bluuebunny/crossref_metadata_2025_split huggingface dataset:
Biomarker testing rates among metastatic cancer patients with receipt of anti-cancer systemic therapy: A real-world data analysis.
Authors: DaCosta Byfield Stacey , Bapat Bela , Becker Laura , Schroeder Brock , Chatzitheofilou Ismini , Hostin Damon , Reyes Carolina , Fox John Leonard 
Published: October 2023

550 Background: Biomarker testing to direct individualized therapy options can optimize cancer patient outcomes, particularly for patients with advanced disease. Guideline-recommended molecular testing, comprehensive genomic profiling [CGP] and non-CGP testing, is covered by both Medicare and commercial private insurers (at least non-CGP).. This study examined real-world utilization of biomarker testing among cancer patients with metastatic disease who received anti-cancer systemic therapy. Methods: A retrospective analysis was conducted using de-identified administrative claims data from the Optum Labs Data Warehouse. Adults identified with 1 of 6 advanced cancer types from 1/2018 to 8/2021 were identified; index date was the first claim date for advanced disease. Patients with diagnosis codes indicating only lymph node involvement around the primary cancer were excluded. Continuous enrollment in a commercial (COM) or Medicare Advantage (MA) health plan with both medical and pharmacy benefits was required for 12 months prior to the index date (baseline), and ≥6 months after the index date (follow-up); patients with <6 months follow-up due to death were included. Receipt of anti-cancer systemic therapy during the follow-up was required. Biomarker testing was captured using Current Procedural Terminology (CPT®) codes indicating CGP (> 50 gene panels) or non-CGP (≤50 gene panels or single gene tests) during the study period. Rates of biomarker testing by cancer and insurance type, and receipt of targeted therapy and/ were assessed. Results: Result: There were 27,434 metastatic cancer patients meeting study criteria: 10,320 non-small cell lung (NSCLC), 5,525 breast (BC), 5,429 colorectal (CRC), 2,314 ovarian (OC), 872 gastric (GC) and 2,974 pancreatic (PC) cancer patients. 66% of patients were MA enrollees. The median age was 70 years and ranged from 67 years for BC patients to 71 years for NSCLC and PC patients. The median follow-up was 383 days, ranging from 246 days (for PC patients) to 564 days (for BC patients). Overall biomarker testing rates in the follow-up period were: 47% for NSCLC, 34% BC, 58% CRC, 61% OC, 48% GC, and 47% PC. Testing rates were lower among MA patients compared to COM patients (46% vs 53%). Receipt of targeted/monoclonal antibody therapy was higher (47% vs 33%, p<0.01) among patients with biomarker testing compared to those without. Conclusions: Rates of biomarker testing across metastatic tumor types are far from optimal despite guideline recommendations and insurance coverage for testing, and may affect quality of care. To improve adherence to biomarker testing guidelines, interventions to help overcome obstacles to biomarker testing are needed. Future analysis with this cohort will examine patient management and outcomes by receipt of testing, timing of testing and type of therapy received.
Smart phone application for early detection of adverse clinical outcomes in solid malignancy.
Authors: Sainamthip Panot , Kudtiyakarn Sirintip , Sakulma Palita , Supamontri Kittipoom , Sarojpaponpat Thithanan 
Published: October 2023

562 Background: The widespread application of mobile technology in healthcare, particularly in the realm of telemedicine and Patient-Reported Outcomes (PRO), is revolutionizing care. We have developed a mobile application that leverages artificial intelligence (AI) and The Internet of Medical Things (IoMT) using machine learning (ML) assisted dashboard to enable real-time documentation and communication between patients and healthcare providers. The aim of our research is to explore the potential of this innovative combination in boosting patient quality of life (QoL) and enabling prompt, effective patient management. Methods: We conducted a pilot randomized controlled trial. The trial focused on comparing the quality of life at baseline, 1 month, and 3 months of cancer patients receiving standard conventional follow-up care versus those using our intervention. Patients receive a smartwatch and download our application, while a monitoring care team was established. Our application incorporates guidelines from MacMillan Cancer providing ML-guided symptom assessment. If adverse drug reactions or treatment effects occurred, the application provided prompt self-care suggestions. The secondary outcomes evaluated included cumulative emergency visits, cancer care knowledge assessment results, and the feasibility. Results: Between December 1, 2022, and June 1, 2023, a total of 30 patients were recruited for the trial. The average age of the patients was 51 years, with a majority being women (76.6%). The cohort comprised five types of solid malignancies, with breast cancer accounting for 56.6%. Most patients were in stage 4, and the aim of treatment for them was palliative (60%). The mean FACT-G scores were similar between the two groups at all time points. However, the control group showed a significant decrease in Functional Well-Being at 3 months compared to baseline (P = .025). Additionally, while the Intervention group had zero emergency visits, the Control group experienced a total of five visits (P = .016). The literacy test scores showed no significant difference. On average, individuals spent 60 minutes per day using the application, indicating high user engagement and overall feasibility. Conclusions: Mobile application, powered by AI and IoMT, has the potential to significantly impact patient care and further research is warranted to explore its broader implementation in healthcare settings. Clinical trial information: TCTR20230331002 .[Table: see text]
Automating the identification of patients admitted to tertiary care hospital with immunotherapy related adverse events (irAEs).
Authors: Manela Alon , Chaudhuri Anamika , Pimkina Julia S. , Camacho Svetlana , Ali Mohamed , Grosse Danielle , Day Angela , Jacobson Joseph 
Published: October 2023

