bluuebunny/crossref_metadata_2025_split huggingface dataset:
Spreading routine serious illness conversations at a single cancer center using a multidisciplinary and patient-family advisory approach.
Authors: Wasp Garrett Thomas , Guo Karen , Wilson Matthew , Vergo Maxwell Thomas , Willaims Andy , Perry James J. , Holthoff Megan M. , Buus-Frank Madge E. , Cullinan Amelia M. 
Published: September 2022

367 Background: ASCO Guidelines recommend oncologists conduct serious illness conservations (SIC) for all patients with advanced cancer. We describe the spreading of a multidisciplinary and patient-family advisor (PFA) quality improvement (QI) initiative to conduct routine SIC at a NCI-designated comprehensive cancer center. Methods: This single center study describes the second phase of a learning health system initiative to conduct routine SIC for all seriously ill patients with cancer. Prior work completed included defining patient eligibility (e.g. two-year surprise question), and deploying a SIC template in the electronic health record (EHR) to capture SIC in a centralized location. Phase II can be categorized into three steps: a) increasing communication coach and PFA capacity; b) refining EHR tools to automate tracking and reporting of outcomes; and c) adoption of the Model for Improvement as the QI methodology to guide testing and implementation. For the first three months, each team engaged in preparatory work including: process mapping, key driver diagram development, and SIC Guide training. In the last six months, each team met weekly to set their own team goals, conducted iterative PDSAs, and reviewed run charts of their performance. Patient-level data on SIC documentation was collected through automated EHR tools and provided to the teams on a weekly basis. Results: Over nine months (3/1/2021 to 12/31/21), four teams screened 510 patients with cancer, identified 272 (53%) patients as eligible for SIC, and 178 (65%) of those eligible had a documented SIC from a baseline of 0%. The breakdown of this combined SIC patient count by clinician author is as follows: team A 40 (22%); team B 45 (25%); team C 9 (5%); team D 14 (8%) and specialty palliative care 70 (39%). Each team set modest, initial SIC documentation goals (range 5-15%), and attainment of first SIC documentation goal varied (range 2 to 4 months). We retained all clinician team members during the study period, but 2 out of 3 PFAs left by study period end. Noted challenges with PFA recruitment and retention included: lengthy recruitment, integration into clinical teams, and resistance to change by teams. Conclusions: The multidisciplinary approach, inclusive of specialty palliative care, increased SIC documentation. PFA involvement, as implemented, met with challenges and yielded mixed results. Additional follow-up will be required to assess if gains can be sustained and/or increased.
Electronic Frailty Index: A risk stratification tool for survival and health care outcomes in veterans with colorectal cancer.
Authors: Zhu Huili , Razjouyan Javad , Yarlagadda Sudha , Naik Aanand , Sada Yvonne H 
Published: September 2022

375 Background: Frailty reflects decreased physiologic reserve and subsequent increased vulnerability to stress. Frailty has been associated with poor health outcomes in patients with cancer, however traditional frailty indices are time consuming and not widely implemented in clinical oncology practice. An electronic frailty index (EFI) derived from electronic medical records (EMR) is an objective and standardized approach to frailty that can be automated. Little is known about the association between EFI and survival and healthcare utilization outcomes in patients with colorectal cancer (CRC). Methods: We used VA administrative files from 2016 – 2020 and the VA Central Cancer Registry to identify a cohort of patients diagnosed with CRC. EFI was calculated based on the Veterans Affairs-FI (VA-FI), a validated 31-item cumulative deficit FI, to define three groups: robust (≤0.1), prefrail (0.1-0.2), and frail (> 0.2). We conducted Cox proportional hazard analyses to evaluate survival. We performed logistic regression to examine prolonged length of stay (LOS) ≥ median (11 days) during all-cause hospitalization and emergency department (ED) visits within one year after diagnosis. All models were adjusted for age, gender, race, Charlson comorbidity index, and stage. Confidence intervals (CI) were calculated as 95%. Results: Of 6,043 CRC patients (age: 69.7 ± 10.6 years), 45.6% were