Resource allocation and health technology assessment in Australia: Views from the local level

Objectives: Several studies have shown that a key determinant of successful health technology assessment (HTA) uptake is a clear, fair, and consistent decision-making process for the approval and introduction of health technologies. The aim of this study was to gauge healthcare providers' and managers' perceptions of local level decision making and determine whether these processes offer a conducive environment for HTA. An Area Health Service (AHS) aimed to use the results of this study to help design a new process of technology assessment and decision making. Methods: An online survey was sent to all health service managers and healthcare providers working in one AHS in Sydney, Australia. Questions related to perceptions of current health technology decisions in participants' own institution/facility and opinions on key criteria for successful decision-making processes. Results: Less than a third of participants agreed with the statements that local decision-making processes were appropriate, easy to understand, evidence-based, fair, or consistently applied. Decisions were reportedly largely influenced by total cost considerations as well as by the central state health departments and the Area executive. Conclusions: Although there are renewed initiatives in HTA in Australia, there is a risk that such investments will not be productive unless policy makers also examine the decision-making contexts within which HTA can successfully be implemented. The results of this survey show that this is especially true at the local level and that any HTA initiative should be accompanied by efforts to improve decision-making processes. Copyright © 2009 Cambridge University Press

How much of Australia's health expenditure is allocated to general practice and primary healthcare?

ackground and objectives Understanding resource allocation is important to ensure that limited health resources are spent where they bring the greatest benefit. The aim of this study was to explore how much of Australia’s national health expenditure is allocated specifically to general practice services, and more broadly to primary healthcare (PHC) services. Methods This study used multiple Australian institutional reports – produced by the Australian Institute of Health and Welfare, Productivity Commission and Services Australia – to classify, compare and quantify general practice and PHC expenditure. Results National statistics report that approximately 34% of Australian health expenditure is spent on PHC. However, less than 20% of PHC expenditure (approximately 6.5% of total health expenditure) is allocated to delivering general practice services. Spending on general practitioners and general practice services varies between 4.2% and 6.8% of total health expenditure (between $7.8 billion and$12.4 billion) depending on the classification used. Discussion Significant differences exist in how different institutions classify general practice and PHC spending. Clearer, agreed and more precise methods of classification and reporting of health expenditure are needed

Bleeding Hearts, Profiteers, or Both: Specialist Physician Fees in an Unregulated Market

Copyright © 2016 John Wiley & Sons, Ltd. This study shows that, in an unregulated fee-setting environment, specialist physicians practise price discrimination on the basis of their patients' income status. Our results are consistent with profit maximisation behaviour by specialists. These findings are based on a large population survey that is linked to administrative medical claims records. We find that, for an initial consultation, specialist physicians charge their high-income patients AU$26 more than their low-income patients. While this gap equates to a 19% lower fees for the poorest patients (bottom 25% of the household income distribution), it is unlikely to remove the substantial financial barriers they face in accessing specialist care. There are large variations across specialties, with neurologists exhibiting the largest fee gap between the high-income and low-income patients. Several possible channels for deducing the patient's income are examined. We find that patient characteristics such as age, health concession card status and private health insurance status are all used by specialists as proxies for income status. These characteristics are particularly important to further practise price discrimination among the low-income patients but are less relevant for the high-income patients. Copyright © 2016 John Wiley & Sons, Ltd

A systematic review of scabies transmission models and data to evaluate the cost-effectiveness of scabies interventions

© 2019 van der Linden et al. Background: Scabies is a common dermatological condition, affecting more than 130 million people at any time. To evaluate and/or predict the effectiveness and cost-effectiveness of scabies interventions, disease transmission modelling can be used. Objective: To review published scabies models and data to inform the design of a comprehensive scabies transmission modelling framework to evaluate the cost-effectiveness of scabies interventions. Methods: Systematic literature search in PubMed, Medline, Embase, CINAHL, and the Cochrane Library identified scabies studies published since the year 2000. Selected papers included modelling studies and studies on the life cycle of scabies mites, patient quality of life and resource use. Reference lists of reviews were used to identify any papers missed through the search strategy. Strengths and limitations of identified scabies models were evaluated and used to design a modelling framework. Potential model inputs were identified and discussed. Findings: Four scabies models were published: a Markov decision tree, two compartmental models, and an agent-based, network-dependent Monte Carlo model. None of the models specifically addressed crusted scabies, which is associated with high morbidity, mortality, and increased transmission. There is a lack of reliable, comprehensive information about scabies biology and the impact this disease has on patients and society. Discussion: Clinicians and health economists working in the field of scabies are encouraged to use the current review to inform disease transmission modelling and economic evaluations on interventions against scabies

Natural Illumination from Multiple Materials Using Deep Learning

Recovering natural illumination from a single Low-Dynamic Range (LDR) image is a challenging task. To remedy this situation we exploit two properties often found in everyday images. First, images rarely show a single material, but rather multiple ones that all reflect the same illumination. However, the appearance of each material is observed only for some surface orientations, not all. Second, parts of the illumination are often directly observed in the background, without being affected by reflection. Typically, this directly observed part of the illumination is even smaller. We propose a deep Convolutional Neural Network (CNN) that combines prior knowledge about the statistics of illumination and reflectance with an input that makes explicit use of these two observations. Our approach maps multiple partial LDR material observations represented as reflectance maps and a background image to a spherical High-Dynamic Range (HDR) illumination map. For training and testing we propose a new data set comprising of synthetic and real images with multiple materials observed under the same illumination. Qualitative and quantitative evidence shows how both multi-material and using a background are essential to improve illumination estimations

Evaluation of the effectiveness of HDR tone-mapping operators for photogrammetric applications

