bluuebunny/crossref_metadata_2025 huggingface dataset:
Clinical treatment pathways utilization in an outpatient academic cancer center.
Authors: Chen Anna , Park Seojin , Csik Valerie Pracilio , Maio Vittorio , Walsh Karen , Binder Adam F
Published: September 2021

323

Background: Clinical treatment pathway (CTP) tools have been incorporated into oncology practices to help standardize treatment, reduce care variation in cancer patients, and move towards value-based care. CTP tools incorporate the latest guidelines and are developed through multidisciplinary committees. Treatment decisions are prioritized by efficacy, toxicity, and cost to improve quality and value. This is especially important in cancer care which is associated with high drug costs. However, use and implementation of CTP tools has been met with mixed reviews and published data from academic medical centers are limited. Methods: Outpatient treatment decisions for patients across different cancer types, including colorectal, gastroesophageal, and breast cancers from July 2018 to March 2020 at an urban NCI-designated cancer center were evaluated. Every treatment decision was evaluated in relation to physician utilization of CTP tools. Treatment decisions were categorized as choosing a CTP recommended option ( pathways dependent), not choosing a CTP recommended option ( pathways independent), or not using the CTP tool at all ( non-pathways). Descriptive statistics and significance testing were used to explore CTP utilization over time, physician years in practice, cancer type, and cancer stage. Multinomial and binary logistic regression models were used to evaluate associated factors with CTP utilization. Results: During the study period, 1,007 treatment decisions were identified. Of these, 76% were pathways dependent, 13% were pathways independent, and 11% were non-pathways. CTP utilization was statistically significant in association with cancer type, cancer stage, physician years in practice, and year of encounter. In multinomial logistic regression models, physicians with more years in practice were more likely to be pathways independent and non-pathways than pathways dependent. Physicians treating colorectal or gastroesophageal cancer were more likely to be pathways independent [OR 3.27; 95% CI: 1.98-5.42 and OR 2.25; 95% CI: 1.12-4.49, respectively] or non-pathways [OR 2.98; 95% CI: 1.65-5.37 and OR 3.21; 95% CI: 1.58-6.54, respectively] than pathways dependent compared to physicians treating breast cancer. Treatment decisions for patients with either stage 2 and stage 4 cancers were more likely to be pathways independent than stage 1 cancers [OR 4.13; 95% CI: 1.44-11.84 and OR 4.37; 95% CI: 1.27-15.04, respectively]. Conclusions: This study found that a large proportion of treatment decisions for patients across different cancer types were made utilizing CTP tools, but utilization varies according to cancer type, stage, and physician years in practice. Further research is needed to better understand the physician decision making process and implementation of CTP tools to drive improvement in design, education, and training.
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]
Barriers to patient-centered oncology care: Pilot study of home infusion of anticancer immunotherapy.
Authors: Gill David Michael , Burr Wendy , Bell Mckenzie , Thomas Alisa , Simmonds Jenny , Mullalley Megan , Petersen Libby , Sandhu Zoya , Kator Sarah , Banner Melissa , Rhodes Terence Duane , Thota Ramya , Haslem Derrick S.
Published: September 2021

36

Background: ASCO published a position statement regarding home infusion of anticancer therapy in June 2020. This statement recommends independent research to evaluate the safety and effectiveness of home infusions. Intermountain Healthcare (IM) incorporated this statement into its oncology care with an IRB-approved, prospective single-arm pilot study to determine the safety and feasibility of home administration of checkpoint inhibitor (CPI) immunotherapy with synchronous telemedicine visits. Methods: Patients with cancer receiving treatment at Intermountain Medical Center and Intermountain Cancer Center St. George were screened for enrollment into an IRB-approved, non-randomized pilot study of 20 patients. Eligibility criteria required patients to receive a CPI for an FDA-approved indication, live in Washington County or Salt Lake County, Utah, and have commercial payer coverage of CPI home infusion. Eligible patients were required to receive 2 doses of CPI at an infusion center, and patients who experienced an infusion reaction were excluded from receiving home infusion. Home infusion nurses are trained in oncology, CPIs, and home infusion reaction protocol. During synchronous video visits, infusion nurses are trained to perform the hands-on portions of the physical exam. A financial analysis estimated cost to IM and commercial payers for routine and home CPI infusions. Results: 622 patients were screened, of which 104 were receiving a CPI. 64 patients lived in an eligible county and 19 patients had commercial payer coverage. Of patients on CPIs, 8.7% (9/104) met all eligibility criteria accounting for 1.4% (9/622) of all patients with cancer screened (Table). Financial analysis estimated $829 cost (excluding drug cost) to IM for standard infusion reimbursement compared to $599 for in-home CPI infusions, accounting for savings of $230 per infusion. Majority of cost savings are from elimination of infusion center facilities fee ($495). Analysis includes $269 for home infusion nurse wages. Subsequent analysis for commercial payer SelectHealth estimates $270 reimbursement savings for the payer. Conclusions: Home immunotherapy infusions are estimated to be cost effective for both IM and commercial payers. However, lack of drug coverage and the rural demographics of Utahns with cancer are barriers to home CPI infusions. The pilot study was discontinued per infeasibility stopping criteria.[Table: see text]
Medication nonadherence and patient cost responsibility for rural and urban cancer survivors.
Authors: Williams Courtney , Davidoff Amy J. , Halpern Michael T. , Mollica Michelle , Castro Kathleen M. , De Moor Janet
Published: September 2021

