{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T17:21:44Z","timestamp":1775841704220,"version":"3.50.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T00:00:00Z","timestamp":1619568000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T00:00:00Z","timestamp":1619568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Inf Syst Front"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s10796-021-10137-5","type":"journal-article","created":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T09:03:39Z","timestamp":1619600619000},"page":"2179-2195","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["Responsible Artificial Intelligence in Healthcare: Predicting and Preventing Insurance Claim Denials for Economic and Social Wellbeing"],"prefix":"10.1007","volume":"25","author":[{"given":"Marina","family":"Johnson","sequence":"first","affiliation":[]},{"given":"Abdullah","family":"Albizri","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0407-9217","authenticated-orcid":false,"given":"Antoine","family":"Harfouche","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,28]]},"reference":[{"issue":"4","key":"10137_CR1","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.2307\/41703508","volume":"36","author":"A Abbasi","year":"2012","unstructured":"Abbasi, A., Albrecht, C., Vance, A., & Hansen, J. (2012). Metafraud: A meta-learning framework for detecting financial fraud. MIS Quarterly: Management Information Systems, 36(4), 1293.","journal-title":"MIS Quarterly: Management Information Systems"},{"issue":"10","key":"10137_CR2","doi-asserted-by":"publisher","first-page":"1340","DOI":"10.1093\/bioinformatics\/btq134","volume":"26","author":"A Altmann","year":"2010","unstructured":"Altmann, A., Tolo\u015fi, L., Sander, O., & Lengauer, T. (2010). Permutation importance: A corrected feature importance measure. Bioinformatics, 26(10), 1340\u20131347.","journal-title":"Bioinformatics"},{"key":"10137_CR3","doi-asserted-by":"crossref","unstructured":"Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., et al. (2019). Software engineering for machine learning: A case study. In IEEE\/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (pp. 291\u2013300). Institute of Electrical and Electronics Engineers Inc.","DOI":"10.1109\/ICSE-SEIP.2019.00042"},{"issue":"1","key":"10137_CR4","first-page":"1089","volume":"5","author":"Y Bengio","year":"2004","unstructured":"Bengio, Y., & Grandvalet, Y. (2004). No unbiased estimator of the variance of K-fold cross-validation. Journal of Machine Learning Research, 5(1), 1089\u20131105.","journal-title":"Journal of Machine Learning Research"},{"issue":"4","key":"10137_CR5","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1002\/phar.1569","volume":"35","author":"M Bounthavong","year":"2015","unstructured":"Bounthavong, M., Watanabe, J. H., & Sullivan, K. M. (2015). Approach to addressing missing data for electronic medical records and pharmacy claims data research. Pharmacotherapy, 35(4), 380\u2013387.","journal-title":"Pharmacotherapy"},{"key":"10137_CR6","unstructured":"Cam, A., Chui, M., & Hall, B. (2018). Global AI survey: AI proves its worth, but few scale impact. McKinsey. https:\/\/www.mckinsey.com\/featured-insights\/artificial-intelligence\/global-ai-survey-ai-proves-its-worth-but-few-scale-impact. Accessed 30 September 2020."},{"key":"10137_CR7","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2011","unstructured":"Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2011). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321\u2013357.","journal-title":"Journal of Artificial Intelligence Research"},{"key":"10137_CR8","unstructured":"Chollet, F. (2017). Deep learning with Python (1st ed.). Manning Publications."},{"key":"10137_CR9","unstructured":"Cyrus, H. (2020). Leveraging machine learning to identify quality issues in the Medicaid claim adjudication process."},{"key":"10137_CR10","unstructured":"Delmolino, D., & Whitehouse, M. (2018). Responsible AI: A framework for building Trust in Your AI solutions."