Procedure code overutilization detection from healthcare claims using unsupervised deep learning methods
Abstract Background Fraud, Waste, and Abuse (FWA) in medical claims have a negative impact on the quality and cost of healthcare. A major component of FWA in claims is procedure code overutilization, where one or more prescribed procedures may not be relevant to a given diagnosis and patient profile...
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| Format: | Article |
| Language: | English |
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BMC
2023-09-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-023-02268-3 |
| _version_ | 1797452381073440768 |
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| author | Michael Suesserman Samantha Gorny Daniel Lasaga John Helms Dan Olson Edward Bowen Sanmitra Bhattacharya |
| author_facet | Michael Suesserman Samantha Gorny Daniel Lasaga John Helms Dan Olson Edward Bowen Sanmitra Bhattacharya |
| author_sort | Michael Suesserman |
| collection | DOAJ |
| description | Abstract Background Fraud, Waste, and Abuse (FWA) in medical claims have a negative impact on the quality and cost of healthcare. A major component of FWA in claims is procedure code overutilization, where one or more prescribed procedures may not be relevant to a given diagnosis and patient profile, resulting in unnecessary and unwarranted treatments and medical payments. This study aims to identify such unwarranted procedures from millions of healthcare claims. In the absence of labeled examples of unwarranted procedures, the study focused on the application of unsupervised machine learning techniques. Methods Experiments were conducted with deep autoencoders to find claims containing anomalous procedure codes indicative of FWA, and were compared against a baseline density-based clustering model. Diagnoses, procedures, and demographic data associated with healthcare claims were used as features for the models. A dataset of one hundred thousand claims sampled from a larger claims database is used to initially train and tune the models, followed by experimentations on a dataset with thirty-three million claims. Experimental results show that the autoencoder model, when trained with a novel feature-weighted loss function, outperforms the density-based clustering approach in finding potential outlier procedure codes. Results Given the unsupervised nature of our experiments, model performance was evaluated using a synthetic outlier test dataset, and a manually annotated outlier test dataset. Precision, recall and F1-scores on the synthetic outlier test dataset for the autoencoder model trained on one hundred thousand claims were 0.87, 1.0 and 0.93, respectively, while the results for these metrics on the manually annotated outlier test dataset were 0.36, 0.86 and 0.51, respectively. The model performance on the manually annotated outlier test dataset improved further when trained on the larger thirty-three million claims dataset with precision, recall and F1-scores of 0.48, 0.90 and 0.63, respectively. Conclusions This study demonstrates the feasibility of leveraging unsupervised, deep-learning methods to identify potential procedure overutilization from healthcare claims. |
| first_indexed | 2024-03-09T15:07:28Z |
| format | Article |
| id | doaj.art-02555d8075b94df6af5d8c4ed9e785df |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-03-09T15:07:28Z |
| publishDate | 2023-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-02555d8075b94df6af5d8c4ed9e785df2023-11-26T13:32:35ZengBMCBMC Medical Informatics and Decision Making1472-69472023-09-0123111110.1186/s12911-023-02268-3Procedure code overutilization detection from healthcare claims using unsupervised deep learning methodsMichael Suesserman0Samantha Gorny1Daniel Lasaga2John Helms3Dan Olson4Edward Bowen5Sanmitra Bhattacharya6AI Center of Excellence, Deloitte & Touche LLPProgram Integrity, Deloitte & Touche LLPProgram Integrity, Deloitte & Touche LLPAI Center of Excellence, Deloitte & Touche LLPProgram Integrity, Deloitte & Touche LLPAI Center of Excellence, Deloitte & Touche LLPAI Center of Excellence, Deloitte & Touche LLPAbstract Background Fraud, Waste, and Abuse (FWA) in medical claims have a negative impact on the quality and cost of healthcare. A major component of FWA in claims is procedure code overutilization, where one or more prescribed procedures may not be relevant to a given diagnosis and patient profile, resulting in unnecessary and unwarranted treatments and medical payments. This study aims to identify such unwarranted procedures from millions of healthcare claims. In the absence of labeled examples of unwarranted procedures, the study focused on the application of unsupervised machine learning techniques. Methods Experiments were conducted with deep autoencoders to find claims containing anomalous procedure codes indicative of FWA, and were compared against a baseline density-based clustering model. Diagnoses, procedures, and demographic data associated with healthcare claims were used as features for the models. A dataset of one hundred thousand claims sampled from a larger claims database is used to initially train and tune the models, followed by experimentations on a dataset with thirty-three million claims. Experimental results show that the autoencoder model, when trained with a novel feature-weighted loss function, outperforms the density-based clustering approach in finding potential outlier procedure codes. Results Given the unsupervised nature of our experiments, model performance was evaluated using a synthetic outlier test dataset, and a manually annotated outlier test dataset. Precision, recall and F1-scores on the synthetic outlier test dataset for the autoencoder model trained on one hundred thousand claims were 0.87, 1.0 and 0.93, respectively, while the results for these metrics on the manually annotated outlier test dataset were 0.36, 0.86 and 0.51, respectively. The model performance on the manually annotated outlier test dataset improved further when trained on the larger thirty-three million claims dataset with precision, recall and F1-scores of 0.48, 0.90 and 0.63, respectively. Conclusions This study demonstrates the feasibility of leveraging unsupervised, deep-learning methods to identify potential procedure overutilization from healthcare claims.https://doi.org/10.1186/s12911-023-02268-3Fraud, waste, and abuseProcedure code overutilizationUnsupervised learningDeep autoencoderFeature-weighted loss function |
| spellingShingle | Michael Suesserman Samantha Gorny Daniel Lasaga John Helms Dan Olson Edward Bowen Sanmitra Bhattacharya Procedure code overutilization detection from healthcare claims using unsupervised deep learning methods BMC Medical Informatics and Decision Making Fraud, waste, and abuse Procedure code overutilization Unsupervised learning Deep autoencoder Feature-weighted loss function |
| title | Procedure code overutilization detection from healthcare claims using unsupervised deep learning methods |
| title_full | Procedure code overutilization detection from healthcare claims using unsupervised deep learning methods |
| title_fullStr | Procedure code overutilization detection from healthcare claims using unsupervised deep learning methods |
| title_full_unstemmed | Procedure code overutilization detection from healthcare claims using unsupervised deep learning methods |
| title_short | Procedure code overutilization detection from healthcare claims using unsupervised deep learning methods |
| title_sort | procedure code overutilization detection from healthcare claims using unsupervised deep learning methods |
| topic | Fraud, waste, and abuse Procedure code overutilization Unsupervised learning Deep autoencoder Feature-weighted loss function |
| url | https://doi.org/10.1186/s12911-023-02268-3 |
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