Does Medical School Training Relate to Practice? Evidence from Big Data

Feldman, Keith; Chawla, Nitesh V.

doi:10.1089/big.2014.0060

Cited by 26 publications

(15 citation statements)

References 14 publications

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“…A number of other research groups have explored the use of CMS Medicare and LEIE data for the purpose of identifying patterns, anomalies, and potentially fraudulent activity. Feldman and Chawla [42] explored the relationship between medical school training and the procedures performed by physicians in practice in order to detect anomalies. The 2012 Medicare Part B data set was linked with provider-level medical school data obtained through the CMS physician compare data set [43].…”

Section: Medicare Fraud Detectionmentioning

confidence: 99%

Medicare fraud detection using neural networks

Johnson

Khoshgoftaar

2019

J Big Data

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Access to affordable healthcare is a nationwide concern that impacts a large majority of the United States population. Medicare is a Federal Government healthcare program that provides affordable health insurance to the elderly population and individuals with select disabilities. Unfortunately, there is a significant amount of fraud, waste, and abuse within the Medicare system that costs taxpayers billions of dollars and puts beneficiaries' health and welfare at risk. Previous work has shown that publicly available Medicare claims data can be leveraged to construct machine learning models capable of automating fraud detection, but challenges associated with class-imbalanced big data hinder performance. With a minority class size of 0.03% and an opportunity to improve existing results, we use the Medicare fraud detection task to compare six deep learning methods designed to address the class imbalance problem. Data-level techniques used in this study include random over-sampling (ROS), random under-sampling (RUS), and a hybrid ROS-RUS. The algorithm-level techniques evaluated include a cost-sensitive loss function, the Focal Loss, and the Mean False Error Loss. A range of class ratios are tested by varying sample rates and desirable class-wise performance is achieved by identifying optimal decision thresholds for each model. Neural networks are evaluated on a 20% holdout test set, and results are reported using the area under the receiver operating characteristic curve (AUC). Results show that ROS and ROS-RUS perform significantly better than baseline and algorithm-level methods with average AUC scores of 0.8505 and 0.8509, while ROS-RUS maximizes efficiency with a 4× speedup in training time. Plain RUS outperforms baseline methods with up to 30× improvements in training time, and all algorithm-level methods are found to produce more stable decision boundaries than baseline methods. Thresholding results suggest that the decision threshold always be optimized using a validation set, as we observe a strong linear relationship between the minority class size and the optimal threshold. To the best of our knowledge, this is the first study to compare multiple data-level and algorithm-level deep learning methods across a range of class distributions. Additional contributions include a unique analysis of the relationship between minority class size and optimal decision threshold and state-of-the-art performance on the given Medicare fraud detection task.

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Section: Medicare Fraud Detectionmentioning

confidence: 99%

Medicare fraud detection using neural networks

Johnson

Khoshgoftaar

2019

J Big Data

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“…There have been a number of studies conducted, by our research group and others, using Public Use Files (PUF) data from CMS in assessing potential fraudulent activities through data mining and other analytics methods. The vast majority of these studies use only Part B data [17,[31][32][33][34][35][36][37], neglecting to account for other parts of Medicare when detecting fraudulent behavior. Within the healthcare system, anywhere money is being exchanged, there is an opportunity for a bad actor to manipulate the process and siphon funds, affecting the efficiency and effectiveness of the Medicare healthcare process.…”

Section: Related Workmentioning

confidence: 99%

“…As technology advances and its use increases, so does the ability to perform data mining and machine learning on Big Data, which can improve the state of healthcare and medical insurance programs for patients to receive quality medical care. The Centers for Medicare and Medicaid Services (CMS) joined in this effort by releasing "Big Data" Medicare datasets to assist in identifying fraud, waste and abuse within Medicare [17]. CMS released a statement that "those intent on abusing Federal health care programs can cost taxpayers billions of dollars while putting beneficiaries' health and welfare at risk.…”

mentioning

confidence: 99%

Big Data fraud detection using multiple medicare data sources

2018

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Healthcare in the United States (U.S.) is important in the lives of many citizens, but unfortunately the high costs of health-related services leave many patients with limited medical care. In response, the U.S. government has established and funded programs, such as Medicare [1], that provide financial assistance for qualifying people to receive needed medical services [2]. There are a number of issues facing healthcare and

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“…For example, people with diabetes use mobile devices to communicate with each other, share information or search for information, thus, forming a large group of big data networks 18 . The US Department of Health and Human Services has issued a policy to increase the transparency of the US healthcare system, which constitutes big data sharing for many patients, physicians, and medical‐related information 19 . Faced with a huge amount of different types of electronic data, new requirements for R&D‐related electronic products have been put forward to adapt to complex and competitive big data and its logical way 20,21 .…”

Section: Introductionmentioning

confidence: 99%

“…18 The US Department of Health and Human Services has issued a policy to increase the transparency of the US healthcare system, which constitutes big data sharing for many patients, physicians, and medical-related information. 19 Faced with a huge amount of different types of electronic data, new requirements for R&D-related electronic products have been put forward to adapt to complex and competitive big data and its logical way. 20,21 From the massive electronic medical record data, we found that the new efficacy of existing drugs-metformin for cancer treatment can also be used to treat diabetes.…”

mentioning

confidence: 99%

Brief introduction of medical database and data mining technology in big data era

Yang

Liu

et al. 2020

J Evidence Based Medicine

362

248

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Data mining technology can search for potentially valuable knowledge from a large amount of data, mainly divided into data preparation and data mining, and expression and analysis of results. It is a mature information processing technology and applies database technology. Database technology is a software science that researches manages, and applies databases. The data in the database are processed and analyzed by studying the underlying theory and implementation methods of the structure, storage, design, management, and application of the database. We have introduced several databases and data mining techniques to help a wide range of clinical researchers better understand and apply database technology.

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Does Medical School Training Relate to Practice? Evidence from Big Data

Cited by 26 publications

References 14 publications

Medicare fraud detection using neural networks

Medicare fraud detection using neural networks

Big Data fraud detection using multiple medicare data sources

Brief introduction of medical database and data mining technology in big data era

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