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

ABSTRACT:

Importance

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.

Objective

To determine whether changes to the candidate variables in CMS models would improve risk models predicting patient total payment within 30 days of hospitalization for acute myocardial infarction (AMI), heart failure (HF), and pneumonia.

Design, setting, and participants

This comparative effectiveness research study used data from Medicare fee-for-service hospi

SUBMITTER: Krumholz HM 

PROVIDER: S-EPMC6694388 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature