Bayesian inference of local government audit outcomes.

The scandals in publicly listed companies have highlighted the large losses that can result from financial statement fraud and weak corporate governance. Machine learning techniques have been applied to automatically detect financial statement fraud with great success. This work presents the first application of a Bayesian inference approach to the problem of predicting the audit outcomes of financial statements of local government entities using financial ratios. Bayesian logistic regression (BLR) with automatic relevance determination (BLR-ARD) is applied to predict audit outcomes. The benefit of using BLR-ARD, instead of BLR without ARD, is that it allows one to automatically determine which input features are the most relevant for the task at hand, which is a critical aspect to consider when designing decision support systems. This work presents the first implementation of BLR-ARD trained with Separable Shadow Hamiltonian Hybrid Monte Carlo, No-U-Turn sampler, Metropolis Adjusted Langevin Algorithm and Metropolis-Hasting algorithms. Unlike the Gibbs sampling procedure that is typically employed in sampling from ARD models, in this work we jointly sample the parameters and the hyperparameters by putting a log normal prior on the hyperparameters. The analysis also shows that the repairs and maintenance as a percentage of total assets ratio, current ratio, debt to total operating revenue, net operating surplus margin and capital cost to total operating expenditure ratio are the important features when predicting local government audit outcomes using financial ratios. These results could be of use for auditors as focusing on these ratios could potentially speed up the detection of fraudulent behaviour in municipal entities, and improve the speed and quality of the overall audit.

Audit Data Analysis and Application Based on Correlation Analysis Algorithm.

Traditional audit data analysis algorithms have many shortcomings, such as the lack of means to mine the hidden audit clues behind the data, the difficulty of finding increasingly hidden cheating techniques caused by the electronic and networked environment, and the inability to solve the quality defects of the audited data. Correlation analysis algorithm in data mining technology is an effective means to obtain knowledge from massive data, which can complete, muffle, clean, and reduce defective data and then can analyze massive data and obtain audit trails under the guidance of expert experience or analysts. Therefore, on the basis of summarizing and analyzing previous research works, this paper expounds the research status and significance of audit data analysis and application; elaborates the development background, current status, and future challenges of correlation analysis algorithm; introduces the methods and principles of data model and its conversion and audit model construction; conducts audit data collection and cleaning; implements audit data preprocessing and its algorithm description; performs audit data analysis based on correlation analysis algorithm; analyzes the hidden node activation value and audit rule extraction in correlation analysis algorithm; proposes the application of audit data based on correlation analysis algorithm; discusses the relationship between audit data quality and audit risk; and finally compares different data mining algorithms in audit data analysis. The findings demonstrate that by analyzing association rules, the correlation analysis algorithm can determine the significance of a huge quantity of audit data and characterise the degree to which linked events would occur concurrently or sequentially in a probabilistic manner. The correlation analysis algorithm first inputs the collected audit data through preprocessing module to filter out useless data and then organizes the obtained data into a format that can be recognized by data mining algorithm and executes the correlation analysis algorithm on the sorted data; finally, the obtained hidden data is divided into normal data and suspicious data by comparing it with the pattern in the rule base. The algorithm can conduct in-depth analysis and research on the company's accounting vouchers, account books, and a large number of financial accounting data and other data of various natures in the company's accounting vouchers; reveal its original characteristics and internal connections; and turn it into an audit. People need more direct and useful information. The study results of this paper provide a reference for further researches on audit data analysis and application based on correlation analysis algorithm.

Reporting of declarations and conflicts of interest in WHO guidelines can be further improved.

OBJECTIVES: We aimed to examine the declaration of interests (DOIs), management of conflict of interest (COI), and the funders for World Health Organization (WHO) guidelines. STUDY DESIGN AND SETTING: We examined all Guidelines Review Committee (GRC)-approved WHO guidelines published in English from January 2007 (inception of the GRC) to November 2016. We obtained a list of all such guidelines from the GRC Secretariat. Characteristics of guidelines including funders and individual contributors' DOI were independently extracted by two researchers. Binary logistic regression was used to assess the association between declarations and the number of organizations involved in development. RESULTS: A total of 176 guidelines fulfilled inclusion criteria, encompassing 14 clinical or public health fields. Funders were reported in 128 (73%) of the guidelines: the most common were governments. DOI for external contributors were reported in 157 (89%) of the guidelines: 75 (48%) indicated no contributors with COI, 57 (36%) reported contributors with COI, and 25 (16%) reported collecting DOI but not whether COI existed. Financial COI were reported more frequently than nonfinancial COI. Of 57 guidelines that reported COI, 45 (79%) indicated how the COI were managed. CONCLUSION: The majority of WHO guidelines reported their funding sources and the DOI and COI of external contributors in their guideline documents. However, there is a need for improvement, in particular for reporting of funders and their role, declaration processes, and management of COI.

National expenditure on health research in South Africa: what is the benchmark?

The Mexico (2004), Bamako (2008) and Algiers (2008) declarations committed the South African (SA) Ministry of Health to allocate 2% of the national health budget to research, while the National Health Research Policy (2001) proposed that the country budget for health research should be 2% of total public sector health expenditure. The National Health Research Committee has performed an audit to determine whether these goals have been met, judged by: (i) health research expenditure as proportions of gross expenditure on research and development (GERD) and the gross domestic product (GDP); and (ii) the proportion of the national health and Department of Health budgets apportioned to research. We found that total expenditure on health research in SA, aggregated across the public and private sectors, was R3.5 billion in 2009/10, equating to 16.7% of GERD. However, the total government plus science council spend on health research that year was only R729 million, equating to 3.5% of GERD (0.03% of the GDP) or 0.80% of the R91.4 billion consolidated government expenditure on health. We further found that R418 million was spent through the 2009/2010 Health Vote on health research, equating to 0.46% of the consolidated government expenditure on health or 0.9% of the R45.2 billion Health Vote. Data from other recent years were similar. Current SA public sector health research allocations therefore remain well below the aspirational goal of 2% of the national health budget. We recommend that new, realistic, clearly defined targets be adopted and an efficient monitoring mechanism be developed to track future health research expenditure.

