bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development and outcomes of a virtual triage pathway for cancer diagnosis in vague symptom patients.
Authors: Tremble Kathryn , Monroy-Iglesias Maria J. , Martin Sabine , Jones Geraint , Moss Charlotte , Russell Beth , Steward Lindsay , Murtagh Colette , Cargaleiro Carlos , Roberts Graham , Van Hemelrijck Mieke , De Michele Luigi , Dolly Saoirse 
Published: September 2021

326 Background: At the Rapid Access Diagnostic Unit at Guy’s Hospital London, we review patients with vague symptoms that are concerning for malignancy. As part of our response to the COVID-19 pandemic, we developed a virtual triage pathway with the aim to reduce face-to-face appointments and prioritise resources towards patients with an underlying cancer diagnosis. Methods: Patients were triaged by clinicians based on a telephone consultation with the patient and history and blood tests provided in the referral. Those triaged as high risk were either directly booked for investigation (“straight-to-test”) or booked for a face-to-face consultation for further history and examination. Low risk patients were either put on a watch-and-wait pathway with a telephone follow-up in 3-4 weeks or discharged back to the GP with a robust plan on symptom management. The patient outcomes were tracked and compared to the outcomes from the face-to-face assessment service used prior to the COVID-19 pandemic (Dec 2016-Feb 2020). Patients triaged as low risk and discharged were tracked to monitor for any subsequent cancer diagnoses. Results: There were 804 referrals triaged between March 2020-January 2021. 75% were triaged to a face-to-face assessment and 18% triaged straight-to-test. 4% were placed on the watch-and-wait pathway and 3% were returned to the GP with advice. In those triaged as high risk, 8.2% were diagnosed with cancer, 54% were diagnosed with a serious-benign condition and 38% with a non-serious or no condition. In the patients triaged as low risk and placed on the watch-and-wait pathway, 14% were brought in for a face-to-face assessment based on their follow-up telephone assessment. None of the patients on the watch-and-wait pathway were found to have a cancer diagnosis, 11% were diagnosed with a serious-benign condition, and 89% were diagnosed with a non-serious or no condition. There was an overall cancer diagnosis rate of 7.6% compared with a pre-COVID-19 diagnosis rate of 6.6%. Conclusions: The virtual triage pathway effectively risk-assessed patients, with those triaged as high risk having an 8.2% cancer diagnosis rate compared to a 0% cancer diagnosis rate in those triaged as low risk. Furthermore, the virtual triage service had a higher cancer diagnosis rate compared to the pre-COVID-19 face-to-face assessment service. Therefore the virtual triage service provides an efficient pathway for cancer diagnosis in patients presenting with vague symptoms, reducing the number of face-to-face appointments and supporting management of low risk patients in the community.
Using clinical pathways to prospectively identify poor prognosis cancer patients: A preliminary analysis.
Authors: Sanyal Abhijit , Hamilton Joanna M. , Martin Ariel , Selesnick Victoria H. , Rice Matthew , Scalzo Michael , Siegel Jocelyn H. , Tulsky James A. , Jacobson Joseph O. , Jackman David Michael 
Published: September 2021

329 Background: Cancer patients in the last year of life may have different clinical needs and evolving goals of care. Helping clinicians consistently identify such patients in a systematic and prospective fashion, at a discrete moment in the care trajectory, is an important step towards optimizing the care of these patients. Methods: Physician medical directors for each clinical pathway at our institution were tasked with identifying nodes within their respective treatment pathways associated with poor prognosis, specifically a median overall survival of < 12 months. This information was embedded into the underlying data model of the pathways platform, allowing us to determine how often clinicians navigated through poor prognosis nodes. Results: Pathways medical directors identified 44 nodes associated with poor prognosis. Some of these nodes encompassed a broader set of patients (metastatic pancreatic cancer, any line), whereas others were more specific (metastatic non-small cell lung cancer, squamous cell histology, second line or beyond). For the period 3/11/20 – 5/31/21, clinicians navigated in the platform for 11,057 unique patients. Of these, 1,234 (11.1%) unique patients were associated with poor prognosis nodes. Such navigations were much more common in patients with solid tumors (table). Conclusions: A well-maintained clinical pathways program can be a mechanism for defining clinical settings associated with poor prognosis and for routinely identifying patients in these settings. By embedding this into the pathways data model, we can provide physicians with important reminders and resources related to goals of care conversations, supportive care resources, and appropriate treatment options and clinical trials.[Table: see text]
Implementing clinical decision support for oncology advanced care planning: A systems engineering framework to optimize the usability and utility of a machine learning predictive model in clinical practice.
Authors: Ganta Teja , Lehrman Stephanie , Pappalardo Rachel , Crow Madalene , Will Meagan , Liu Mark , Freeman Robbie , Kia Arash , Parchure Prathamesh , Keyzner Alla , Mazumdar Madhu , Bhardwaj Aarti Sonia , Smith Cardinale B. 
Published: September 2021

