Norwood’s Post

JobKash Location: Pembroke Pines, FloridaAt Memorial, we are dedicated to improving the health, well-being and, most of all, quality of life for the people entrusted to our care. An unwavering commitment to our service vision is what makes the difference. It is the foundation of The Memorial Experience.Summary:Collects, manages, analyzes, and reports on cancer/tumor incidence data.Responsibilities:Prepares statistical reports on mortality rates, treatment effects, incidence rates of various diagnostic categories and demographic variables.Complies with all reporting requests and requirements, including but not limited to requests by the NCDB, quarterly reports to the State Cancer Registry and the annual ACoS report.Contributes information to the Cancer Committee for the purpose of developing criteria for patient care evaluation, collecting data for protocol studies, and conducting audits.Identifies, analyzes and interprets the history, diagnosis, treatment, disease status and survival data of cancer patients treated in the organization.Competencies:ACCOUNTABILITY, ACCURACY, CUSTOMER SERVICE, HEALTH INFORMATION MANAGEMENT (HIM) SYSTEMS, HUMAN ANATOMY, MEDICAL TERMINOLOGY (1), PROBLEM SOLVING, RESPONDING TO CHANGE, STANDARDS OF BEHAVIOREducation and Certification Requirements:High School Diploma or Equivalent (Required)Certified Tumor Registrar (CTR CERT) - National Cancer Registrars Association (NCRA)Additional Job Information:Complexity of Work: Requires critical thinking skills, decisive judgment and the ability to work with minimal supervision. Must be able to work in a stressful environment and take appropriate action.Required Work Experience: One (1) year in an approved cancer registry.Working Conditions and Physical Requirements: Bending and Stooping = 0% Climbing = 0% Keyboard Entry = 80% Kneeling = 0% Lifting/Carrying Patients 35 Pounds or Greater = 0% Lifting or Carrying 0 - 25 lbs Non-Patient = 80% Lifting or Carrying 2501 lbs - 75 lbs Non-Patient = 0% Lifting or Carrying > 75 lbs Non-Patient = 0% Pushing or Pulling 0 - 25 lbs Non-Patient = 80% Pushing or Pulling 26 - 75 lbs Non-Patient = 0% Pushing or Pulling > 75 lbs Non-Patient = 0% Reaching = 80% Repetitive Movement Foot/Leg = 0% Repetitive Movement Hand/Arm = 80% Running = 0% Sitting = 80% Squatting = 0% Standing = 80% Walking = 80% Audible Speech = 80% Hearing Acuity = 80% Smelling Acuity = 0% Taste Discrimination = 0% Depth Perception = 80% Distinguish Color = 80% Seeing - Far = 80% Seeing - Near = 80% Bio hazardous Waste = 0% Biological Hazards - Respiratory = 0% Biological Hazards - Skin or Ingestion = 0% Blood and/or Bodily Fluids = 0% Communicable Diseases and/or Pathogens = 0% Asbestos = 0% Cytotoxic Chemicals = 0%

A Year of Journal Articles (Day 176/365): Musser, Robert Clayton, Rashaud Senior, Laura J. Havrilesky, Jordan Buuck, David J. Casarett, Salam Ibrahim, and Brittany A. Davidson. "Randomized Comparison of Electronic Health Record Alert Types in Eliciting Responses about Prognosis in Gynecologic Oncology Patients." Applied Clinical Informatics (2024). Summary: Objectives: - Compare response rates for different EHR alert types in eliciting GOC conversations for cancer patients. Methods: - Developed a validated "surprise at 6-month mortality" question as an Epic BestPractice Advisory (BPA) alert. Three alert versions: 1. Required on Open chart (pop-up) 2. Required on Close chart (navigator) 3. Optional Persistent (Storyboard) - Randomized alert versions based on patient medical record number. - Defined "meaningful responses" as "Yes" or "No", excluding deferrals. - Data collection over 6 months. Results: - Alerts triggered for 685 patients across 1,786 outpatient encounters. -Encounter level response rates: -----Highest for Required on Open (94.8%) -----Lower for Required on Close (90.1%) -----Lowest for Optional Persistent (19.7%) - Individual alert response rates: -----Highest for Optional Persistent (98.3%) -----Lowest for Required on Open (68.0%) -----"No" responses (suggesting poor prognosis): --------More likely with Optional Persistent (13.6%) and Required on Open (10.3%) --------Less likely with Required on Close (7.0%) Conclusions: - Required alerts triggered more responses than optional alerts (nearly 5x higher). - Alert timing impacts response rate and potential content. - The alert with the highest meaningful response rate also caused the most interruptions and deferrals. - Balancing these factors is crucial when designing clinical decision support for maximum impact.