robust, 34.5% were prefrail, and 19.9% were frail. The cohort included 22.9% rectal cancer and 77.1% colon cancer cases, distributed across stages I (29.2%), II (25.0%), III (25.2%), and IV (20.6%). Increased risk of death was found in prefrail (adjusted hazard ratio [aHR] 1.21; CI 1.10-1.32) and frail (aHR 1.90; CI 1.71-2.11) patients when compared to robust patients. Increased odds of one-year ED visits were identified in prefrail (adjusted odds ratio [aOR] 1.18; CI 1.06 – 1.35) and frail (aOR, 1.66, CI 1.43-1.94) patients. Frailty status was associated with increased odds of one-year hospitalization of prolonged LOS among prefrail (aOR 1.38; CI 1.11-1.70) and frail patients (aOR 1.79; 95% CI 1.39-2.30). Conclusions: EFI was significantly associated with survival and healthcare utilization among patients with CRC, independent of stage. Frail patients had nearly 80% increased likelihood of prolonged hospitalization. EFI has the potential to be an automated and objective decision support tool at the point of care for risk assessment prior to CRC treatment-related stressors, such as surgery or chemotherapy. Future work is needed to develop a cancer specific EFI with dynamic variables that can be followed prospectively, as well as evaluating barriers to implementation within the EMR to guide treatment decisions and improve the quality of cancer care.
A pilot study to increase cascade genetic risk education and testing in families with hereditary cancer syndromes.
Authors: Katz Steven , Hawley Sarah T. , Tocco Rachel , Kurian Allison W. 
Published: September 2022

378 Background: There is great need to build and evaluate tools and strategies to improve cascade genetic risk evaluation in families at high risk for hereditary cancer. The Genetic Information and Family Testing (GIFT) Trial is a population-based intervention that will examine features of a virtual platform that provides genetic risk education (GRE) and low-cost genetic testing (GT) to relatives of 5000 adult patients diagnosed with cancer in 2018-19 in Georgia and California and who tested positive for a clinically actionable germline pathogenic variant (PV). We present findings of a pilot study that informed the GIFT Trial protocol and platform features. Methods: We surveyed 277 women diagnosed with breast cancer in 2017, reported to the Georgia SEER registry, and who tested positive for a clinically actionable PV in a cancer-associated gene. We then invited respondent patients to enroll in the intervention phase which provided online GRE, human pretest genetic navigator support, and an offer of low-cost GT through Color Health, Inc. to all untested 1st or 2nd degree relatives. Respondent patients were eligible for the intervention if they reported a PV on genetic testing and had at least one relative who had not received GT. Enrolled patients invited relatives through the platform by providing email addresses. Family clusters were block randomized to free vs $50 test costs at the time of the initial patient invitation. Results: 125 of 277 patients (45.2%) returned surveys: median age was 51 and 22% were African American. The most common PV reported by patients were BRCA2 (25.7%), BRCA1 (19.5%), CHEK2 (20.4%), ATM (8.8%) and PALB2 (7.1%). Disclosure of genetic test results to relatives was quite high: 83.6% of patients reported that they discussed results with all their daughters vs 69.4% with sons; 78.1% discussed their results with all sisters vs 61.8% with all brothers. The median number of patient-reported untested relatives in a family was 9 (25th-75th percentile: 4-14). Most respondents were eligible for the trial offer (113 of 125, 90.4%). Overall, results were similar between the free test arm and $50 test arm: In the free testing arm, 20 patient participants invited 28 relatives; 12 relatives enrolled, and all 12 relatives ordered testing. In the $50 arm, 16 patient participants invited 38 relatives of whom 18 enrolled and all 18 ordered testing. For the 66 relatives invited by the patient participants: 47 (71.2%) were first degree (8 parents, 22 siblings, and 17 children) and about half of the relatives were men (47.0%). Conclusions: Breast cancer patients with PVs make substantial efforts to communicate with family members about genetic risk but most relatives do not receive GT. Our study findings suggest that a low-cost online navigator-supported intervention can directly engage relatives to increase cascade genetic risk education and testing with little difference in uptake by test cost arms.
