Data Science in Healthcare

Electronic Health Records (EHRs) are the backbone of healthcare data. Building a solid understanding of EHR systems is non-negotiable if you’re aiming for a career in Healthcare Data Analytics. While direct hands-on access to real EHR platforms usually requires employment within a healthcare organization, gaining knowledge of their structure and workflows can significantly set you apart in the job market. Here’s what employers want and where to learn it:  Top EHR Skills in Demand 1. 𝐄𝐩𝐢𝐜 𝐂𝐥𝐚𝐫𝐢𝐭𝐲 (SQL-based reporting): Epic dominates the EHR market, and Clarity is its reporting database. 💠Access limited free training via Epic’s 𝐔𝐬𝐞𝐫𝐖𝐞𝐛 (if your employer is an Epic partner). 💠Study Clarity Data Models documentation (publicly available). 2. Cerner Millennium: Cerner is a major EHR player, especially in large health systems. 💠Explore Cerner’s 𝐃𝐞𝐯𝐙𝐨𝐧𝐞 for data structure guides. 💠Check out Cerner Ignite APIs for hands-on practice. 3. MEDITECH & Allscripts: Common in community hospitals and smaller practices. 💠Review MEDITECH’s Expanse documentation. 💠Search for Allscripts developer resources (limited free materials available). 4. HL7 & FHIR (Data Exchange Standards): Essential for interoperability (sharing data between systems). 💠Take free courses on HL7 𝑰𝒏𝒕𝒆𝒓𝒏𝒂𝒕𝒊𝒐𝒏𝒂𝒍’𝒔 𝒘𝒆𝒃𝒔𝒊𝒕𝒆. 💠Practice with FHIR sandbox tools (e.g., HAPI FHIR). 5. DHIS2: Especially important for global and public health analytics. Many free resources are available through 𝐃𝐇𝐈𝐒𝟐 𝐀𝐜𝐚𝐝𝐞𝐦𝐲 and GitHub. 6. SQL (The #1 Skill for EHR Analytics): Every EHR query runs on SQL. Check out: W3Schools SQL Tutorial, Khan Academy’s SQL Course, Programming with Mosh In most healthcare analytics roles, you won't interact with EHRs directly. You’ll extract, clean, and analyze data from these systems, usually via SQL queries. Thus, strong SQL skills are essential. Other Free Learning Resources to Build Your Knowledge: ✅Coursera: Healthcare Data Literacy by Johns Hopkins University — great for foundational understanding. ✅edX: Data Science for Healthcare by MIT — dives into analytics applications. ✅YouTube: Search for EHR data structure tutorials to visualize how clinical data is organized. ✅EHRGo: Explore this academic tool that offers realistic EHR simulation environments.   No access to real EHRs? No problem! Practice with: 𝐒𝐲𝐧𝐭𝐡𝐞𝐚 (synthetic patient data generator) 𝐌𝐈𝐌𝐈𝐂-𝐈𝐈𝐈 (de-identified ICU EHR dataset) These open datasets simulate real-world clinical data and are perfect for honing your querying and analytical skills. 💡 Challenge for You: This week, download a sample EHR dataset (like Synthea or MIMIC-III) and practice running SQL queries on it. Think: How would you extract a list of diabetic patients over 65? Next post: Which companies need healthcare data analysts, and how do you stand out? #ehr #healthcareanalytics

