References
- Potentially avoidable hospitalizations. Rockville (MD): Agency for Healthcare Research and Quality; Last Updated June 2018. https://www.ahrq.gov/research/findings/nhqrdr/chartbooks/carecoordination/measure3.html
- Moy E, Chang E, Barrett M, et al. Potentially preventable hospitalizations – United States, 2001–2009. MMWR. 2013;62(3):139–143.
- The IBM cost of care model: using claims data to help predict healthcare resource consumption. Somers (NY): IBM Watson Health; 2018. https://www.ibm.com/downloads/cas/W62MR7MD
- The Evolution of DxCG, the gold standard in risk adjustment and predictive modeling. Waltham, MA: Cotiviti; 2016. https://resources.cotiviti.com/white-paper/the-evolution-of-dxcg
- The Johns Hopkins ACG® System: white paper. Baltimore (MD): Johns Hopkins University; 2018. https://www.healthy.works/wp-content/uploads/2018/11/The-Johns-Hopkins-ACG-System-White-Paper-SMALLER.pdf
- Hileman G, Steele S. Accuracy of claims-based risk scoring models. Schaumburg, Illinois: Society of Actuaries; 2016. https://www.soa.org/globalassets/assets/files/research/research-2016-accuracy-claims-based-risk-scoring-models.pdf
- Pope GC, Kautter J, Ingber MJ, et al. Evaluation of the CMS-HCC risk adjustment model. Maryland: Centers for Medicare and Medicaid in Baltimore; 2011.
- DxCG intelligence model directory verisk health. Waltham (MA): Verisk Health; 2014.
- Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–2830.
- Berchick ER, Hood E, Barnett JC. Health insurance coverage in the United States, Current Population Reports, p. 60–264. 2017. U.S. Government Printing Office, Washington, DC, 2018. https://www.census.gov/content/dam/Census/library/publications/2018/demo/p60-264.pdf