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Pecora AL, Norden AD, Hervey J, et al. Development of a Precise, Clinically Relevant, Digital Classification Schema for Cancer. JCO Clin Cancer Inform. 2018;2:1-10doi: 10.1200/CCI.18.00006.
Pecora, A. L., Norden, A. D., Hervey, J., Schultz, E. V., Gallucci, T. L., Rushforth, E., & Goldberg, S. L. (2018). Development of a Precise, Clinically Relevant, Digital Classification Schema for Cancer. JCO clinical cancer informatics, 21-10. https://doi.org/10.1200/CCI.18.00006
Pecora, Andrew L, et al. "Development of a Precise, Clinically Relevant, Digital Classification Schema for Cancer." JCO clinical cancer informatics vol. 2 (2018): 1-10. doi: https://doi.org/10.1200/CCI.18.00006
Pecora AL, Norden AD, Hervey J, Schultz EV, Gallucci TL, Rushforth E, Goldberg SL. Development of a Precise, Clinically Relevant, Digital Classification Schema for Cancer. JCO Clin Cancer Inform. 2018 Dec;2:1-10. doi: 10.1200/CCI.18.00006. PMID: 30652606.
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JCO Clin Cancer Inform. 2018 Dec;2:1-10. doi: 10.1200/CCI.18.00006.

Development of a Precise, Clinically Relevant, Digital Classification Schema for Cancer.

JCO clinical cancer informatics

Andrew L Pecora, Andrew D Norden, John Hervey, Eric V Schultz, Thomas L Gallucci, Elizabeth Rushforth, Stuart L Goldberg

Affiliations

  1. All authors: Cota, New York, NY.

PMID: 30652606 DOI: 10.1200/CCI.18.00006

Abstract

PURPOSE: Health care transactions depend on the efficiency of digital codes. The International Classification of Diseases and Related Health Problems (ICD) coding system, which is the most commonly used digital system, fails to capture the complexity of oncologic diseases. Because important prognostic information such as cancer stage and genomic markers are missing, the potential for ICD codes to define and compare patient cohorts is severely limited. A more precise, clinically relevant, digital classification schema that incorporates prognostic elements would address these needs.

METHODS: Working with cancer disease-specific experts, a new digital classification scheme, known as the Cota Nodal Address (CNA) system, was developed. The CNA has six components that define the disease of interest and incorporate all standard-of-care prognostic and predictive markers related to the particular cancer, including patient features.

RESULTS: Properly sorted into homogeneous groupings of patients with similar prognostic characteristics, the CNA system facilitated big data analytic approaches, such as evaluations of population health, identification of variation in treatment decisions, and the enablement of value-based payment models. The schema has been applied to patients with breast cancer at a large tertiary cancer care hospital and a regional community cancer care network and has facilitated the creation and application of value-based payment models.

CONCLUSION: The development and potential uses of a prognosis-based classification system are reviewed herein. Compared with ICD coding, the greater precision of the schema permits improved analyses of variance in treatment, outcomes, and costs in cancer care management.

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