Readers should consider our inability to independently
confirm utilization when interpreting results.
In future studies, we will embed the OSPRO tools into
electronic medical record (EMR) databases to refine and
test outcomes prediction models at the health care systems
level. Importantly, we will collect clinical encounter data
through the EMR and combine it with administrative or
billing data to confirm the results of this study with more
objective measures of health care use. These studies will
also allow us to provide better guidance on how to use the
OSPRO tools to identify serious psychiatric involvement or
systemic sources of pain that require medical referral. Fi-
nally, we will explore alternative scoring strategies for the
tools, such as weighted scoring for the OSPRO-ROS and
use of predicted full-length p sychological questionnaire
scores for the OSPRO-YF. Healthcare providers could then
use the collective informatio n from these studies to build
learning health systems that facilitate effective, real-time
clinical decision-making support to improve value of care
for patients with musculoskeletal pain.
Conclusion
Baseline disability and change in pain intensity were im-
portant predictors of any subsequent pain-related
healthcare utilization, while predictors of individual ser-
vice utilization were outcome-specific. Identification of
risk is improved through treatment monitoring for pain
and, in some cases, disability and pain-related psycho-
logical distress. Comorbidity burden was an important
predictor of subsequent utilization of opioids and diag-
nostic tests and imaging, both of which have been recent
targets of healthcare policy to constrain their unneces-
sary use. Future research is needed to refine these pre-
dictor variables and incorporate them into risk models
that support clinical decision-making so that treatment
effectiveness and efficiency are optimized in value-based
systems.
Lentz et al. BMC Health Services Research  (2018) 18:648 Page 12 of 14