Abstract
Correct code assignment of Medicare Severity Diagnosis-Related Group (MS-DRG) is critical for healthcare. However, there is a gap currently on automatically identifying all potentially miscoded cases and prioritizing manual reviews over these cases. This paper reports a new process using a data-driven machine learning approach to flag potentially misclassified cases for manual review. We investigated using a stack of regularized logistic/softmax regression, random forest, and support vector machine to suggest potential cases for manual review by care providers, provided details addressing the data imbalance, and explored using features from various source including diagnosis and procedure codes, length of stay and the access log data from the electronic health record system. This potentially improves the efficiency of the coding review by care providers, providing another line of defense against miscoding to enhance coding compliance, and reduce the negative effects of upcoding and downcoding. We tested the new method with four common pediatric conditions and demonstrated its feasibility.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Rimler, S.B., Gale, B.D., Reede, D.L.: Diagnosis-related groups and hospital inpatient federal reimbursement. Radiographics 35(6), 1825–1834 (2015)
Verdon, D.R.: Report: medicare coding errors signal need for physician education. Med. Econ. 91(13), 55 (2014)
National Health Expenditures 2016 Highlights. Centers for Medicare and Medicaid Services. https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/downloads/highlights.pdf
CMS financial report FY2017. Centers for Medicare and Medicaid Services. https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/CFOReport/Downloads/2017_CMS_Financial_Report.pdf
Stavrakas-Souba, L.: Avoiding audits by benchmarking your E/M coding. J. Med. Pract. Manage. 21(1), 51–53 (2005)
Medicare Fee-For-Service 2016 Improper Payments Report. Centers for Medicare and Medicaid Services. https://www.cms.gov/Research-Statistics-Data-and-Systems/Monitoring-Programs/Medicare-FFS-Compliance-Programs/CERT/Downloads/MedicareFeeforService2016ImproperPaymentsReport.pdf
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the ICLR (2015)
Van Poucke, S., Thomeer, M., Heath, J., Vukicevic, M.: Are randomized controlled trials the (g) old standard? From clinical intelligence to prescriptive analytics. J. Med. Internet Res. 18(7), e185 (2016)
Wu, J., Roy, J., Stewart, W.F.: Prediction modeling using EHR data: challenges, strategies, and a comparison of machine learning approaches. Med. Care 1, S106–S113 (2010)
Radovanovic, S., Vukicevic, M., Kovacevic, A., Stiglic, G., Obradovic, Z.: Domain knowledge based hierarchical feature selection for 30-day hospital readmission prediction. In: Holmes, John H., Bellazzi, R., Sacchi, L., Peek, N. (eds.) AIME 2015. LNCS (LNAI), vol. 9105, pp. 96–100. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19551-3_11
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., Elhadad, N.: Intelligible models for healthcare: predicting pneumonia risk and hospital 30-day readmission. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1721–1730. ACM, 10 August 2015
Jovanovic, M., Radovanovic, S., Vukicevic, M., Van Poucke, S., Delibasic, B.: Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression. Artif. Intell. Med. 1(72), 12–21 (2016)
Xu, Y., Biswal, S., Deshpande, S.R., Maher, K.O., Sun, J.: RAIM: recurrent attentive and intensive model of multimodal patient monitoring data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2565–2573. ACM, 19 July 2018
Zhang, W., Bjarnadóttir, M.V., Proaño, R.A., Anderson, D., Konrad, R.: Accelerating the adoption of bundled payment reimbursement systems: a data-driven approach utilizing claims data. IISE Trans. Healthcare Syst. Eng. 8(1), 22–34 (2018)
Chekroud, A.M., et al.: Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry 3(3), 243–250 (2016)
Hughes, M.C., et al.: Prediction-constrained topic models for antidepressant recommendation. In: Neural Information Processing Systems (NIPS) Workshop on Machine Learning for Healthcare (2017)
Bauder, R., Khoshgoftaar, T.M., Seliya, N.: A survey on the state of healthcare upcoding fraud analysis and detection. Health Serv. Outcomes Res. Meth. 17(1), 31–55 (2017)
Rosenberg, M.A., Fryback, D.G., Katz, D.A.: A statistical model to detect DRG upcoding. Health Serv. Outcomes Res. Meth. 1(3–4), 233–252 (2000)
Luo, W., Gallagher, M.: Unsupervised DRG upcoding detection in healthcare databases. In: 2010 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 600–605. Sydney, NSW, Australia; IEEE 2010, 13 December 2010
Acknowledgment
Richard Hoyt and Beth Burkhart of Nationwide Children’s Hospital helped prepare the data. Steve Rust and the rest of the data science team assisted in the project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Feng, Y., Lin, S., Lin, EJ., Farley, L., Huang, Y., Liu, C. (2019). Identifying Candidates for Medical Coding Audits: Demonstration of a Data Driven Approach to Improve Medicare Severity Diagnosis-Related Group Coding Compliance. In: Wang, H., Siuly, S., Zhou, R., Martin-Sanchez, F., Zhang, Y., Huang, Z. (eds) Health Information Science. HIS 2019. Lecture Notes in Computer Science(), vol 11837. Springer, Cham. https://doi.org/10.1007/978-3-030-32962-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-32962-4_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32961-7
Online ISBN: 978-3-030-32962-4
eBook Packages: Computer ScienceComputer Science (R0)