Abstract
BACKGROUND: Low back pain (LBP) is a debilitating condition that is complex to manage. One reason is that clinicians lack means to identify early on patients who are likely to become high care utilizers. OBJECTIVE: To explore the feasibility of developing a 'dynamic' predictive model using electronic health record data to identify costly LBP patients within the first year after their initial LBP encounter with a primary care provider. Dynamic, in this context, indicates a process in which the decision on how to manage patients is dependent on whether they are at their first, second or third LBP visit with the provider. METHODS: A series of logistic regression models was developed to predict who will be a high-cost patient (defined as top 30% of the cost distribution) at each of the first three LBP visits. RESULTS: The c-statistics of the three logistic regression models corresponding to each of the first three visits were 0.683, 0.795 and 0.741, respectively. The overall sensitivity of the model was 42%, the specificity was 86% and the positive predictive value was 48%. Men were more likely to become expensive than women, while patients who had workers' compensation as their primary payer type had higher use of prescription opioid drugs or were smokers before the first LBP visit were also more likely to become expensive. CONCLUSION: The results suggest that it is feasible to develop a dynamic, primary care provider visit-based predictive model for LBP care based on longitudinal data obtained via electronic health records.