Highlights from American College of Rheumatology 2023   Precision Medicine for autoimmunity was highlighted at the international @ACR2023 meeting Nov 11-14 by autoimmune patients, rheumatologists, pharma, and entrepreneurs all interested in personalized, next gen care powered by human intelligence AND machine learning.   ·        Genomic testing of RNA from blood or biopsy (skin or kidney) may provide information to physicians to assist them in prioritizing the biologic or small molecule medications for an individual patient.     ·        Machine learning algorithms that incorporate both clinical and molecular data guided by experienced autoimmune HCPs are the enablers of personalized care.    ·        The future of rheumatology for patient care is built on the foundation of experienced physicians and scientists whose discoveries reveal insights from real-world patient management. Patients living with autoimmune disease face complex challenges in their lives every day and THEIR input, captured in self report, biometric data, and mobile apps, can help inform needs for day-to-day disease management.   ·        Social determinants of health (SDOH):  The SDOH, such as income, education, access, and social support, have a significant impact on a person's health. If a doc writes a prescription, but the patient can’t get to a pharmacy in their neighborhood, there is a problem. Healthcare providers are increasingly recognizing the urgency and importance of addressing these SDOH to improve patient outcomes.   Looking Ahead to 2024 and Beyond Precision medicine tools are emerging that will allow optimized management of autoimmune disease.    ·        Genomic blood and biopsy tests may provide a physician with information that a patient is stable on their current medication or that a change is necessary to stabilize disease and decrease the frequency/severity of flares.   ·        Education of stakeholders is important to overcome barriers to adoption of #precisonmedicine testing, particularly in terms of clinician energy/capacity/willingness to onboard new tools.   Call to Action As rheumatology evolves, as always, rheumatology practitioners will take the lead in understanding and incorporating new technologies that improve patient care. By embracing collaboration and blending clinical experience and judgement with evolving genomic technologies, all stakeholders hope to shape a future of rheumatology that provides personalized care, accessible for all patients.    What do you think the future of healthcare holds? Share your thoughts in the comments below.   #precisonmedicine #futureofrheumatology #patientcare #autoimmunedisease #machinelearning #genomictesting

https://lnkd.in/ema7pE-K. The Cure Mito Foundation, www.CureMito.org, a leading mitochondrial Leigh Syndrome patient advocacy group with data and standards patient registry expertise, recently published their second peer-reviewed article in the Journal of the Society for Clinical Data Management based on modeling and testing in their Leigh patient registry database. The project focused on interoperability of Leigh syndrome patient registry data with CDISC standards, converting the data to CDASH and SDTM standards. This project is a key milestone for all stakeholders who collect real world data in rare diseases and need to make their data and findings interoperable. "Cure Mito Foundation is proud to be the first in the mitochondrial disease community to align our registry data with regulatory submission standards. In our published paper we have described the entire process of data mapping with the intent that it will help others who want to learn from our experience or undertake similar efforts.," said Sophia Zilber 🌺, Cure Mito board member and patient registry director.   MitoWorld, which is partnered with Cure Mito, is committed to supporting standards research, clinical applications with the data and patient group adoption as part of trying to speed the process to therapies in the rare mitochondrial disease area.  #ShineOnRare #mitochondrialdisease #mitoawareness #CureMito #nhs #cell #buckinstitute #fastercures #STATnews #SIAM #mitobridge #mitocanada #globalhealth #drugdevelopment #datasharing #collaboration ##mitochondria #drugdiscovery #drugdevelopment #cancer #neurodegenerativedisease #basicresearch #NCATS #Duchenne #PPMD #DMD #neuromuscular #musculardystrophy #CPath #FDA #raredisease #curelbsl #MitoWorld #Astellas #MitoFoundation #globalhealth #fastercures #drugdevelopment #datasharing

