Radformation’s Post

Increasing reliability of patient test results while maintaining efficiencies in a lab is crucial for comprehensive QC evaluation and reporting. Discover a top-tier peer reporting tool that benchmarks against thousands, elevating #QC lab performance, test reliability, and sustaining compliance. Learn more about QC peer reporting programs and Unity data management. https://bit.ly/3REE3YF #LabQuality #QC #LabManagement #ClinicalLabTesting #QualityControl #ReliableResults #ContinuousLearning

#Clinical terms# Safety set 安全性数据集 Sample size 样本量 Secondary variable 次要变量 Serious adverse event, SAE 严重不良事件 Serious adverse reaction, SAR 严重不良反应 Severity 严重程度 Significant level 显著性水平 Simple randomization 简单随机 Standard operating procedure, SOP 标准操作规程 Source data verification, SDV 原始数据核准 Source data, SD 原始数据 Source document, SD 原始文件

Learn how the IQM System enhances the efficiency, accuracy, and consistency of your Machine QA processes #iqm #futureofqa #qualityassurance #mqa

MEDDICC is all the rage right now 🏌♂️ Its great to want something, but its another thing all together to implement it. With that in mind, I present to you.... The MEDDICC cheat sheet. Comment below if you would like HD, can also provide a editable version What you will get MEDDICC Cheat sheet: - MEDDICC Definitions - 80 questions aligned to the MEDDICC framework - Structured against a standard sales cycle MEDDICC Risk Sheet: - Risk scales against each item in the framework - Art of Questioning: - Question styles and definitions Questioning frameworks - 45 examples of effective and usable questions to use in phone and discovery calls.

Tip for optimizing your auditing time: A bunch of clinics use Brellium to screen for things that weren’t possible with a manual QA team. Like copy-and-pasting, modifiers, and care plans across 10’s of thousands of individual documents. More data is great, but only if used correctly. Especially if you’re a large clinic. Start with the most mission critical errors for your clinical team & payers. Like SI/HI misdocumentation, copy-and-pasted notes, and hours billed <> session time mismatches. Fix those first. Turn on bulk error reminder emails. Then watch your error % decrease week-by-week, as your providers get better at documenting. Once your critical errors are fixed, then drill down to your tier-2 errors. Then tier 3, on and on. Fix the big ticket errors first, then focus on what’s next!

📌 CRA Challenge #19 : Rethinking eTMF Metrics 📊📁 As we continue our discussion on metrics, I'd like to delve into the eTMF metrics. Common metrics like completeness, quality, and timeliness are widely used, but as a former CRA and CL, I question their effectiveness in reflecting the true status of an eTMF. From my experience, even towards the end of studies, we often find ourselves realising that numerous documents are missing. This is largely due to the inadequacy of quarterly or yearly reviews, which tend to be superficial. So, this begs the question: are there better metrics that could more accurately represent the real state of an eTMF? I might be totally incorrect and still these are the best ways to measure eTMF status. I'm eager to hear your thoughts and experiences. What do you think could be a more effective way to measure eTMF metrics? Are the current standards sufficient, or do we need a shift in our approach? #CRAChallenges #ClinicalResearch #eTMFMetrics #DataQuality

Surestep’s QA manager, Mark, explains everything you need to know about Surestep’s unique SMO trim lines. 👍 They often seem counterintuitive to clinicians who see them for the first time. Check out this video and see how to order the best trim lines for your patients!

https://lnkd.in/gPYvVpJS The Most Complete and Current Update To The Extensive Changes In the HCC Model.

Honest or dishonest? Users are dishonest, we cannot be believed when we tell you something (you need proof about drug use from a urine screen), we cannot be trusted to be honest about taking our medication (hence the enhanced use of supervised pick up). One can only conclude that this must mean NDTMS data is invalid & misleading and that an organizational client satisfaction survey cannot be trusted. When claims are made of confidential Hep C screening we will have to dismiss this if they cannot provide proof. Honest or dishonest?

What do we gain in underwriting by evaluating medical claims data in addition to traditional prescription data? RGA recently conducted an in-depth analysis of the differences between underwriter rules and risk scores using Milliman’s Irix® Risk Score solution. Click here to access the whitepaper and discover the insights provided. https://gag.gl/YQJZve