bluuebunny/crossref_metadata_2025_split huggingface dataset:
Living systematic reviews: A novel mechanism for improving efficiency and quality of evidence synthesis in oncology.
Authors: Riaz Irbaz Bin , Siddiqi Rabbia , Asghar Noureen , Cathcart-Rake Elizabeth Jane , Herasevich Vitaly , Montori Victor , Wang Zhen , Go Ronald S. , Rajkumar S. Vincent , Murad M. Hassan 
Published: October 2019

241 Background: In a rapidly moving field, such as cancer immunotherapy, where immune checkpoint inhibitors (ICIs) are used across 14 different tumor types, patients may receive suboptimal treatment or even be harmed if information on toxicity is not readily translated for use in clinical practice. Every single systematic review and meta-analysis which attempted to summarize toxicity of immune checkpoint inhibitors (ICIs) quickly became outdated. A living systematic review, which is defined as a systematic review that is continually updated to incorporate relevant new evidence as it becomes available, is necessary in this situation. Methods: The process of creating a living systematic review started with the creation of a comprehensive search designed by a librarian experienced in systematic reviews in collaboration with the study’s principle investigator. Search was constantly updated every 3 months and evidence is synthesized in a series of steps (microtasks) using a combination of human and augmented intelligence. A complete infrastructure is being developed and it includes automated cumulative meta-analysis and an online reporting platform which will constantly update information for clinicians and patients in a live manner. Results: We screened 6746 studies during Sep 2018-March 2019 and identified 6746 studies and we were able to successfully maintain up-to-date toxicity estimates for immune mediated adverse events over this period while maintaining the rigor of a conventional systematic review. Eventually, we will integrate the steps of LSR into one, user-friendly, semi-automated format which can independently provide accurate estimates and feed into and support a living guidelines platform through shared Application Programing Interface (APIs). Conclusions: LSRs are feasible, efficient, and when fully developed can reduce redundancy and waste in medical research, improve the quality of evidence, reduce human effort and support living and dynamic guidelines to facilitate truly informed shared decision making.
Retrospective evaluation of clinical evidence submitted with off-label requests at a community-based oncology infusion center.
Authors: Stankowicz Matthew , Heyer David M. 
Published: October 2019

254 Background: A systematic grading system was created to evaluate literature submitted with off-label requests to the Inova Schar Cancer Institute (ISCI) oncology P&T. Level of evidence (LOE) 1A, 1B, and 1C are granted immediate approval. LOE 2A, 2B, 2C require two members (MD and PharmD) to approve. LOE 3A, 3B, 3C require 3/4 (2MDs, 2PharmDs) members to approve. LOE no benefit (NB) is denied approval. Methods: Identified off-label requests for hematology/oncology patients from December 23, 2016 to January 2, 2019. Primary objective was retrospective evaluation of literature submitted using a systematic grading system through the oncology P&T. Secondary objective was to evaluate financial and clinical outcomes of off-label treatments, including compliance with Quality Oncology Practice Initiative measures. Future aim is to compare LOE and treatment outcomes pre- and post-implementation. Results: There were 73 off-label requests with 126 references submitted as supporting literature. The most frequently requested treatments were nivolumab (21.9%) and pembrolizumab (20.5%). The most common disease state was breast cancer. The LOE for the literature submitted was 1A (10.3%), 1B (23%), 1C (8.7%), 2A (9.5%), 2B (7.9%), 2C (4.8%), 3A (1.6%), 3B (7.1%), 3C (5.6%), and NB (21.4%). Sixty-six treatments were approved and 54 patients received at least one dose of off-label treatment. Thirty off-label requests were made based on molecular tumor board recommendations. Off-label treatment was discontinued for disease progression (29.6%), toxicity (25.9%), or death (14.8%). Three patients completed therapy and 11 received subsequent treatment after discontinuation of the off-label treatment. Average time from last treatment to death was 41.8 days (median 21, range 6-175). Eleven of 55 expired patients received hospice care prior to expiring. Most off-label treatments were approved by third party insurance (55.6%) while 37% were covered by manufacturer provided free drug and 7.4% were paid for by the ISCI Foundation. Conclusions: The standardized grading system for off-label treatments can be implemented to ensure safe and effective use of off-label treatments in oncology patients.
Improving quality of cancer care: Lessons learned from the journey towards QOPI Certification.
Authors: Abdelhafiez Nafisa , Alshami Mona Mohamed , Al-Kaiyat Mohammad Omar , Pasha Tabrez , Mutairi Nashmia , Gilmore Terry , Jahanzeb Mohammad , Jazieh Abdul Rahman 
Published: October 2019