330 Background: Machine learning models are well-positioned to transform cancer care delivery by providing oncologists with more accurate or accessible information to augment clinical decisions. Many machine learning projects, however, focus on model accuracy without considering the impact of using the model in real-world settings and rarely carry forward to clinical implementation. We present a human-centered systems engineering approach to address clinical problems with workflow interventions utilizing machine learning algorithms. Methods: We aimed to develop a mortality predictive tool, using a Random Forest algorithm, to identify oncology patients at high risk of death within 30 days to move advance care planning (ACP) discussions earlier in the illness trajectory. First, a project sponsor defined the clinical need and requirements of an intervention. The data scientists developed the predictive algorithm using data available in the electronic health record (EHR). A multidisciplinary workgroup was assembled including oncology physicians, advanced practice providers, nurses, social workers, chaplain, clinical informaticists, and data scientists. Meeting bi-monthly, the group utilized human-centered design (HCD) methods to understand clinical workflows and identify points of intervention. The workgroup completed a workflow redesign workshop, a 90-minute facilitated group discussion, to integrate the model in a future state workflow. An EHR (Epic) analyst built the user interface to support the intervention per the group’s requirements. The workflow was piloted in thoracic oncology and bone marrow transplant with plans to scale to other cancer clinics. Results: Our predictive model performance on test data was acceptable (sensitivity 75%, specificity 75%, F-1 score 0.71, AUC 0.82). The workgroup identified a “quality of life coordinator” who: reviews an EHR report of patients scheduled in the upcoming 7 days who have a high risk of 30-day mortality; works with the oncology team to determine ACP clinical appropriateness; documents the need for ACP; identifies potential referrals to supportive oncology, social work, or chaplain; and coordinates the oncology appointment. The oncologist receives a reminder on the day of the patient’s scheduled visit. Conclusions: This workgroup is a viable approach that can be replicated at institutions to address clinical needs and realize the full potential of machine learning models in healthcare. The next steps for this project are to address end-user feedback from the pilot, expand the intervention to other cancer disease groups, and track clinical metrics.
Predictive modeling to create a proactive approach to patient blood management in the oncology population.
Authors: Rivas Sandra , Cox Hecca , Mescher Benjamin , Villamin Colleen , Bates Tonita , Martinez Fernando , Correa Medina Mayrin , Klein Kimberly 
Published: September 2021

332 Background: Patient safety concerns that arose during COVID-19, related to blood shortages at a large oncological transfusion center, foregrounded the need for predictive modeling tools to optimize blood product inventory control. A maximum surgical blood ordering schedule (MSBOS), is a tool used to assist clinicians in predicting intraoperative blood usage based on retrospective historical data within an institution. Although MSBOS proves to be valuable, it is rudimentary in nature. Not only is data collection cumbersome but the data generated may not reflect current surgical practices and inter-patient variability may skew procedural averages. Predictive blood modeling is contingent generation of a digital health dashboard (DHB). DHB are electronically embedded in the electronic health record (EHR) to collect perioperative data. Coupling the generated informatics (patient demographics, diagnosis, laboratory results, procedural type, medications/supplements, surgeon) with machine learning allows for creation of patient-centered predictive blood modeling algorithms and better inventory control. Methods: To characterize blood use across various procedures at our institution, we engaged information technology specialists to create a Web Intelligence report by integrating data from both an EHR and a lab information system (LIS) into a single repository. Information obtained illuminated a master procedure list, blood product usage patterns, and characterized patient demographics during January - March, 2020. Data is continuously extracted to create a perpetually updated MSBOS while secondarily functioning to cultivate data for future predictive machine learning algorithms. Results: Data analysis demonstrated 5598 procedures were performed during the first quarter of 2020. Procedures not transfused with packed red blood cells (pRBCs) totaled to 4,156 and 1,442 had a greater than or equal to 10% probability of requiring pRBCs. Our current practices reflected our overall crossmatch to transfusion ratio ( C:T) was 5.4 to 1. Concerted collaboration, resulting in preparation of pre-surgical blood product orders according laboratory generated MSBOS schedule could decrease the C:T to 1.7 to 1. Additionally, high intraprocedural pRBCs variability was identified in current procedural subtypes. Conclusions: Traditionally generated MSBOS are functionally limited and may not be reflective of current surgical practices. Additionally, inter-patient variability may distort some procedural type guidance. Creating an integrated data report, eliminates some of the inherent limitations of traditional MSBOS. Moving forward, the cultivated data if coupled with machine learning has the potential to create transferable proprietary algorithms that proactively predict individual patient transfusion needs.
Machine learning-based approach to the risk assessment of potentially preventable outpatient cancer treatment-related emergency care and hospitalizations.
Authors: Miao Kevin , Dahle Justice , Yousefi Sasha , Buchake Bilwa , Kaur Parambir , Odisho Anobel Y. , Cinar Pelin , Hong Julian C. 
Published: September 2021

