bluuebunny/crossref_metadata_2025_split huggingface dataset:
Process modeling radiation oncology clinic workflow from therapeutic simulation to treatment: Identifying impending strain and possible treatment delays.
Authors: Chowdhry Varun , Simpson Natalie 
Published: September 2022

39 Background: The administration of safe, high-quality radiotherapy requires the systematic completion of a series of steps from CT simulation, physician contouring, dosimetric treatment planning, pre-treatment quality assurance, plan verification, and ultimately treatment delivery. Nevertheless, due consideration to the cumulative time required to complete each of these steps is often not given sufficient attention when determining patient start date. On one hand, the nature of cancer therapy relies on timely treatment delivery. On the other hand, overly ambitious treatment turnaround times can lead to staff burnout, and result in medical errors. We sought to better understand how changes in patient volume could impact turnaround time through a simulation-based study. Methods: We developed a process model workflow for a single physician, single linear-accelerator clinic that simulated arrival rates and processing times for patients undergoing radiation treatment using AnyLogic Simulation Modeling software (AnyLogic 8 University edition, v8.7.9). We varied the new patient arrival rate from 1 to 10 patients per week to understand the impact treatment turnaround times from simulation to treatment. We utilized processing time estimates for each of the required steps as was determined in a departmental focus group study. We assumed an eight hour workday, and that contouring, treatment planning, verification, and treatment delivery followed the following distributions, respectively (in hours): triangular (2, 4, 16); triangular (8, 16, 60); triangular (0.25, 0.5, 1). Results: Altering the number of patients simulated per week from 1-10 patients resulted in a corresponding increase average processing time from simulation to treatment from 4 to 7 days. The maximum processing time for patient from simulation to treatment ranged from 6-14 days. To compare individual distributions, we utilized the Kolmogorov-Smirnov (K-S) statistical test. We found that altering the arrival rate from 4 patients per week to 5 patients per week resulted in a statistically significant change in the distributions of processing times (p = 0.03) and resulted in a corresponding increase in the maximum patient processing time from approximately 6 days to 12 days, or from approximately 1 week to 2 weeks. Conclusions: The results of this simulation-based modeling study confirm the appropriateness of current staffing levels to ensure timely patient delivery while minimizing staff burnout. However, sustained increases in volume could require increased staffing to ensure timely treatment delivery while minimizing staff burnout. Simulation modeling can help guide staffing and workflow models to ensure timely treatment delivery.
Outcomes of a quality improvement project aimed at improving delivery of new anticancer drug education to nursing in an academic-community hybrid model.
Authors: Blouin Gayle C. , Derosier Crystal , Man Yun , Morris Amy , Roberts Daniel Aaron 
Published: September 2022

50 Background: Educating staff on new anticancer medications is imperative for patient safety. Infusion nurses at Dana Farber Cancer Institute (DFCI) satellite infusion centers were surveyed on their comfort level administering newer oncology drugs. Results revealed only 57% of nurses were “extremely comfortable or somewhat comfortable,” while 32% of nurses were “somewhat uncomfortable or very uncomfortable.” This project aimed to increase percentage of nurses at the satellites indicating they are “extremely comfortable” or “somewhat comfortable” from baseline of 57% to ≥75%. Methods: An interdisciplinary team including pharmacy, medical oncology, and nursing was formed to analyze workflows around new drug onboarding, staff education, and satellite communication. The team utilized Plan-Do-Study-Act (PDSA) cycle methodology. Tools included process maps and fishbone to identify areas of improvement. Intervention development tools included a Pareto chart and a priority matrix. Results were analyzed using statistical process control charts. Results: PDSA cycle 1 consisted of making the pharmacy driven education sheet, At-A-Glance (AAG), more accessible for new drugs. AAGs for drugs approved by the FDA in the previous 18 months were posted on the DFCI pharmacy intranet page. Nurses were educated on how to access AAG weekly. For PDSA cycle 2, the survey was modified to target individual nurses administering new drugs rather than all nurses. A “Nursing consideration” section was added to the AAG template and workflow developed; Navigation and review of AAG was added to nursing competency. Monthly nursing/pharmacy huddles were established to optimize communication. These interventions increased the percentage of nurses indicating they are “extremely comfortable” or “somewhat comfortable” to ≥75% which was sustainable through PDSA cycle 2 (see Table). Conclusions: Making an easy-to-use nursing education resource accessible improved nursing comfort level. There were challenges with nursing survey due to COVID-19 surge and staff shortages. Routine huddles are essential for sustainability.[Table: see text]
Improving the uptake of prehabilitation interventions for older adults prior to cancer treatment using telehealth in the Geriatric Oncology One-Stop Clinic.
Authors: Ng Yean Shin , Said Noorhanah Mohd , Chen Matthew Zhixuan , Eng Wan Nghee , Ling Natalie Mun Wai , Low Jia Li , Yao Yao , Loy Yijun , Chun Meiling , Wong Wei Yee , Ong Jie Xin , Luah Vivian , Pang Shien Ling Angela , Ho Francis 
Published: September 2022

