Designing and Validating a Comprehensive Patient-Reported Outcomes Measure for Ambulatory Cancer Settings: The Revised Edmonton Symptom Assessment System for Cancer.

PURPOSE: Patient-reported outcomes (PROs) information has been routinely collected in Cancer Care Alberta (CCA) for years using the revised Edmonton Symptom Assessment System (ESAS-r) and Canadian Problem Checklist (CPC). There was interest in combining these into a more comprehensive single measure tailored to ambulatory cancer settings. The purpose of this study was to validate an expanded and redesigned ESAS-r called the ESAS-r Cancer. METHODS: Stakeholder engagement, a review of the literature, and 2 years of CPC data collected in the cancer program informed the addition of six symptoms to the ESAS-r. To assess and validate the measure, 1,600 randomly sampled patients were mailed paper copies of the ESAS-r Cancer, ESAS-r, and a validated, comprehensive PRO measure called the Memorial System Assessment Scale-Short Form (MSAS-SF), which is often used with patients with cancer. Canonical Correlation Analysis and exploratory factor analyses were performed to assess concurrent and construct validity of the ESAS-r Cancer against ESAS-r, using MSAS-SF as the reference measure for comparison. Cronbach α was calculated to assess reliability. RESULTS: Four hundred and sixty-one patients (29% response rate) completed all three questionnaires. ESAS-r Cancer showed higher numerical correlation than ESAS-r and accounted for more information included on MSAS-SF, explaining slightly more variance than ESAS-r (75.2% v 73.5%). The three-dimensional factor structure of ESAS-r Cancer outperformed the two-dimensional factor structure of ESAS-r. The reliability of ESAS-r Cancer was verified and found to be slightly higher than ESAS-r (Cronbach α = .903 v .884). CONCLUSION: ESAS-r Cancer is now in use with patients throughout CCA. This valid and reliable PRO measure can be used by other cancer or specialized health care programs who wish to routinely assess common symptoms.

Testing a modified electronic version of the Edmonton symptom assessment system-revised for remote online completion with ambulatory cancer patients in Alberta, Canada.

Objective: The cancer program in Alberta, Canada routinely collects patient-reported outcomes using the Edmonton symptom assessment system-revised (ESAS-r). The program recently launched the province's new clinical information system which has expanded functionality, allowing patients to complete symptom questionnaires remotely online, instead of completing a paper form at the clinic. This study aimed to test a modified electronic version of the ESAS-r [(e)ESAS-r] with patients, to assess the feasibility of completion and questionnaire clarity. Methods: Staff, patients, and other stakeholders worked to create modified definitions for ESAS-r symptoms, to aid in patient understanding. Patient and family advisors were recruited to test the questionnaire. Participants completed an online mock-up of the (e)ESAS-r and answered questions about technical issues. One-to-one cognitive interviews were held to discuss each symptom definition in detail. Modifications were made based on the feedback and a second round of interviews was held to finalize the wording. Results: In total, 19 patients and 7 family advisors participated. All but one (96.2%) completed the questionnaire without assistance and had no technical issues. Participants requested certain wording modifications and that definitions be added for all symptoms for consistency. Very few participants reported any confusion with the final definitions. Conclusions: The (e)ESAS-r was tested for clarity and ease of completion and was determined to be suitable for remote online use with ambulatory cancer patients. The enhanced definitions on the new questionnaire were clear to patients and helped ensure they understood the meaning of each symptom they were asked to rate.

Virtual Cancer Care During the COVID-19 Pandemic in Alberta: Evidence From a Mixed Methods Evaluation and Key Learnings.

PURPOSE: This study reports on a mixed methods evaluation conducted within a provincial cancer program in Alberta, Canada. The purpose was to capture key learnings from a rapid virtual care implementation because of the COVID-19 pandemic and to understand the impact on patient and staff experiences. METHODS: Administrative data were collected for 21,362 patients who had at least one virtual or in-person visit to any provincial cancer center from April 1, 2020, to June 10, 2020. Patient surveys were conducted with 397 randomly selected patients who had received a virtual visit. Surveys were also conducted with 396 Cancer Care Alberta staff. RESULTS: 14,906 virtual visits took place in this period, and about 40% of weekly visits were virtual. Significant differences were observed in both patient-reported symptom questionnaire completion rates and referrals to supportive care services between patients seen in-person and virtually. Patients receiving active treatments reported significantly lower levels of satisfaction with virtual visits than those seen for follow-up, but overall 90% of patients indicated interest in receiving virtual care in the future. Staff thought virtual visits increased patients' access to care but less than one third (31.5%) felt confident meeting patients' emotional needs and having conversations about disease progression and/or end of life virtually. CONCLUSION: The COVID-19 pandemic has driven the rapid implementation of virtual visits for cancer care delivery in health care settings. The findings from this mixed methods evaluation provide a concrete set of considerations for organizations looking to develop a large-scale, enduring virtual care strategy.

Using Autoregressive Integrated Moving Average (ARIMA) Modelling to Forecast Symptom Complexity in an Ambulatory Oncology Clinic: Harnessing Predictive Analytics and Patient-Reported Outcomes.

An increasing incidence of cancer has led to high patient volumes and time challenges in ambulatory oncology clinics. By knowing how many patients are experiencing complex care needs in advance, clinic scheduling and staff allocation adjustments could be made to provide patients with longer or shorter timeslots to address symptom complexity. In this study, we used predictive analytics to forecast the percentage of patients with high symptom complexity in one clinic population in a given time period. Autoregressive integrated moving average (ARIMA) modelling was utilized with patient-reported outcome (PRO) data and patient demographic information collected over 24 weeks. Eight additional weeks of symptom complexity data were collected and compared to assess the accuracy of the forecasting model. The predicted symptom complexity levels were compared with observation data and a mean absolute predicting error of 5.9% was determined, indicating the model's satisfactory accuracy for forecasting symptom complexity levels among patients in this clinic population. By using a larger sample and additional predictors, this model could be applied to other clinics to allow for tailored scheduling and staff allocation based on symptom complexity forecasting and inform system level models of care to improve outcomes and provide higher quality patient care.