4

Background: Little is known about the specific out-of-pocket costs which may lead to prescription nonadherence in older cancer survivors, and how patterns may differ for those living in rural areas. This study quantified patient costs overall and by residence for older cancer survivors who did and did not report cost-related prescription nonadherence. Methods: This retrospective cohort study used data from the Surveillance, Epidemiology, and End Results Program, Medicare claims, and the Consumer Assessment of Healthcare Providers and Systems survey linked data resource (SEER-CAHPS) from 2007-2015. Older cancer survivors self-reported cost-related prescription nonadherence in the prior six months. Patient cost responsibility (deductibles, coinsurance, copayments) was summed for all medical care received in the year prior to survey. Differences in patient cost responsibility by cost-related adherence was estimated using gamma generalized linear models adjusted for patient age, race, sex, education, dual Medicaid enrollment status, residence, comorbidity count, cancer type, stage, and phase of care. Models stratified by urban/rural residence as designated by Rural-Urban continuum codes assessed effect modification. Results: Of 11,829 older adult survivors of prostate (37%), breast (32%), colorectal (14%), gynecologic (10%), or lung (6%) cancer, 12% reported any cost-related prescription nonadherence in the prior year. Median age of survivors was 76 (interquartile range [IQR] 71-82), 15% had less than a high school degree, 59% had at least one non-cancer comorbidity, and 16% had ever been dual eligible. Prevalence of cost-related nonadherence was similar by patient characteristics. Median cost responsibility in the year prior to survey was $1,529 (IQR $744-$2,959) for patients reporting nonadherence and $1,123 (IQR $572-$2,362) for those reporting adherence. In adjusted models, patients reporting nonadherence had $656 higher patient cost responsibility in the year prior (95% CI $564-$760) compared to those reporting adherence. Approximately half of the difference in cost was outpatient spending (β = $277, 95% CI $210-$359). Differences in cost responsibility for patients reporting nonadherence compared to adherence were smaller for patients residing in rural areas (18% of respondents; β = $341, 95% CI $177-$564) compared to those residing in urban areas (82% of respondents; β = $715, 95% CI $613-$830). Conclusions: Compared to those reporting adherence, cost-related prescription nonadherence was associated with higher health care cost responsibility in cancer survivors. Furthermore, prescription adherence decisions may be more cost-sensitive for patients living in rural compared to urban areas. Interventions to address out-of-pocket health care costs, particularly for rural cancer survivors, could aid in increased prescription adherence and subsequent health outcomes.
Improving referrals to survivorship: A patient-driven initiative.
Authors: Corcoran Stacie , Licciardi Frank , Weber Eliza , Duggan Erika , Woodside Alexandria , Correa Zana , lachaud-Richard Margot , Kassa Alyse , Jakubowski Ann Alice
Published: September 2021