Unleashing the Potential of Healthcare Data: Combining Machine-Readable Files with Aggregated and Deidentified 837/835 Data The healthcare data landscape is about to transform, opening the door to unprecedented insights and opportunities. With the release of machine-readable files (MRFs) under the CMS Transparency in Coverage Rule and the continued aggregation, normalization, and deidentification of 837 (claims) and 835 (payment remittance) data, organizations across the healthcare spectrum are poised to create new business models, solve longstanding challenges, and deliver more personalized experiences. When these two datasets are combined, the result is a comprehensive, high-resolution view of healthcare economics and clinical outcomes—unlocking applications for employers, life sciences companies, pharma, payers, providers, and even direct-to-consumer innovators. A New Era of Healthcare Insights By merging the granular pricing and network data in MRFs with detailed claims and payment data from 837/835s, organizations can answer critical questions with precision: - Machine-readable files provide a view of negotiated rates, in-network/out-of-network pricing, and coverage tiers. - 837/835 data adds claims-level detail, including service utilization, diagnosis codes, procedural outcomes, and payment remittances. The integration of these datasets offers a unique opportunity to: - Normalize disparate data sources. - Tokenize and deidentify data for privacy compliance. - Create insights that span cost, quality, and outcomes across the healthcare continuum. Use Cases and Business Models 1. Employer Benefits Optimization Employers can use this dataset to: - Benchmark provider pricing: Identify high-value care by comparing costs and outcomes across regions, networks, and providers. - Optimize plan designs: Tailor benefits to steer employees toward cost-effective, high-quality providers. - Proactive cost management: Predict high-cost conditions and implement wellness or care navigation programs to improve outcomes and reduce expenses. Business Model: Data-as-a-service platforms offering real-time insights for benefits managers and human resource teams. 2. Life Sciences and Pharma Continued (see bio)…

Clinical trials are crucial for medical innovation, but they can be slow and costly. Data is changing that. By leveraging data-driven approaches, we can streamline every aspect of clinical trials: * Better Patient Recruitment: Data analytics helps find and engage the right patients faster, improving recruitment and retention rates. * Smarter Trial Design: Predictive analytics optimize trial design, reduce risks, and minimize delays. * Real-Time Monitoring: Advanced tools enable real-time monitoring and adaptive trial designs, ensuring trials are efficient and effective. * Higher Data Integrity: Technologies like AI and blockchain ensure data accuracy and security, building trust with regulators and stakeholders. * Faster Approvals: Robust data helps accelerate regulatory reviews, bringing new therapies to market sooner. * Incorporating Real-World Evidence: Integrating data from wearables and patient-reported outcomes gives a fuller picture of treatment effects. Data is revolutionizing clinical trials—making them faster, smarter, and more effective. It’s time to fully embrace this potential to advance healthcare. #ClinicalTrials #DataScience #HealthcareInnovation #RealWorldEvidence #FasterApprovals

🔍 𝗛𝗼𝘄 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗶𝗻 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗶𝘀 𝗥𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗖𝗼𝘀𝘁𝘀 📊 The healthcare industry is witnessing a revolutionary transformation through data analytics, delivering both improved patient outcomes and significant cost reductions. 𝗛𝗲𝗿𝗲'𝘀 𝗵𝗼𝘄 𝘀𝗺𝗮𝗿𝘁 𝗱𝗮𝘁𝗮 𝗶𝘀 𝗿𝗲𝘀𝗵𝗮𝗽𝗶𝗻𝗴 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰𝘀: 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 is transforming preventive care. By analyzing patient data patterns, healthcare providers can identify high-risk patients before conditions worsen, reducing expensive emergency interventions and hospitalizations. One health system reported a 30% reduction in preventable hospital readmissions through predictive modeling. 𝗦𝘂𝗽𝗽𝗹𝘆 𝗖𝗵𝗮𝗶𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 is slashing operational costs. Real-time inventory tracking and demand forecasting are helping hospitals reduce waste in medical supplies and optimize procurement. Major healthcare networks have reported saving millions annually through data-driven supply chain management. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗔𝗹𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻 is becoming more efficient. By analyzing patient flow patterns and staff utilization data, hospitals are optimizing scheduling and resource deployment. This has led to reduced wait times and better staff utilization, with some facilities reporting 15-20% cost savings in operational expenses. 𝗣𝗼𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗛𝗲𝗮𝗹𝘁𝗵 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 is enabling targeted interventions. Data analytics helps identify community health trends and at-risk populations, allowing for more focused and cost-effective healthcare initiatives. 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗜𝗺𝗽𝗮𝗰𝘁: A leading healthcare network implemented comprehensive data analytics and achieved: - 25% reduction in administrative costs - 18% decrease in treatment variation - $42 million annual savings in operational expenses - Improved patient satisfaction scores The future of healthcare isn't just about treating patients – it's about using data intelligently to provide better care while managing costs effectively. What's your experience with data analytics in healthcare? Share your thoughts below! Curious to hear: Which aspect of healthcare data analytics excites you the most? #HealthcareAnalytics #DigitalHealth #HealthTech #Healthcare #DataDriven #HealthcareInnovation #CostOptimization #HealthcareLeadership #HealthIT #FutureOfHealthcare