Patient preferences for telehealth versus in-person oncology visits.
Authors: Kumar Deepika , Gordon Nancy P. , Neeman Elad , Zamani Constanza , Sheehan Tammy Rene , Martin Ernesto , Payne Jessica , Egorova Olga , Kolevska Tatjana , Liu Raymond 
Published: September 2022

386 Background: The COVID-19 pandemic led to rapid adoption of telehealth (video or phone) visit modalities for oncology encounters not requiring in-person visits. We surveyed oncology patients regarding preferences for in-person versus telehealth modalities for different types of clinical encounters. Methods: We surveyed adults who were undergoing treatment at Kaiser Permanente Northern California infusion centers between 11/2021 – 05/2022 using a self-administered questionnaire. Patients were asked about modality preferences for 6 types of clinical discussions, overall advantages and disadvantages of telehealth versus in-person encounters, and barriers to video visit use. Results: A total of 757 patients who completed surveys in English answered questions about visit modality preferences for different types of discussions with their oncologist. Respondents were 63% female, median age 63y, and majority White (61% White, 19% Asian, 11% Latino, 7% Black) and college-educated (28% some college, 45% ≥ bachelor’s degree). For the first post-diagnosis discussion, most patients preferred in-person (IP) visits (83%), followed by video visits (27%) and phone visits (18%). For follow-up visits during treatment, patients preferred IP (52%), video (50%) and phone (37%) visits. For discussions of bad news and sensitive topics, IP visits were preferred by 68% and 62%, video visits by 44% and 48%, and phone visits by 32% and 41%, respectively. Good news could come at IP (49%), video (52%) or phone (49%) visits. Approximately 20% of patients had no overall preference for IP versus telehealth visits. However, 58% of patients felt more personally connected with their doctor at IP visits. Patients also had more confidence in IP examinations (73%) and felt IP was easier for showing things (67%) and talking (51%) to the doctor. Patients felt telehealth visits saved them time (72%), reduced infection exposure (64%) and travel issues (45%), were cheaper (38%), and enabled inclusion of more people (28%). Of 24% of patients who felt video visits would be hard, 51% cited poor internet, 41% lack of an adequate device, and 28% difficulty signing on. Conclusions: The majority of oncology patients consider telehealth visits acceptable for most types of clinical discussions, with the exception of the first post-diagnosis visit. Only one-fourth of patients indicated potentially modifiable barriers to video visits. Our results support use of telehealth visits for most types of oncology encounters.
Comorbidities among patients with breast cancer during COVID-19: Agreement between patient-reported data and electronic medical records.
Authors: Maculaitis Martine C. , Liu Xianchen , Thompson Jeffrey A. , Berk Alexandra , Massa Angelina , Weiss Marisa C. , Li Benjamin , Kurosky Samantha K. , McRoy Lynn 
Published: September 2022

409 Background: Comorbidities are a major cause of complications in cancer patients and can increase the risk of severe illness from coronavirus disease 2019 (COV). Yet, in real-world studies of cancer patients, comorbidities are often not well captured in electronic medical records (EMR); self-reported comorbidities may be limited by recall error. Combining and comparing self-reported and EMR data may help identify key data gaps in comorbidity diagnosis. We aimed to estimate self-reported and EMR-documented comorbidities and examine agreement between these data sources in US patients with early-stage (eBC) or metastatic (mBC) breast cancer. Methods: From March 30 to July 6, 2021, patients (aged ≥18 years) who self-reported a BC diagnosis (no current Stage 0 or ductal carcinoma in situ) and provided consent were recruited via Ciitizen, a patient-mediated health records and real-world evidence platform, and patient advocacy groups to complete a cross-sectional online survey. EMR data from Invitaes Ciitizen platform, covering November 1, 2019-September 28, 2021, were collected. The datasets were then linked; in preliminary assessment, depression (DEP), anxiety (ANX), and COV were the only comorbidities sufficiently populated to enable agreement analysis. DEP, ANX, and COV prevalence rates in EMR and survey data were computed. Agreement between data sources was estimated using Cohens kappa. Results: Overall, 542 patients in the linked sample were included in the analyses. A majority was female (99%), aged ≥50 years (52%), and diagnosed with mBC (53%). Patients were similarly distributed by US geographic region. DEP, ANX, and COV prevalence rates were 40%, 50%, and 6% in EMR and 20%, 16% and 10% in survey, respectively (Table). The kappa values for agreement were.5 (DEP),.3 (ANX), and.8 (COV). Conclusions: Mental health conditions and COV were prevalent in BC patients during the pandemic. It is important for oncologists to consider that DEP and ANX are often underreported among patients with BC. Agreement between data sources was low for mental health conditions. Self-report and EMR data may thus provide complementary information on comorbidities.[Table: see text]
Improving routine use of clinical pathway decision support through EMR integration.
