Abstract
Objectives
There are several mechanisms for monitoring the quality of care in long-term care
(LTC), including the use of quality indicators derived from resident assessments and
formal inspections. The LTC inspection process is time and resource-intensive, and
there may be opportunities to better target inspections. In this study, we aimed to
examine whether quality indicators could predict future inspection performance in
LTC homes across Ontario, Canada.
Setting and Participants
In total, 594 LTC homes across Ontario.
Methods
Using a database compiling detailed inspection reports for the period from 2017 to
2018, we classified each home into 1 of 3 categories (in good standing, needing improvement,
needing significant improvement). Machine learning techniques were used to examine
whether publicly available Resident Assessment Instrument‒Minimum Data Set quality
indicators for the period 2016‒2017 could predict facility classification based on
inspection results.
Results
After running a wide range of models, only a weak relationship was found between quality
indicators and future inspection performance. The best-performing model was able to
achieve a classification accuracy of 40.1%. Feature analysis was performed on the
final model to identify which quality indicators were most indicative of predicted
poor performance. Experiencing worsened pain, restraint use, and worsened pressure
ulcers were correlated with homes predicted as needing significant improvement. Counterintuitively,
improved physical functioning had an inverse relationship with homes predicted as
being in good standing.
Conclusions and implications
Most quality indicators are poor predictors of inspection performance. Further work
is required to explore the limited relationship between these 2 measures of LTC quality,
and to identify other quality measures that may be useful as predictors of facilities
facing difficulty in meeting quality standards.
Keywords
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Article info
Publication history
Published online: October 29, 2019
Footnotes
Funding for this study was provided by the Walter and Maria Schroeder Institute for Brain Innovation & Recovery, a National Sciences and Engineering Research Council of Canada (NSERC) discovery grant (BT; RGPIN 435653), and an Academic Scholar Award, Department of Psychiatry, University of Toronto (AI).
Identification
Copyright
© 2019 AMDA - The Society for Post-Acute and Long-Term Care Medicine.
