Development of a Common Patient Assessment Scale across the Continuum of Care: A Nested Multiple Imputation Approach

TL;DR

This paper introduces a novel statistical method combining nested multiple imputation and the multivariate ordinal probit model to create a common assessment scale for patients across various post-acute care settings, enabling better comparison of functional status improvements.

Contribution

It develops a new two-step procedure that imputes unmeasured assessments and standardizes functional status measurement across diverse healthcare providers, improving cross-setting evaluation.

Findings

MVOP model outperforms existing imputation methods in simulations.

The procedure successfully creates a common assessment scale for stroke patients.

Application to real data demonstrates improved comparability across care settings.

Abstract

Evaluating and tracking patients' functional status through the post-acute care continuum requires a common instrument. However, different post-acute service providers such as nursing homes, inpatient rehabilitation facilities and home health agencies rely on different instruments to evaluate patients' functional status. These instruments assess similar functional status domains, but they comprise different activities, rating scales and scoring instructions. These differences hinder the comparison of patients' assessments across health care settings. We propose a two-step procedure that combines nested multiple imputation with the multivariate ordinal probit (MVOP) model to obtain a common patient assessment scale across the post-acute care continuum. Our procedure imputes the unmeasured assessments at multiple assessment dates and enables evaluation and comparison of the rates of functional improvement experienced by patients treated in different health care settings using a common measure. To generate multiple imputations of the unmeasured assessments using the MVOP model, a likelihood-based approach that combines the EM algorithm and the bootstrap method as well as a fully Bayesian approach using the data augmentation algorithm are developed. Using a dataset on patients who suffered a stroke, we simulate missing assessments and compare the MVOP model to existing methods for imputing incomplete multivariate ordinal variables. We show that, for all of the estimands considered, and in most of the experimental conditions that were examined, the MVOP model appears to be superior. The proposed procedure is then applied to patients who suffered a stroke and were released from rehabilitation facilities either to skilled nursing facilities or to their homes.