Real-World Data Analysis Applications

Real-time data analytics is transforming businesses across industries. From predicting equipment failures in manufacturing to detecting fraud in financial transactions, the ability to analyze data as it's generated is opening new frontiers of efficiency and innovation. But how exactly does a real-time analytics system work? Let's break down a typical architecture: 1. Data Sources: Everything starts with data. This could be from sensors, user interactions on websites, financial transactions, or any other real-time source. 2. Streaming: As data flows in, it's immediately captured by streaming platforms like Apache Kafka or Amazon Kinesis. Think of these as high-speed conveyor belts for data. 3. Processing: The streaming data is then analyzed on-the-fly by real-time processing engines such as Apache Flink or Spark Streaming. These can detect patterns, anomalies, or trigger alerts within milliseconds. 4. Storage: While some data is processed immediately, it's also stored for later analysis. Data lakes (like Hadoop) store raw data, while data warehouses (like Snowflake) store processed, queryable data. 5. Analytics & ML: Here's where the magic happens. Advanced analytics tools and machine learning models extract insights and make predictions based on both real-time and historical data. 6. Visualization: Finally, the insights are presented in real-time dashboards (using tools like Grafana or Tableau), allowing decision-makers to see what's happening right now. This architecture balances real-time processing capabilities with batch processing functionalities, enabling both immediate operational intelligence and strategic analytical insights. The design accommodates scalability, fault-tolerance, and low-latency processing - crucial factors in today's data-intensive environments. I'm interested in hearing about your experiences with similar architectures. What challenges have you encountered in implementing real-time analytics at scale?

Causal inference using real-world evidence (#RWE) is a methodology focused on establishing causal relationships between exposures or interventions and outcomes. It utilizes observational data from real-world settings like electronic health records, claims databases, and patient registries. RWE complements randomized controlled trials (RCTs) by assessing effectiveness across diverse populations and guiding healthcare decisions. The credibility of RWE for causal inference hinges on clear study design, appropriate real-world data (#RWD), effective communication, and robust statistical analysis. Regulatory efforts, such as the FDA’s Advancing Real-World Evidence Program, aim to enhance evidence generation by incorporating patient perspectives and promoting RWE alongside traditional research methods. Despite its valuable insights, RWE encounters limitations in determining causality due to potential biases. To mitigate these challenges, techniques like target trial emulation and causal frameworks are suggested. By integrating RWE with RCTs, a more holistic understanding of healthcare interventions and their real-world impacts can be attained. This integration facilitates the advancement of evidence-based healthcare practices, ensuring a comprehensive evaluation of healthcare strategies and outcomes.

NEW PUBLICATION - Results from our latest Real-World Evidence Pilot demonstrate how treatment response rate can be measured using real-world data. Data obtained from clinical practice, or real-world data (#RWD), can provide valuable insights about treatment outcomes - particularly for patient populations not fully represented in prior clinical studies or for exploring potential other uses for new medicines. However, use of RWD as a research tool can require different methods and study considerations. In our new study published in JCO CCI, we show that different sources of data can be used to implement a common approach and consistently evaluate response rates. This aligned method and reproducibility in results show that rwResponse can be a valuable metric to assess treatment effectiveness outside of traditional clinical trials. Full publication: https://lnkd.in/diTaFg2Z RWE project page: https://lnkd.in/eEaJqjH9 Receive regular updates: https://lnkd.in/dCvFvkeX Many thanks to our collaborators: American Society of Clinical Oncology (ASCO), ConcertAI, COTA, FDA, Flatiron Health, Friends of Cancer Research, Guardian Research Network, IQVIA, Memorial Sloan Kettering Cancer Center, Ontada, Syapse, Syneos Health, and Tempus AI #RWEFriends #cancerresearch