263 Background: To assess the quality of cancer care provided at our institution, we participated in Quality Oncology Practice Initiative (QOPI) of American Society of Clinical Oncology (ASCO). However, our initial two rounds revealed lower score than required for QOPI Certification. Our goal was to implement interventions based on lessons learned from the initial rounds. Methods: Prior to the third round and using plan-do-study-act (PDSA) cycles, we identified and worked on three processes: clarifying and enhancing the function of the team, optimizing communication and improving documentation. We created QOPI multi-disciplinary team to include more members from different disciplines. We enhanced the knowledge of the team regarding our electronic health records system (EHRS) by conducting educational sessions and nominating super users who are very competent in EHRS and peer-to-peer support was created. Members were entering data in group sessions with the help of super users. We established double check system for records to be reviewed by two team members before submission. communication was assured among team members through weekly in person meetings and with ASCO QOPI team via virtual meetings to address queries. Documentation was improved by creating newer templates that conform with QOPI requirements including chemotherapy treatment plan, end of treatment summary and documentation of treatment consent. Results: The implementation of these interventions over three PDSA cycles made noticeable improvement in the previously unmet standards resulting in a score that exceeded the benchmark in fall of 2017 (score of 93%). This made our practice eligible for on-site certification visit by ASCO QOPI surveyors on May 2018 to assess practice compliance with QOPI standards. After addressing the unmet standards from the visit, our center became the first QOPI Certified Center in the Middle East and Asia in October/2018. Conclusions: Our journey towards QOPI Certification highlights the importance of fundamental principles in health care: coordinated multidisciplinary team, effective communication and proper documentation that captures essential and critical items reflecting better quality of care.
Increasing screening for latent tuberculosis in patients with hematologic malignancy: A quality improvement project.
Authors: Veloce Nicole , Chan Maverick , Seemangal Julie , Zwolinski Andrea , Al Saleh Abdullah S , Garcia Luciana , Habib Laura Anne , Jayakar Jai , Kimpton Miriam , Batt Jane , Hicks Lisa K. 
Published: October 2019

264 Background: Latent tuberculosis infection (LBTI) affects about one quarter of the world population. Treating LTBI is the most effective way of preventing active TB. Patients with hematologic malignancy (HM) and LTBI are at heightened risk (2-40 times baseline) of progressing to active TB. Universal TB screening is recommended for this population. In 2016, a patient receiving chemotherapy for myeloma at our center developed active TB. This triggered an intensive contact tracing investigation involving local public health units, infection control, and specialists. Over 800 patients had one or more exposures to the index case. No cases of secondary TB were identified; however, the contact tracing was resource intensive and stressful for patients and staff. This project aims to prevent future incidents by developing a standardized process for completing and documenting TBSTs, ensuring LTBIs are identified and managed prior to systemic therapy for HM. We aimed to complete and document TBSTs in ≥85% of patients starting systemic therapy for HM by Feb 2018. Methods: Baseline data was determined via retrospective review of 25 patients receiving chemotherapy for HM Jul-Nov 2017. An order set, work flow process map and standard operating procedure were created over a series of plan-do-study act cycles. TBST was added to a pre-existing “ready for treatment” checklist employed by pharmacists at our site. When pharmacists could find no record of a TBST result prior to treatment, the treating physician was notified. The go-live date was Aug 15, 2018. The impact of change was evaluated via chart review of patients who started chemotherapy for HM between Aug 27, 2018-Feb 27, 2019. Results: Prior to implementation of the standardized process, 3/25 patients (12%) had documentation of prior TBST. Following implementation of the standardized process, 14/30 patients (47%) had documentation of TBST prior to starting systemic therapy for HM. All 14 patients had negative TBSTs. Conclusions: Developing a standardized process for ordering, completing, and documenting TBST for patients starting systemic therapy for HM is feasible and effective. Additional revisions of the process are required to meet our target.
Improving quality through A.I.: Applying machine learning to predict unplanned hospitalizations after radiation.
Authors: Christopherson Kaitlin M. , Berlind Christopher G. , Ahern Christopher A. , Holmes Angela , Lindsay William David , Smith Benjamin D. , Das Prajnan , Smith Grace L. 
Published: October 2019