Excited to share that my tutorial paper "Bayesian transition models for ordinal longitudinal outcomes" coauthored with Frank Harrell, Thomas Stewart, and Ben French has been published open-access in Statistics in Medicine! Link: https://lnkd.in/e9XP7wsX Here is a brief description of our paper: Many COVID-19 clinical trials collect ordinal longitudinal outcome data, which is ordered data collected at multiple points in time. As an example, the ACTT-1 clinical trial collected ordinal patient status every day for 28 days. The ordinal states included asymptomatic, at home with moderate symptoms, hospitalized (with or without ventilation), recovered, or deceased. However, many of these clinical trials only analyze part of the data, such as looking at time-to-recovery, rather than analyzing the full ordinal longitudinal data. Ordinal longitudinal outcomes capture disease progression more fully than outcomes such as time-to-recovery, and the longitudinal dimension of the data can quantify when the treatment is most effective. Furthermore, ordinal longitudinal outcomes can accommodate terminal events such as death and recurrent events such as hospitalization. This tutorial shows how Bayesian ordinal transition models are a flexible modeling framework for analyzing ordinal longitudinal outcomes. These models can estimate clinically interpretable estimands such as the treatment difference in the mean number of days recovered over the course of the trial. This tutorial paper develops the theory from first principles and provides an application using data from ACTT-1 with code examples in R.

Bridging the Gap in Clinical Trial Diversity with Real-Time Data It is no hidden secret that the persistent lack of diversity in clinical trials remains a significant barrier to equitable healthcare outcomes. Sharing an insightful article I read this morning on why harnessing real-time data is essential for truly driving diversity in clinical trials: Why We Need to Harness Real-Time Data to Truly Drive Diversity in Clinical Trials At MaxisIT Inc., we recognize the critical need to enhance trial inclusivity and effectiveness through advanced data solutions. Our platform empowers researchers to: Monitor demographic data efficiently, ensuring diverse enrollment aligns with the epidemiology of the disease under study. Adjust recruitment strategies in real-time, thanks to instant data insights that highlight underrepresentation or recruitment challenges. Utilize data-driven narratives to tailor recruitment efforts, making sure that trial populations reflect the diversity of conditions across different racial and ethnic groups. We are committed to empowering clinical researchers with the tools needed to break down barriers and enhance the inclusivity and efficacy of their studies. Let us help you leverage the power of near real-time data to not only meet but exceed modern clinical trial standards and improve health outcomes for all. #clinicaltrials #clinicalresearch #diversityinresearch #realtimedata #dataanalytics #insights #MaxisIT

It is sometime now that I have this very interesting paper that is reflecting many of my experiences, believes, but also worries on the use of #RWD and reproducibility of clinical readouts in the real world. Therefore, I like to focus on the broader picture and not so much on the results, by highlighting few of the conclusions made by the authors: -  The authors compared the results of a clinical study with those obtained from 5 different #EHRs in the US, after using common inclusion / exclusion criteria for the extraction of the cohorts. - The example used was in patients with metastatic #NSCLC and focusing on the estimation of #rwOS. - Among the many objectives of this research was the: 1) “reproducibility and performance of rwOS across five real-world mNSCLC patient data sets”, and 2) if use of consistent inclusion / exclusion criteria that have applied in the clinical study would have led to similar estimates, or 3) if potential differences could be explained by data-specific characteristics. - The authors concluded that the estimands varied across the data sources, with rwOS being shorter in real-world cohorts compared to the trial population. - Potential reasons were related to missing data (like ECOG PS, lab values, incomplete mortality data), cohorts composition (like % of patients with brain metastasis, comorbidities, socioeconomic status, health-insurance coverage, etc), definition of endpoints (RECIST criteria vs clinical practise), data collection patterns (i.e., cross-over effect, variable treatment timing & dosing, duration of therapy, etc), real vs clinical setting (i.e., community vs academic practises associated to the severity of cases treated). - The authors concluded that “Building on this research, agreement on minimum reporting and performance standards and capturing of post-baseline events (e.g., frequency and timing of treatment cross-over) or subsequent treatments, as well as a process to evaluate real- world end points across data sets could inform best practices that may help unlock the potential of EHR-derived RWD.” A lot of those observations reflect many of my believes on #RWD, with only two exceptions (all below can be found in my previous posts): - In both #Ophthalmology and #Oncology TAs, different US data sources led to very consistent outcomes. This was not the case when RWD from different geographies have been used, though. - Matching the #nAMD real-world cohorts to the trial populations on no more than 3 inclusion / exclusion criteria, we have been in the position to closely reproduce clinical readouts in the US, UK and Australia. Very nice paper for those friends that have time for some reading this weekend. Disclaimer: Opinions expressed are solely my own and do not express the views or opinions of any of my employers.

