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Updated: Jul 17, 2025

Computerized Adaptive Testing System of Functional Assessment of Stroke

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

Addressing non-response data for standardized post-acute functional items.

Chih -Ying Li^1,2, Hyunkyoung Kim³, Brian Downer⁴

¹Department of Occupational Therapy, School of Health Professions, University of Texas Medical Branch, Rm 3.906, 301 University Blvd., Galveston, TX, 77555-1142, USA. chili@utmb.edu.

BMC Health Services Research

|September 6, 2023

Summary

This summary is machine-generated.

Non-response in Section GG functional data is common, especially for mobility, and linked to poorer patient outcomes. Four imputation methods showed similar results, but variations exist, impacting quality reporting in inpatient rehabilitation facilities.

Keywords:

Critical care outcomes Functional status Health care Health services administration Medicare payment advisory commission Mobility Outcome and process assessment Patient outcome assessment Self-care Subacute care

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Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit

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Published on: September 30, 2020

Area of Science:

Rehabilitation Medicine
Health Services Research
Data Science

Background:

Standardized functional assessments like Section GG in post-acute care include non-response options (refuse, not attempt, not applicable).
Non-response patterns in functional data can significantly impact patient outcomes and quality reporting.
Understanding and addressing non-response is crucial for accurate assessment in inpatient rehabilitation facilities (IRFs).

Purpose of the Study:

To examine non-response patterns in Section GG functional data within nationwide IRFs.
To compare the effectiveness of four different methods for addressing non-response in functional data.
To assess the impact of imputation methods on the correlation between Section GG and site-specific functional items.

Main Methods:

Utilized 100% Medicare 2018 data for characterization of non-response patterns.
Applied four imputation methods: Monte Carlo Markov Chains (MCMC), Fully Conditional Specification (FCS), Pattern-mixture model (PMM), and the Centers for Medicare and Medicaid Services (CMS) approach.
Compared Spearman correlations and weighted kappa between Section GG and site-specific items before and after imputation.

Main Results:

High rates of non-response were observed at discharge (3.9% self-care, 61.6% mobility).
Patients with non-response data generally exhibited worse functional status and less improvement.
All four imputation methods yielded similar changes in correlations, but variations were noted across impairments between multiple imputation techniques and the CMS approach.

Conclusions:

Reasons for non-response are linked to varying functional status, with 'refused' and 'not applicable' indicating different functional independence levels.
The high rate of non-response for mobility items raises concerns about biased quality reporting in IRFs.
Findings offer insights for improving patient care, outcomes, quality reporting, and payment models in post-acute settings.