A Robust Interrupted Time Series Model for Analyzing Complex Healthcare Intervention Data

TL;DR

This paper introduces a new Robust-ITS model for analyzing complex healthcare intervention data, accounting for changes in variability and correlation post-intervention, and identifying change points in time series data.

Contribution

The paper presents a novel Robust-ITS model that overcomes limitations of existing methods by modeling changes in variance, correlation, and change point detection in interrupted time series analysis.

Findings

Successfully applied to hospital patient satisfaction data

Identified significant changes in correlation and variance post-intervention

Provides a freely available R Shiny toolbox for implementation

Abstract

Current health policy calls for greater use of evidence based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and interdependent components that challenge traditional statistical analytic techniques, in particular when modeling a time series of outcomes data that might be "interrupted" by a change in a particular method of health care delivery. Interrupted time series (ITS) is a robust quasi-experimental design with the ability to infer the effectiveness of an intervention that accounts for data dependency. Current standardized methods for analyzing ITS data do not model changes in variation and correlation following the intervention. This is a key limitation since it is plausible for data variability and dependency to change because of the intervention. Moreover, present methodology either assumes a pre-specified interruption time point with an instantaneous effect or removes data for which the effect of intervention is not fully realized. In this paper, we describe and develop a novel `Robust-ITS' model that overcomes these omissions and limitations. The Robust-ITS model formally performs inference on: (a) identifying the change point; (b) differences in pre- and post-intervention correlation; (c) differences in the outcome variance pre- and post-intervention; and (d) differences in the mean pre- and post-intervention. We illustrate the proposed method by analyzing patient satisfaction data from a hospital that implemented and evaluated a new nursing care delivery model as the intervention of interest. The Robust-ITS model is implemented in a R Shiny toolbox which is freely available to the community.