Machine learning approaches for predicting high cost high need patient expenditures in health care
Abstract Background This paper studies the temporal consistency of health care expenditures in a large state Medicaid program. Predictive machine learning models were used to forecast the expenditures, especially for the high-cost, high-need (HCHN) patients. Results We systematically tests temporal...
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2018-11-01
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| Series: | BioMedical Engineering OnLine |
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| Online Access: | http://link.springer.com/article/10.1186/s12938-018-0568-3 |
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doaj-0eda411cf6df430e9f2bf0ee1e148d7e2020-11-25T02:13:00ZengBMCBioMedical Engineering OnLine1475-925X2018-11-0117S112010.1186/s12938-018-0568-3Machine learning approaches for predicting high cost high need patient expenditures in health careChengliang Yang0Chris Delcher1Elizabeth Shenkman2Sanjay Ranka3Department of Computer & Information Science & Engineering, University of FloridaDepartment of Health Outcomes & Biomedical Informatics, University of FloridaDepartment of Health Outcomes & Biomedical Informatics, University of FloridaDepartment of Computer & Information Science & Engineering, University of FloridaAbstract Background This paper studies the temporal consistency of health care expenditures in a large state Medicaid program. Predictive machine learning models were used to forecast the expenditures, especially for the high-cost, high-need (HCHN) patients. Results We systematically tests temporal correlation of patient-level health care expenditures in both the short and long terms. The results suggest that medical expenditures are significantly correlated over multiple periods. Our work demonstrates a prevalent and strong temporal correlation and shows promise for predicting future health care expenditures using machine learning. Temporal correlation is stronger in HCHN patients and their expenditures can be better predicted. Including more past periods is beneficial for better predictive performance. Conclusions This study shows that there is significant temporal correlation in health care expenditures. Machine learning models can help to accurately forecast the expenditures. These results could advance the field toward precise preventive care to lower overall health care costs and deliver care more efficiently.http://link.springer.com/article/10.1186/s12938-018-0568-3High-costHigh need patientsMachine learningPredictive modeling |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Chengliang Yang Chris Delcher Elizabeth Shenkman Sanjay Ranka |
| spellingShingle |
Chengliang Yang Chris Delcher Elizabeth Shenkman Sanjay Ranka Machine learning approaches for predicting high cost high need patient expenditures in health care BioMedical Engineering OnLine High-cost High need patients Machine learning Predictive modeling |
| author_facet |
Chengliang Yang Chris Delcher Elizabeth Shenkman Sanjay Ranka |
| author_sort |
Chengliang Yang |
| title |
Machine learning approaches for predicting high cost high need patient expenditures in health care |
| title_short |
Machine learning approaches for predicting high cost high need patient expenditures in health care |
| title_full |
Machine learning approaches for predicting high cost high need patient expenditures in health care |
| title_fullStr |
Machine learning approaches for predicting high cost high need patient expenditures in health care |
| title_full_unstemmed |
Machine learning approaches for predicting high cost high need patient expenditures in health care |
| title_sort |
machine learning approaches for predicting high cost high need patient expenditures in health care |
| publisher |
BMC |
| series |
BioMedical Engineering OnLine |
| issn |
1475-925X |
| publishDate |
2018-11-01 |
| description |
Abstract Background This paper studies the temporal consistency of health care expenditures in a large state Medicaid program. Predictive machine learning models were used to forecast the expenditures, especially for the high-cost, high-need (HCHN) patients. Results We systematically tests temporal correlation of patient-level health care expenditures in both the short and long terms. The results suggest that medical expenditures are significantly correlated over multiple periods. Our work demonstrates a prevalent and strong temporal correlation and shows promise for predicting future health care expenditures using machine learning. Temporal correlation is stronger in HCHN patients and their expenditures can be better predicted. Including more past periods is beneficial for better predictive performance. Conclusions This study shows that there is significant temporal correlation in health care expenditures. Machine learning models can help to accurately forecast the expenditures. These results could advance the field toward precise preventive care to lower overall health care costs and deliver care more efficiently. |
| topic |
High-cost High need patients Machine learning Predictive modeling |
| url |
http://link.springer.com/article/10.1186/s12938-018-0568-3 |
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1724906957156909056 |