271 Background: Unplanned hospitalizations may diminish quality of care among cancer patients receiving radiotherapy (RT). In patients undergoing RT for gastrointestinal (GI) cancers, we hypothesized that a machine learning approach would enable prediction of unplanned hospitalizations within 30 days of RT. Methods: We analyzed 836 abdominal (gastric, pancreatic, biliary, hepatic) and 514 pelvic (rectal, anal) courses of RT for GI cancers treated at our institution (3/2016—1/2019). Over 700 clinical/treatment variables and unplanned hospitalizations during or within 30 days after RT were mined from institutional databases. Using machine learning, we developed random forest (RF), gradient boosted decision trees (XGB), and logistic models for unplanned hospitalizations. Models were trained on 670 abdominal and 423 pelvic cases. Five-fold cross-validation (CV) was used to select model type and hyperparameters, using area under the ROC curve (AUC) to measure performance. The best model was validated on the subsequent 166 abdominal and 91 pelvic cases. AUC>0.70 was deemed clinically valid. Results: Among 1,350 cases, incidence of 30-day unplanned hospitalization was 12.3% (13.3% abdominal cohort; 10.7% pelvic cohort). Model CV AUCs are shown in table. The best models were XGB and RF for the abdominal and pelvic cohorts, respectively. Their validation testing AUCs are shown in table. For all models tested, lab values (e.g. potassium, lipoproteins, hemoglobin) prior to RT were significant predictors of unplanned hospitalizations. In the abdominal cohort, pancreatic primary and total RT dose were important. For the pelvic cohort, body mass index was important. Median healthcare costs from RT start - 30 days post-RT were $69,108 in non-hospitalized patients and $119,844 in hospitalized patients. Conclusions: In GI cancer patients undergoing RT, a machine learning model identified patients at risk of 30-day unplanned hospitalization. Predictive analytics may be a key tool to help providers identify high-risk patients and optimize interventions, while improving quality and value of care. [Table: see text]
Launch of telemedicine in community oncology practice.
Authors: Blakely L. Johnetta , Dickson Natalie R. , Erter Jack William , Craig Carolyn , Bushart Holly , Stewart David , Darden Mary , Ripley Ann , Poole Stacey L , Frailey Susan , Patton Jeffrey 
Published: October 2019

275 Background: Tennessee Oncology (TO) is a community medical, radiation and gynecology oncology practice with 90 physicians and 40 advanced practice providers (APPs) in 33 locations in Tennessee. TO participates in the Oncology Care Model (OCM), a CMMI experimental payment model to improve access, quality of care, patient experience and lower costs. Methods: To promote provider-patient communication to improve outcomes and lower healthcare costs, TO launched a telemedicine pilot. The pilot was designed to understand Tennessee’s rules and regulations, reimbursement policies for Medicare, Medicaid and commercial payers and technology requirements. As survivorship was aligned with clinical workflow, supported by existing technology and required minimal staff training, the Survivorship Program for OCM was selected as proof of concept for telemedicine. Education surrounding Survivorship is required as part of the OCM model. A portion of the MEOS payment was considered as reimbursement for this initiative. The goal was to increase the delivery and review of survivorship documents to eligible breast cancer patients using the telemedicine platform from 0% to 80%. Results: 4000 potential patients were eligible for survivorship visits within TO. The selection was narrowed to include only OCM patients with breast cancer. TO identified 4 APPs who were given special training. TO’s front office staff coordinated scheduling of the technology, provider and space available. There were 99 patients eligible for a Telemedicine visit. 36 patients completed a Telemedicine visit with 23 patients declining. 19 patients did not respond to requests for these visits. 10 patients completed surveys and were 100% satisfied with their visits. The APPs felt Telemedicine visits were more productive than in person visits. Conclusions: Telemedicine is an effective tool for delivery of health care. There are challenges that make this technology difficult to implement such as reimbursement and limitations to the use of technology in elderly. In our pilot we found that the APPs and patients found this to be an effective way of communication and delivery of care. In the future telemedicine could answer some of the shortages in health care delivery and could also improve coordination of care.
Evaluation of scheduled video visits for cancer care at the Sidney Kimmel Cancer Center at Thomas Jefferson University.
Authors: Handley Nathan , Binder Adam , Sabonjian Megan , Lopez Ana Maria 
Published: October 2019

