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Unsupervised Learning for Anomaly De...

Kemp, James,

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Unsupervised Learning for Anomaly Detection in Australian Medical Payment Data /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Unsupervised Learning for Anomaly Detection in Australian Medical Payment Data // James Kemp.
Author:	Kemp, James,
Description:	1 electronic resource (181 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-08, Section: B.
Contained By:	Dissertations Abstracts International86-08B.
Subject:	Government agencies. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31863411
ISBN:	9798302895769

Unsupervised Learning for Anomaly Detection in Australian Medical Payment Data /
Kemp, James,

Unsupervised Learning for Anomaly Detection in Australian Medical Payment Data /James Kemp. - 1 electronic resource (181 pages)

Source: Dissertations Abstracts International, Volume: 86-08, Section: B.

Fraudulent or wasteful medical insurance claims made by health care providers are costly for insurers. Typically, OECD healthcare organisations lose 3-8% of total expenditure due to fraud. As Australia's universal public health insurer, Medicare Australia, spends approximately $A 34 billion per annum on the Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme, wasted spending of $A1-2.7 billion could be expected. However, fewer than 1% of claims to Medicare Australia are detected as fraudulent, below international benchmarks.Variation is common in medicine, and health conditions, along with their presentation and treatment, are heterogenous by nature. Increasing volumes of data and rapidly changing patterns bring challenges which require novel solutions. Machine learning and data mining are becoming commonplace in this field, but no gold standard is yet available.In this project, requirements are developed for real-world application to compliance analytics at the Australian Government Department of Health and Aged Care (DoH), covering: unsupervised learning; problem generalisation; human interpretability; context discovery; and cost prediction. Three novel methods are presented which rank providers by potentially recoverable costs. These methods used association analysis, topic modelling, and sequential pattern mining to provide interpretable, expert-editable models of typical provider claims. Anomalous providers are identified through comparison to the typical models, using metrics based on costs of excess or upgraded services. Domain knowledge is incorporated in a machine-friendly way in two of the methods through the use of the MBS as an ontology. Validation by subject-matter experts and comparison to existing techniques shows that the methods perform well. The methods are implemented in a software framework which enables rapid prototyping and quality assurance. The code is implemented at the DoH, and further applications as decision-support systems are in progress. The developed requirements will apply to future work in this field.

English

ISBN: 9798302895769Subjects--Topical Terms:

3562805
Government agencies.
Subjects--Index Terms:

Medicare Benefits Schedule

Unsupervised Learning for Anomaly Detection in Australian Medical Payment Data /
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520 $a Fraudulent or wasteful medical insurance claims made by health care providers are costly for insurers. Typically, OECD healthcare organisations lose 3-8% of total expenditure due to fraud. As Australia's universal public health insurer, Medicare Australia, spends approximately $A 34 billion per annum on the Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme, wasted spending of $A1-2.7 billion could be expected. However, fewer than 1% of claims to Medicare Australia are detected as fraudulent, below international benchmarks.Variation is common in medicine, and health conditions, along with their presentation and treatment, are heterogenous by nature. Increasing volumes of data and rapidly changing patterns bring challenges which require novel solutions. Machine learning and data mining are becoming commonplace in this field, but no gold standard is yet available.In this project, requirements are developed for real-world application to compliance analytics at the Australian Government Department of Health and Aged Care (DoH), covering: unsupervised learning; problem generalisation; human interpretability; context discovery; and cost prediction. Three novel methods are presented which rank providers by potentially recoverable costs. These methods used association analysis, topic modelling, and sequential pattern mining to provide interpretable, expert-editable models of typical provider claims. Anomalous providers are identified through comparison to the typical models, using metrics based on costs of excess or upgraded services. Domain knowledge is incorporated in a machine-friendly way in two of the methods through the use of the MBS as an ontology. Validation by subject-matter experts and comparison to existing techniques shows that the methods perform well. The methods are implemented in a software framework which enables rapid prototyping and quality assurance. The code is implemented at the DoH, and further applications as decision-support systems are in progress. The developed requirements will apply to future work in this field.
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