🧠 Generative AI, Combined with Big Data, Offers Benefits to Health Care Industry 🏥 Generative AI revolutionizes healthcare by analyzing unstructured data like clinical notes, radiology reports, and patient histories. It's leading to more accurate and timely diagnoses, unlocking new potentials in patient care. 🌟 Harvey Castro, MD,, a physician and healthcare consultant, sheds light on the vast and varied sources of unstructured data in healthcare, including genomic data and wearable devices. He emphasizes the need for sophisticated data processing and machine learning techniques. 📊 For instance, natural language processing (NLP) can extract meaningful information from text-based sources, while predictive modeling can forecast health outcomes. Image recognition algorithms can aid in diagnosing and detecting abnormalities in medical images like X-rays and MRIs. 🖥️ Dr. Castro also stresses the importance of patient data protection, recommending data anonymization techniques, robust security measures, and privacy-preserving AI techniques like differential privacy and federated learning. 🔒 Read the full insights here: Generative AI and Big Data in Healthcare 📘 What do you think about integrating Generative AI in healthcare? How can we ensure the ethical use of AI in patient care? Share your thoughts and follow @harvey for more insights! 💭 https://lnkd.in/g7TViD4f #chatgpthealthcare #harveycastromd #thegptpodcast Ai ChatGPT Healthcare Questions to Reflect on: How can Generative AI further personalize healthcare delivery? What are the ethical considerations for harnessing unstructured data for AI applications in healthcare? How can healthcare organizations ensure robust data security while leveraging AI?

Johnson & Johnson is paving the way in healthcare innovation with a robust data science and AI approach. Here are 5 key achievements and discoveries that highlight their pioneering efforts: 1️⃣ Advanced Diagnostics: Utilizing algorithms to analyze heart tests, identifying deadly high blood pressure earlier than human capabilities. Plus, voice-recognition tech aiding in early Alzheimer's detection and VR goggles enhancing surgeon training for complex procedures. 2️⃣ AI in Drug Discovery: Amidst skepticism, J&J stands out, leveraging med. AI, a vast database, and machine learning for faster drug development. They're pioneering an experimental cancer drug set for human testing next year, a significant leap forward. 3️⃣ Integrated Data Science Approach: Not just employing data scientists but also individuals skilled in chemistry, biology, and drug development. Their strategy involves tight integration of data scientists into crucial drug research decisions. 4️⃣ Precision Medicine Leap: J&J's collaboration analyzed genetic traits in diseases using data from over 50,000 people, discovering thousands of new genetic variants influencing certain blood proteins. AI-driven insights aim to yield new drugs or diagnostics targeting these findings. 5️⃣ Impactful Collaborations: With over 50 external partnerships, including startups and health-tech companies, J&J is accelerating healthcare advancements. Notably, a collaboration aims to drastically reduce diagnosis time for pulmonary hypertension, potentially saving lives. Johnson & Johnson's dedication to transforming healthcare through AI and data science is setting a new standard. With breakthroughs on the horizon and their commitment to innovation, they're spearheading the future of healthcare. #HealthcareInnovation #DataScience #AIInHealthcare #FutureOfHealthcare #innovation #ai #technology

🚀 Proud to share our latest study! 🚀 Health Care Professionals and Data Scientists' Perspectives on a Machine Learning System to Anticipate and Manage the Risk of Decompensation From Patients With Heart Failure: Qualitative Interview Study. 🔎 Why does this matter? Heart failure (HF) affects over 64 million people worldwide, with decompensation being a major cause of hospitalization and healthcare costs. Machine learning (ML) models hold great promise for early detection, risk stratification, and personalized care, but their implementation comes with challenges. 💡 What did we find? Through qualitative interviews with healthcare professionals and data scientists, we explored key insights into using ML models for HF management: ✅ ML models can support early risk detection and patient stratification. ✅ Variable selection is critical. ✅ Wearables could improve monitoring, but adoption barriers exist, especially for older patients. ✅ Successful implementation requires a human-in-the-loop approach, where clinicians validate ML alerts before acting. ✅ Key barriers include technical, regulatory, ethical, and adoption challenges that must be addressed for real-world use. 📌 A big thank you to my co-authors and everyone involved! 📖 Read the full study: https://lnkd.in/gveYQsJn #ArtificialIntelligence #MachineLearning #HeartFailure #AIinHealthcare #DigitalHealth #HealthInnovation #PredictiveAnalytics Escola Nacional de Saúde Pública, Universidade Nova de Lisboa Anna Hirata Ana Rita Pedro Rui Santana Teresa Magalhães