},{"issue":"2","key":"10137_CR11","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1145\/2844110","volume":"59","author":"N Diakopoulos","year":"2016","unstructured":"Diakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59(2), 56\u201362.","journal-title":"Communications of the ACM"},{"key":"10137_CR12","unstructured":"Doshi-Velez, F., & Kim, B. (2017). Towards A Rigorous Science of Interpretable Machine Learning. http:\/\/arxiv.org\/abs\/1702.08608. Accessed 27 November 2020."},{"key":"10137_CR13","doi-asserted-by":"publisher","unstructured":"Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62\u201370. https:\/\/doi.org\/10.1016\/j.infoandorg.2018.02.005.","DOI":"10.1016\/j.infoandorg.2018.02.005"},{"issue":"5","key":"10137_CR14","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189\u20131232.","journal-title":"Annals of Statistics"},{"key":"10137_CR15","unstructured":"Gee, E., & Spiro, T. (2019). Excess administrative costs burden the U.S. health care system - Center for American Progress. Center for American Progress. https:\/\/www.americanprogress.org\/issues\/healthcare\/reports\/2019\/04\/08\/468302\/excess-administrative-costs-burden-u-s-health-care-system\/. Accessed 27 August 2020."},{"key":"10137_CR16","doi-asserted-by":"crossref","unstructured":"Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018). Explaining explanations: An approach to evaluating interpretability of machine learning.","DOI":"10.1109\/DSAA.2018.00018"},{"issue":"2","key":"10137_CR17","doi-asserted-by":"publisher","first-page":"337","DOI":"10.25300\/MISQ\/2013\/37.2.01","volume":"37","author":"S Gregor","year":"2013","unstructured":"Gregor, S., & Hevner, A. (2013). Positioning and presenting design science research for maximum impact. MIS Quarterly, 37(2), 337\u2013355.","journal-title":"MIS Quarterly"},{"issue":"6","key":"10137_CR18","doi-asserted-by":"publisher","first-page":"1622","DOI":"10.17705\/1jais.00649","volume":"21","author":"S Gregor","year":"2020","unstructured":"Gregor, S., Chandra Kruse, L., Seidel, S., & Kruse, C. (2020). The anatomy of a design principle. Article in Journal of the Association for Information Systems, 21(6), 1622\u20131652.","journal-title":"Article in Journal of the Association for Information Systems"},{"key":"10137_CR19","unstructured":"Hall, P., Gill, N., & Schmidt, N. (2019). Proposed Guidelines for the Responsible Use of Explainable Machine Learning. http:\/\/arxiv.org\/abs\/1906.03533. Accessed 27 November 2020."},{"key":"10137_CR20","doi-asserted-by":"crossref","unstructured":"Hansen, K. (2020). The virtue of simplicity: On machine learning models in algorithmic trading. Big Data & Society.","DOI":"10.1177\/2053951720926558"},{"key":"10137_CR21","unstructured":"Heaton, J. (2008). Introduction to neural networks for Java (2nd ed.). Heaton Research, Inc."},{"issue":"1","key":"10137_CR22","doi-asserted-by":"publisher","first-page":"75","DOI":"10.2307\/25148625","volume":"28","author":"AR Hevner","year":"2004","unstructured":"Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly: Management Information Systems, 28(1), 75\u2013105.","journal-title":"MIS Quarterly: Management Information Systems"},{"key":"10137_CR23","doi-asserted-by":"publisher","unstructured":"Hopp, W. J., Li, J., & Wang, G. (2018). Big Data and the Precision Medicine Revolution. Production and Operations Management, 27(9), 1647\u20131664. https:\/\/doi.org\/10.1111\/poms.12891.","DOI":"10.1111\/poms.12891"},{"key":"10137_CR24","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4614-7138-7","volume-title":"An introduction to statistical learning","author":"G James","year":"2013","unstructured":"James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 103). New York: Springer New York."},{"key":"10137_CR25","unstructured":"Jimenez, R. (2013). Proper coding can help prove medical necessity. Smart Billing Solutions: Medical Billing. https:\/\/smartbillingsolutions.blogspot.com\/2013\/08\/proper-coding-can-help-prove-medical.html. Accessed 1 September 2020."