IntroductionThe purpose of this study was to compare operative times, surgical outcomes, resource utilization, and hospital charges before and after the implementation of a sentinel lymph node (SLN) mapping algorithm in endometrial cancer.MethodsAll patients with clinical stage I endometrial cancer were identified pre- (2012) and post- (2017) implementation of the SLN algorithm. Clinical data were summarized and compared between groups. Total hospital charges incurred on the day of surgery were extracted from the hospital financial system for each patient and all charges were adjusted to 2017 US dollars.ResultsA total of 203 patients were included: 71 patients in 2012 and 130 patients in 2017. There was no difference in median age, body mass index, or stage. In 2012, 35/71 patients (49.3%) underwent a lymphadenectomy. In 2017, SLN mapping was attempted in 120/130 patients (92.3%) and at least one SLN was identified in 110/120 (91.7%). Median estimated blood loss was similar between groups (100 mL vs 75 mL, p=0.081). There was a significant decrease in both median operative time (210 vs 171 min, p=0.007) and utilization of intraoperative frozen section (63.4% vs 14.6%, p<0.0001). No significant differences were noted in intraoperative (p=1.00) or 30 day postoperative complication rates (p=0.30). The median total hospital charges decreased by 2.73% in 2017 as compared with 2012 (p=0.96).DiscussionImplementation of an SLN mapping algorithm for high- and low-risk endometrial cancer resulted in a decrease in both operative time and intraoperative frozen section utilization with no change in surgical morbidity. While hospital charges did not significantly change, further studies are warranted to assess the true cost of SLN mapping.
The COVID-19 pandemic has had a dramatic impact on care delivery among health care institutions and providers in the United States. As a categorical cancer center, MD Anderson has prioritized care for our patients based on acuity of their disease. We continue to implement measures to protect patients and employees from acquiring the infection within our facilities, and to provide acute management of cancer patients with concomitant COVID-19 infections who are considered at high risk of death. The Division of Patient Experience, formerly established in October 2016, has played an integral role in the institution's pandemic response from its inception. The team actively supported programs and processes in anticipation of the pandemic's effect on our patients and employees. We will describe how the team continues to serve in the ever-dynamic environment as we approach the expected surge in
Background: With increasing implementation of enhanced recovery programs (ERPs) in clinical practice, standardised data collection and reporting have become critical in addressing the heterogeneity of metrics used for reporting outcomes. Opportunities exist to leverage electronic health record (EHR) systems to collect, analyse, and disseminate ERP data. Objectives: (i) To consolidate relevant ERP variables into a singular data universe; (ii) To create an accessible and intuitive query tool for rapid data retrieval. Method: We reviewed nine established individual team databases to identify common variables to create one standard ERP data dictionary. To address data automation, we used a third-party business intelligence tool to map identified variables within the EHR system, consolidating variables into a single ERP universe. To determine efficacy, we compared times for four experienced research coordinators to use manual, five-universe, and ERP Universe processes to retrieve ERP data for 10 randomly selected surgery patients. Results: The total times to process data variables for all 10 patients for the manual, five universe, and ERP Universe processes were 510, 111, and 76 min, respectively. Shifting from the five-universe or manual process to the ERP Universe resulted in decreases in time of 32% and 85%, respectively. Conclusion: The ERP Universe improves time spent collecting, analysing, and reporting ERP elements without increasing operational costs or interrupting workflow. Implications: Manual data abstraction places significant burden on resources. The creation of a singular instrument dedicated to ERP data abstraction greatly increases the efficiency in which clinicians and supporting staff can query adherence to an ERP protocol.
Background and Objective: With the inclusion of Enhanced Recovery Programs (ERPs) into routine clinical practice, scaling programs across an institution is important to drive sustainable change in a patient-centric care delivery paradigm. A review of ERP implementation within a large institution was performed to understand key components that hinder or facilitate success of scaling an ERP. Methods: From January 2018 to March 2018, a needs assessment was completed to review implementation of enhanced recovery across the institution. Implementation progress was categorized into one of 5 phases including Define, Implement, Measure, Analyze, and Optimize. Results: Only 25% of service line ERPs reached the optimization phase within 5 years. One hundred percent of respondents reported more strengths (n = 41) and opportunities (n = 41) than weaknesses or threats (n = 25 and 14, respectively). Commonly identified strengths included established enhanced recovery pathways, functional team databases, and effective provider education. Weaknesses identified were inconsistencies in data quality/collection and a lack of key personnel participation including buy-in and time availability. Respondents perceived the need for data standardization to be an opportunity, while personnel factors were viewed as key threats. Conclusion: Identification of strengths, weaknesses, opportunities, and threats could prove beneficial in helping scale an ERP across an institution. Successful optimization and expansion of ERPs require robust data management for continuous quality improvement efforts among clinicians, administrators, executives, and patients.
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