Do physicians within the same practice setting manage osteoporosis patients similarly? Implications for implementation research

University of Alabama at Birmingham Department of Epidemiology Birmingham AL USA
Osteoporosis International (Impact Factor: 4.17). 11/2009; 20(11):1921-1927. DOI: 10.1007/s00198-009-0900-7


Using data from long-term glucocorticoid users and long-term care residents, we evaluated osteoporosis prescribing patterns related to physician behavior and common practice settings. We found no significant clustering effect for common practice setting, suggesting that osteoporosis quality improvement (QI) efforts may be able to ignore this factor in designing QI interventions.
Patients’ receipt of prescription therapies are significantly influenced by their physician’s prescribing patterns. If physicians in the same practice setting influence one another’s prescribing, evidence implementation interventions must consider targeting the practice as well as individual physicians to achieve maximal success.
We examined receipt of osteoporosis treatment (OP Rx) from two prior evidence implementation studies: long-term glucocorticoid (GC) users and nursing home (NH) residents with prior fracture or osteoporosis. Common practice setting was defined as doctors practicing at the same address or in the same nursing home. Alternating logistic regression evaluated the relationship between OP Rx, common practice setting, and individual physician treatment patterns.
Among 6,281 GC users in 1,296 practices, the proportion receiving OP Rx in each practice was 6–100%. Among 779 NH residents in 66 nursing homes, the proportion in each NH receiving OP Rx was 0–100%. In both, there was no significant relationship between receipt of OP Rx and common practice setting after accounting for treatment pattern of individual physicians.
Physicians practicing together were not more alike in prescribing osteoporosis medications than those in different practices. Osteoporosis quality improvement may be able to ignore common practice settings and maximize statistical power by targeting individual physicians.

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Available from: Andrew O Westfall, Jul 28, 2014
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