Robert McElroy’s Post

💉 Unlocking the Differences: Point-of-Care (POC) Tests vs. Extreme Point-of-Care (EPOC) Tests Recently, I was asked to clarify the distinction between POC and EPOC tests in a technical meeting. Here's a breakdown of the key attributes: Point-of-Care (POC) Tests: 1. Rapid test results at the patient site 2. May require auxiliary instruments for support 3. Typically used in controlled or semi-controlled environments 4. Ideal for routine testing, monitoring, and screening 5. Examples: Glucometers Extreme Point-of-Care (EPOC) Tests: 1. Instrument-free, rapid test results at the patient site 2. No auxiliary instruments required for sample handling or dispensing 3. Accurate results even in harsh, remote, or resource-limited settings 4. Employs frugal technologies, eliminating the need for resources or power connections 5. Examples: WHO-approved HIV self-tests, HCS . . . Request services: DM if you are facing challenges in developing your POC /EPOC test. Join MedTech Innovators Cohort: https://lnkd.in/dcHdnM5U Book a call: https://lnkd.in/g9TUG3Ji #innovators #startup #innovation #pointofcare #diagnostics #medtech #sujaykbiswas #POCTestDevelopment #IVD

🚨 Are your protocols hurting more than helping? Our very own Kathryn Gentz, PharmD, MBA, has analyzed over 1,250 protocols spanning grades I-IV, RWE, and beyond. She has seen it all and knows exactly where things go sideways (like footnotes that confuse 🤪). In her latest blog, Kathryn shares 3 common mistakes that add unnecessary complexity and costs to #clinicaltrials. Are you guilty of any of these!? Read her blog to find out - and the solution might be easier than you think! Link in comments 👇🏼 #ProtocolComplexity #ProtocolDeviation #ClinicalResearch #ResearchConduct #ClinOps

On the risks of #reidentifying individuals from pseudonymized data or results -- a good scientific read that I came across yesterday -- beautifully-written, clear article on health data. I particularly liked the categories "identity" and "sensitive attribute" disclosure #datascience #rwd #health #science https://lnkd.in/diqfjPrp

Discover how Real-World Evidence (#RWE) is revolutionizing clinical trials and leveraging Real-World Data (RWD) to identify optimal patient populations, improve diversity, and inform clinical trial design at the RWEsearch Annual Summit 2024. Join us from September 4-6 for a unique opportunity to delve into the transformational world of RWE. #RWEsearchSummit2024 #RealWorldEvidence #ClinicalTrials #RWD #PatientDiversity #HealthcareInnovation

Did you know the EU's new regulation for health technology assessment is now in force? The new framework is intended to make health technology assessment faster and more effective by streamlining processes and pooling resources.   It will affect all applications for new marketing authorisations in the EU for: ⭐ new cancer drugs and gene, stem cell and tissue engineering therapies (ATMPs) immediately ⭐ orphan drugs from January 2028 ⭐ all medicines from 2030. Selected high-risk medical devices will also begin to be assessed under the new framework beginning in 2026. It will be interesting to see whether the implementation of this regulation in relation to orphan drugs results in greater divergence between the UK and the EU – at the moment, the parallel regimes work in very similar ways, but we are beginning to see some differences with a practical impact. 🌍💉#OrphanDrugs #ATMPs #EU #HealthTechnology #Pharma https://lnkd.in/em82gvn8

Key Challenges in Real-World Evidence (RWE) Research – What to Watch Out For #Real_World_Evidence (#RWE) is transforming healthcare by providing insights from real-world patient data. However, recent studies remind us that data definitions and limitations can greatly impact research results. * Clear Disease Definitions    - Using only diagnosis codes (e.g., ICD-10) to identify diseases can lead to overestimations.    - In South Korea, an additional code (V193) helps confirm cases more accurately.    - Researchers should carefully define diseases to avoid bias. * Differences in Patient Groups    - Patients often have different health conditions, which can affect study results.    - Even with methods like propensity score matching, differences may still exist.    - Consider how findings apply to different populations before making conclusions. * Data Quality and Completeness    - Insurance claims data is useful for large-scale studies but lacks detailed clinical information.    - Combining it with hospital-based data can provide a more complete picture.    - Always check data accuracy and completeness before drawing conclusions. * Study Design and Bias Control    - Methods like matching and multivariable analysis help reduce bias, but additional sensitivity checks are needed.    - Changes in treatment guidelines over time can also affect study outcomes. * Meaningful Interpretation    - Statistical significance doesn’t always mean clinical significance.    - Policymakers and healthcare providers need clear, actionable insights.    - Transparency about limitations is key to building trust in RWE findings. * RWE has the power to shape better healthcare decisions, but careful planning and critical evaluation are essential to ensure reliable and useful results.

🌍 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐢𝐧 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 𝐨𝐟 𝐡𝐞𝐚𝐥𝐭𝐡! 🏥HIV/SIDA management is a complex process that requires quick access to updated data, therapeutic guidelines, and information about drug interactions. 🧑⚕️HChat meets these needs with intelligent features designed to simplify the work of professionals and enhance the efficiency of clinical decisions. 🔗Visit www.hchat.ro and discover the usefulness of this tool.

An interesting update below from my colleague Clemency on the new EU-wide rules on Health Technology Assessments. Although the rules currently only cover new cancer medicines and ATMPs, their scope will be extended to cover orphan medicines from January 2028 and will apply to all new medicinal products by 2030.

Machine learning-based algorithms applied to drug prescriptions and other healthcare services in the Sicilian claims database to identify acromegaly as a model for the earlier diagnosis of rare diseases. https://lnkd.in/gTUnfs9g #machinelearning #drhub #drugprescriptions #rarediseases #artificialintelligence #ai #healthcare #earlierdiagnosis

Synthetic data as external control arms in scarce single-arm clinical trials | PLOS Digital Health: Synthetic data quality assessment. A synthetic control arm must retain essential characteristics of the original dataset to serve as a viable ...