277 Background: Telehealth is currently undergoing exponential growth as a result of improved access through less costly, more portable, and better quality technology and through increased broadband internet proliferation. Telehealth has been shown to improve access and decrease costs while maintaining quality of care and high levels of patient satisfaction. One approach to telehealth is the video visit, in which audiovisual technology is used to connect patients and providers virtually. We implemented a pilot scheduled video visit program in a large academic cancer center (SKCC). Methods: Oncology clinical teams were trained in the use of JeffConnect, the video visit platform developed at our institution. Clinical teams were encouraged to utilize video visits in instances they deemed appropriate (i.e. post hospital follow-up, post-chemotherapy evaluations). All participating patients received a link to an evaluation following completion of the video visit. A subset of patients receiving more detailed survey questions. We reviewed evaluations occurring between 9/1/2016 and 3/31/2019. Results: Sixty-nine unique clinicians and 759 unique patients engaged in 865 completed telemedicine visits. For these, 118 surveys were completed and returned (14.03% of total visits), a similar response rate for other services. Average likelihood to recommend JeffConnect to a friend or colleague was 9.1 (scale 0-10). The majority of patients reported video visits saved them time (Figure 1) and money. For the subset of patients receiving the more detailed survey (n=30), average distance from SKCC at the time of the visit was 125 miles (range 10-963 miles, median 50) and average financial savings were $61.15 (range $8-$300, median $45). Conclusions: Scheduled cancer care video visits were feasible, resulted in high patient satisfaction, and saved patients time and money. Evaluating patient and clinician perceptions of the benefits of and barriers to telemedicine in greater detail is necessary for broader implementation. Patient-reported time saved by telemedicine. [Table: see text]
Survivorship: Leveraging the EMR to better care.
Authors: Walde Amy , Bootle Emma 
Published: October 2019

283 Background: UCHealth is a 10-hospital health system located on the Front Range of the Rocky Mountains. In 2018, UCHealth Cancer Care diagnosed or treated more than 8,400 analytic cases. In this presentation, we will discuss how we formulated a response to our organizational need for the creation of a Cancer Survivorship Program that not only met the ASCO QOPI & Commission on Cancer (CoC) Standards but also the needs of our patients. Methods: Our response plan included devising processes for the development and delivery of treatment summary care plans (TS/SCP). The development phase comprised the formation of UCHealth Cancer Care governance structure, creation of 22 disease specific templates containing the Institute of Medicine (IOM) / National Comprehensive Care Network (NCCN) recommended elements of survivorship care, Electronic Health Record (EHR) automation to increase efficiency and finally designing dashboard reports to identify eligible patients and capture compliance. The delivery phase consisted of securing dedicated resources, a communication plan and education for staff and providers regarding the benefits of a Cancer Survivorship program. Results: A jump in compliance from 7% in 2015 to 29% in 2018, demonstrates the success and impact of TS/SCPs at UCHealth. We will also discuss the 2019 work plan to achieve 50% compliance as required by the CoC. Conclusions: Leveraging the EHR is necessary when developing a Cancer Survivorship Program. UCHealth will provide examples of the treatment summary care plans, reference and reporting metrics to get other programs started on their journey.
Improving biobehavioral outcomes with progressive muscle relaxation in patients with advanced lung cancer.
Authors: Arrato Nicole A , Lo Stephen B. , Grogan Madison , Stec Nathan J. , Eastep Christine , Tenney Kathryn M. , Palmer Sara A. , Andersen Barbara L. , Presley Carolyn J 
Published: October 2019

