Capturing discrete complete staging elements in EPIC for new and progressing breast (B) and prostate (P) cancer patients: A quality training program pilot.

2019 ◽  
Vol 37 (27_suppl) ◽  
pp. 117-117
Author(s):  
Linda D. Bosserman ◽  
Denise Morse ◽  
Kathleen McNeese ◽  
Ridaa Atcha ◽  
Niki Himat Patel
Keyword(s):  
Cancer Patients ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Discrete Elements ◽  
Quality Metric ◽  
Clinical Notes ◽  
Leadership Support ◽  
Medical Oncologists ◽  
Abstract Data ◽  
Survey Results

117 Background: Staging is a fundamental quality metric. Entry of discrete elements of staging in the EHR supports & improves efficiencies for clinical notes, therapy summaries, survivorship plans, clinical decision making, pathway use, timely authorizations, clinical trial eligibility evaluations, as well as clinical, financial & quality outcomes. Staging of all new & progressing patients in our EPIC EHR was set as a priority in 2018 but baseline entry through 2018 was below target goals. Methods: A Staging QTP project group was formed & participated in the COH-ASCO QTP Fall training. Baseline capture of staging data in EPIC for campus and community (enterprise) sites for Breast (B) & Prostate (P) patients was collected. QTP processes were used to develop and implement a 12-week pilot with PDSA cycles at weeks 8 and 12. B & P cancer patients seen by 3 B & 2 P medical oncologists (MO), 5B surgeons (S) & 3P urologists (U) agreed to participate in a pilot study. Disease leads determined the elements of complete staging: 9-10 for B & 8 for P. Training was done for all campus APPs & for doctors in the pilot. Follow up by the QTP team was done at clinics during the pilot. Reports were built to capture specific staging elements for B & P patients by doctor. Reports were sent out starting week 9 indicating patients without complete staging with missing elements. A survey about the process was done by participant MDs & APPS weeks 8 & 12. Results: Baseline enterprise wide complete staging in Epic was 6% for B cancer & 3% for P cancer. 6 week PDSA showed capture of complete staging for B-MO & S was 20% and for P-MO & U was 12%. QTP Pilot ends 6/19 & abstract data will be updated for the staging & survey results. Additional results on complete staging for progressing patients is being captured. Conclusions: Complete staging in the EHR requires definition, training, resourcing, leadership support and feedback. Initial results show all new patients had initial staging data entered and complete staging rates increased significantly for new B & P cancer patients before weekly feedback was sent. Analysis of survey results will inform future PDSA cycles & plans for complete staging enterprise wide.

scholarly journals Impact of the 12-Gene Colon Cancer Assay on Clinical Decision Making for Adjuvant Therapy in Stage II Colon Cancer Patients

2016 ◽  
Vol 19 (1) ◽  
pp. 82-87 ◽  
Author(s):  
Baruch Brenner ◽  
Ravit Geva ◽  
Megan Rothney ◽  
Alexander Beny ◽  
Ygael Dror ◽  
...  
Keyword(s):  
Decision Making ◽  
Colon Cancer ◽  
Adjuvant Therapy ◽  
Cancer Patients ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Stage Ii ◽  
Stage Ii Colon Cancer

A community-based program for personalized cancer care using next-generation sequencing (NGS).

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 11102-11102
Author(s):  
Shile Liang ◽  
Pranil Chandra ◽  
Zeqiang Ma ◽  
Debbie Haynes ◽  
James Prescott ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Cancer Patients ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Molecular Profiling ◽  
Targeted Agents ◽  
Community Based ◽  
Off Label ◽  
Molecular Abnormalities ◽  
Tumor Types

11102 Background: Despite growing interest and need, molecular profiling of tumor samples is largely unavailable in community cancer centers, where nearly 80% of cancer patients (pts) are treated. In 10/12, Sarah Cannon Research Institute (SCRI) launched a community-based molecular profiling program to: 1) better understand the molecular constituency of cancer patients, 2) identify appropriate pts for phase I and II clinical trials of targeted agents, and 3) identify pts with molecular abnormalities responsive to FDA-approved agents. Methods: Eligible pts consented to testing of available biospecimens, which were interrogated for alterations in 35 cancer-related genes using NGS (1000X average coverage) in a CLIA/CAP laboratory. Results were reported to the treating physician within 14 days and stored in a database to enable correlation with clinical outcomes. Results: As of 1/13, 209 pts had been enrolled with 84% having sufficient material for assay. At least 1 mutation was detected in 46% of tumors. Results in the 3 most commonly assayed tumor types are summarized (Table). Mutations for which there are FDA-approved targeted agents were found in 14 off-label tumors (EGFR 4, KIT 3, SMO 3, BRAF 2, HER2 2). 40 pts (27%) were subsequently enrolled in clinical trials; in 19 of these, assay results influenced clinical trial selection. Conclusions: This program provides molecular profiling data to community oncologists for clinical decision making. Experience to date indicates this information can be provided in a timely manner for incorporation into clinical practice. Profiling results will enable: 1) selection of pts with appropriate tumor targets for investigational targeted agents, 2) enhanced study enrollment, 3) evaluation of FDA approved targeted agents in off-label tumor types, and 4) correlation of treatment outcomes with patterns of tumor molecular abnormalities. [Table: see text]