566 Background: Immune checkpoint inhibitors (ICIs) have revolutionized oncology by offering prolonged survival in advanced cancers that were previously treatment-refractory. However, the effectiveness of these agents is accompanied by the potential for autoimmune toxic effects known as immune-related adverse events (irAEs). These adverse effects are difficult to predict, can be long-lasting, and occasionally fatal. Early identification and treatment of irAEs can reduce morbidity and enable prompt resumption of anti-cancer therapy, ultimately improving patient outcomes. Methods: We manually reviewed 658 patient charts of individuals who received ICIs and were admitted to a tertiary care hospital over a three-month period. This process identified 750 phrases indicative of irAEs. Subsequently, an in-house Epic reporting team developed a program to search for these specific phrases within the charts of patients admitted to the hospital who had received ICIs in the 24 months prior to their admission. During the program's development, its performance was compared to the previous manual chart review process. Results: A comparative analysis was conducted between the new program and the current manual chart review over a three-month period. Implementing the new program resulted in a 68% reduction in the average number of charts requiring manual review (69 vs. 219 per month). Furthermore, the program identified an additional 17% of cases (21 vs. 18 per month). The positive predictive value increased by 400% (32% vs. 8% per month), and the time required to complete the task decreased by 70% (4.6 vs. 15 hours per month). Conclusions: Utilizing an EMR-based program that automatically searches for patients admitted with irAEs can significantly shorten the time required for identification and subsequent management of these complex patients.
Natural language processing identification of adverse events on immunotherapy clinical trials: Accuracy compared to trial reporting and physician review.
Authors: Friesner Isabel D , Kwon Daniel H , Ni Lisa , Bridge Mark , Fong Lawrence , Hong Julian C. 
Published: October 2023

571 Background: Adverse event (AE) assessment is an important component of clinical trials and routine clinical care. Natural language processing (NLP) approaches may facilitate automated methods to identify AEs, potentially allowing both early intervention and streamlined trial AE documentation. We previously demonstrated that an NLP pipeline had high accuracy for identifying toxicities associated with radiotherapy from clinical notes compared to physician reviewers. This study aims to evaluate this pipeline in comparison with clinical trial reporting and physician reviewers. Methods: We identified a total of 53 encounters across 20 patients enrolled over four immunotherapy trials with both corresponding notes and associated AEs. Two independent physician reviewers (DK & LN) identified symptoms documented in notes using a National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) v5.0 reporting tool. Likewise, a previously described NLP pipeline based on Apache clinical Text Analysis Knowledge Extraction System was used on the notes to identify CTCAE symptoms. NLP-extracted symptoms and physician reviewer identification were assessed for sensitivity and specificity compared to trial-reported AEs. Cohen kappa score was used to assess inter-rater reliability between physician reviewers. Results: The five most reported trial AEs were diarrhea (17%), fever (17%), fatigue (15%), constipation (13.2%), and nausea (11.3%) (Table).Except for anorexia, NLP had greater or equal sensitivity for reported AEs compared to both reviewers (R1, R2). However, both reviewers had greater specificity than NLP. Between the reviewers, vomiting and anorexia had the highest Cohen kappa scores (1, 0.88), while fever and constipation had the lowest (0.31, 0.47, Table). Conclusions: NLP can detect toxicity symptoms from clinical notes with higher or equal sensitivity than physician reviewers, while physician reviewers had greater specificity. These results indicate that our NLP approach over-identifies symptoms while manual reviewers are more conservative in their approach. Therefore, NLP may best be served in the screening setting. NLP may also help reduce reviewer inter-rater variability, particularly in fever, constipation, and fatigue symptoms.[Table: see text]
Streamlining chemotherapy prior authorizations: Leveraging resources to cut delays and boost revenue.
Authors: Bhardwaj Aarti Sonia , Liu Mark , Pintova Sofya , Williams Chanda , Noble-Kirk Alana , Sheng Tianxiang , Liggins Jennifer , Favre Daniel , Smith Cardinale B. 
Published: October 2023