🔬 Invited to assess research novelty in Real-World Evidence — and one paper stood out Last week, I was invited by the University of Sussex Metascience Unit (UK Government) to contribute expert assessments to the Metascience Novelty Indicators Challenge. The task was refreshingly thoughtful. 📍 Instead of “rate this paper good/bad,” the survey asked reviewers to judge: • methodological originality • conceptual advancement • practical impact for the field • and whether ideas genuinely change practice A simple but mindful scale that separates true innovation from incremental noise. I reviewed five publications in Real-World Evidence (RWE), spanning ethics, transportability, AI imaging, and data science methods. One clearly stood above the rest: 📄 Data Science Methods for Real-World Evidence Generation in Real-World Data — Fang Liu, Annual Review of Biomedical Data Science ❓ Why? Because it doesn’t propose just another technique. It reframes the entire way we generate evidence from real-world data. 📍 Key messages from the paper: ✅ RWD are messy, heterogeneous, incomplete — traditional RCT-era methods are insufficient ✅ RWE requires an end-to-end pipeline, not isolated analytics ✅ Study design matters first (target trial emulation, pragmatic trials) ✅ Causal inference + ML must be combined, not confused ✅ Trustworthiness is non-negotiable: validity, uncertainty quantification, explainability, privacy, fairness ✅ Evidence must be regulatory-grade, not exploratory dashboards 🔊 In short: methodology + governance + ethics = credible RWE This resonates strongly with what we see daily across regulators, HTA bodies, and pharma teams. At Helios Academy Ltd – Where Science Meets Compassion, this is exactly the space we operate in: 🔺 Helping organisations move beyond “data access” toward decision-grade evidence that stands up to scrutiny. 🔺 Not more dashboards. 🔺 Better science. If you work in RWE, HTA, or evidence strategy, this paper is genuinely worth your time. Sometimes the most novel idea isn’t a new algorithm — it’s a better way to think. 🏷️ Keywords: #RealWorldEvidence #RWD #HealthData #CausalInference #HTA #EvidenceBasedMedicine #HeliosAcademy #Metascience ⚠️ Disclaimer: Views expressed here are my own. Helios Academy Ltd — “Where Science Meets Compassion” — is an independent educational initiative. This post does not represent the views of Astellas Pharma, my employer, and contains no confidential or company-related information.

Struggles of doing data science in the real world 🤦: What do you do when there’s no A/B test but you still need insights? I recently faced that challenge (again): 👉 The growth team asked me to evaluate the impact of a new mobile app feature on conversions (a week after it launched) In the real world, data is messy, and A/B tests aren’t always an option. As a Data Scientist, you need to learn to be resourceful Here’s how I approached it: 1️⃣ Segmented analysis: I created pre- and post-launch groups based on user signup dates. 2️⃣ Exploratory data analysis (EDA): Visualized conversion trends, layering in cohort and seasonal comparisons. 3️⃣ Statistical testing: Ran an independent t-test to validate observed changes, carefully checking assumptions like normality and variance equality. Result? A clear signal of increased conversions on iOS, while Android showed minimal impact. 💡 Key takeaway: T-tests (or similar methods) can still deliver actionable insights outside traditional A/B testing, but validating assumptions and adding context is critical to making reliable conclusions. I broke down my full workflow and the lessons learned in my latest newsletter article (If you’re curious, check the link in the comments👇) What’s your go-to method for analyzing feature impacts without a perfect experimental setup?