scholarly journals Deliberações clínicas na atuação em pacientes com câncer de cabeça e pescoço: atualidades e desafios na disfagia

2020 ◽  
Vol 31 (4) ◽  
pp. 693-697
Author(s):  
Guilherme Maia Zica ◽  
Andressa Silva de Freitas
Keyword(s):  
Decision Making ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer ◽  
Clinical Decision Making ◽  
Clinical Decision

Starmer H, Edwards J. Clinical Decision Making with Head and Neck Cancer Patients with Dysphagia. Semin Speech Lang. 2019 Jun;40(3):213-226.

scholarly journals Medical Liability in Cancer Care During COVID-19 Pandemic: Heroes or Guilty?

2020 ◽  
Vol 8 ◽  
Author(s):  
Rosario Barranco ◽  
Carlo Messina ◽  
Alessandro Bonsignore ◽  
Carlo Cattrini ◽  
Francesco Ventura
Keyword(s):  
Cancer Patients ◽  
Cancer Care ◽  
Clinical Decision Making ◽  
Clinical Performance ◽  
Performance Status ◽  
Clinical Decision ◽  
Hospital Environment ◽  
Tumor Board ◽  
Medical Liability ◽  
Careful Examination

Background: The COVID-19 outbreak rapidly became a public health emergency affecting particularly the frail category as cancer patients. This led oncologists to radical changes in patient management, facing the unprecedent issue whether treatments in oncology could be postponed without compromising their efficacy.Purpose: To discuss legal implications in oncology practice during the COVID-19 pandemic.Perspective: Treatment delay is not always feasible in oncology where the timing often plays a key role and may impact significantly in prognosis. During the COVID-19 pandemic, the oncologists were found between the anvil and the hammer, on the one hand the need to treat cancer patients aiming to improve clinical benefits, and on the other hand the goal to reduce the risk of COVID-19 infection avoiding or delaying immunosuppressive treatments and hospital exposure. Therefore, two rising scenarios with possible implications in both criminal and civil law are emerging. Firstly, oncologists may be “accused” of having delayed or omitted the diagnosis and/or treatments with consequent worsening of patients' outcome. Secondly, oncologists can be blamed for having exposed patients to hospital environment considered at risk for COVID-19 transmission.Conclusions: During the COVID-19 pandemic, clinical decision making should be well-balanced through a careful examination between clinical performance status, age, comorbidities, aim of the treatment, and the potential risk of COVID-19 infection in order to avoid the risk of suboptimal cancer care with potential legal repercussion. Moreover, all cases should be discussed in the oncology team or in the tumor board in order to share the best strategy to adopt case by case.

Graphical representations of patient tolerability data: Recommendations from the National Cancer Institute (NCI) Cancer Moonshot Standardization Working Group.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18612-e18612
Author(s):  
Gillian Gresham ◽  
Gina L. Mazza ◽  
Blake Langlais ◽  
Bellinda King-Kallimanis ◽  
Lauren J. Rogak ◽  
...  
Keyword(s):  
Working Group ◽  
Best Practice ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Section 508 ◽  
Graphical Displays ◽  
Patient Reported ◽  
Tolerability Data ◽  
Survey Results ◽  
Data Incorporate

