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.
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.
Cancer patients experience numerous distressing symptoms and concerns across the course of their illness, which negatively influence their quality of life. Regardless of cancer type, unmanaged symptoms can lead to adverse downstream consequences. Patient Reported Outcome Measures (PROMs) can be used to inform patient care and lead to targeted symptom management but simply gathering this information does not improve outcomes for the patient. Patient generated information must be easy for the clinicians to access and interpret if it is to be used to inform care delivery in ambulatory oncology facilities. This pragmatic work responded to this need. One Canadian provincial ambulatory oncology jurisdiction implemented digital tracking of PROMs over time in the provincial Electronic Medical Record (EMR) to support full integration of PROMs into standard care workflows and processes. Due to an inability within the EMR for direct patient entry, a hybrid data-entry was designed where the patient completes a paper-based PROM in the waiting room, and after clinical review, a clinician documents this along with their clinical assessment in the EMR. Several digital dashboards were developed which report PROMs data at the micro (individual), meso (clinic) and macro (program) levels. Using PROMs routinely in these provincial practice settings has numerous benefits including enhanced patient-clinician communication, assisting with problem detection, management of symptoms, and improving outcomes for patients. There are over 60,000 unique patients represented in our PROMs database, and over 300,000 unique screening events captured. The PROMs data is now used at all levels of the provincial cancer jurisdiction to provide targeted person centred care (micro), to staff appropriately at a clinic or program level (meso), and for capacity planning for provincial programs (macro). A new provincial EMR is currently being implemented which has an associated patient portal. Based on the success of this work, integration of direct entry of PROMs by the patient prior to the appointment and an associated workflow for symptom management is underway in this jurisdiction.
This article examines the relationship between gender, class and unpaid care for children and elderly household members across twenty-five countries. Using the microdata files of the 2015–2017 Luxembourg Income Study, we demonstrate that household income quintile shapes the relationship between resident caregiving and a) women’s diminished share of household income and b) the associated “wage penalty” women experience in paid employment, examining dual-headed heterosexual households and grouping countries at varying levels of GDP per capita. Our analyses demonstrate that both eldercare and childcare have a negative impact on women’s economic outcomes, yet the effects of both types of unpaid care vary across class. Overall, childcare has a larger impact for women in lower income households, while eldercare has a larger impact for women in higher income households. However, the wage penalties experienced by wealthier women due to either type of potential care responsibilities are considerably less than those experienced by women in poorer households. Together, these data suggest that unpaid resident caregiving has effects that are both highly gendered and highly classed, leading to intersectional disadvantages for women performing unpaid care within poorer households across countries, and with effects that, in some cases, are further amplified within low-GDP countries.
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