55 Background: Prehabilitation in older patients aged ≥ 65 years undergoing cancer surgery and systemic therapy is crucial in reducing treatment complications and improving physical health, psychological well-being, quality of life (QOL) and cancer-related outcomes. However, a majority of older patients (80%) attending our Geriatric Oncology (GO) One-Stop clinic do not receive same day Comprehensive Geriatric Assessment (CGA)-directed Allied Health Interventions (AHI) for prehabilitation during their First Visit (FV). Objectives: The objective of this study is to increase the uptake of same day CGA-directed AHI for prehabilitation from 20% to 60% using telehealth. Methods: Older patients planned for an FV at the GO One-Stop clinic in an academic tertiary center, the National University Cancer Institute, Singapore (NCIS) were recruited. Our workflow was revised so that all FV patients underwent CGA via telehealth prior to the physical visit. Based on the preliminary CGA findings, a multidisciplinary geriatric oncology board planned for prehabilitation interventions which would be performed on the same day as the physical visit. Interim follow-up via telehealth by GO nurses helped to monitor patients for treatment-related toxicities and development of new geriatric syndromes. QOL was assessed during the FV and 3 months later. Results: Two hundred seventy-five patients were recruited from July 2020 to January 2022. 60% (n = 165) received prehabilitation interventions in the One-Stop clinic. The average time spent per visit was shortened from 4 hours to 2.5hours, but completed interventions on same day rose from 1 to 3. The proportion of patients who responded that they benefited from the program on the patient satisfactory survey remained high at 96%. 84.8% were satisfied with the hybrid telehealth model and 80.8% of them reported a maintained or improved QOL after being enrolled into the program. Conclusions: Adoption of telehealth for CGA is a feasible and effective method in improving prehabilitation interventions uptake for GO patients. GO hybrid model of care is a sustainable practice with no additional cost incurred.
Fostering a high-functioning team in cancer care using the 4R Oncology model: Assessment in a large health system.
Authors: Liu Raymond , Weldon Christine B. , Linehan Elizabeth Shurell , Gordon Nancy P. , Abbe Thea , Hennings Marti , James Henie , Katzel Jed Abraham , Ng Chun Fai , Tomita Megumi , Velotta Jeffrey B. , Ossowski Stephanie , Sakoda Lori C. , Sprague Sharon , Dowling Anna , Beringer Kimberly , Ravelo Arliene , Yu Elaine , Trosman Julia R. 
Published: September 2022

