Identification of high-risk beneficiaries in private healthcare insurance
The objective of this study was to apply the Knowledge Discovery in Databases process to find out if beneficiaries of a private healthcare insurance would belong, at least once, to the ‘very high cost’ and ‘complex cases’ groups throughout the 12 months after the month when algorithms were applied....
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| Published in | Health informatics journal Vol. 30; no. 1; p. 14604582241230384 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London, England
SAGE Publications
01.01.2024
SAGE PUBLICATIONS, INC |
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| Abstract | The objective of this study was to apply the Knowledge Discovery in Databases process to find out if beneficiaries of a private healthcare insurance would belong, at least once, to the ‘very high cost’ and ‘complex cases’ groups throughout the 12 months after the month when algorithms were applied. Datasets were built containing information on beneficiaries’ effective use of their health plan, as well as their characteristics. Five machine learning algorithms were used, namely Random forest, Extra tree, Xgboost, Naive bayes and K-nearest neighbor. The K-nearest neighbor algorithm had a recall rate of 81.12%, 83.77% precision and an Area Under the Curve (AUC) value of 0.9045. The study also revealed that categorization occurs, on average, 8.11 months before a beneficiary entering, for the first time, a high-risk group, considering the dataset classification from January 2019 to June 2020. |
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| AbstractList | The objective of this study was to apply the Knowledge Discovery in Databases process to find out if beneficiaries of a private healthcare insurance would belong, at least once, to the ‘very high cost’ and ‘complex cases’ groups throughout the 12 months after the month when algorithms were applied. Datasets were built containing information on beneficiaries’ effective use of their health plan, as well as their characteristics. Five machine learning algorithms were used, namely Random forest, Extra tree, Xgboost, Naive bayes and K-nearest neighbor. The K-nearest neighbor algorithm had a recall rate of 81.12%, 83.77% precision and an Area Under the Curve (AUC) value of 0.9045. The study also revealed that categorization occurs, on average, 8.11 months before a beneficiary entering, for the first time, a high-risk group, considering the dataset classification from January 2019 to June 2020. The objective of this study was to apply the Knowledge Discovery in Databases process to find out if beneficiaries of a private healthcare insurance would belong, at least once, to the 'very high cost' and 'complex cases' groups throughout the 12 months after the month when algorithms were applied. Datasets were built containing information on beneficiaries' effective use of their health plan, as well as their characteristics. Five machine learning algorithms were used, namely Random forest, Extra tree, Xgboost, Naive bayes and K-nearest neighbor. The K-nearest neighbor algorithm had a recall rate of 81.12%, 83.77% precision and an Area Under the Curve (AUC) value of 0.9045. The study also revealed that categorization occurs, on average, 8.11 months before a beneficiary entering, for the first time, a high-risk group, considering the dataset classification from January 2019 to June 2020.The objective of this study was to apply the Knowledge Discovery in Databases process to find out if beneficiaries of a private healthcare insurance would belong, at least once, to the 'very high cost' and 'complex cases' groups throughout the 12 months after the month when algorithms were applied. Datasets were built containing information on beneficiaries' effective use of their health plan, as well as their characteristics. Five machine learning algorithms were used, namely Random forest, Extra tree, Xgboost, Naive bayes and K-nearest neighbor. The K-nearest neighbor algorithm had a recall rate of 81.12%, 83.77% precision and an Area Under the Curve (AUC) value of 0.9045. The study also revealed that categorization occurs, on average, 8.11 months before a beneficiary entering, for the first time, a high-risk group, considering the dataset classification from January 2019 to June 2020. |
| Author | Santos, Adauto Leal, Gislaine Camila Lapasini Balancieri, Renato |
