Introduction: Intraoperative radiation therapy (IORT) is a minimally invasive radiation option for select patients with early stage breast cancer. This prospective, single institution, pilot study summarizes patient-reported quality of life (QoL) outcomes and clinician-reported toxicity following IORT following breast conservation therapy. Methods: Forty-nine patients were enrolled in a prospective study from 2013 until 2015 to assess QoL and toxicity following breast conservation therapy and IORT. Nine patients did not meet criteria for IORT alone on final pathology and required whole breast irradiation afterwards. These patients were evaluated separately. Validated QoL questionnaires were provided to patients at 1-week, 1-month, and subsequent 6-month intervals for 2 years. Radiation-related toxicity symptoms were evaluated by clinicians at the same time intervals. Likert scale responses were converted to continuous variables to depict patient-reported and clinician-reported outcomes. Results: Outcomes were analyzed as weighted averages of the Likert scale for each symptom. Responses for negative QoL symptoms ranged largely from 0 (none) to 2 (moderate). Responses for positive QoL symptoms ranged largely from 3 (quite a bit) to 4 (very much). Seventy-five percent of patients developed a toxicity; however, 99% of the toxicities were grades 1 and 2. All toxicities demonstrated a downward trend over time, with the exception of breast fibrosis and nodularity, which increased over time. There were no local recurrences upon 2-year follow up. Conclusion: Early stage breast cancer treated with IORT yields favorable QoL outcomes and minimal toxicity profiles with adequate short-term local control.

Background: Enhanced recovery protocols (ERPs) after metabolic and bariatric surgery (MBS) may help decrease length of stay (LOS) and postoperative nausea/vomiting but implementation is often fraught with challenges. The primary aim of this pilot study was to standardize a MBS ERP with a real-time data support dashboard and checklist and assess impact on global and individual element compliance. The secondary aim was to evaluate 30 day outcomes including LOS, hospital readmissions, and re-operations. Methods and procedures: An ERP, paper checklist, and virtual dashboard aligned on MBS patient care elements for pre-, intra-, and post-operative phases of care were developed and sequentially deployed. The dashboard includes surgical volumes, operative times, ERP compliance, and 30 day outcomes over a rolling 18 month period. Overall and individual element ERP compliance and outcomes were compared pre- and post-implementation via two-tailed Student’s t-tests. Results: Overall, 471 patients were identified (pre-implementation: 193; post-implementation: 278). Baseline monthly average compliance rates for all patient care elements were 1.7%, 3.7%, and 6.2% for pre-, intra-, and post-operative phases, respectively. Following ERP integration with dashboard and checklist, the intra-operative phase achieved the highest overall monthly average compliance at 31.3% (P < 0.01). Following the intervention, pre-operative acetaminophen administration had the highest monthly mean compliance at ≥ 99.1%. Overall TAP block use increased 3.2-fold from a baseline mean rate of 25.4–80.8% post-implementation (P < 0.01). A significant decrease in average intra-operative monthly morphine milligram equivalents use was noted with a 56% drop pre- vs. post-implementation. Average LOS decreased from 2.0 to 1.7 days post-implementation with no impact on post-operative outcomes. Conclusion: Implementation of a checklist and dashboard facilitated ERP integration and adoption of process measures with many improvements in compliance but no impact on 30 day outcomes. Further research is required to understand how clinical support tools can impact ERP adoption among MBS patients.

The volume and speed of data generation in biomedical literature, social media, and other resources during the COVID-19 pandemic is unprecedented. This mountain of data is growing daily across PubMed, Twitter, Google Scholar, and the World Health Organization's COVID-19 database [1], naming a few. The recently published COVID-19 Twitter dataset may offer insights into multiple topics from compliance with social distancing to assembling homemade masks and mental health tips [2]. Beyond social media, the massive COVID-19 Open Research Dataset (CORD-19) has been assembled from tech giants like Microsoft, the Allen Institute for Artificial Intelligence, and Georgetown University's Center for Security and Emerging Technology [3]. This dataset houses over 12,000 full text articles in “machine-readable form” that can be ingested programmatically into computer software programs and analyzed using machine learning applications like natural language processing (NLP). Furthermore, CovidSurg is a global registry for tracking outcomes in COVID-19 infected surgical patients with over 100 countries registered [4]. This registry represents a unique opportunity to evaluate variation in patient characteristics, peri‑operative management and surgical outcomes. Additionally, guidelines continue to emerge from large international surgical societies like Society of American Gastrointestinal and Endoscopic Surgeons (SAGES). SAGES has developed peri‑operative safety practices involving filtration, smoke evacuation, and personal protective equipment use [5]. It is paramount that prospective data collection efforts across these resources and multiple areas of clinical practice continues both institutionally and globally.