#eucrofwebinar New webinar alert! What types of investigator profiles are the right ones for a successful clinical trial?   In this presentation, we’ll roll the dice and take a side-by-side look at what types of investigators are out there in oncology, CNS, or rare diseases and see how to select the appropriate ones for your trial. Learning Objectives: •Define the importance of adequate data and data standardization •Establish data-driven strategies for solid investigator selection •Understand disease-specific challenges to investigator selection The webinar will be introduced by Barbara Argibay Gonzalez, Vice President, General Manager and Elke Ydens, Associate Director of Business Solutions of Data Division of Anju Software.   When? Tuesday, April 9 · 10 - 11:30am CEST Follow the link to register: https://lnkd.in/e6H2zEVe #clinicaldata #clinicalinvestigator #clinicalresearch #contractresearch

📃Scientific paper: Patient outcomes associated with subcutaneous C1INH prophylaxis for hereditary angioedema: a retrospective analysis Abstract: BACKGROUND: Real-world data on subcutaneous C1INH (C1INH[SC]) usage and patient-level impacts on hereditary angioedema (HAE)-related outcomes and quality of life (QoL) are both lacking and challenging to generate using conventional study methodologies. Using a hybrid study design involving patient interviews supplemented by retrospective medical chart data review, we conducted a real-world assessment of the impact of C1INH(SC) prophylaxis on HAE attack patterns, QoL, and on-demand medication use. METHODS: The study was conducted at seven US sites and included 36 adults with HAE who had been treated with C1INH(SC) long-term prophylaxis following ≥ 12 months of on-demand management only. Patients underwent 30-min interviews, facilitated and analyzed by a trained qualitative research specialist. Medical records were reviewed for 12 months before (pre-index) and after (post-index) initiation of C1INH(SC). Using interview data with descriptive terms converted to numerical values, we compared pre- versus post-index attack frequency, severity, and rescue medication usage. RESULTS: Mean (SD) annualized attack frequency per patient decreased 82.0%, from 38.8 (38.8) attacks/year pre-index to 7.0 (15.3) attacks/year (P < 0.001); the median number of attacks decreased by 97.0% (30 pre-index to 1 post-index). For 20 patients, the annualized attack rate after starting C1INH(SC) prophylaxis was ≤ 1 attack/year; 12 of these patients reported 0 attacks. Mean (SD) attack severity (sc... Continued on ES/IODE ➡️ https://etcse.fr/QvA ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

It is sometime now that I have this very interesting paper that is reflecting many of my experiences, believes, but also worries on the use of #RWD and reproducibility of clinical readouts in the real world. Therefore, I like to focus on the broader picture, and not so much on the results, by highlighting few of the conclusions made by the authors: - The authors compared the results of a clinical study with those obtained from 5 different #EHRs in the US, after using common inclusion / exclusion criteria for the extraction of the cohorts. - The example used was in patients with metastatic #NSCLC and focusing on the estimation of #rwOS. - Among the many objectives of this research was the: 1) “reproducibility and performance of rwOS across five real-world mNSCLC patient data sets”, and 2) if use of consistent inclusion / exclusion criteria applied in the clinical study would have led to similar estimates, or 3) if potential differences could be explained by data-specific characteristics. - The authors concluded that the estimands varied across the data sources, with rwOS being shorter in real-world cohorts compared to the trial population. - Potential reasons were related to missing data (like ECOG PS, lab values, incomplete mortality data), cohorts composition (like % of patients with brain metastasis, comorbidities, socioeconomic status, health-insurance coverage, etc), definition of endpoints (RECIST criteria vs clinical practise), data collection patterns (i.e., cross-over effect, variable treatment timing & dosing, duration of therapy, etc), real vs clinical setting (i.e., community vs academic practises associated to the severity of cases treated). - The authors concluded that “Building on this research, agreement on minimum reporting and performance standards and capturing of post-baseline events (e.g., frequency and timing of treatment cross-over) or subsequent treatments, as well as a process to evaluate real- world end points across data sets could inform best practices that may help unlock the potential of EHR-derived RWD.” A lot of those observations reflect many of my believes on #RWD, with only two exceptions (all below can be found in my previous posts): - In both #Ophthalmology and #Oncology TAs different US data sources led to very consistent outcomes. This was not the case when RWD from different geographies have been used, though. - Matching the #nAMD real-world cohorts to the trial populations on no more than 3 inclusion / exclusion criteria, we have been in the position to closely reproduce clinical readouts in the US, UK and Australia. Very nice paper for those friends that have some time to read this weekend. Disclaimer: Opinions expressed are solely my own and do not express the views or opinions of any of my employers.