573 Background: Prior authorizations (PAs) for systemic cancer treatments are a barrier to timely quality cancer care delivery. The high administrative burden to complete peer to peer’s (P2P) and appeals leads to care delays and revenue losses. At our tertiary academic cancer center, in partnership with our cancer registry and Epic teams, we shifted the clerical work required for PAs from clinicians to certified tumor registrars (CTRs) to increase authorization efficiency and decrease financial losses from denials. Methods: Clinicians place the treatment plan order which goes to the CTR work queue. We leveraged the Epic staging smart form to consolidate and auto-populate common elements needed for PA. The CTRs then complete and validate the form prior to the authorization specialists obtaining the PA. We compared the pre (1/2022-9/2022) and post-implementation (11/2022-5/2023) periods on our primary outcomes: average monthly number of PAs pending review (including P2P, appeals and authorizations requiring clarifying clinical documentation) and average time spent on PAs by the authorization specialists. We compared the same post implementation period to the same time the year before on the financial loss from denied PAs. Analyses were conducted using Welch's t-test. Results: There was an 11% reduction in the average monthly number of PAs pending review (350 vs 313; p=0.27) from pre to post implementation. There was an average improvement of 6 hours spent obtaining a final approval/per authorization (100 vs 106 hours; p=0.64) from pre to post implementation. In addition, there was a trend toward improvement in financial losses from PA denials from Dec 2022 to March 2023 and a cost savings of ~ 7 million dollars from the same time period pre-intervention; p=0.01 (Table). Conclusions: By leveraging the EHR and optimizing existing non-clinical staff workflows, we demonstrated a sustained decrease in the number of ambulatory PAs pending review, time spent on the PAs and significantly decreased financial losses from denials. Future work will include treatment and disease specific optimizations and evaluating the impact on clinician well-being.[Table: see text]
The use of mobile electronic prescribing to drive quality of oncology care delivery.
Authors: Speicher Stephen , Wynn Franklin , Porter Jason 
Published: October 2023

586 Background: This pilot technology enhancement project set out to improve the quality of care for oncology patients by developing a mobile application for writing prescriptions (non-chemotherapy) and narcotics. We describe usage of this new mobile application and associated timeliness of care delivery, provider workflow efficiencies and patient safety metrics. Methods: Agile methodology was used to develop the workflow and software for the mobile application. Internal Patient Safety Usability Testing (PSUT) was used to assess high-risk tasks amongst potential users (n = 7). High-risk tasks were defined as those which, based on human error, could lead to potential patient harm or unsafe conditions. High-risk tasks included the ability for users to understand/acknowledge allergy and drug interactions, edit a selection, select and send to the appropriate pharmacy, and locate the record of the completed prescription within the patient’s chart through the mobile application. A pilot study was also conducted among oncologists (n = 14). A quantitative metric was measured to understand the adoption and usability of the mobile application. Qualitative feedback was also obtained. Results: 1. PSUT identified a need for improvement with recognition of interaction severity and differentiating an allergy versus a drug interaction. A design modification was also made to introduce a three-second hold to acknowledge an allergy. This was done to encourage users to take appropriate time to visualize and comprehend allergy and interaction information before advancing the workflow. 2. During the pilot study, 400 mobile prescriptions were written. After-hours writing of prescriptions in the mobile application increased (11.8%) compared to the EMR (3.5%). The average amount of time from initiation of writing a prescription to completion was 62 seconds in the mobile application. There were no high-risk patient safety incidents observed during the pilot study. Qualitative data from participants included themes around convenience, feasibility, improved access to care, and safety. Conclusions: Usage of the pilot mobile application demonstrated a safe and efficient approach to increase the quality of care delivery for oncology patients. Usage also suggests an increase in patient access to care after hours.
Objective physical activity measures to predict hospitalizations among patients with cancer receiving chemoradiotherapy.
Authors: Scotina Anthony D. , Lavine Jennie S. , Izmailova Elena S. , Kabarriti Rafi , Mehta Keyur J. , Garg Madhur , Kalnicki Shalom , Ohri Nitin 
Published: October 2023

