Predicting symptom complexity: Using autoregressive integrated moving average (ARIMA) models to create responsive clinic scheduling.

e13529 Background: Increasing cancer incidence, coupled with a trend in treating patients for longer periods of time, presents challenges in addressing all patients’ symptoms/concerns within the allotted time for ambulatory clinic appointments. Consequently, the ability to forecast and monitor the percentage of cancer patients with different symptom complexity levels is extremely valuable. Symptom complexity is a summary score that weighs the severity of all patient reported symptom scores at one time point. If a clinic could predict how many patients may need more time due to complex symptom management needs, clinic-scheduling templates could be adjusted to include a set number of longer appointments. Methods: Auto Regressive Integrated Moving Average (ARIMA) models were utilized to forecast the percentage of patients with a high symptom complexity level within one cancer clinic in Alberta, Canada. Goodness-of-fit measures such as Bayesian information criterion (BIC) and Ljung-Box test were used to determine optimal form for the ARIMA model. Following model selection, the autocorrelation function (ACF) was performed. These tests together verified that chosen AR, MA and differencing (I) were appropriate. Model performance on the historical data for model fit was summarized by Mean Absolute Error (MAE) and Root Squared Mean Error (RSME). Forecasting accuracy was assessed using mean absolute prediction error by comparing the forecasts with actual clinic data. Results: Of the multiple model structures tested, ARIMA (0, 0, 1) was selected, with the lowest BIC and non-significant Ljung-Box test. We obtained forecasts of the percentage of patients with high symptom complexity levels, with an MAE at 4.0%. To assess forecast accuracy, we calculated the absolute prediction error by comparing the forecasted percentages of patients with high symptom complexity levels to actual clinic visit data and the mean absolute prediction error was 5.9%. Conclusions: This forecasting model has important implications, allowing clinics to adjust scheduling templates to provide a select number of longer timeslots and therefore, be better prepared to meet the symptom management needs of cancer patients who are considered highly complex. This model could be applied to other clinical populations to allow for a tailored scheduling approach based on each clinic’s symptom complexity forecasting.

Discrete Event Simulation of a Specialty Outpatient Clinic to Improve Patient Flow

The objective of this study was to test the validity and efficacy of discrete-event simulation (DES) in modeling a specialty outpatient clinic, and to apply the model to predict how the clinic could improve their patient flow. Arena software was used to develop this DES model. Real-life model inputs included the time that patients spent in each clinic process, clinic room utilization rate, and physician room schedules. The DES was validated via a comparison between the model’s outputs and raw clinic data, and further validated by clinic leadership. Once validated, the DES was modified to represent different scenarios, such as changes in clinic scheduling and resource usage. Analysis of the models revealed that adding two volunteers to escort patients in the morning and afternoon would decrease the queue time to see a physician by 33.9% and 65.2% respectively. The model results also suggested that there is not enough congestion in the clinic to warrant changingthe clinic scheduling from fixed room scheduling to un-assigned room scheduling. The results of the this study support the usability of DES in the modeling and analysis ofspecialty outpatient clinics to provide decision support.