},{"key":"10137_CR26","doi-asserted-by":"crossref","unstructured":"Jobin, A., Lenca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence.","DOI":"10.1038\/s42256-019-0088-2"},{"key":"10137_CR27","doi-asserted-by":"crossref","unstructured":"Johnson, M., Albizri, A., & Simsek, S. (2020). Artificial intelligence in healthcare operations to enhance treatment outcomes: A framework to predict lung cancer prognosis. Annals of Operations Research, 1\u201331.","DOI":"10.1007\/s10479-020-03872-6"},{"issue":"3","key":"10137_CR28","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s10729-015-9317-3","volume":"19","author":"ME Johnson","year":"2016","unstructured":"Johnson, M. E., & Nagarur, N. (2016). Multi-stage methodology to detect health insurance claim fraud. Health Care Management Science, 19(3), 249\u2013260.","journal-title":"Health Care Management Science"},{"key":"10137_CR29","unstructured":"Jones Sanborn, B. (2017). Change healthcare analysis shows $262 billion in medical claims initially denied, meaning billions in administrative costs | healthcare finance news. Healthcare Finance. https:\/\/www.healthcarefinancenews.com\/news\/change-healthcare-analysis-shows-262-million-medical-claims-initially-denied-meaning-billions. Accessed 28 August 2020."},{"key":"10137_CR30","unstructured":"Khurjekar, N. M. (2017). An integrated three stage predictive framework for health insurance claim denials. Binghamton University."},{"key":"10137_CR31","unstructured":"Kim, B.-H., Sridharan, S., Atwal, A., & Ganapathi, V. (2020). Deep Claim: Payer Response Prediction from Claims Data with Deep Learning. arXiv. http:\/\/arxiv.org\/abs\/2007.06229. Accessed 18 February 2021."},{"key":"10137_CR32","unstructured":"Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Appears in the International Joint Conference on Artificial Intelligence (IJCAI) (pp. 1\u20137)."},{"issue":"3","key":"10137_CR33","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1097\/QMH.0000000000000175","volume":"27","author":"JV Kovach","year":"2018","unstructured":"Kovach, J. V., & Borikar, S. (2018). Enhancing financial performance: An application of lean six sigma to reduce insurance claim denials. Quality Management in Health Care, 27(3), 165\u2013171.","journal-title":"Quality Management in Health Care"},{"key":"10137_CR34","unstructured":"Kuhn, M., & Johnson, K. (2018). Applied predictive modeling (2nd ed.). Springer."},{"key":"10137_CR35","doi-asserted-by":"publisher","DOI":"10.1002\/9781119013563","volume-title":"Statistical analysis with missing data. Statistical analysis with missing data","author":"RJA Little","year":"2002","unstructured":"Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data. Statistical analysis with missing data. Hoboken: John Wiley & Sons, Inc.."},{"key":"10137_CR36","unstructured":"Liu, S., & Vicente, L. (2020). Accuracy and fairness trade-offs in machine learning: A stochastic multi-objective approach."},{"key":"10137_CR37","unstructured":"Lodder, P. (2013). To impute or not impute: That\u2019s the question."},{"key":"10137_CR38","first-page":"131","volume":"9","author":"D Mease","year":"2008","unstructured":"Mease, D., & Wyner, A. (2008). Evidence contrary to the statistical view of boosting. Journal of Machine Learning Research, 9, 131\u2013156.","journal-title":"Journal of Machine Learning Research"},{"key":"10137_CR39","unstructured":"Olson, D. L., & Delen, D. (2008). Advanced data mining techniques. Springer Publishing Company, Incorporated."},{"key":"10137_CR40","doi-asserted-by":"crossref","unstructured":"Papanicolas, I., Woskie, L. R., & Jha, A. K. (2018). Health care spending in the United States and other high-income countries. JAMA - Journal of the American Medical Association. American Medical Association.","DOI":"10.1001\/jama.2018.1150"},{"key":"10137_CR41","doi-asserted-by":"publisher","unstructured":"Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45\u201377. https:\/\/doi.org\/10.2753\/MIS0742-1222240302.","DOI":"10.2753\/MIS0742-1222240302"},{"key":"10137_CR42","unstructured":"Pohlig, C. (2009, August). Investigate Claim Denials. The Hospitalist. https:\/\/www.the-hospitalist.org\/hospitalist\/article\/123900\/investigate-claim-denials. Accessed 1 September 2020."},{"key":"10137_CR43","unstructured":"Polit, D. F. . (2012). Nursing research : Generating and assessing evidence for nursing practice \/. Wolters Kluwer Health\/Lippincott Williams & Wilkins."