Central Arkansas Radiation Therapy Institute

292 Background: Oncology patients living in medically underserved areas are at increased risk of care gaps due to limited communication with their care teams –often stemming from low utilization of the patient portal CareSpace, our EMR's two-way patient portal. These gaps can lead to missed provider communications, missed appointments, and limited access to medical records. Notably, 45% of patients were listed as having declined portal invitations, often without fully understanding the portal’s benefits. To address this issue, we aimed to quantify the number of patients in "declined” status and implement targeted quality improvement interventions at multiple points of care, with the goal of reducing the “declined” status to 35%. Methods: Three Plan-Do-Study-Act (PDSA) cycles were conducted to identify and address the causes of low CareSpace utilization focusing on the functionality of the CareSpace invitation tool and the performance of our Patient Support Services (PSS) staff. Results: PDSA 1: Analysis revealed that patients were automatically assigned to “declined” status if no email address was entered into the system. To address this, we disabled the automation process. PDSA 2: We implemented a new workflow in which email addresses were collected at the referral stage, allowing the Central Referral Office team to automatically send CareSpace portal invitations. PDSA 3: Further analysis showed that new patient packet forms were missing a designated line for email addresses, and that PSS staff were inconsistent in collecting email data and sending invitations. With support from departmental leadership, we revised the patient intake forms to include an email address field and reinforced training for the PSS team to ensure consistent verification and invitation practices. The removal of the automated “declined” designation in PDSA 1 led to an immediate drop in patients listed as “declined,” from 45% to 36%. Following PDSA 2, the rate decreased further to 32%, surpassing our initial target. Finally, the improvements implemented during PDSA 3 reduced the “declined” status rate to 28% (From x to y patients). Conclusions: We identified key barriers within both the CareSpace app and the PSS workflow, and addressed them through three PDSA cycles with input from all stakeholders. As a result, we reduced the proportion of patients in “declined” status to 28%, surpassing our initial goal of 35%, and generating interest in similar initiatives across the organization. These interventions significantly improved patient portal enrollment, particularly in medically underserved areas. Barriers and Next Steps: One ongoing challenge is that portal invitations expire after 48 hours. To address this, we are partnering with our Business Intelligence team to develop an automated bot that will periodically resend invitations. This solution is expected to further reduce barriers to portal adoption and enhance access to care.

Abstract BackgroundGynecological cancers are among the most prevalent cancers in women worldwide. Brachytherapy, often used as a boost to external beam radiotherapy, is integral to treatment. Advances in computation, algorithms, and data availability have popularized machine learning.ObjectiveTo develop and compare machine learning models for predicting grade 3 or higher toxicities in gynecological cancer patients treated with high dose rate (HDR) brachytherapy, aiming to contribute to personalized radiation treatments.MethodsA retrospective analysis on gynecological cancer patients who underwent HDR brachytherapy with Syed-Neblett or Tandem and Ovoid applicators from 2009 to 2023. After exclusions, 233 patients were included. Dosimetric variables for the high-risk clinical target volume (HR-CTV) and organs at risk, along with tumor, patient, and toxicity data, were collected and compared between groups with and without grade 3 or higher toxicities using statistical tests. Six supervised classification machine learning models (Logistic Regression, Random Forest, K-Nearest Neighbors, Support Vector Machines, Gaussian Naive Bayes, and Multi-Layer Perceptron Neural Networks) were constructed and evaluated. The construction process involved sequential feature selection (SFS) when appropriate, followed by hyperparameter tuning. Final model performance was characterized using a 25% withheld test dataset.ResultsThe top three ranking models were Support Vector Machines, Random Forest, and Logistic Regression, with F1 testing scores of 0.63, 0.57, and 0.52; normMCC testing scores of 0.75, 0.77, and 0.71; and accuracy testing scores of 0.80, 0.85, and 0.81, respectively. The SFS algorithm selected 10 features for the highest-ranking model. In traditional statistical analysis, HR-CTV volume, Charlson Comorbidity Index, Length of Follow-Up, and D2cc - Rectum differed significantly between groups with and without grade 3 or higher toxicities.ConclusionsMachine learning models were developed to predict grade 3 or higher toxicities, achieving satisfactory performance. Machine learning presents a novel solution to creating multivariable models for personalized radiation therapy care.