🚀 How AstraZeneca is Using AI to Revolutionize Drug Discovery & Patient Care AI is no longer a "nice to have" in life sciences, it’s a game-changer. At AstraZeneca, AI and data science are transforming drug discovery, clinical trials, and disease detection, bringing life-changing medicines to patients faster. 🔬 Accelerating Drug Discovery Traditionally, drug discovery was a slow, manual process. Now, AI-driven reinforcement learning helps chemists design new molecules with precision, cutting development time significantly. The same approach is being applied to antibody research, where AI can screen millions of antibody sequences per experiment to create better-targeted therapies. 🩺 AI in Clinical Trials In respiratory medicine, AI is being trained to analyze patient cough recordings, replacing manual counting by researchers. This not only improves accuracy but also frees up valuable time for scientists to focus on more complex challenges. James Weatherall , Chief Data Scientist at AstraZeneca, puts it best: 💡 “AI is no longer an add-on, but an essential part of science. If you want to work on truly hard problems with high-stakes solutions, life sciences is an incredible place to be.” The future of healthcare is digital. AI is accelerating breakthroughs, but at its core, it’s about one thing, helping more people live longer, healthier lives. What do you think about AI in healthcare? Let’s discuss! 👇💬 #AI #Healthcare #Innovation #DrugDiscovery #DigitalHealth

As healthcare systems face mounting pressures, including workforce shortages to rising patient demand, data and artificial intelligence are playing a pivotal role in enabling innovation and efficiency. In my recent conversation with Matthew Kull, Chief Information and Digital Officer at Inova Health, we explored how AI, predictive analytics, and automation are reshaping healthcare delivery. Matt shared insights into how Inova is using large language models to streamline administrative processes, freeing clinicians to focus more on patient care. We also discussed how predictive analytics enhances staffing and supply chain planning, and why data privacy and ethical AI governance are critical in healthcare innovation. Listen to the full interview here: https://lnkd.in/eyNnrFqb #DigitalTransformation #AIinHealthcare #PredictiveAnalytics #DataPrivacy #InovaHealth #Technovation #TechnovationPodcast #podcast #podcastinterview #CIO #CDO

The healthcare sector is undergoing a significant transformation propelled by the advancements in machine learning (#ML) and deep learning (#DL) technologies. These data-driven methodologies are reshaping medicine, enhancing diagnostics, and improving patient care. 🔍 **Key Highlights:** - **Rapid Progress:** ML and DL technologies are advancing rapidly, providing robust and efficient solutions for medical data analysis. - **Innovative Applications:** DL algorithms are raising the bar in medical diagnostics, introducing applications like cardiac MRI software and diabetic retinopathy detection. - **Personalized Medicine:** Big data analytics and DL models are enabling personalized medicine, tailoring treatments to individual patients. - **Chatbot Technologies:** Integration of AI-powered chatbots, such as ChatGPT, is transforming patient interactions, offering real-time support and improving healthcare delivery. 🌟 **Impactful Studies:** - **Medical Imaging:** DL models are enhancing the accuracy of medical image analysis, aiding in early cancer detection and other crucial diagnostics. - **Healthcare Data:** The shift from ML to DL is enhancing the analysis of electronic health records (EHR), providing insights into patient care and treatment outcomes. - **Clinical Trials:** DL-powered tools are being employed in clinical trials to validate new diagnostic methods and treatment plans. 🔗 **Future Prospects:** The transition from ML to DL in healthcare is just the beginning. These evolving technologies hold the potential to revolutionize the medical field, making healthcare more efficient, personalized, and accessible. [Citation: Chakraborty, C., Bhattacharya, M., Pal, S., Lee, S.-S. (2024). From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare. Current Research in Biotechnology, 7, 100164.] #MachineLearning #DeepLearning #Healthcare #AI #MedicalInnovation #PersonalizedMedicine #ChatGPT #BigData #MedicalResearch