e18612 Background: Effective communication of treatment tolerability data is essential for clinical decision making and improved patient outcomes, yet standardized approaches to the analysis and visualization of tolerability data in cancer clinical trials are currently limited. To address this need, the Standardization Working Group (SWG) was established within the NCI Cancer Moonshot Tolerability Consortium. This abstract describes the SWG’s initiative to develop a publicly accessible online toolkit with a comprehensive set of guidelines, references, and resources for graphical displays of tolerability data. Methods: A multidisciplinary group of PRO researchers including biostatisticians, clinicians, epidemiologists, and representatives from the NCI and FDA convened monthly to discuss toolkit development and content. Considerations for standardization of graphical displays of tolerability data included (1) types of graphical displays, (2) incorporation of missing data, (3) labeling and color schemes, and (4) software to produce graphical displays. For consistency, considerations of tolerability relied on the Patient-Reported Outcomes version of the CTCAE (PRO-CTCAE), which includes 124 items assessing the frequency, severity, interference, and/or presence of 78 symptomatic adverse events. Graphical displays were generated using simulated PRO-CTCAE data and summarized by composite score (range 0-3).Color schemes that were Section 508 compliant and color blindness accessible were created. Surveys were distributed to 68 consortium members to assess preferences and interpretability of the graphical displays. Results: The SWG created graphical displays for PRO-CTCAE data, including bar charts, butterfly plots, and Sankey diagrams and compiled SAS macros and R functions to do so. Graphical displays made available in the toolkit maximize the use of PRO-CTCAE data, incorporate missingness, support between-arm comparisons, and present data longitudinally over treatment cycles or study timepoints. Survey results for labeling and color schemes were summarized and informed a list of short labels for PRO-CTCAE items (e.g., “radiation burns” for “skin burns from radiation”) and standardized color schemes for use in graphical displays. Survey results were also summarized to provide insight into PRO researchers’ ability to accurately interpret the graphical displays. Conclusions: Standardizinggraphical displays is important for improving the communication and interpretation of tolerability data. The type of graphical display used depends on the purpose of the analysis and should be tailored to the intended audience, including patients. This toolkit will provide a comprehensive resource with best practice recommendations.

The Obesity Paradox in Cancer: How Important Is Muscle?

2018 ◽  
Vol 38 (1) ◽  
pp. 357-379 ◽  
Author(s):  
Elizabeth M. Cespedes Feliciano ◽  
Candyce H. Kroenke ◽  
Bette J. Caan
Keyword(s):  
Cancer Patients ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Obesity Paradox ◽  
Future Research ◽  
Reverse Causality ◽  
Research Directions ◽  
Future Research Directions ◽  
Relationship Of ◽  
The Relationship

Although higher body mass index (BMI) increases the incidence of many cancers, BMI can also exhibit a null or U-shaped relationship with survival among patients with existing disease; this association of higher BMI with improved survival is termed the obesity paradox. This review discusses possible explanations for the obesity paradox, the prevalence and consequences of low muscle mass in cancer patients, and future research directions. It is unlikely that methodological biases, such as reverse causality or confounding, fully explain the obesity paradox. Rather, up to a point, higher BMI may truly be associated with longer survival in cancer patients. This is due, in part, to the limitations of BMI, which scales weight to height without delineating adipose tissue distribution or distinguishing between adipose and muscle tissue. Thus, cancer patients with higher BMIs often have higher levels of protective muscle. We assert that more precise measures of body composition are required to clarify the relationship of body size to cancer outcomes, inform clinical decision-making, and help tailor lifestyle interventions.

scholarly journals On the predictability of postoperative complications for cancer patients: a Portuguese cohort study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Daniel Gonçalves ◽  
Rui Henriques ◽  
Lúcio Lara Santos ◽  
Rafael S. Costa
Keyword(s):  
Postoperative Complications ◽  
Cancer Patients ◽  
Predictive Models ◽  
Clinical Decision Making ◽  
Characteristic Curve ◽  
Postoperative Mortality ◽  
Clinical Decision ◽  
Risk Scores ◽  
Support Tool ◽  
The Creation

AbstractPostoperative complications are still hard to predict despite the efforts towards the creation of clinical risk scores. The published scores contribute for the creation of specialized tools, but with limited predictive performance and reusability for implementation in the oncological context. This work aims to predict postoperative complications risk for cancer patients, offering two major contributions. First, to develop and evaluate a machine learning-based risk score, specific for the Portuguese population using a retrospective cohort of 847 cancer patients undergoing surgery between 2016 and 2018, for 4 outcomes of interest: (1) existence of postoperative complications, (2) severity level of complications, (3) number of days in the Intermediate Care Unit (ICU), and (4) postoperative mortality within 1 year. An additional cohort of 137 cancer patients from the same center was used for validation. Second, to improve the interpretability of the predictive models. In order to achieve these objectives, we propose an approach for the learning of risk predictors, offering new perspectives and insights into the clinical decision process. For postoperative complications the Receiver Operating Characteristic Curve (AUC) was 0.69, for complications’ severity AUC was 0.65, for the days in the ICU the mean absolute error was 1.07 days, and for 1-year postoperative mortality the AUC was 0.74, calculated on the development cohort. In this study, predictive models which could help to guide physicians at organizational and clinical decision making were developed. Additionally, a web-based decision support tool is further provided to this end.

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