A growing sense of the need to define good-quality cancer care has emerged in the past decade of the 20th century. The goals were to assess, improve, and reward quality. Animated debates between cancer care delivery academic and community organizations, governmental agencies, and insurance companies have led to multiple initiatives and pilot projects. ASCO was on the cutting edge of quality in oncology movement. We can define three phases, overlapping rather than sequential, in ASCO's journey. The first phase was generating definitions of good-quality care characterized by the publication of ASCO guidelines. The second phase was the creation of the tools to measure the implementation of standards of care with the creation of the Quality Oncology Practice Initiative (QOPI). The third phase was the launch of a comprehensive approach to cancer care quality as illustrated by QOPI Certification, then the more complete iteration, ASCO Certified Program. The latter is the most elaborate program to define quality from the patient and health care providers' perspective on one hand and governmental agencies and insurance providers' perspective on the other. Since the publication of the Ensuring Quality Cancer Care Report in 1998 to the ASCO Certified in 2023, a quarter century has elapsed. ASCO did not operate in a vacuum. Through collaborative efforts, reacting to and interacting with various players, it has advocated for positive change. During this period, ASCO has led the movement of quality in oncology intelligently and with the upmost sense of responsibility toward the patients, health care professionals, and society at large. While many of these efforts began domestically, their reach is extending globally through research, education, and the promotion of equitable care.

Este trabalho analisa, sob as perspectivas léxico-semântica e cultural, as unidades lexicais registradas na Carta QSL 0237.a - mulato. do Atlas Linguístico de Mato Grosso do Sul – ALMS (2007) como denominação das “pessoas que têm a pele pouco escura porque só a mãe ou só o pai é negro”. Para a execução do estudo, foi traçado o seguinte percurso metodológico: definição da fonte e do corpus, seguido da consulta ao ALMS e respectivo levantamento dos dados lexicais cartografados. Na sequência, os dados foram quantificados e organizados para posterior análise sob a ótica da Teoria Lexical e da Antropologia Linguística. O universo de dados catalogados reuniu unidades léxicas como moreno, mestiço, pardo, negro, misto, tostado, bugre, caboclo moreno, morena canela, café com leite, meio branco, preto, mameluco, bugre, totalizando 21 itens lexicais. O registro da forma lexical moreno claro, com 7,81% de ocorrência, por exemplo, indica que o uso de um termo considerado amenizador (moreno) é reforçado por outro (claro), com maior carga suavizadora. Pela análise realizada, depreende-se, que tais unidades, já constituintes do repertório lexical dos falantes sul-mato-grossenses, além de nomearem o tipo humano descrito pela pergunta, passaram a indicar representações sociais, o que desembocou em traços de apropriação cultural. A análise demonstrou aspectos da relação existente entre fatores linguísticos e extralinguísticos, no conjunto vocabular dos informantes sul-mato-grossenses examinado, validando o argumento de que a língua constitui ferramenta dinâmica da cultura local e que a norma linguística, particularmente a lexical, expressa aspectos significativos, relacionados aos valores veiculados pela sociedade.

Las muestras de broncoaspirado y lavado broncoalveolar se sembraron en agares TSA con 5% de sangre de carnero y chocolate en condiciones de aerobiosis con 7,5% de CO 2 , en agares McConkey y Columbia (CNA) en condiciones de aerobiosis y en agar Brucella en condiciones de anaerobiosis. En la tinción de Gram se observaron cocobacilos gramnegativos, y en agar McConkey se observaron a las 48 h 10000 UFC/ml de crecimiento lento, oxidasa y catalasa positivas, siendo identificadas mediante el sistema API R 20 NE (Biomerieux, Marcyl'Étoile, Francia) como B. bronchiseptica. El antibiograma se realizó mediante la técnica disco-difusión en agar Mueller-Hinton resultando sensible a piperacilina/tazobactam, ceftazidima, carbapenem, aminoglucósidos, quinolonas y colistina, Neumonía asociada a derrame parapneumónico en paciente inmunocompetente causado por Bordetella bronchiseptica