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Annals of Oncology ; 32:S1135, 2021.
Article in English | EMBASE | ID: covidwho-1432863


Background: The impact of active cancer on susceptibility to coronavirus disease 2019 (COVID-19) remains controversial. This study leverages the infrastructure across the University of California (UC) Cancer Consortium, pooling electronic health record (EHR) data to assess the relationship between active cancer diagnoses (n=151,392) and COVID-19 positivity. Methods: In this cohort study, patients with COVID-19 test results and active cancer diagnoses were identified from the UC Health System COVID Research Data Set (CORDS). This data set collects COVID-19 test results from the 5 academic medical centers in the UC Health System and their NCI-designated Comprehensive Cancer Centers. COVID-19 test results were identified by Logical Observation Identifiers Names and Codes (LOINC). Active cancer was defined as an EHR-based malignant diagnosis within 9 months of testing, irrespective of active therapy. Total daily positivity rates were aggregated, and overall rates were compared across patients with and without active cancer using the Pearson’s Chi-squared test. Results: We identified 1,032,588 COVID-19 tests from March 3, 2020 to April 15, 2021, with 151,392 tests (14.7%) associated with an active cancer diagnosis. Monthly trends in positivity rates throughout the pandemic were similar between patients with and without cancer (Table). Overall positivity was lower in patients with active cancer (2.0% versus 4.4%;p<0.001). This was consistent across individual UC sites. [Formula presented] Conclusions: COVID-19 positivity rates were not increased for individuals with active cancer diagnoses in the UC Cancer Consortium. A lower positivity rate amongst cancer patients may be due to demographic, behavioral, occupational or environmental factors, as well as greater asymptomatic testing of cancer patients at some UC sites. Interactions with local prevalence and patient and cancer characteristics will be presented. Legal entity responsible for the study: The authors. Funding: Has not received any funding. Disclosure: All authors have declared no conflicts of interest.

Journal of Clinical Oncology ; 38(29), 2020.
Article in English | EMBASE | ID: covidwho-1076199


Background: Accurate performance status (PS) documentation is essential as poor PS is a strong predictor of treatment-related toxicity. At our institution, a baseline chart review revealed missing PS documentation in 28% of Fellow-seen new patient visits (NPV);PS documentation as unstructured text comprised the remainder. The lack of structured PS documentation represents a missed opportunity for accurate data in registries, trial registration, and supportive care referrals. Methods: To improve standardized documentation of PS for NPV, we designed a Fellowled quality improvement (QI) initiative over the course of 2 PDSA cycles. Specifically, we developed and implemented a structured PS smart data element tool (SDET) into our electronic medical record (EMR). PDSA cycle 1 (7/2019-11/2019) included SDET implementation and publicity using flyers & emails. PDSA cycle 2 (12/2019-4/2020) incorporated individualized feedback to Fellows, biweekly email reminders, and outreach to attendings regarding our SDET. We calculated cumulative usage of our SDET for PS documentation during the 2019-2020 academic year among NPV seen by Fellows. Our aim was to assess and document PS in at least 50% of NPV seen in person. Results: During PDSA cycle 1, cumulative structured PS documentation increased from 8% to 31% (Table). Focus groups revealed that Fellows were not consistently incorporating our SDET into their note templates or were relying on attendingwritten templates. Over PDSA cycle 2, the cumulative structured PS documentation rate increased from 24% to 54%. Overall our cumulative documentation rate is 40%, in large part driven by cycle 1 because of a drop in NPVs and the transition to telehealth during the COVID-19 pandemic. Conclusions: Our Fellow-led QI intervention improved cumulative structured PS documentation from 8% to 40% using two rapid PDSA cycles. Our intervention highlights the importance of real-time data review and stakeholder feedback to identify ongoing challenges. Our third PDSA cycle will include expansion to all clinic providers (Fellows, attendings, and advancedpractice providers), as well as the incorporation of telehealth encounters and follow-up visits. We also hope to align our QI initiative with broader steps toward data interoperability via the ASCO-sponsored mCODE initiative.

Journal of Clinical Oncology ; 38(29), 2020.
Article in English | EMBASE | ID: covidwho-1076191


Background: The adoption of telemedicine in providing patient-centric care has been limited due to concerns related to upfront cost and the uncertain reimbursement models. Telehealth modalities, which encompass broader services, quickly became a central focus of how we delivered care in cancer centers across the nation during the COVID-19 (C19) pandemic. Our aim is to describe five University of California (UC) Cancer Centers' experience with telehealth during the pandemic. Methods: Between March and June 2020, UC Cancer Centers developed or increased the use of telehealth modalities to continue to provide care to our oncology patients during the pandemic. Digital platforms were used to screen for symptoms and exposures related to C19, as well as for symptoms of distress. In addition, providers performed remote visits via video and telephone visits. Each of our centers monitored visit volumes as well as patient satisfaction scores during the pandemic. Results: Our Cancer Centers, each with various levels of pre-pandemic (Jan-Feb) use of telehealth, saw an increase in the volume of patients who were seen via remote visits including video and telephone visits during the pandemic (Mar-Apr). UC Davis, UC Los Angeles and UC San Francisco had implemented telemedicine prior to the pandemic, but the rates of use were 1%, 0.4% and 7%, respectively. In contrast, UC Irvine and UC San Diego did not offer remote visits prior to the pandemic. Despite these differences, during the pandemic, telemedicine rates increased to 50-70% of visits in the cancer centers. In addition, patient satisfaction scores were comparable to in-clinic visits. The use of digital platforms allowed 80% of patients to be screened for risk of C19 prior to their in-clinic visits. Conclusions: While differing levels of implementation was in place for telehealth services in our cancer centers prior to the pandemic, each cancer center was able to continue to see patients via remote visits. In addition, telehealth technology automated activities that would have been performed manually pre-pandemic. The increased use of telemedicine visits with high patient satisfaction scores is an indication that some patients can continue to receive their care via telehealth beyond the pandemic.