Most people say they “know Excel.” But when you give them a real dataset… Sales data with missing values. Customer names typed in ALL CAPS. Dates formatted differently. Duplicates everywhere. That’s when the struggle starts. The Excel Formulas Cheat Sheet (see image for full breakdown) — covering statistical, lookup, logical, cleaning, text, pivot, and date functions every data professional should know. But knowing formulas isn’t enough. You need to know when to use them. Here are real-world scenarios 👇 📊 Scenario 1: Sales Performance Analysis You’re given 5,000 rows of sales data. Use: ● SUM → Total revenue ● AVERAGE → Average order value ● SUMIFS → Revenue by region or product ● COUNTIFS → Orders per sales rep This is typical for business, operations, and finance roles. 🔎 Scenario 2: Customer Data Lookup You have two tables — one with customer IDs and another with transaction history. Use: ● XLOOKUP (or VLOOKUP) → Pull customer details ● INDEX + MATCH → Flexible lookup across columns This happens a lot in CRM and marketing analytics. 🧹 Scenario 3: Cleaning Messy HR or Survey Data Names have extra spaces. Emails have inconsistent formatting. Use: ● TRIM → Remove extra spaces ● CLEAN → Remove non-printable characters ● PROPER → Fix name capitalization ● IFERROR → Handle formula errors gracefully Data cleaning is 60% of real analytics work. 📈 Scenario 4: Conditional Business Insights You want to flag high-performing products. Use: ● IF → Categorize performance (High/Medium/Low) ● AVERAGEIF → Average sales for a specific region ● COUNTIF → Count customers above a threshold That’s how dashboards start. 📅 Scenario 5: Time-Based Reporting Monthly reports. Quarterly revenue. Daily active users. Use: ● TODAY() / NOW() ● YEAR(), MONTH(), DAY() Time intelligence is critical in reporting roles. 🔶️ Where to Get Real-World Datasets to Practice If you want to truly master these formulas, practice with real data: ● Kaggle (free datasets across industries) ● Maven Analytics datasets ● Data.gov (government open data) ● Google Dataset Search ● UCI Machine Learning Repository Don’t practice with perfect datasets. Practice with messy ones. 💡 Pro Tip: Take one dataset and try to answer 5 business questions using only Excel formulas before touching SQL or Power BI. Excel builds your analytical foundation. If you’re serious about becoming better in data, master these formulas first. The image above is your quick reference — save it. ❓ Question for you: Which Excel formula do you use the most — and which one still confuses you? ♻️ Repost for others #Excel #DataAnalytics #DataAnalysis #BusinessIntelligence #SQL #PowerBI #DataCommunity #Analytics #CareerGrowth

Automated real-world data integration improves cancer outcome prediction The digitization of health records and growing availability of tumour DNA sequencing provide an opportunity to study the determinants of cancer outcomes with unprecedented richness. Patient data are often stored in unstructured text and siloed datasets. Here we combine natural language processing annotations1,2 with structured medication, patient-reported demographic, tumour registry and tumour genomic data from 24,950 patients at Memorial Sloan Kettering Cancer Center to generate a clinicogenomic, harmonized oncologic real-world dataset (MSK-CHORD). MSK-CHORD includes data for non-small-cell lung (n��= 7,809), breast (n = 5,368), colorectal (n = 5,543), prostate (n = 3,211) and pancreatic (n = 3,109) cancers and enables discovery of clinicogenomic relationships not apparent in smaller datasets. Leveraging MSK-CHORD to train machine learning models to predict overall survival, we find that models including features derived from natural language processing, such as sites of disease, outperform those based on genomic data or stage alone as tested by cross-validation and an external, multi-institution dataset. By annotating 705,241 radiology reports, MSK-CHORD also uncovers predictors of metastasis to specific organ sites, including a relationship between SETD2 mutation and lower metastatic potential in immunotherapy-treated lung adenocarcinoma corroborated in independent datasets. We demonstrate the feasibility of automated annotation from unstructured notes and its utility in predicting patient outcomes. The resulting data are provided as a public resource for real-world oncologic research. https://lnkd.in/ebvmPCAC

Pharma is using electronic health records (#EHRs) and #claims data, but are missing the clearest picture of the patient. In the race to develop more targeted, effective therapies, pharma is making big investments in real-world data (#RWD). But one critical piece is still often missing from the puzzle: medical imaging. While claims data and EHRs provide a broad picture, imaging reveals what’s happening inside the patient, in ways other data types simply can’t. Here’s why real-world imaging data (#RWiD) matters more than ever for pharma: 🔍 Stronger Biomarker Discovery Imaging plays a crucial role in identifying and validating #biomarkers, especially in fields like #oncology, #neurology, and #cardiology. Access to real-world imaging helps researchers understand disease progression across diverse populations and clinical settings. ⏱ Faster, Smarter Clinical Trials Imaging endpoints are increasingly common in clinical trials. Using RWiD, sponsors can simulate trial cohorts, optimize inclusion criteria, and assess historical controls (or external control arms), saving both time and cost. 📊 Post-Market Evidence Generation Need to understand how a therapy is working in the real world, beyond the trial population? Imaging data helps track therapeutic response, safety signals, and long-term outcomes with anatomical and physiological detail. 🧠 Enabling AI and Radiomics From radiomics to imaging-based predictive models, RWiD is essential for training and validating the next generation of data-driven tools in drug development and personalized medicine. At Segmed, Inc., we’re making this data accessible: curated, standardized, and de-identified. So pharma innovators can build with confidence and clinical relevance. If you’re in drug development and haven’t yet explored real-world imaging data, now is the time. Because the body often tells a story that charts and codes alone can’t. Link: https://lnkd.in/gS8hiU4M #PharmaInnovation #RealWorldData #MedicalImaging #ClinicalResearch #Biomarkers #Radiomics #DrugDevelopment #AIinHealthcare #Segmed #EvidenceGeneration