A comparative study of financial data sources for critical access hospitals: audited financial statements, the Medicare cost report, and the Internal Revenue Service form 990.

PURPOSE: Medicare cost reports (MCR), Internal Revenue Service form 990s (IRS 990), and audited financial statements (AFS) vary in their content, detail, purpose, timeliness, and certification. The purpose of this study was to compare selected financial data elements and characterize the extent of differences in financial data and ratios across the MCR, IRS 990, and AFS for a sample of nonprofit critical access hospitals (CAHs). METHODS: Line items from AFS of 47 CAHs were compared to data reported in the hospitals' MCR and IRS 990s. Line items were based on 9 financial indicators commonly used to assess hospital financial performance. FINDINGS: Of the indicators examined, the equity financing ratio most frequently matched between the 3 reports, while salaries and benefits to total expenses and debt service coverage were often different. Variances were driven by differences in individual account balances used to construct the ratios. Relative to AFS, cash was frequently lower on the IRS 990 while marketable securities and unrestricted investments were often higher. Other revenue and net income were consistently lower on the MCR and IRS 990, and depreciation was often higher on the MCR. The majority of total assets and fund balance (equity) values matched across the 3 reports, suggesting differences in classification among detailed accounts were more common than variances between the component totals (total assets, total liabilities, and fund balance). CONCLUSIONS: Health policy researchers should consider the impact of these variances on study results and consider ways to improve the availability and quality of financial accounting information.

The costs in provision of haemodialysis in a developing country: a multi-centered study.

BACKGROUND: Chronic Kidney Disease is a major public health problem worldwide with enormous cost burdens on health care systems in developing countries. We aimed to provide a detailed analysis of the processes and costs of haemodialysis in Sri Lanka and provide a framework for modeling similar financial audits. METHODS: This prospective study was conducted at haemodialysis units of three public and two private hospitals in Sri Lanka for two months in June and July 2010. Cost of drugs and consumables for the three public hospitals were obtained from the price list issued by the Medical Supplies Division of the Department of Health Services, while for the two private hospitals they were obtained from financial departments of the respective hospitals. Staff wages were obtained from the hospital chief accountant/chief financial officers. The cost of electricity and water per month was calculated directly with the assistance of expert engineers. An apportion was done from the total hospital costs of administration, cleaning services, security, waste disposal and, laundry and sterilization for each unit. RESULTS: The total number of dialysis sessions (hours) at the five hospitals for June and July were 3341 (12959) and 3386 (13301) respectively. Drug and consumables costs accounted for 70.4-84.9% of the total costs, followed by the wages of the nursing staff at each unit (7.8-19.7%). The mean cost of a dialysis session in Sri Lanka was LKR 6,377 (US$ 56). The annual cost of haemodialysis for a patient with chronic renal failure undergoing 2-3 dialysis session of four hours duration per week was LKR 663,208-994,812 (US$ 5,869-8,804). At one hospital where facilities are available for the re-use of dialyzers (although not done during study period) the cost of consumables would have come down from LKR 5,940,705 to LKR 3,368,785 (43% reduction) if the method was adopted, reducing costs of haemodialysis per hour from LKR 1,327 at present to LKR 892 (33% reduction). CONCLUSIONS: This multi-centered study demonstrated that the costs of haemodialysis in a developing country remained significantly lower compared to developed countries. However, it still places a significant burden on the health care sector, whilst possibility of further cost reduction exists.

Assessing the differences and similarities between hospital chains and independents regarding revenues, profits, and community contributions.

Hospital systems or chains continue to grow their market share relative to independent hospitals. This trend generates concerns among health care industry observers as historical performance suggests chains charge more for health care services than the independents while providing reduced contributions to their community. This study empirically assesses key performance measures of 67 acute-care hospitals in Virginia by testing if there are differences between chains and independents regarding total patient revenues, revenues per admission, profitability and community support, including charity care, bad debt, taxes paid and Medicaid participation. Implications to industry policy-makers as well as to hospital executives and marketing managers are then presented.

Financial standards for HMOs.

In today's health care environment, it is important to assess the liquidity and profitability performance of HMOs. This study focuses on three liquidity ratios and three profitability ratios derived from national databases of between 740 and 776 HMOs from 1996 to 1999. Most of the HMOs appear to be using more debt and are less liquid now than they were in 1995. Since administrative overhead costs and dollars spent on medical costs have been increasing, HMOs' margins have been consistently negative. A more careful analysis of overhead costs and the cost of the delivery of medical services could result in improved HMO quality of care, efficiencies, and a return to positive profit margins.

Management strategies and financial performance in rural and urban hospitals.

This study identifies the difference in financial performance between rural and urban hospitals and examines whether or not that difference may be attributed to the emphasis of revenue enhancement over cost management strategies. Hospitals in Virginia were included in this study except for the two state university medical centers. Rural and urban hospitals were compared on 10 performance indicators grouped into four categories: revenues, costs, profits, and productivity. The results suggest that rural hospital profitability is dependent on cost management. Since rural hospitals achieved lower cost, better efficiency and productivity level than urban hospitals in Virginia, they demonstrate a significant higher level of profit.