333 Background: Patients undergoing outpatient infusion chemotherapy for cancer are at risk for potentially preventable, unplanned acute care in the form of emergency department (ED) visits and hospital admissions. This can impact outcomes, patient decisions, and costs to the patient and healthcare system. To address this need, the Centers for Medicare & Medicaid Services developed the Chemotherapy Measure (OP-35). Recent randomized controlled data indicate that electronic health record (EHR)-based machine learning (ML) approaches accurately direct supportive care to reduce acute care during radiotherapy. As this may extend to systemic therapy, this study aims to develop and evaluate ML approaches to predict the risk of OP-35 qualifying, potentially preventable acute care within 30 days of infusional systemic therapy. Methods: This study included data from UCSF cancer patients receiving infusional chemotherapy from July 1, 2017, to February 11, 2021, (total 7,068 patients over 84,174 treatments). The data incorporated into the ML included 430 EHR-derived variables, including cancer diagnosis, therapeutic agents, laboratory values, vital signs, medications, and encounter history. Three ML approaches were trained to predict an OP-35 acute care risk following a systemic therapy infusion with least absolute shrinkage selection operator (LASSO), random forest, and gradient boosted trees (GBT; XGBoost) approaches. The models were trained on a subset (75% of patients; before October 12, 2019) of the dataset and validated on a mutually exclusive subset (25% patients; after October 12, 2019) based on the receiver operating characteristic (ROC) curves and calibration plots. Results: There were 1,651 total acute care visits (244 ED visits and 1,407 ED visits converted into hospitalization); 1,310 infusions included a qualifying acute care visit (200 with ED visits only, 0 direct hospital admissions, and 1,110 with both ED visit and hospitalization). Each ML approach demonstrated good performance in the internal validation cohort, with GBT (AUC 0.805) outpacing the random forest (0.750) and LASSO logistic regression (0.755) approaches. Visualization of calibration plots verified concordance between predicted and observed rates of acute care. All three models shared patient age and days elapsed since last treatment as important contributors. Conclusions: EHR-based ML approaches demonstrate high predictive ability for OP-35 qualifying acute care rates on a per-infusion basis, identifying 30-day potentially preventable acute care risk for patients undergoing chemotherapy. Prospective validation of these models is ongoing. Early prediction can facilitate interventional strategies which may reduce acute care, improve health outcomes, and reduce costs.
Implementation of ePROs in the care of long-term cancer survivors.
Authors: Palos Guadalupe R. , Gilmore Katherine Ramsey , Chapman Patricia Hansberry , Rodriguez Maria Alma 
Published: September 2021

334 Background: Standard methods for implementation of electronic patient reported outcomes (ePROs) in the routine care of cancer survivors remain unclear. To address this gap, we assessed the technical and operational feasibility of implementing ePROs into the clinical workflow of four survivorship clinics, Genitourinary (GU), Head & Neck (HN), Thyroid (THY), and Stem Cell Transplant (SCT). Methods: This quality improvement study was conducted from 9/1/2019 to 4/30/2021. Benchmarks of ≥ 50% were established for two outcomes including: (1) Delivery Rate - percentage of patients who received the ePRO prior to their clinic visit and (2) Adherence Rate - percentage of patients who completed one ePRO. Data were collected at T0 (Baseline) (09/01/2019 to 12/31/2020) and T1 (1/1/2021 to 4/30/2021). Patients completed the survey 3-7 days prior to a clinic visit. The timing to capture ePROs’ data was tailored to the specific cancer site. Operational steps were: 1) questionnaire was ordered for scheduled survivorship appointment; 2) electronic medical record system (EMRS) pushed the questionnaire to the patient portal app with a message asking the patient to complete and submit the ePROs survey, 3) data was pushed back to the provider by the EMRS; and 4) results stored in the medical record were accessed by the provider prior to or on the day of the patient’s visit. Data were collected and stored using the EPIC Data Universes and reported through Microsoft Power BI. This institutional Quality Improvement Assessment Board approved this study. Results: The Table shows the improvement in the benchmark for sent and adherence rates between T0 and T1. A few clinics showed room for improvement. Patient engagement facilitators included being scheduled for a virtual visit and having staff sent prompts prior to visits. Challenges were: (1) variation in the time ePROs were available in the medical records, 2) inconsistent linking of scheduled visits and prompt to send the survey, and 3) patients’ not opening, completing, or submitting the survey even when prompted to do so. Opportunities included (1) developing a patient education flyer describing ePROs’ role in patient care, (2) providing instruction on the completion and completion of how to complete the ePRO, and (3) conducting staff education sessions on how to link scheduled visits with ePROs’ patient prompts. Conclusions: Our preliminary results indicate that integration ePROs can be considered an acceptable approach to improve follow-up care of cancer survivors but with room for improvement in some clinics. Future evaluation will further explore patients’ ePROs’ impact on clinical outcomes and patient engagement.[Table: see text]
Patterns of virtual care among long-term cancer survivors.
Authors: Gilmore Katherine Ramsey , Chapman Patricia Hansberry , Palos Guadalupe R. , Rodriguez Maria Alma 
Published: September 2021