44

Background: Patients often feel lost in the transition from cancer patient to survivor. As cancer treatment improves, the number of survivors in the US is expected to approach 22 million by the year 2029. Smaller increases in the MD workforce coupled with long-term effects of new treatments necessitate the creation survivorship clinics led by advanced practice providers (APP) who are disease-specialized and specifically trained to manage unique survivor needs. Although the institutions Survivorship program was established more than a decade ago, the average referral rate to the program is only 30%. Given the high levels of patient satisfaction with the program, we sought to understand and address clinician awareness and referral obstacles. Methods: Based on patient input, members of our Patient & Family Advisory Council for Quality (PFACQ) put forth a proposal aimed at increasing referrals to the Survivorship program. A multidisciplinary working group comprising patients, clinicians, administrators and process engineers was assembled. Process Improvement training was conducted, and a primary goal was developed: increase Survivorship referrals by 25%. Central to the approach was engagement with referring physicians and APPs on a service-by-service basis. A four-question survey was developed to address timing of initial discussion, challenges to referring, knowledge of visit elements, and materials needed to support referrals. A customized ‘roadshow’ presentation was created and delivered by a PFACQ member at a faculty meeting. Highlights included: the importance of Survivorship care to patients and details of the visit, survey responses, and additional faculty discussion and input. Results: Four roadshows were completed from August 2020-June 2021: Gynecologic Surgery, Endocrine, Breast Medicine and Lymphoma. Based on survey responses and dialogue with providers, new material to support patient discussions and education have been developed: a patient-facing brochure, clinician- & patient-facing video, and clinician talking points. Methods to facilitate identifying eligible patients were also discussed, as well as automatic referrals to Survivorship for selected services. Data reveal a significant increase in referral rates from GYN and Endocrine services (follow up > 6 months) of 63% and 228%, respectively. (Breast Medicine and Lymphoma with shorter follow up will be reported.) Engagement with other services is underway. Conclusions: Results have exceeded the goal of increasing referral rates to the program. This process suggests that ongoing engagement with referring clinicians is essential to achieving and maintaining higher referral rates. The patient voice throughout this process has been a powerful tool in raising clinician awareness of their experiences and expectations for comprehensive follow up care, which can be delivered on a greater scale by survivorship providers.
Challenges and opportunities of alternative payment models for physicians/quality officers in community/academic oncology practice sites via a national prospective survey.
Authors: Kharat Aditi , Panchal Rupesh , Ilham Sabrina , Brendle Madeline , Au Trang , Copher Ronda , Huggar David , McBride Ali , Willis Connor , Brixner Diana I.
Published: September 2021

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Background: Alternative payment models (APMs) aim to address the rising cost of cancer care ($173 billion in 2020) by promoting high-value care. This study explored the challenges and opportunities associated with implementation of APMs in oncology practice sites across the U.S. Methods: A novel survey was created through literature review and a focus group of content experts and researchers. This survey was distributed electronically (Qualtrics) to oncology/hematology physicians (MDs) and quality officers (QOs) at U.S academic (AC) and community (CO) cancer centers from August 2020 to November 2020. Each participant reported on their center’s experience with APMs. A descriptive analysis was conducted. Results: A total of 136 sites were contacted and 28 sites (13AC/15CO) participated (21% response rate). There were 30 (67%) sites that implemented an APM, the most common was the CMS Oncology Care Models (CMS OCM) (87%, n=26), which typically took 6 months or less for implementation (66%, n=20) and was reported to improve benchmark performance (63%, n=10). The most common reasons APMs were not implemented were administrative complexity (53%, n=8); difficulty with alignment of multiple payers to APM (47%, n=7); and need for a median of 5 full-time hires. A larger number of QOs indicated participation in CMS OCM compared to MDs [94% (n=15) vs. 76% (n=11)]. The biggest difficulty experienced by MDs was adoption of innovation at site’s financial risk (43%, n=6) compared to QOs who focused on administrative and financial burden (43%, n=7). Community cancer sites were more likely to use APMs (100%, n=23) compared to AC sites (31%, n=7). The largest difficulty experienced by CO sites was adoption of innovations at their own financial risk (26%, n=6) compared to high costs, administrative burden and lack of payer engagement (29%, n=2 each) for AC sites. The most common challenge with incentives among CO sites was concerns with risk adjustment of quality measures (57%, n=13) compared to increased complexity of incentive compensation (57%, n=4) for AC sites. CO sites indicated that quality measure performance reviews were primarily conducted by administrative staff compared to clinical leaders for AC sites. Conclusions: Administrative burden and access to appropriate patient data inhibit the impact of APMs. MDs are primarily concerned about the penalties and financial risk, whereas QOs focus on administrative complexities. For CO and AC sites, the adoption and implementation of APMs to improve cost and care delivery was not without significant challenges. This study demonstrates the multifaceted impact APMs have on care delivery.
Establishing the need for provider education on cancer and nutrition (PECAN): Survey results on nutrition practices at a large community oncology practice.
Authors: Smith Karen , Ortega Lance , Wilfong Lalan S. , Mikan Sabrina Q. , Sandbach John F. , Hudson Kathryn Elizabeth
Published: September 2021