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| Cites_doi | 10.1148/radiology.143.1.7063747 10.1007/s10994-006-6226-1 10.1016/j.compbiomed.2014.07.005 10.7812/TPP/04-119 10.1109/ICHI.2017.86 10.1145/3368756.3369103 10.1177/14604582211043920 10.3390/ijerph18020565 10.1590/S0103-73312008000400009 10.1056/NEJMp1511131 10.1023/A:1010933404324 10.1038/s41746-020-00354-8 10.1145/2939672.2939785 10.1016/j.hlpt.2020.11.002 10.1111/epi.14729 10.1787/f2b7ee85-pt 10.1016/S0031-3203(96)00142-2 10.5694/j.1326-5377.2006.tb00289.x 10.1016/j.archger.2020.104121 10.1016/S0034-4257(97)00083-7 10.1007/978-0-387-39940-9_565 10.1136/bmjopen-2017-017775 10.2196/jmir.4976 |
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| Keywords | knowledge discovery in databases machine learning health insurance data mining healthcare |
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| References | Crooks 2005; 9 Mukhiya, Lamo 2021; 27 Chechulin, Nazerian, Rais 2014; 9 Nomura, Ishii, Chiba 2021; 18 dos Santos, Dias, Chiavegatto Filho 2021; 10 Schubert-Bast, Zöllner, Ansorge 2019; 60 Bradley 1997; 30 Fayyad, Piatetsky-Shapiro, Smyth 1996; 17 Geurts, Ernst, Wehenkel 2006; 63 Stehman 1997; 62 Calver, Brameld, Preen 2006; 184 Breiman 2001; 45 Qin, Lv, Wang 2020; 90 Hanley, McNeil 1982; 143 Osawa, Goto, Yamamoto 2020; 3 Pietrobon, Prado, Caetano 2008; 18 Powers, Chaguturu 2016; 374 Wammes, Tanke, Jonkers 2017; 7 Hu, Hao, Jin 2015; 17 Shenas, Raahemi, Tekieh 2014; 53 BRASIL (bibr4-14604582241230384) 2013 bibr30-14604582241230384 bibr5-14604582241230384 bibr20-14604582241230384 bibr18-14604582241230384 bibr28-14604582241230384 bibr15-14604582241230384 Chechulin Y (bibr12-14604582241230384) 2014; 9 Matta GC (bibr32-14604582241230384) 2021 bibr2-14604582241230384 bibr22-14604582241230384 bibr35-14604582241230384 bibr25-14604582241230384 bibr7-14604582241230384 bibr31-14604582241230384 bibr14-14604582241230384 bibr27-14604582241230384 bibr11-14604582241230384 bibr17-14604582241230384 bibr1-14604582241230384 bibr34-14604582241230384 bibr21-14604582241230384 Fayyad U (bibr8-14604582241230384) 1996; 17 Mitchell TM (bibr24-14604582241230384) 1997 bibr3-14604582241230384 bibr19-14604582241230384 bibr29-14604582241230384 bibr9-14604582241230384 bibr36-14604582241230384 bibr23-14604582241230384 bibr10-14604582241230384 bibr13-14604582241230384 bibr6-14604582241230384 bibr33-14604582241230384 bibr16-14604582241230384 bibr26-14604582241230384 |
| References_xml | – volume: 62 start-page: 77 issue: 1 year: 1997 end-page: 89 article-title: Selecting and interpreting measures of thematic classification accuracy publication-title: Rem Sens Environ contributor: fullname: Stehman – volume: 45 start-page: 5 issue: 1 year: 2001 end-page: 32 article-title: Random forests publication-title: Mach Learn contributor: fullname: Breiman – volume: 27 start-page: 14604582211043920 issue: 3 year: 2021 article-title: An HL7 FHIR and GraphQL approach for interoperability between heterogeneous electronic health record systems publication-title: Health Inf J contributor: fullname: Lamo – volume: 17 start-page: 37 issue: 3 year: 1996 end-page: 37 article-title: From data mining to knowledge discovery in databases publication-title: AI Mag contributor: fullname: Smyth – volume: 143 start-page: 29 issue: 1 year: 1982 end-page: 36 article-title: The meaning and use of the area under a receiver operating characteristic (ROC) curve publication-title: Radiology contributor: fullname: McNeil – volume: 60 start-page: 911 issue: 5 year: 2019 end-page: 920 article-title: Burden and epidemiology of status epilepticus in infants, children, and adolescents: a population-based study on German health insurance data publication-title: Epilepsia contributor: fullname: Ansorge – volume: 53 start-page: 9 year: 2014 end-page: 18 article-title: Identifying high-cost patients using data mining techniques and a small set of non-trivial attributes publication-title: Comput Biol Med contributor: fullname: Tekieh – volume: 9 start-page: 68 issue: 3 year: 2014 article-title: Predicting patients with high risk of becoming high-cost healthcare users in Ontario (Canada) publication-title: Healthc Policy contributor: fullname: Rais – volume: 374 start-page: 203 issue: 3 year: 2016 end-page: 205 article-title: ACOs and high-cost patients publication-title: N Engl J Med contributor: fullname: Chaguturu – volume: 3 start-page: 148 issue: 1 year: 2020 article-title: Machine-learning-based prediction models for high-need high-cost patients using nationwide clinical and claims data publication-title: NPJ Digit Med contributor: fullname: Yamamoto – volume: 17 start-page: e4976 issue: 9 year: 2015 article-title: Online prediction of health care utilization in the next six months based on electronic health record information: a cohort and validation study publication-title: J Med Internet Res contributor: fullname: Jin – volume: 63 start-page: 3 issue: 1 year: 2006 end-page: 42 article-title: Extremely randomized trees publication-title: Mach Learn contributor: fullname: Wehenkel – volume: 9 start-page: 93 issue: 2 year: 2005 article-title: Managing high-risk, high-cost patients: the southern California kaiser permanente experience in the medicare ESRD demonstration project publication-title: Perm J contributor: fullname: Crooks – volume: 18 start-page: 767 year: 2008 end-page: 783 article-title: Suplemental health in Brazil: the role of the national agency of suplemental health in the sector’s regulation publication-title: Physis: Revista de Saude Coletiva contributor: fullname: Caetano – volume: 184 start-page: 393 issue: 8 year: 2006 end-page: 397 article-title: High-cost users of hospital beds in Western Australia: a population-based record linkage study publication-title: Med J Aust contributor: fullname: Preen – volume: 90 start-page: 104121 year: 2020 article-title: Health status prediction for the elderly based on machine learning publication-title: Arch Gerontol Geriatr contributor: fullname: Wang – volume: 30 start-page: 1145 issue: 7 year: 1997 end-page: 1159 article-title: The use of the area under the ROC curve in the evaluation of machine learning algorithms publication-title: Pattern Recogn contributor: fullname: Bradley – volume: 10 start-page: 79 issue: 1 year: 2021 end-page: 86 article-title: Machine learning and national health data to improve evidence: finding segmentation in individuals without private insurance publication-title: Health Policy Technol contributor: fullname: Chiavegatto Filho – volume: 7 start-page: e017775 issue: 11 year: 2017 article-title: Characteristics and healthcare utilization patterns of high-cost beneficiaries in The Netherlands: a cross-sectional claims database study publication-title: BMJ Open contributor: fullname: Jonkers – volume: 18 start-page: 565 issue: 2 year: 2021 article-title: Does last year's cost predict the present cost? An application of machine learning for the Japanese area-basis public health insurance database publication-title: Int J Environ Res Publ Health contributor: fullname: Chiba – ident: bibr19-14604582241230384 – ident: bibr28-14604582241230384 doi: 10.1148/radiology.143.1.7063747 – volume-title: Ministério da Saúde; Organização Pan-Americana da Saúde year: 2013 ident: bibr4-14604582241230384 contributor: fullname: BRASIL – ident: bibr21-14604582241230384 doi: 10.1007/s10994-006-6226-1 – volume-title: Machine learning year: 1997 ident: bibr24-14604582241230384 contributor: fullname: Mitchell TM – ident: bibr6-14604582241230384 – ident: bibr13-14604582241230384 doi: 10.1016/j.compbiomed.2014.07.005 – volume: 17 start-page: 37 issue: 3 year: 1996 ident: bibr8-14604582241230384 publication-title: AI Mag contributor: fullname: Fayyad U – ident: bibr33-14604582241230384 doi: 10.7812/TPP/04-119 – volume-title: Série Informação para ação na Covid-19 year: 2021 ident: bibr32-14604582241230384 contributor: fullname: Matta GC – ident: bibr34-14604582241230384 doi: 10.1109/ICHI.2017.86 – ident: bibr35-14604582241230384 doi: 10.1145/3368756.3369103 – ident: bibr3-14604582241230384 – ident: bibr30-14604582241230384 – ident: bibr1-14604582241230384 – ident: bibr31-14604582241230384 doi: 10.1177/14604582211043920 – ident: bibr18-14604582241230384 doi: 10.3390/ijerph18020565 – ident: bibr23-14604582241230384 – ident: bibr5-14604582241230384 doi: 10.1590/S0103-73312008000400009 – ident: bibr10-14604582241230384 doi: 10.1056/NEJMp1511131 – ident: bibr20-14604582241230384 doi: 10.1023/A:1010933404324 – ident: bibr16-14604582241230384 doi: 10.1038/s41746-020-00354-8 – ident: bibr22-14604582241230384 doi: 10.1145/2939672.2939785 – ident: bibr7-14604582241230384 – ident: bibr36-14604582241230384 doi: 10.1016/j.hlpt.2020.11.002 – volume: 9 start-page: 68 issue: 3 year: 2014 ident: bibr12-14604582241230384 publication-title: Healthc Policy contributor: fullname: Chechulin Y – ident: bibr15-14604582241230384 doi: 10.1111/epi.14729 – ident: bibr2-14604582241230384 doi: 10.1787/f2b7ee85-pt – ident: bibr27-14604582241230384 doi: 10.1016/S0031-3203(96)00142-2 – ident: bibr9-14604582241230384 doi: 10.5694/j.1326-5377.2006.tb00289.x – ident: bibr29-14604582241230384 – ident: bibr17-14604582241230384 doi: 10.1016/j.archger.2020.104121 – ident: bibr26-14604582241230384 doi: 10.1016/S0034-4257(97)00083-7 – ident: bibr25-14604582241230384 doi: 10.1007/978-0-387-39940-9_565 – ident: bibr11-14604582241230384 doi: 10.1136/bmjopen-2017-017775 – ident: bibr14-14604582241230384 doi: 10.2196/jmir.4976 |
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| SubjectTerms | Algorithms Bayes Theorem Databases, Factual Health care Health insurance Humans Insurance Machine Learning |
| Title | Identification of high-risk beneficiaries in private healthcare insurance |
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