Radiotherapy treatment scheduling: Implementing operations research into clinical practice

Robust optimization of a radiotherapy pretreatment preparation workflow

Objective. Increasing cancer incidence, staff shortage and high burnout rate among radiation oncologists, medical physicists and radiation technicians are putting many departments under strain. Operations research (OR) tools could optimize radiotherapy processes, however, clinical implementation of OR-tools in radiotherapy is scarce since most investigated optimization methods lack robustness against patient-to-patient variation in duration of tasks. By combining OR-tools, a method was developed that optimized deployment of radiotherapy resources by generating robust pretreatment preparation schedules that balance the expected average patient preparation time (Fmean) with the risk of working overtime (RoO). The method was evaluated for various settings of an one-stop shop (OSS) outpatient clinic for palliative radiotherapy.Approach. The OSS at our institute sees, scans and treats 3-5 patients within one day. The OSS pretreatment preparation workflow consists of a fixed sequence of tasks, which was manually optimized for radiation oncologist and CT availability. To find more optimal sequences, with shorterFmeanand lowerRoO, a genetic algorithm was developed which regards these sequences as DNA-strands. The genetic algorithm applied natural selection principles to produce new sequences. A decoder translated sequences to schedules to find the conflicting fitness parametersFmeanandRoO. For every generation, fitness of sequences was determined by the distance to the estimated Pareto front ofFmeanandRoO. Experiments were run in various OSS-settings.Main results. According to our approach, the expectedFmeanof the current clinical schedule could be reduced with 37%, without increasingRoO. Additional experiments provided insights in trade-offs betweenFmean,RoO, working shift length, number of patients treated on a single day and staff composition.Significance. Our approach demonstrated that OR-tools could optimize radiotherapy resources by robust pretreatment workflow scheduling. The results strongly support further exploration of scheduling optimization for treatment preparation also outside a one-stop shop or radiotherapy setting.

Treatment times for delivering adjuvant breast radiotherapy at a cancer centre in Ontario, Canada

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.

An operations research approach to automated patient scheduling for eye care using a multi-criteria decision support tool

Inefficient management of resources and waiting lists for high-risk ophthalmology patients can contribute to sight loss. The aim was to develop a decision support tool which determines an optimal patient schedule for ophthalmology patients. Our approach considers available booking slots as well as patient-specific factors. Using standard software (Microsoft Excel and OpenSolver), an operations research approach was used to formulate a mathematical model. Given a set of patients and clinic capacities, the model objective was to schedule patients efficiently depending on eyecare measure risk factors, referral-to-treatment times and targets, patient locations and slot availabilities over a pre-defined planning horizon. Our decision support tool can feedback whether or not a patient is scheduled. If a patient is scheduled, the tool determines the optimal date and location to book the patients' appointments, with a score provided to show the associated value of the decisions made. Our dataset from 519 patients showed optimal prioritization based on location, risk of serious vision loss/damage and the referral-to-treatment time. Given the constraints of available slots, managers can input hospital-specific parameters such as demand and capacity into our model. The model can be applied and implemented immediately, without the need for additional software, to generate an optimized patient schedule.