Multichannel mixture models for time-series analysis and classification of engagement with multiple health services: An application to psychology and physiotherapy utilization patterns after traffic accidents.

Background: Motor vehicle accidents (MVA) represent a significant burden on health systems globally. Tens of thousands of people are injured in Australia every year and may experience significant disability. Associated economic costs are substantial.

Correction: Analysis of healthcare service utilization after transport-related injuries by a mixture of hidden Markov models.

[This corrects the article DOI: 10.1371/journal.pone.

Analysis of healthcare service utilization after transport-related injuries by a mixture of hidden Markov models.

Background: Transport injuries commonly result in significant disease burden, leading to physical disability, mental health deterioration and reduced quality of life. Analyzing the patterns of healthcare service utilization after transport injuries can provide an insight into the health of the affected parties, allow improved health system resource planning, and provide a baseline against which any future system-level interventions can be evaluated. Therefore, this research aims to use time series of service utilization provided by a compensation agency to identify groups of claimants with similar utilization patterns, describe such patterns, and characterize the groups in terms of demographic, accident type and injury type.