292 Background: Advanced lung cancer involves severe and distressing symptoms, especially anxiety, breathlessness, insomnia, and pain. Progressive muscle relaxation (PMR) is an empirically supported treatment for reducing stress and the emotional, cognitive, and physiological concomitants of anxiety. Methods: A quality improvement effort involved enhancing standard of care (SOC) by offering patients with newly diagnosed lung cancer one brief (20-minute) PMR session during a clinic visit for treatment determination. A static group comparison design was used. The PMR group ( n= 40) received PMR with pre (initial visit) and post (3-4 week follow-up) assessments of anxiety (Generalized Anxiety Disorder-7 Scale), breathlessness (American Thoracic Society for the Division of Lung Diseases Questionnaire), insomnia (Medical Outcomes Study Sleep Scale), and pain (Brief Pain Inventory). The static group ( n= 32) was assessed on one occasion only, during the 3-4 week follow-up visit. Pre/post analyses for the PMR group used paired samples t-tests. Linear regressions compared post data between the two groups. An alpha of .10 determined significance. Results: Analyses showed pre- to post-PMR differences in pain ( p= .036, d= .309) and breathlessness ( p= .084, d= .663), with null effects for anxiety ( p= .149, d= .178) and insomnia ( p= .811, d= .067). Controlling for performance status, smoking history, and toxicities, regression showed that the PMR group reported significantly less pain ( p= .046, b= 2.48). Conclusions: PMR effectively reduced symptoms of breathlessness and pain among patients with advanced lung cancer. Interventions based on the education and training of PMR may provide an improvement to SOC lung cancer treatment.
Centralizing mixing of infusion drugs: A process to comply with USP 797/800, improve efficiency, and reduce costs in a multisite oncology infusion practice.
Authors: Perry Manuel , Dolson Theresa , Lindros Gianna , Welyczko Courtney , Hines Scott 
Published: October 2019

295 Background: In oncology practices with multiple infusion sites, compliance with USP 797/800 will be difficult to achieve due to increased building costs. Mixing oncology medications in a single-site referred to as “centralized mixing” is an attempt to comply with USP regulations as well to improve safety, quality and decrease costs. Methods: The centralized mixing process was initiated in June 2017 and required patients to have labs drawn 24-48 hours prior to infusion. If results were adequate, infusion nurses contacted the patient to review a symptom checklist to report findings which would preclude treatment. The next day, pharmacists began drug mixing at 6 am and distributed them by courier to five other practice sites by 10am. When patients arrived for treatment, they went directly to the infusion suite. A follow up visit was arranged mid-cycle to see their oncologist prior to the next scheduled infusion. If weekly infusions were required, then follow up visits were scheduled at the discretion of the oncologist. Results: Data from the fourth quarter of 2016 (prior to centralized mixing) was compared to the fourth quarter of 2018. Although physician total work RVUs increased from 12,198 to 16,254, labor costs decreased by $15,194. After implementation of centralized mixing, there were four fewer infusion nurse FTE’s while two registered pharmacists were hired. Labor costs per total work RVU decreased from $38.51 to $27.97 resulting in a quarterly savings of $42,790. Conclusions: Centralized mixing of oncology medication has reduced the cost per physician work RVU of administering chemotherapy by 27.3% and has lead to an annualized savings of $171,160. Increased infusion capacity and improved efficiency were due to streamlining the patient experience: there is no need to wait for drugs to be mixed, for lab results to return or to see their oncologist prior to treatment. The practice is now able to improve environmental safety with USP 797/800 at a single site, improve quality by limiting staff who mix chemotherapy to only two pharmacists, reduce training time for our infusion nurses by six weeks and to delay the need to build additional infusion suites.