57 Background: Delivering cancer care by high-functioning multidisciplinary teams promises to address care fragmentation and complexity, which threaten care quality, impact patient outcomes, and strain the oncology workforce. We assessed whether the 4R Oncology model (Trosman et al 2021) for team-based interdependent care delivery and patient self-management impacted team functioning at Kaiser Permanente Northern California (KPNC), a large community-based health system. Methods: We deployed the 4R intervention in breast and lung cancers at 4 KPNC centers. The intervention encompassed team development and care delivery optimizations enabling teamwork. 4R Care Sequences – novel patient-facing care plans outlining timing and sequence of care – informed team formation, activities and functioning. We assessed intervention along 4 characteristics of high-functioning teams (Vogel JOP 2016). Results: 4R facilitated development of a high-functioning team along all 4 characteristics. (1) We formed an internally and externally recognized multidisciplinary team of 24 specialties who assumed team responsibilities for relevant care. (2) All participants committed to a shared goal of delivering interdependent care at the optimal time and sequence based on Care Sequences. (3) The team conducted 40 optimizations to enable effective interdependent care: 22 in breast, 7 in lung, and 11 in both cancers (Table). The optimizations impacted 6 care domains at 4 points in care continuum. Half of optimizations entailed low effort; 70% resulted in improved process efficiency. (4) An ongoing teamwork adaptation process was established. Conclusions: 4R is an effective and feasible approach to fostering high-functioning teams, which may contribute to improvement of multidisciplinary care delivery and support viability of the oncology workforce. Our intervention and taxonomy of results inform other institutions motivated to strengthen teamwork and improve delivery of complex interdependent care.[Table: see text]
Expanding the role of advanced practice providers (APPs) in cancer clinical trials.
Authors: Williams Erin Fenske , Macchiaroli Alyssa , Bazzell Angela F. , Brucker Edie , Gerber David E 
Published: September 2022

60 Background: Advanced Practice Providers (APPs) represent a growing and increasingly important component of the oncology workforce. However, APP roles in clinical research are not clearly understood, and certain policies may limit participation. For instance, National Cancer Institute (NCI) Cancer Therapy Evaluation Program (CTEP) historically prohibited APPs from signing orders on CTEP-sponsored studies, even at centers where APPs have antineoplastic privileges allowing them to order cancer treatments as standard therapy or on non-CTEP studies. At the UT Southwestern Harold C. Simmons Comprehensive Cancer Center (SCCC), more than 100 active CTEP-sponsored trials account for approximately 30% of therapeutic enrollment. Given these considerations, we approached CTEP regarding potential policy change. Methods: In early 2020, we raised the issue of disparities in allowed APP delegation between (1) CTEP trials and (2) standard practice and non-CTEP trials to NCI/CTEP leadership. Later that year, CTEP organized a discussion on the topic with SCCC representatives and other stakeholders. Subsequently, NCI deployed a survey to National Clinical Trials Network (NCTN) and Experimental Therapeutics Clinical Trials Network (ETCTN) centers asking about the role of APPs at their respective institutions. Results: Following the survey, CTEP revised its policy, allowing appropriately credentialed APPs to sign treatment orders on CTEP-sponsored trials, effective September 1, 2021. After completing required regulatory steps to update trial protocols at UTSW SCCC, approved APPs began signing cancer clinical trial treatment orders in January 2022. Since that time, the proportion of trial treatment orders signed by APPs at SCCC has increased from 1% in January 2022 to 20% in May 2022. Conclusions: The role of APPs in cancer care can be further enhanced by alleviating barriers and allowing providers to perform at the full scope of practice, thereby further integrating care teams critical to compliant and safe clinical trial participation.
Decreases in emergency department and hospitalization visits over 5 years of the Oncology Care Model.
Authors: Jensen Terry Lynn , Mikan Sabrina Q. , Books Holly , Escudier Susan , Pearson Lisa 
Published: September 2022