},{"key":"10137_CR44","unstructured":"Powers, D. M. W. (2011). Evaluation: From precision, recall, and F-measure to ROC, Informedness, Markedness & Correlation. Journal of Machine Learning Technologies, 2(1), 37\u201363. http:\/\/dspace.flinders.edu.au\/dspace\/http:\/\/www.bioinfo.in\/contents.php?id=51. Accessed 24 August 2020."},{"key":"10137_CR45","doi-asserted-by":"crossref","unstructured":"Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science. Springer.","DOI":"10.1007\/s11747-019-00710-5"},{"key":"10137_CR46","unstructured":"Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Model-Agnostic Interpretability of Machine Learning. http:\/\/arxiv.org\/abs\/1606.05386. Accessed 27 Nov 2020."},{"key":"10137_CR47","doi-asserted-by":"crossref","unstructured":"Saripalli, P., Tirumala, V., & Chimmad, A. (2017). Assessment of healthcare claims rejection risk using machine learning. In 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services, Healthcom 2017 (Vol. 2017-December, pp. 1\u20136).","DOI":"10.1109\/HealthCom.2017.8210758"},{"key":"10137_CR48","doi-asserted-by":"crossref","unstructured":"Simsek, S., Albizri, A., Johnson, M., Custis, T., & Weikert, S. (2020). Predictive data analytics for contract renewals: A decision support tool for managerial decision-making. Journal of Enterprise Information Management.","DOI":"10.1108\/JEIM-12-2019-0375"},{"key":"10137_CR49","unstructured":"Tikkanen, R., & Abrams, M. (2020). U.S. Health Care from a Global Perspective, 2019 | Commonwealth Fund. The Commonwealth Fund. https:\/\/www.commonwealthfund.org\/publications\/issue-briefs\/2020\/jan\/us-health-care-global-perspective-2019. Accessed 27 August 2020."},{"key":"10137_CR50","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xiong, M., & Olya, H. (2020). Toward an understanding of responsible artificial intelligence practices. In Proceedings of the 53rd Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences.","DOI":"10.24251\/HICSS.2020.610"},{"key":"10137_CR51","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1038\/s42256-019-0022-7","volume":"1","author":"O Wearn","year":"2019","unstructured":"Wearn, O., Freeman, R., & Jacoby, D. (2019). Responsible AI for conservation. Nature Machine Intelligence, 1, 72\u201373.","journal-title":"Nature Machine Intelligence"},{"key":"10137_CR52","doi-asserted-by":"crossref","unstructured":"Wojtusiak, J., Ngufor, C., Shiver, J., & Ewald, R. (2011). Rule-based prediction of medical claims\u2019 payments: A method and initial application to medicaid data. In Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011 (Vol. 2, pp. 162\u2013167).","DOI":"10.1109\/ICMLA.2011.126"},{"key":"10137_CR53","unstructured":"Yong, P. L., Saunders, R. S., & Olsen, L. (2010). Excess administrative costs. Institute of Medicine (US) roundtable on evidence-based medicine. National Academies Press (US). https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK53942\/. Accessed 1 September 2020."},{"issue":"1","key":"10137_CR54","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.jeconom.2015.02.006","volume":"187","author":"Y Zhang","year":"2015","unstructured":"Zhang, Y., & Yang, Y. (2015). Cross-validation for selecting a model selection procedure. Journal of Econometrics, 187(1), 95\u2013112.","journal-title":"Journal of Econometrics"}],"container-title":["Information Systems Frontiers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10796-021-10137-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10796-021-10137-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10796-021-10137-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,24]],"date-time":"2023-11-24T08:22:14Z","timestamp":1700814134000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10796-021-10137-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,28]]},"references-count":54,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["10137"],"URL":"https:\/\/doi.org\/10.1007\/s10796-021-10137-5","relation":{},"ISSN":["1387-3326","1572-9419"],"issn-type":[{"value":"1387-3326","type":"print"},{"value":"1572-9419","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,28]]},"assertion":[{"value":"18 April 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}