identifying risk for additional utilization has emerged due
to the growth of cost-sharing and capitated payment
models, particularly in the United States (US). As a result,
many US health care services organizations have begun
to prioritize early identification of individuals at risk for
downstream healthcare use at the onset of treatment
[10, 11]. Early risk assessment allows systems to deliver
greater value by 1) focusing limited health care re-
sources towards patients who are most in need, and 2)
identifying those who may require coordination of mul-
tiple providers and services to optimize outcomes.
Prospective identification of risk for high subsequent
healthcare utilization is a different approach to out-
comes prediction for musculoskeletal pain [ 12, 13] and
one that has not been evaluated in physical therapy set-
tings in the US. Most existing outcomes prediction
models focus on pain and disability endpoints [ 12– 14].
They also concentrate on condition-specific and psycho-
logical predictors, with less attention to factors that could
influence healthcare utilization more directly [ 15– 17].
These factors include insurance, comorbidities, symp-
toms unrelated to the pain condition, and treatment
response. As a result, predictors of pain-related
healthcare utilization beyond physical therapy are un-
known. A better understanding of these predictors
will have significant implications for future healthcare
pathway development. For instance, an influence of
modifiable factors like pain-related psychological dis-
tress might imply the need to build clinical pathways
that address those factors directly through physical
therapist provided intervention. Additionally, under-
standing the relative predictive capabilities of baseline
versus change estimates for modifiable factors would
clarify whether prediction is improved by routinely
assessing outcomes during the course of treatment
(i.e. treatment monitoring) [ 18].
This study was undertaken in a nationwide, US cohort
of patients receiving outpatient physical therapy for a
primary complaint of knee, shoulder, back or neck pain.
The primary aim of the analysis was to predict incidence
of additional pain-related healthcare utilization in the
year following the episode of physical therapy for mus-
culoskeletal pain. We considered factors not commonly
assessed in outcomes prediction for musculoskeletal
pain, like insurance, comorbidities, and treatment re-
sponse, as well as those more often associated with
pain-related outcomes (e.g. psychological distress). This
project will lead to the development of potentially novel
outcome prediction models for this population in a com-
mon, non-pharmacological US healthcare setting. The
results of this study will be particularly important in
value-based payment settings where enhanced clinical
decision-making drives treatment effectiveness and sys-
tem efficiency.
Methods
Dataset and patient population
This study used data from the Orthopedic Physical Ther-
apy – Investigative Network’s (OPT-IN) Optimal Screen-
ing for Prediction of Referral and Outcome (OSPRO)
validation cohort study, a longitudinal prospective study
of individuals with knee, shoulder, back or neck pain seek-
ing Physical Therapy in the US. A convenience sample
was recruited from December 2014 and December 2015
by participating OPT-IN clinics. The OPT-IN clinics that
participated in data collection represented multiple geo-
graphic regions in the US including the Mideast, South-
east, Great Lakes, Rocky Mountain States and Far West,
with an attempt to balance recruitment between urban
and rural settings over the entire OPT-IN network. Phys-
ical therapists practicing in these clinics identified eligible
participants at initial evaluation and directed them to a se-
cure study website for the informed consent process and
baseline self-report assessment. Eligibility criteria have
been thoroughly reported elsewhere [ 19] and were