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Background: Oncology providers and patients benefit from evidence-based nutritional resources to support cancer care. For patients, proper nutrition care can prevent inappropriate weight loss, improve treatment tolerance, and improve quality of life. Recent literature has noted the lack of both adequate dietitian resources at outpatient centers and comprehensive nutrition guidelines in cancer care. At a large community practice with 210 locations across Texas and Oklahoma, a survey was developed and administered to assess nutrition practices. Methods: Clinic directors in the practice completed a survey to assess for the presence or absence of a malnutrition screen; how nutrition concerns are addressed; and the availability of a dietitian. Results: A total of 26 responses detailed the nutrition processes at 50 unique locations. At 37 (74%) locations, a malnutrition screening tool was utilized and of these, 30 (81%) locations reported utilizing the Malnutrition Screening Tool (MST), a brief, validated tool for outpatient settings including oncology. Seven (19%) sites did not specify what tool was used. Of the clinics that reported completing a malnutrition screen for patients, the screen was completed by the advanced practice provider (APP)/physician in 18 (49%) clinics, a nurse in 4 (11%) clinics, a dietitian in 4 (11%) clinics and a medical assistant in 1 (3%) clinic. At 10 sites more than one provider type was responsible for completing the screen. Nutrition issues were addressed by physicians, APPs, and nurses in 15 (50%), 35 (70%), and 9 (14%) sites respectively (see table). Twenty-two locations (44%) reported referring patients to a dietitian and 10 sites (20%) had a dietitian available in clinic. Forty-one (82%) clinics employed more than one method for addressing nutrition issues. Sixteen of 26 respondents (62%) reported that increased dietitian access would be helpful; this included respondents whose locations already had a dietitian available. Conclusions: Overall, 74% of reporting locations used a malnutrition screening tool and 20% had access to a dietitian on site. Forty-four percent of sites referred to a dietitian if there was a need. This indicates that multiple providers are involved in the nutritional care of oncology patients. In response to these findings, the multidisciplinary PECAN Taskforce was created to develop comprehensive malnutrition screening, implement a referral process for all at-risk patients, enhance nutrition education, and expand dietitian services. Further study will measure the impact of expanded nutrition access on patient outcomes.[Table: see text]
Impact of multidisciplinary tumor board (MTB) discussion on the outcome of patients with liver-limited metastasis from colorectal cancer (LMCRC).
Authors: Guimarães Paula Tavares , Coimbra Felipe José Fernández , Mello Celso Lopes , Felismino Tiago
Published: September 2021

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Background: MTB is a valuable tool in complex cases, since they involve different medical fields aiming for a more comprehensive decision-making process. Our aims are to describe MTB decisions in complex LMCRC, to analyze decisions compliance and evaluate outcomes. Methods: This is a retrospective analysis of medical records of patients diagnosed with liver metastasis of colorectal carcinoma (LMCRC) discussed at a single center, weekly-basis MTB from January 2019 to December 2020. Descriptive statistics was used for demographic and treatment characteristics and adhesion to MTB decisions. Survival was estimated by Kaplan-Meier method and log-rank tests. Results: Out of 1033 cases discussed at MTB, 63 met the inclusion criteria. Median age was 56.5 years (range 32 – 81), 61.9% were male, ECOG was 0-1 in 96.8%. Regarding primary tumor sidedness: 81% and 19% were left and right, respectively. Synchronous metastasis was present in 69.8% and wild-type RAS was found in 52.4% and MSI-H in 4.8%. At time of MTB, 68.3% and 4.8% had realized a first or second-line chemotherapy (CT) respectively. No previous therapy was described in 19% and 7.9% had received adjuvant CT. Previous hepatic resections occurred in 20.6%. After MTB, liver resection was recommended for 44.5%. Preoperative CT followed by one or two-stage surgery for 23.8%. Non-surgical treatments were offered for 17.5% and palliative CT for 14.3%. Decisions from the MTB were implemented in 93.6%. Reason for not performing (N = 4) were: one for disease progression, one for bureaucratic restriction and two for other reasons. Median follow-up time was 17.2m. At date of analysis, 31 patients (49.2%) had relapsed or progressed and 8 had died. Restricting for patients who underwent MTB decision (N = 59), mPFS was 14.71m. In resected cases (N = 40), who were discussed at the onset of liver metastasis diagnosis (without CT in the metastatic setting) had mPFS not reached, while who were discussed after first or second-line had mPFS of 9.39m (p 0.002). R0 resections were reached in 92.5%. Evaluating the 30-day postoperative mortality, there was one death. mPFS for palliative CT was 6.83m. Conclusions: In our analysis the adherence to MTB recommendation was high (above 90%) and patients selected for surgery presented a prolonged PFS. Our data suggest that selection of patients for liver metastasis resection in a MTB discussion should occur early in the course of treatment.
Characteristics, treatment patterns, and economic burden of patients with metastatic cervical cancer receiving systemic anticancer therapy.
Authors: Pan Xiaoyun , Lal Lincy S. , White John , Mahmoudpour Seyed Hamidreza , Valencia Christian
Published: September 2021