Authors: Kotak Dinesh , Ichiuji Mary Mitsuko , Asakura Laura , Hanifin Shawn , Aye Nancy , Kolevska Tatjana , Cain Carol , Rabrenovich Violeta , Lindberg Mark , Li Yan 
Published: September 2022

432 Background: There has been an explosive growth in oncology with an evolution of precision oncology based on molecular profiling. Providing evidence-based, high quality, equitable care is a foundational mission for Kaiser Permanente (KP). Thus, KP created a systematic approach toward oncology care pathways that incorporated organizational leadership, structured participation and feedback, novel integration of clinical decision support (CDS) tools, and evaluation of efficacy, safety, and value. Our goal was to make it easy and preferable for physicians to access clinical decision support for every patient, every time. Methods: KP’s inter-regional oncology chiefs, supported by relevant teams in evidence analysis, pharmacy, and other clinical specialties, convened monthly meetings in 10 oncology sub-specialties to synthesize evidence and create care pathways for priority conditions. The pathways are published to KP’s Clinical Library (CL), a trusted, curated resource of clinical knowledge guidance and tools, available via KP’s Epic Systems electronic medical record (EMR). The pathways are represented as intuitive, graphical, clickable process diagrams, with additional clinical content available as hover text. CL uses standard Epic interfaces to receive contextual information when accessed from a patient encounter. When the physician browses the CL oncology pathway and selects a treatment recommendation, CL returns to the EMR with the specific treatment plan queued up for ordering. We began initial deployment of these inter-regional pathways in Feb 2020. We combined EMR data with CL data to understand the impact of this clinical decision support. Results: We experienced dramatic uptake of oncology clinical pathways despite offering minimal training and no external incentives. KP orders over Beacon treatment plans annually in the clinical conditions supported by our pathways. In the first quarter of deployment, clinicians ordered 9% of these protocols through clicking on CDS in CL. Over the course of 2 years, we saw pathway-based ordering grow to over 75% of eligible patients. Conclusions: We find that we have made considerable progress toward our goal of having physician users consult clinical pathways decision support before ordering oncology drug treatment protocols. As with all clinical guidance, we would not expect 100% of patients to follow the recommendation, due to clinical variation and co-morbidity, but we find that by making clinical pathways easily accessible and actionable within normal clinical workflow, physicians are consulting the latest evidence in their treatment.
Changing the electronic health record to optimize pre-chemotherapy lab orders.
