  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.
