, CKD cohort classification, the rate of change in eGFR and direct health costs were assessed.Results: About 90.66% of patients had a lower eGFR when calculated using the EKFC equation. About 12.6% of individuals were classified into a different CKD stage using the EKFC equation with 97.43% of these patients classified into a higher (more advanced) stage. There was a 25.9% increase in the number of patients identified as having CKD when calculated using the EKFC equation. Direct health costs also increased with the use of EKFC reporting. Conclusion:Use of the EKFC equation will increase population prevalence of CKD and will result in a shift to higher stages of CKD. This has implications for monitoring and referral of patients within specialist services and has the potential to increase the need for multidisciplinary care.