64 Background: In the last five years of the Oncology Care Model (OCM) our community oncology practice developed standardized processes throughout the practice to impact hospital and emergency department (ED) utilization. OCM feedback reports were evaluated and compared to practice baseline data to assess the effectiveness of these interventions in improving patient care. Within the practice continuous tracking allowed for timely reviews and process improvement. Methods: A multimodal triage approach was created, including development of individual patient treatment plans and weekly clinical team huddles where new treatment patients or high-risk patients were discussed. The “Call Us First” campaign was initiated through wallet cards. These cards were provided to patients outlining regimen details and indications for patients seeking urgent or emergent medical care. An improved triage program with Navigating Cancer (NC) reduced call back times to patients regarding patient symptoms and care needs, increased faster symptom management and decreased ED visits. Additionally, nursing check in calls were made to patients after initial chemotherapy treatments and hospital admissions. These statewide programs were rolled out through a systematic process over the first year of the OCM. Results: Admissions for the practice’s OCM patients were 22% in 2015 and decreased to 17% in 2021. For the practice’s OCM population in 2015, the baseline ED visits were 16% and decreased to 14% in late Fall 2021. Home health utilization increased from 17.9% at baseline to 22.3% after five years. Long term care days decreased from 10.7 days to 4.5 days. Skilled nursing facility days decreased from 67.6 days in 2015 to 36.80 days in 2021. By comparison, overall national OCM practices had similar declines in ED visits and hospital admissions. Conclusions: Through OCM participation there were systematic changes reducing ED visits and hospital admissions over five years. We believe that early intervention projects enable patients to avoid ED/hospital visits. Home health care visits may have contributed to less admissions. Patient outcomes were favorably impacted by the practice wide processes guided by the feedback reports. Future iterations of value-based care are undecided, and practices will build on these enhancements.
Impact of biopsychosocial screening program on hospital admissions: Observational study from a Brazilian cancer center.
Authors: Bergerot Cristiane Decat , Bergerot Paulo Gustavo , Molina Lorena Nascimento Manrique , Freitas Alici Natalia de Sousa , Nascimento Karla Lúcia Lucia , Philip Errol James , Lee David , Sacchi Luana , Nazario Jose , Vieira Natalia Barros Salgado , Matos Neto Joao Nunes , Buso Marco Murilo , Soto Pérez de Celis Enrique , Florez Narjust 
Published: September 2022

72 Background: The integration of biopsychosocial screening programs (BSP) has been promoted as a way by which to identify unmet needs and to provide tailored treatments to patients. Studies have reported mixed outcomes regarding the impact of such services on hospital admissions during cancer treatment. This study sought to evaluate the effect of a BSP on hospital admissions and length of stay among a large, heterogenous sample of patients diagnosed with cancer (March 2020 to December 2021). Methods: We enrolled consecutive patients diagnosed with cancer receiving treatment at a single institution located in the capital of Brazil. We assessed patients’ characteristics via chart review (e.g., age, sex, histology, hospital admission, length of hospitalization). In addition, as per the BSP protocol, patients were assessed via standardized self-report questionnaires (Distress Thermometer, FACT-G, Patient-Generated Subjective Global Assessment), and appropriate interventions are provided. This program was offered at no cost to all patients and engagement was voluntary. We compared the number of hospital admissions and length of stay between groups (patients who participated in the BSP vs. those who did not). Mixed linear models adjusted for selected characteristics (age, type of cancer and disease stage) were assessed. Results: A total of 1014 patients were included in this analysis. From the total sample, 84% participated in the BSP and 20% were hospitalized for an average of 9 days (ranging from 1-80 days). Mostly patients were female (63%), median age was 63 years old. Breast cancer (26%), hematological (18%), and gastrointestinal cancer (14%) were the most common types of cancer, and the majority had advanced disease (stage III-IV; 67%). Compared to those who engaged in the BSP, patient’s characteristics were well balanced, however, the proportion of patients hospitalized during their cancer treatment was higher among patients who did not participate in the BSP (27% vs 8%: P = 0.001), as was the length of hospitalization spent more days in the hospital than patients who has participated in the BSP (M = 9.5 days vs M = 4.2 days; P = 0.001). Conclusions: Our findings suggest the benefit of a BSP in reducing hospitalizations and length of stay among patients with cancer. An integrated model of care may assist in targeting patient’s unmet needs and may positively impact clinical and hospital-based outcomes. This is the first study to evaluate the effects of a BSP among a Brazilian population using real world data.
Characterizing “no treatment” decisions in patients with advanced-stage cancers.
Authors: Akpoviroro Ogheneyoma , Akpoviroro Oghenetejiro Princess , Yossef Kristena , Castagne Myriam , Rodrigues Elga , Uwandu Queeneth , Bolden Brian , Mirza Wasique 
Published: September 2022

