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Outlier detection is a fast-moving method in healthcare data and it is the major concern for the health insurance providers. Most of the Medicare data is related to real-world data. Outlier analysis plays a crucial role in data validity and reliability. To detect outlier for medical data is a complex task as it is having more number of variables and is of multivariate in nature. The paper presents a model-based approach in which outliers are detected and they were assigned with labels. The outlier or suspicious is defined as some outcome, which is expected that it is going to commit fraud. The methodology carried out in two phases to develop a Supervised Outlier Detection Approach in healthcare Claims (SODAC). Initially, the data preprocessing stage for feature selection it uses the filter method and set grouping hierarchy to select the best subset and to organize the features. The outlier detection phase uses the combination of classic methods of statistical and distance-based approach. To evaluate the distribution of data the Gaussian probability density function is applied for the data values. The distance-based approach which reflects the outputs as outlier codes. The partitioning of the input dataset and applies statistical mean to each subset and further uses derived multi aggregate metric to consolidate the data instances of the partitions(subsets). The distance-based outlier detection (dod) is done by calculating the maximum distance from the inner average statistical mean measure of the neighborhood from the data objects (instances) of the input. The data object value goes beyond the maximum or minimum of calculated measure is termed as suspicious. This type of detection of outliers is called as indicative fraud potential. The results performed relatively stable for a large scale data as illustrated in the experimentation part over publicly available real world data. [ABSTRACT FROM AUTHOR]

Background: Evidence-based health workforce policies are essential to ensure the provision of high-quality health services and to support the attainment of universal health coverage (UHC). This paper describes the main characteristics of available health workforce data for 74 of the 75 countries identified under the 'Countdown to 2015' initiative as accounting for more than 95% of the world's maternal, newborn and child deaths. It also discusses best practices in the development of health workforce metrics post-2015.Methods: Using available health workforce data from the Global Health Workforce Statistics database from the Global Health Observatory, we generated descriptive statistics to explore the current status, recent trends in the number of skilled health professionals (SHPs: physicians, nurses, midwives) per 10 000 population, and future requirements to achieve adequate levels of health care in the 74 countries. A rapid literature review was conducted to obtain an overview of the types of methods and the types of data sources used in human resources for health (HRH) studies.Results: There are large intercountry and interregional differences in the density of SHPs to progress towards UHC in Countdown countries: a median of 10.2 per 10 000 population with range 1.6 to 142 per 10 000. Substantial efforts have been made in some countries to increase the availability of SHPs as shown by a positive average exponential growth rate (AEGR) in SHPs in 51% of Countdown countries for which there are data. Many of these countries will require large investments to achieve levels of workforce availability commensurate with UHC and the health-related sustainable development goals (SDGs). The availability, quality and comparability of global health workforce metrics remain limited. Most published workforce studies are descriptive, but more sophisticated needs-based workforce planning methods are being developed.Conclusions: There is a need for high-quality, comprehensive, interoperable sources of HRH data to support all policies towards UHC and the health-related SDGs. The recent WHO-led initiative of supporting countries in the development of National Health Workforce Accounts is a very promising move towards purposive health workforce metrics post-2015. Such data will allow more countries to apply the latest methods for health workforce planning. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]