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Background: In 2021, 14,480 patients are estimated to be diagnosed with cervical cancer in the US; 16% of patients are expected to have metastatic disease for whom the 5-year survival rate is 17.6% per SEER estimates. Patients with metastatic cervical cancer (mCC) are treated mainly with systemic therapy. This study aims to describe the clinical characteristics, demographics, treatment patterns, and economic burden of patients with mCC receiving systemic therapy. Methods: Eligible women had been diagnosed with cervical cancer, as evidenced by >2 outpatient or >1 inpatient claim in the Optum Research Database from January 2014 through January 2020. Patients were included if they had metastasis within 6 months before or after cervical cancer diagnosis, with evidence of systemic treatment on or after the latter of a claim date for cervical cancer disease or metastatic disease. The index date was the first-line treatment initiation date. Patients were required to have ≥6 months of pre-index continuous enrollment. The top 3 treatment regimens and median treatment duration by line of therapy were described. All-cause per-patient-per-month (PPPM) costs (2019 US dollars), including plan and patient paid amounts, were reported for full follow-up period from first-line and second-line therapy initiation. Results: The study sample consisted of 778 patients (mean age, 59 years; commercial, 58%; Medicare Advantage, 42%). The mean (median) follow-up period was 14 (9) months. Top baseline comorbidities were diseases of the urinary system (71%) and diseases of the female genital organs (70%), and the median Charlson comorbidity index was 7. In the first line, 80% of patients received platinum-based therapy and 23% received bevacizumab (bev). Of 778 patients, only 294 (38%) received second-line therapy, with 34% receiving bev. Top first-line treatment regimens were carboplatin + paclitaxel (27%), cisplatin (21%), and bev + carboplatin + paclitaxel (10%); the median (95% CI) duration of treatment was 3.4 (3.1-3.7) months. Top second-line treatment regimens were bev + carboplatin + paclitaxel (13%), carboplatin + paclitaxel (11%), and pembrolizumab (6%); the median duration of treatment was 3.8 (3.1-4.2) months. Mean all-cause total PPPM costs were $19,519 from first-line and $22,660 second-line therapy initiation (table). Conclusions: This study indicates that real-world mCC patients have short treatment durations and significant economic burden with first-line and second-line therapy. Novel therapies associated with greater clinical benefits in patients with mCC may provide economic benefit.[Table: see text]
Medicare Part D spending for the 10 costliest oral anticancer drugs.
Authors: McBride Kaitlyn , Snyder Sophie
Published: September 2021

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Background: Novel oral targeted drugs are being used more frequently to treat many cancers and have substantially improved clinical and survival outcomes. Due to the long treatment durations of many of these medications, which are given continuously until patient progression, per-patient lifetime costs can be high. This study aimed to identify the ten oral anticancer therapies with the highest annual Medicare Part D spending. Methods: Descriptive statistics were performed on data obtained from the CMS Medicare Part D Drug Spending Dashboard and IBM Micromedex RED BOOK, from 2018-2019. Medicare Part D dashboard includes total and average drug spending, and number of beneficiaries utilizing the drug. RED BOOK provides current and historical average wholesale pricing (AWP) data for all prescription drugs. The wholesale acquisition cost (WAC) was calculated from the AWP to evaluate trends in price. We identified the ten anticancer brand-name medications in 2019 with the highest annual Part D spending and reported changes in average spending and number of unique beneficiaries for each drug from 2018-2019. Results: In 2019, Revlimid had the highest annual total Part D spending at $4.6 billion, followed by Imbruvica with $2.4 billion; these drugs also had the greatest number of beneficiary utilizers. From 2018-2019, change in average spending per dosage unit was greatest for Zytiga (34%), however among all drugs, average spending per beneficiary in 2019 was lowest for this medication ($58,074). From 2018-2019, WAC and average spending per dosage unit increased for all of the top ten drugs, as well as the number of total beneficiaries utilizing each drug, except for Zytiga and Sprycel. Conclusions: Oral anticancer therapies provide high value for patients, including improved quality of life and survival. Annual costs for these drugs are high, however spending on inpatient hospital services remains a greater share of total Medicare spending in aggregate and on a per beneficiary basis. Emerging one-time curative treatments for cancer may prove most cost-effective in the long-term by eliminating the need for continuous medication use and hospital care, while improving patient outcomes.[Table: see text]