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Development and Testing of Improved Models to Predict Payment Using Centers for Medicare & Medicaid Services Claims Data.

Harlan M Krumholz | Frederick Warner | Andreas Coppi | Elizabeth W Triche | Shu-Xia Li | Shiwani Mahajan | Yixin Li | Susannah M Bernheim | Jacqueline Grady | Karen Dorsey | Nihar R Desai | Zhenqiu Lin | Sharon-Lise T Normand
JAMA network open | 2019

Predicting payments for particular conditions or populations is essential for research, benchmarking, public reporting, and calculations for population-based programs. Centers for Medicare & Medicaid Services (CMS) models often group codes into disease categories, but using single, rather than grouped, diagnostic codes and leveraging present on admission (POA) codes may enhance these models.

Pubmed ID: 31411709

Research resources used in this publication

None found

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Associated grants

  • Agency: NCATS NIH HHS, United States
    Id: UL1 TR001863

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MATLAB (tool)

RRID:SCR_001622

Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.

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