𝐇𝐨𝐰 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐃𝐚𝐭𝐚 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐬 𝐃𝐞��𝐢𝐬𝐢𝐨𝐧-𝐌𝐚𝐤𝐢𝐧𝐠 𝐟𝐨𝐫 𝐇𝐞𝐚𝐥𝐭𝐡 𝐏𝐥𝐚𝐧𝐬 𝐚𝐧𝐝 𝐏𝐚𝐲𝐞𝐫𝐬 Clinical trials tell you what COULD work. Real-world data shows you what ACTUALLY works. And it's changing everything for health plans. Healthcare executives are sitting on a goldmine of untapped data. Claims records. EHR data. Wearable device insights. Patient registries. But most aren't using it to its full potential. Here's what you're missing: 𝐓𝐡𝐞 𝐂𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐓𝐫𝐢𝐚𝐥 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 Traditional studies are perfect. Too perfect. • Controlled environments • Carefully selected patients • Ideal conditions But your members don't live in a lab. They have messy, complex health conditions. They skip medications. They have different backgrounds and lifestyles. 𝐑𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐝𝐚𝐭𝐚 𝐜𝐚𝐩𝐭𝐮𝐫𝐞𝐬 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐩𝐢𝐜𝐭𝐮𝐫𝐞. 𝐓𝐡𝐞 𝐆𝐚𝐦𝐞-𝐂𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 When health plans leverage RWD properly, magic happens: 𝐏𝐫𝐞𝐝𝐢𝐜𝐭 𝐰𝐡𝐨 𝐠𝐞𝐭𝐬 𝐬𝐢𝐜𝐤 𝐛𝐞𝐟𝐨𝐫𝐞 𝐭𝐡𝐞𝐲 𝐝𝐨 Identify high-risk members early Intervene with preventive care Save millions in avoided complications 𝐌𝐚𝐤𝐞 𝐜𝐨𝐯𝐞𝐫𝐚𝐠𝐞 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 See how treatments actually perform Base formulary decisions on real outcomes Reduce costly trial-and-error approaches 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 𝐥𝐢𝐤𝐞 𝐧𝐞𝐯𝐞𝐫 𝐛𝐞𝐟𝐨𝐫𝐞 Streamline provider networks Eliminate inefficiencies Improve resource allocation 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐞 𝐦𝐞𝐦𝐛𝐞𝐫 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞𝐬 Tailor interventions to individual needs Deliver relevant benefits and outreach Improve satisfaction and retention 𝐓𝐡𝐞 𝐒𝐩𝐞𝐞𝐝 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞 Modern analytics platforms process RWD in real-time. No more waiting months for insights. No more outdated reports. 𝐅𝐚𝐬𝐭 𝐝𝐚𝐭𝐚 = 𝐟𝐚𝐬𝐭 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 = 𝐛𝐞𝐭𝐭𝐞𝐫 𝐨𝐮𝐭𝐜𝐨𝐦𝐞𝐬. 𝐓𝐡𝐞 𝐁𝐨𝐭𝐭𝐨𝐦 𝐋𝐢𝐧𝐞 Health plans using real-world data are: -Reducing costs by millions -Improving member outcomes -Making faster, smarter decisions -Staying ahead of competition Those who don't? They're flying blind. 𝐘𝐨𝐮𝐫 𝐝𝐚𝐭𝐚 𝐢𝐬 𝐲𝐨𝐮𝐫 𝐜𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐯𝐞 𝐚𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞. The question is: Are you using it? How is your organization leveraging real-world data? Share your wins (and challenges) below! #HealthcareData #RealWorldData #HealthPlans #DataAnalytics #HealthTech #PayerInsights