RESUMO
BACKGROUND: Time is a valuable commodity that impacts hospital flow, patient experience and economic resources. This study aims to identify factors that affect daily treatment time over a course of radiation therapy (RT) in patients who underwent adjuvant breast RT. METHODS: In all adjuvant breast/chestwall RT patients treated from October 2017 to May 2018, daily set-up, beam delivery time, and overall treatment times were collected. A multivariable linear regression analysis was conducted to identify significant predictive factors related to treatment time. A general linear regression model was used to determine whether there was a learning curve effect throughout the course of treatment that decreased treatment time as patient and staff familiarity with the treatment procedure increased. RESULTS: A total of 567 patients were included with a median age of 61 years. The average overall treatment time for 2-field and 4-field RT was 8.3 (SD 2.4) and 13.1 (SD 5.6) minutes, respectively. Factors that significantly increased overall treatment times in patients prescribed 2-field RT were: bilateral techniques, breath-hold (BH) techniques, prone techniques (PR), reverse decubitus techniques (RD), wide tangents techniques, the use of bolus and number of segments delivered. (p < 0.05). Factors that significantly increased overall treatment times in patients who received 3-field and 4-field RT were: wide tangents volumes, a higher number of monitor units (MUs), bilateral techniques and BH techniques (p < 0.05). Older patients (≥60) who underwent 3-field and 4-field RT demonstrated a statistically significant increase in set-up time (p < 0.0001). Overall treatment time decreased from 10.0 to 9.3 min over the course of treatment, suggesting a minor learning curve (p = 0.009). CONCLUSION: The use of bilateral RT, BH, PR, RD, wide tangents, bolus, increasing treatment volumes, and increasing plan complexity were associated with increased treatment times. Future research should quantify the impact of other factors (BMI, mobility, patient care assessments, and imaging protocols) and utility of technological tools (time-predicting models, machine learning tools, and operations research models) on treatment time to optimize RT scheduling and improve resource management.