[EN] The ability of High Dynamic Range (HDR) imaging to capture the full range of lighting in a scene has meant that it is being increasingly used for Cultural Heritage (CH) applications. Photogrammetric techniques allow the semi-automatic production of 3D models from a sequence of images. Current photogrammetric methods are not always effective in reconstructing images under harsh lighting conditions, as significant geometric details may not have been captured accurately within under- and over-exposed regions of the image. HDR imaging offers the possibility to overcome this limitation, however the HDR images need to be tone mapped before they can be used within existing photogrammetric algorithms. In this paper we evaluate four different HDR tone-mapping operators (TMOs) that have been used to convert raw HDR images into a format suitable for state-of-the-art algorithms, and in particular keypoint detection techniques. The evaluation criteria used are the number of keypoints, the number of valid matches achieved and the repeatability rate. The comparison considers two local and two global TMOs. HDR data from four CH sites were used: Kaisariani Monastery (Greece), Asinou Church (Cyprus), Château des Baux (France) and Buonconsiglio Castle (Italy).We would like to thank Kurt Debattista, Timothy Bradley, Ratnajit Mukherjee, Diego Bellido Castañeda and TomBashford Rogers for their suggestions, help and encouragement. We would like to thank the hosting institutions: 3D Optical Metrology Group, FBK (Trento, Italy) and UMR 3495 MAP CNRS/MCC (Marseille, France), for their support during the data acquisition campaigns. This project has received funding from the European Union’s 7 th Framework Programme for research, technological development and demonstration under grant agreement No. 608013, titled “ITN-DCH: Initial Training Network for Digital Cultural Heritage: Projecting our Past to the Future”.Suma, R.; Stavropoulou, G.; Stathopoulou, EK.; Van Gool, L.; Georgopoulos, A.; Chalmers, A. (2016). Evaluation of the effectiveness of HDR tone-mapping operators for photogrammetric applications. Virtual Archaeology Review. 7(15):54-66. https://doi.org/10.4995/var.2016.6319SWORD546671

Rates of Low-Value Service in Australian Public Hospitals and the Association With Patient Insurance Status.

Importance: Low-value services have limited or no benefit to patients. Rates of low-value service in public hospitals may vary by patient insurance status, given that there may be different financial incentives for treatment of privately insured patients. Objective: To assess the variation in rates of 5 low-value services performed in Australian public hospitals according to patient funding status (ie, private or public). Design, Setting, and Participants: This retrospective cross-sectional study analyzed New South Wales public hospital data from January 2013 to June 2018. Patients included in the sample were over age 18 years and eligible to receive low-value services based on diagnoses and concomitant procedures. Data analysis was conducted from June to December 2020. Main Outcomes and Measures: Hospital-specific rates of low-value knee arthroscopic debridement, vertebroplasty for osteoporotic spinal fractures, hyperbaric oxygen therapy, oophorectomy with hysterectomy, and laparoscopic uterine nerve ablation for chronic pelvic pain were measured. For each measure, rates within each public hospital were compared by patient funding status descriptively and using multilevel models. Results: A total of 219 862 inpatients were included in analysis from 58 public hospitals across the 5 measures. A total of 38 365 (22 904 [59.7%] women; 12 448 [32.4%] aged 71-80 years) were eligible for knee arthroscopic debridement for osteoarthritis; 2520 (1924 [76.3%] women; 662 [26.3%] aged 71-80 years), vertebroplasty for osteoporotic spinal fractures; 162 285 (82 046 [50.6%] women; 28 255 [17.4%] aged 61-70 years), hyperbaric oxygen therapy; 15 916 (7126 [44.8%] aged 41-50 years), oophorectomy with hysterectomy; and 776 (327 [42.1%] aged 18-30 years), uterine nerve ablation for chronic pelvic pain. Overall rates of low-value services varied considerably between measures, with the lowest rate for hyperbaric oxygen therapy (0.3 procedures per 1000 inpatients [47 of 158 220 eligible inpatients]) and the highest for vertebroplasty (30.8 procedures per 1000 eligible patients [77 of 2501 eligible inpatients]). There was significant variation in rates between hospitals, with a few outlying hospitals (ie, <10), particularly for knee arthroscopy (range from 1.8 to 21.0 per 1000 eligible patients) and vertebroplasty (range from 13.1 to 70.4 per 1000 eligible patients), with higher numerical rates of low-value services among patients with private insurance than for those without. However, there was no association overall between patient insurance status and low-value services. Overall differences in rates among those with and without private insurance by individual procedure type were not statistically significant. Conclusions and Relevance: There was significant variation in rates of low-value services in public hospitals. While there was no overall association between private insurance and rate of low-value services, private insurance may be associated with low-value service rates in some hospitals. Further exploration of factors specific to local hospitals and practices are needed to reduce this unnecessary care

Semantic-Context-Based Augmented Descriptor For Image Feature Matching

Abstract. This paper proposes an augmented version of local features that enhances the discriminative power of the feature without affecting its invariance to image deformations. The idea is about learning local features, aiming to estimate its semantic, which is then exploited in conjunction with the bag of words paradigm to build an augmented feature descriptor. Basically, any local descriptor can be casted in the proposed context, and thus the approach can be easy generalized to fit in with any local approach. The semantic-context signature is a 2D histogram which accumulates the spatial distribution of the visual words around each local feature. The obtained semantic-context component is concatenated with the local feature to generate our proposed feature descriptor. This is expected to handle ambiguities occurring in images with multiple similar motifs and depicting slight complicated non-affine distortions, outliers, and detector errors. The approach is evaluated for two data sets. The first one is intentionally selected with images containing multiple similar regions and depicting slight non-affine distortions. The second is the standard data set of Mikolajczyk. The evaluation results showed our approach performs significantly better than expected results as well as in comparison with other methods.