592 Background: There is an unmet need for measures that predict adverse events during cancer therapy. Previous studies have demonstrated feasibility and value of collecting physical activity (PA) data from cancer patients undergoing curative-intent chemoradiotherapy using a commercial fitness tracker. Here, we compare the predictive value of objective PA measures versus standard clinical information and assess whether additional PA measures beyond daily step count help predict hospitalization events during treatment. Methods: Cancer patients enrolled in NCT03115398 (2017-2020) wore a Garmin VivoFit continuously during chemoradiotherapy for a variety of solid tumors. ECOG performance status (PS) was assessed at baseline. PA measures tabulated for each day included total step count, peak 30-minute cadence (average step rate over the 30 most active minutes [not necessarily consecutive]), and sedentary time. Baseline PA measures were defined as mean values from up to 14 days prior to start of chemoradiotherapy. Cox regression models with time-dependent (3-day rolling averages of PA measures and recent EORTC QLQ-C30 quality of life [QoL] score) and time-fixed (baseline PA variables, age, sex, cancer diagnosis) covariates were used to identify predictors of hospitalization. The incremental predictive value of PA measures was assessed using logistic regression models and five-fold cross validation. Results: One hundred thirty-eight patients were included in the analysis. The most common cancer diagnoses were gastrointestinal cancer (29%) and head and neck cancer (28%). Twenty-four patients (17%) were hospitalized during chemoradiotherapy. Univariate Cox regression models identified baseline step count (hazard ratio [HR] = 0.85 per 1000 steps, 95% confidence interval [CI]: 0.74-0.98, p = 0.03) and baseline peak 30-minute cadence (HR = 0.86 per 10 steps/min, 95% CI: 0.74-0.99, p = 0.03) as the most significant prognostic predictors of hospitalization. Neither baseline PS nor baseline sedentary time were significant predictors of hospitalization risk. Including both baseline step count and peak 30-minute cadence in multivariable models did not improve predictive power. During treatment, peak 30-minute cadence was identified as the most powerful dynamic predictor of short-term hospitalization risk (HR = 0.83 per 10 steps/min, 95% CI: 0.75-0.92, p < 0.001). Conclusions: This study examined the relationship between a suite of PA measures and risk of hospitalization during concurrent chemoradiotherapy. Our results suggest that before treatment, daily step count average can serve as a simple and effective metric for identifying high-risk patients. During treatment, measures of peak activity may have additional utility for identifying patients in need of enhanced supportive care.
Impact of medically integrated pharmacies on oral anticancer medication prescription abandonment.
Authors: Doshi Gury K. , Wilfong Lalan S. , Dave Neal , Hemmen Wendy 
Published: October 2023

66 Background: Due to the high cost of oral anticancer medications, financial toxicity may limit patient access to treatment. Abandonment occurs when a patient does not fill a prescription. Higher out of pocket costs have been associated with higher rates of prescription abandonment of treatment across cancers with national abandonment rates typically reported as 18%. An integrated patient assistance process within the care team as occurs in a medically integrated pharmacy is postulated to lower abandonment rates. We measured prescription abandonment rates in the Texas Oncology (TXO) medically integrated pharmacy (MIP). Methods: We reviewed prescription data from the electronic health record (EHR) and our pharmacy dispensing system, for all new oral anticancer medications prescribed from January 2022 to December 2022. Prescriptions that were sent electronically from the EHR but did not have a corresponding dispense in the pharmacy dispensing software were classified as having a barrier to fill. Among the prescriptions that had a barrier to fill, we randomly selected cases for further detailed review to identify cause of barrier. By reviewing the EHR, we also examined whether patients filled any alternate oral or injectable anticancer agent within 60 days due to a formulary block, patient choice or physician choice. Abandonment was defined when patients did not receive their treatment or appropriate alternative treatment within 60 days of the written prescription date. Results: 17,442 prescriptions for oral anticancer medications were prescribed by Texas Oncology physicians. Of those 8460 were filled by the TXO MIP without extensive barriers. 8982 of prescriptions were not filled at our TXO MIP and of those 2895 (32%) prescriptions were further reviewed to determine cause of barrier to fill. 1435 (49.5%) prescriptions had a pharmacy benefit manager (PBM) design that routed the prescription to an external pharmacy and out of the continuity of care of the MIP. 827 (29%) of patients experienced financial toxicity and received free drug assistance with the help of the MIP patient assistance team. 72 (1.3 %) of patients were started on an appropriate alternative therapy with input from the medically integrated care team. 15 patients did not start their oral anticancer treatment which resulted in an abandonment rate of less than 1%. Conclusions: National abandonment is reported as high as 18% largely due to high out of pocket costs. The Texas Oncology MIP has a dedicated patient assistance team. Within the MIP, although 29% of patients had a financial barrier to fill, the prescription abandonment rate was <1%. Oral anticancer medications filled in the MIP led to decreased prescription abandonment due to the integrated patient assistance, pharmacy, and clinical teams. A medically integrated pharmacy should be the preferred approach for specialty cancer medications.