335 Background: The recent pandemic led to an untypical demand for virtual care visits in our comprehensive cancer center. Our institution responded to this paradigm shift by offering virtual care to all cancer patients, including cancer survivors. In place of an in-person visit, patients were offered a live video or telephone (audio) appointment for their survivorship clinic visit. We conducted a quality improvement study to identify patterns in the type of virtual visits preferred by patients scheduled for appointments in eight dedicated survivorship clinics. Methods: We conducted a retrospective review of 4784 patients’ medical records who were seen in eight site-specific survivorship clinics from April 2020 – April 2021. Variables reviewed included type of visit, i.e. telephone, live video, or in-person, and patient characteristics such as age, gender, clinic, and distance from cancer center. Descriptive statistics were performed to assess for patterns of virtual care. The review was approved by the Quality Improvement Assessment Board. Results: Virtual care visits varied by clinic (0-86%). Five clinics ((breast, lymphoma, sarcoma, melanoma, and gynecological) performed < 3% of visits using virtual format. A sub-analysis of the 8 clinics performing virtual visits demonstrates 59.5% virtual activity (19.23% phone compared to 39.3% video). Older patients were more likely to use any form of virtual visit compared to in-person (58.9% for those ≥ 65 vs. 54.9% for 18-39). Older patients were more likely to prefer phone visits (25.5% for ≥ 65 vs. 11.2% for 18-39). Local patients were more likely to utilize virtual visits (63% for patients < 75 miles vs. 45% for patients > 500 miles). Patients who lived furthest from the cancer center were more likely to travel in for their appointment, though this population contained fewer patients than those within 75 miles. Use of virtual visits for survivorship care varied, depending on the types of clinical diagnostic tests or examinations required for an annual visit. Conclusions: Our results indicate the use of virtual care was optimal when only lab testing was ordered by their provider. Older adults, with no or limited access to and knowledge of digital technology (computers, smartphones, and others similar tools) may need additional support to engage in video visits. Our findings suggest virtual care can be successfully implemented in the care of survivors. Further research would help determine best-practice approaches to facilitating patient engagement in virtual visits since telemedicine continues to evolve as a standard method to deliver safe and optimal cancer care.
Can high-dose methotrexate be safely administered to outpatients?
Authors: Genge Briana , Carasco Tricia , Young Leslie , Laporte Leigha , Baetz Tara D. 
Published: September 2021

34 Background: High-dose methotrexate (HD-MTX) is administered as prophylaxis for central nervous system (CNS) relapse in Diffuse Large B Cell Lymphoma (DLBCL). It is traditionally administered in an inpatient setting due to its complex supportive care regimen. We developed an outpatient protocol and evaluated the safety of this approach. Methods: In 2019 a multidisciplinary team at Kingston Health Sciences Centre developed an outpatient HD-MTX protocol for CNS prophylaxis in DLBCL. Select eligible patients received their HD-MTX infusion in the day unit and returned daily for bloodwork and monitoring. Leucovorin and continuous intravenous hydration were administered via ambulatory infusion pumps. A single centre retrospective cohort analysis was conducted on all patients who received outpatient HD-MTX. These patients were compared to a historical control group who underwent inpatient HD-MTX and who would have met outpatient eligibility criteria. To evaluate the safety of the outpatient protocol we compared the risk of significant toxicity, defined as an elevation in serum creatinine or oral mucositis of grade III or higher, or development of febrile neutropenia. We also evaluated the time to MTX serum clearance, patient admissions and delays of subsequent chemotherapy cycles. Results: From June 2017 to March 2021, 6 outpatients undergoing 14 HD-MTX cycles and 13 inpatients undergoing 28 cycles were evaluated. Significant toxicity occurred in one outpatient cycle compared to five inpatient cycles (7.1% vs 17.9%, p = 0.79). Average time to MTX clearance was 4.1 days for outpatients and 3.5 days for inpatients (p = 0.013). Only one outpatient was admitted to hospital, resulting in an average length of hospital stay of 0.5 days for outpatients compared to 4.96 days for inpatients. There was no significant difference in the need to delay a subsequent cycle of chemotherapy. Conclusions: Outpatient administration of HD-MTX can be administered safely and effectively as CNS prophylaxis in select patients with DLBCL.[Table: see text]