Authors: Lam Barbara , Celmen Melis , Graham Tim , Dechen Tenzin , Zerillo Jessica Ann 
Published: September 2022

434 Background: National guidelines support screening for infectious diseases and pregnancy before starting cancer-directed treatment. Providers also check liver and kidney function to ensure safe medication dosing. Forgetting to order these tests has implications for patient safety and treatment area throughput. Our aim was to modify the provider order entry (POE) screen in the electronic health record (EHR) to reduce missed laboratory orders for hepatitis B, pregnancy tests, and organ function.Methods: We revised the hematology/oncology POE screens so that labs were grouped by organ system rather than listed alphabetically. A new Pre-Treatment Screening (PTS) order set includes urine pregnancy and a hepatitis B panel. We collected retrospective ordering data at our medical center’s ambulatory academic and community sites from the pre-intervention (10/20 – 9/21) and post-intervention (10/21 – 4/22) time periods. Data was analyzed to determine the pre/post change in percentage of add-on chemistry tests, rate of ordered urine pregnancy and hepatitis B tests in new patients, and proportion of positive hepatitis B tests. Results: There were 165,360 total chemistry lab orders pre-intervention and 91,948 post-intervention. The percentage of add-ons decreased from 0.50% (834/165,360) to 0.38% (351/91,948). There were 1,109 pre-intervention new treatment starts (92/month) and 673 post-intervention (96/month). The rate of hepatitis B screening increased from 86/month to 99/month and the rate of urine pregnancy testing remained the same at 4/month. The proportion of positive hepatitis B surface antigen tests increased from 0.72% (8/1109) to 0.89% (6/673). The proportion of positive hepatitis B core antibody tests decreased from 9.83% (109/1109) to 9.51% (64/673). Conclusions: Simple changes to an electronic ordering screen were associated with increased adherence to pre-treatment screening guidelines. Further PDSA cycles are needed to explore what changes to POE can optimize appropriate utilization of lab orders, especially for pregnancy screening. This has far-reaching consequences not only for new chemotherapy patients but also for other populations receiving immunosuppressive treatments.
Comparison between subjective and objective fatigue in patients with lung cancer and cancer-free participants: Evaluation of diagnostic criteria for cancer-related fatigue.
Authors: Huang Tsai-Wei , Shih Chi-Huang , Chou Pai-Chien , Chou Ting-Ling 
Published: September 2022

440 Background: Cancer-related fatigue is one of the most distressing symptoms of cancer patients. Finding a patient's fatigue often requires the use of a subjective assessment scale, such as the Brief Fatigue Inventory (BFI). There are few objective ways to measure fatigue. The aim of this study was to evaluate whether objective cancer-related fatigue (CRF) diagnostic criteria can differentiate cancer from cancer-free participants and to explore the relationship between subjective and objective fatigue. Methods: In this study, we used a photoplethysmography (PPG) smartband device to collect the PPG information from the patients. During PPG measurements, the heart rate variability (HRV) signal was included in the data collection process for subsequent calculation of the LF/HF ratio. Participants completed a self-report measure to assess demographics, fatigue levels, and brief sleep diaries for the next 7 days. At the same time, the participants wore a wristband to collect HRV signals, which triggered HRV every hour over a 24-hour period for 7 consecutive days. Results: Cancer patients (n = 71) and cancer-free controls (n = 75) were studied. Study participants completed CRF questionnaires and heart rate variability (HRV) records. The smartband can be worn continuously for 120 hours as an objective measure of activity phase, sleep phase and HRV. Compared with controls, cancer patients were more fatigued and were more likely to be disturbed by fatigue (t = -3.73, p < 0.001), especially general activity (t = -2.93, p < 0.001), walking ability (t = -3.6, p < 0.001) and normal work (t = -2.18, p = 0.03). Wearing the bracelet can get the LF/HF value of HRV in the time domain. The LF to HF and LF/HF disorder ratios were further divided into active and sleep phases. During sleep phase, the LF/HF disorder ratio was higher in the cancer group than in the control group regardless of whether fatigue was mild or moderate (t = -2.5, p = 0.01; t = -2.8, p = 0.01, respectively). The LF/HF disorder ratios was higher in cancer patients with mild fatigue than in cancer-free participants during sleep phase (40% vs 20%, respectively). Cancer patients with moderate fatigue had higher incidence of LF/HF disorder ratios than cancer-free participants (50% vs 20%, respectively). Conclusions: It can be concluded that the diagnostic criteria of subjective and objective CRF can distinguish cancer and cancer-free cases. Using this objective fatigue device could provide an indicator for further clinical monitoring. Clinical trial information: NCT04300842.