Optimizing Patient Positioning to Reduce Variation in the Measurement of Breast Cancer-Related Lymphedema.

Background: Prospective lymphedema screening is recommended for breast cancer patients. We observed interoperator variation in perometer-acquired arm volume measurements (P-AVMs) due to patient instability during measurements. We hypothesized that improved positioning during perometry would reduce P-AVM variability. Methods and Results: Each arm was measured three times by each operator using a perometer. With the original configuration, P-AVM was performed by 2 operators in 30 patients and four cohorts of 5 to 6 operators in 5 volunteers. Repeatability, reproducibility, and gage precision/tolerance (P/T) ratio were calculated. A customized handlebar was installed to optimize patient positioning. P-AVMs were performed in 20 patients with both configurations. Student's t-test was used to compare variation. With the new configuration, P-AVMs were performed by three operators in five volunteers and five operators in three volunteers. Repeatability, reproducibility, and gage P/T ratio were calculated. For the original configuration, gage P/T ratio was 19.9% for two operators and 35.9% for four cohorts of five to six operators. One operator using the new handlebar decreased P-AVM variability by 28% (p = 0.02). For the new configuration, gage P/T ratio was 6.5% for three operators and 18.7% for five operators. Conclusions: Optimizing patient setup improved P-AVM accuracy. P-AVM accuracy is critical as lack of accuracy results in either overdiagnosis or underdiagnosis of lymphedema, which in turn results in either over- or undertreatment of this dreaded condition. A higher number of operators were associated with greater P-AVM variability.

Prevalence, predictors and economic consequences of no-shows.

BACKGROUND: Patients not attending to clinic appointments (no-show) significantly affects delivery, cost of care and resource planning. We aimed to evaluate the prevalence, predictors and economic consequences of patient no-shows. METHOD: This is a retrospective cohort study using administrative databases for fiscal years 1997-2008. We searched administrative databases for no-show frequency and cost at a large medical center. In addition, we estimated no-show rates and costs in another 10 regional hospitals. We studied no-show rates in primary care and various subspecialty settings over a 12-year period, the monthly and seasonal trends of no-shows, the effects of implementing a reminder system and the economic effects of missed appointments. RESULTS: The mean no-show rate was 18.8% (2.4%) in 10 main clinics with highest occurring in subspecialist clinics. No-show rate in the women clinic was higher and the no-show rate in geriatric clinic was lower compared to general primary care clinic (PCP). The no-show rate remained at a high level despite its reduction by a centralized phone reminder (from 16.3% down to 15.8%). The average cost of no-show per patient was $196 in 2008. CONCLUSIONS: Our data indicates that no-show imposed a major burden on this health care system. Further, implementation of a reminder system only modestly reduced the no-show rate.