76 Background: Cancer (CA) is one of the leading causes of death worldwide. In 2020 CA was the second leading cause of mortality in the US. Novel treatments for various malignancies are discovered each passing year. Despite these advancements, some patients refuse to receive treatment. Our study focused on characterizing the group of patients with advanced malignancies who refused therapy. Methods: We performed a retrospective chart review of patients diagnosed with advanced malignancies, aged 18-75 years, between 01/01/ 2010 and 12/31/2015, who refused any therapy. We used Geisinger Health System’s data. Results: Our search provided 644 patients who met our inclusion criteria. The mean age of patients was 63.2 years (standard deviation (σ) 8.3). Female patients made up 40.4% (260/644), while 59.6% (384/644) were male. Most patients were Caucasian (96.9%, 624/644), 1.9% (12/644) were Black; 0.6% (4/644) were Asian, and 0.6% (4/644) were of unknown racial background. Marital status was evenly distributed with 50.0% (322/644) being married and 50.0% (322/644) being non-married. Most of the study population (52.3%, 337/644) had government-funded insurance at the time of CA diagnosis; 46.6% (300/644) had private insurance, and 1.1% (7/644) had no insurance. Stage 3 disease, according to American Joint Committee on Cancer staging was noted in 21.1% (136/644), while 78.9% (508/644) had stage 4 disease. The mean BMI was 28.2 (σ 8.3). Most patients were either overweight or obese (60.6%, 350/577; 67 patients did not have data on BMI available). A history of cigarette smoking was present in 73.0% (438/600). Adenocarcinomas (323/639, 50.5%) were the most prominent histological subtype. The most common site of malignancy was the respiratory system (220/644, 34.2%). Only 9.6% (62/644) of patients had a history of previous CA, while a family history of CA was present in 29.5% (190/644). Most patients (62.1%, 400/644) had a Charleston Comorbidity Index (CCI) > 5; the mean CCI was 6.4 (σ 3.7). In 17.1% (110/644) a palliative medicine referral was made. Substance use disorders were present in 23.8% (153/644); 14.4% (93/644) had a history of depression, and anxiety 10.1% (65/644). Conclusions: Male patients made up a larger proportion of the group of advanced-stage patients who declined therapy. The racial distribution in this group of patients appeared to reflect the local population. Most patients who refused therapy had stage 4 disease, and they appeared to have severe underlying comorbidities, with an average CCI >5. Despite treatment refusal, a surprisingly low percentage of patients were referred to palliative medicine.[Table: see text][Table: see text]
Use of non-curative oncological care in osteosarcoma.
Authors: McMahon Kevin , Eaton Vincent , Cichon Gregory , Griffin Julia , Dahl Mary , Silberstein Peter T. , McKillip Kate 
Published: September 2022

78 Background: Osteosarcoma is the most common form of bone cancer, but the utilization of palliative care (PC) in patients with this cancer has not previously been investigated in the NCDB. Methods: This study retrospectively evaluated the use of palliative care in osteosarcoma patients diagnosed and recorded in the National Cancer Database (NCDB) between 2004 and 2017. Patients were identified by the International Classification of Diseases for Oncology, Third Edition (ICD-O-3) coding, and patients with other malignancies or missing follow-up data were excluded. Cross tabulations with Chi-square analysis were performed to evaluate frequencies of different patient and tumor characteristics. Multivariable logistic binary regression was performed to evaluate relationships between patient and tumor characteristics and the use of palliative care. Results: A total of 7498 patients were analyzed with 2.8% of all patients diagnosed having any form of palliative care utilization. Of this group, 53.37% received PC within the first 12 months after diagnosis. Those receiving PC were most likely to be treated with non-curative surgery, radiation, chemotherapy, or any combination of these modalities (56.7%). No significant increase in PC utilization was noted over the 13-year duration of this study. Palliative care usage was increased in patients with greater tumor diameter, tumors in the bones of the trunk & skull, or stage IV tumors. Palliative care usage was decreased in patients living 25-49 miles of their treatment facility, those living in pacific states, those with chondroblastic osteosarcoma, or those with private insurance. Conclusions: Palliative care use in patients with osteosarcoma increases with tumor stage, tumor size, or more proximal tumors, but overall utilization remains markedly low. Future studies should further define these patterns of care and help expand the utilization of PC.