Sequential pattern mining of electronic healthcare reimbursement claims: Experiences and challenges in uncovering how patients are treated by physicians

Malhotra, Kunal; Hobson, Tanner; Valkova, Silvia; Pullum, Laura L.; Ramanathan, Arvind

doi:10.1109/bigdata.2015.7364067

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…The three dimensional similarity metrics based approach namely transaction global similarity metric, feature local similarity metric and feature global similarity metric, is developed to handle the imbalance classification problem. In [78], heart disease, breast cancer and autism spectrum disease procedures administered to patients are analyzed but fraudulent activities are detected by considering costs of these procedures.…”

Section: Background and Related Workmentioning

confidence: 99%

A Sequence Mining-Based Novel Architecture for Detecting Fraudulent Transactions in Healthcare Systems

et al. 2022

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With the exponential rise in government and private health-supported schemes, the number of fraudulent billing cases is also increasing. Detection of fraudulent transactions in healthcare systems is an exigent task due to intricate relationships among dynamic elements, including doctors, patients, and services. Hence, to introduce transparency in health support programs, there is a need to develop intelligent fraud detection models for tracing the loopholes in existing procedures, so that the fraudulent medical billing cases can be accurately identified. Moreover, there is also a need to optimize both the cost burden for the service provider and medical benefits for the client. This paper presents a novel process-based fraud detection methodology to detect insurance claim-related frauds in the healthcare system using sequence mining concepts. Recent literature focuses on the amount-based analysis or medication versus disease sequential analysis rather than detecting frauds using sequence generation of services within each specialty. The proposed methodology generates frequent sequences with different pattern lengths. The confidence values and confidence level are computed for each sequence. The sequence rule engine generates frequent sequences along with confidence values for each hospital's specialty and compares them with the actual patient values. This identifies anomalies as both sequences would not be compliant with the rule engine's sequences. The process-based fraud detection methodology is validated using last five years of a local hospital's transactional data that includes many reported cases of fraudulent activities.

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Section: Background and Related Workmentioning

confidence: 99%

A Sequence Mining-Based Novel Architecture for Detecting Fraudulent Transactions in Healthcare Systems

et al. 2022

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“…Furthermore, these anomalies are analyzed using Prediction based engine. Sequential pattern mining of electronic healthcare reimbursement claims: Experiences and challenges in uncovering how patients are treated by physicians [43] Sequence mining approach Heart Disease, Breast cancer and autism spectrum disease Procedures administered to patients are analyzed but fraudulent activities are detected by considering costs of these procedures. But our methodology self learns from historical medical data and generate frequent and rare sequences and based on these sequences anomalous sequences are detected for 62 specialties.…”

Section: Study Name and Referencesmentioning

confidence: 99%

Sequence Mining and Prediction-Based Healthcare Fraud Detection Methodology

2020

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This paper presents a novel methodology to detect insurance claim related frauds in the healthcare system using concepts of sequence mining and sequence prediction. Fraud detection in healthcare is a non-trivial task due to the heterogeneous nature of healthcare records. Fraudsters behave as normal patients and with the passage of time keep on changing their way of planting frauds; hence, there is a need to develop fraud detection models. The sequence generation is not the part of previous researches which mostly focus on amount based analysis or medication versus diseases sequential analysis. The proposed methodology is able to generate sequences of services availed or prescribed by each specialty and analyse via two cascaded checks for the detection of insurance claim related frauds. The methodology addresses these challenges and self learns from historical medical records. It is based on two modules namely "Sequence rule engine and Prediction based engine". The sequence rule engine generates frequent sequences and probabilities of rare sequences for each specialty of the hospital. The comparison of such sequences with the actual patient sequences leads to the identification of anomalies as both sequences are not compliant to the sequences of the rule engine. The system performs further in detail analysis on all non-compliant sequences in the prediction based engine. The proposed methodology is validated by generating patient sequences from last five years transactional data of a local hospital and identifies patterns of service procedures administered to patients using Prefixspan algorithm and Compact prediction tree. Various experiments have been performed to validate the applicability of the developed methodology and the results demonstrate that the methodology is pertinent to detect healthcare frauds and provides on average 85% of accuracy. Thus can help in preventing fraudulent claims and provides better insight into how to improve patient management and treatment procedures.

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“…Given a cohort of patients with their event sequences, like [9] the pipeline for non-responder classification has two more additional steps than the pipeline described in previous benchmark experiments: 1) Feature construction to convert patient event sequence data into numerical feature vectors. This step can be quite time-consuming as advanced feature construction techniques like sequential mining [29] and tensor factorization [19] can be expensive to compute. On this medical dataset, we consider two kinds of parameters in this step: i) frequency threshold to remove rare events (frequency ranging from 2 to 5) ii) various aggregation functions (including binary, count, sum and average) to aggregate multiple occurrence of events into features.…”

Section: Real-world Experimentsmentioning

confidence: 99%

FLASH: Fast Bayesian Optimization for Data Analytic Pipelines

Zhang¹,

Bahadori²,

Su³

et al. 2016

Preprint

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Modern data science relies on data analytic pipelines to organize interdependent computational steps. Such analytic pipelines often involve different algorithms across multiple steps, each with its own hyperparameters. To achieve the best performance, it is often critical to select optimal algorithms and to set appropriate hyperparameters, which requires large computational efforts. Bayesian optimization provides a principled way for searching optimal hyperparameters for a single algorithm. However, many challenges remain in solving pipeline optimization problems with high-dimensional and highly conditional search space. In this work, we propose Fast LineAr SearcH (FLASH), an efficient method for tuning analytic pipelines. FLASH is a two-layer Bayesian optimization framework, which firstly uses a parametric model to select promising algorithms, then computes a nonparametric model to fine-tune hyperparameters of the promising algorithms. FLASH also includes an effective caching algorithm which can further accelerate the search process. Extensive experiments on a number of benchmark datasets have demonstrated that FLASH significantly outperforms previous state-of-the-art methods in both search speed and accuracy. Using 50% of the time budget, FLASH achieves up to 20% improvement on test error rate compared to the baselines. FLASH also yields state-of-the-art performance on a real-world application for healthcare predictive modeling.

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Sequential pattern mining of electronic healthcare reimbursement claims: Experiences and challenges in uncovering how patients are treated by physicians

Cited by 6 publications

References 30 publications

A Sequence Mining-Based Novel Architecture for Detecting Fraudulent Transactions in Healthcare Systems

A Sequence Mining-Based Novel Architecture for Detecting Fraudulent Transactions in Healthcare Systems

Sequence Mining and Prediction-Based Healthcare Fraud Detection Methodology

FLASH: Fast Bayesian Optimization for Data Analytic Pipelines

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