Data Science in Healthcare

“Foundations of Healthcare Data Science for Underserved Populations" https://lnkd.in/gn7N6SNP A free, open source, auto-updating online textbook. This textbook attempts to cover the messy realities that don't make it into papers: fairness metrics that masked disparities we later discovered, beautiful models that crumbled under missing data, and the humbling experience of watching algorithmic decisions affect patient lives. Key components: - Auto-updating weekly with high-impact research, because the field moves faster than traditional texts - Production-grade code throughout—not pseudocode, but implementations with the error handling, type hints, and validation frameworks that survive institutional review - Equity engineering embedded in every chapter—stratified evaluation, bias correction, and generalizability aren't separate sections, they're woven into each algorithm's design - Beyond LLMs—covering key additional methods that solve clinical problems: doubly-robust causal inference, neural ODEs for longitudinal data, integrated gradients for multimodal attribution, and onwards… This isn't comprehensive—it can't be. It's one physician-data scientist's attempt to document what worked (and what failed) when coding, validating, and deploying ML and AI solutions to patients across safety-net systems. If you're wrestling with the distance between research and reality as a “clinician who codes”, maybe you'll find something useful here. And if you've learned important lessons I've missed—which is inevitable—I'd genuinely value your suggestions. Table of Contents here: https://lnkd.in/g3KHmmgQ Pranav Rajpurkar Benjamin Huynh Michael Barnett Hilary Seligman Elaine Khoong Urmimala Sarkar MD MPH Dean Schillinger Margot Kushel MD Dr. Kedar Mate Robert Wachter James Zou Andrew Ng Nigam Shah Jonathan H. Chen Ethan Goh, MD Alejandro Schuler Bob Phillips, MD MSPH Russ Phillips Bruce E. Landon Zirui Song Asaf Bitton Chris Koller Courtney Lyles, PhD Paulius Mui, MD Jeremy Sussman Reshma Gupta, MD, MSHPM Michael Pencina C. Brandon Ogbunu Jason Andrews Martin McKee David Stuckler Professor Sir Michael Marmot Kasia Lipska Steven Yadlowsky Nathan Lo Kirsten Bibbins-Domingo Kevin Volpp, MD, PhD, FAHA Harlan Krumholz john yudkin Eric Topol, MD

This Special Issue highlights the transformative role of data-driven methodologies in healthcare. It features ten studies employing artificial intelligence (#AI), machine learning, operations research, and advanced analytics to address clinical prediction, quality improvement, resource optimization, and behavioral insights. Key contributions include AI-based COVID-19 mortality prediction, NLP for mental health detection, and models for nurse scheduling and home healthcare teams. The collection underscores the importance of robust data governance and ethical frameworks, demonstrating how systematic analysis of observational data can improve patient outcomes, operational efficiency, and policy development, inspiring continued innovation in healthcare research and practice. Reference: Prybutok VR, Prybutok GL. Data-Driven Insights in Healthcare. Healthcare (Basel). 2025 Oct 22;13(21):2658. doi: 10.3390/healthcare13212658. PMID: 41228025; PMCID: PMC12609396.

Most people think healthcare data scientists build models to predict diseases. While that's certainly part of the job, it's far from where most of the value is created. In my experience, healthcare data science is less about prediction and more about helping organizations anticipate challenges before they become problems. Think about a typical day in a hospital: An Emergency Department becomes overwhelmed, patient wait times climb, beds become scarce, and clinicians feel increasing pressure. Meanwhile, operational leaders are forced to make critical decisions in real time with limited visibility into what happens next. By that point, the organization is responding to the problem instead of anticipating it. The opportunity for data science is to help organizations move from reacting to anticipating. 🚑 Forecasting models can help predict ED volumes, admission rates, and staffing needs before bottlenecks occur. 🏥 Advanced Analytics and machine learning can identify factors driving prolonged length of stay, readmission/mortality rates allowing teams to intervene before discharge delays impact patient flow. 📊 Statistical analysis can help healthcare organizations understand whether quality improvement initiatives are actually improving outcomes or simply adding more work to already stretched teams. 🤖 RAG-powered systems can help clinicians access relevant protocols, policies, and institutional knowledge in seconds, reducing time spent searching for information. ⚡ AI agents can automate administrative tasks such as chart reviews, quality abstraction, documentation support, patient outreach, and care coordination, giving healthcare professionals more time to focus on patient care. We often spend a lot of time talking about algorithms, dashboards, and AI. But healthcare organizations don't benefit from technology alone. They benefit when technology helps the right people make better decisions at the right time. The real value comes from improving workflows, reducing friction, and enabling better decisions across clinical and operational teams. That's when data science starts creating meaningful impact in healthcare. I'm curious: Where do you see the biggest opportunity for data science and AI to improve healthcare workflows today? #HealthcareDataScience #HealthcareAI #DigitalHealth #HealthIT #HealthcareInnovation

If I had to start my job search in data science today, I wouldn’t be chasing tech startups or AI research labs. I’d be looking at banks and hospitals first. After working with banks for three years, I’ve seen firsthand how they leverage data science at scale, but I’ve also seen the massive gaps in talent they’re trying to fill. And it’s the same in healthcare. 1. These institutions handle more data than almost any other industry + Banks and hospitals sit on mountains of data. Every transaction, loan, medical record, and patient interaction is a potential goldmine for analytics, risk modeling, and predictive insights. Unlike tech startups that often struggle to get quality data, these industries are drowning in it. 2. They have real business impact + Tech companies use data to optimize ad clicks. Banks and hospitals use data to save billions and, in some cases, save lives. + In banking, data science powers fraud detection, risk modeling, credit scoring, and regulatory compliance. In healthcare, it’s used for predictive patient care, operational efficiency, and disease modeling. +The stakes are higher, and so is the impact. 3. They are willing to train & invest in data talent + Unlike many tech companies that expect candidates to already have elite experience, banks and hospitals actively invest in upskilling their workforce. Many have internal training programs, partnerships with universities, and well-structured career paths for data professionals. 4. They offer more stability in a volatile job market + The past two years have shown that tech layoffs are brutal. Banks and hospitals, on the other hand, aren’t going anywhere. Even in downturns, they remain essential industries that need data professionals to operate efficiently. 5. They are expanding their use of AI & ML + Financial institutions are automating risk assessments, improving fraud detection, and optimizing investments using AI. Hospitals are using machine learning for predictive diagnostics and patient outcome modeling. These industries are aggressively hiring data talent to modernize their operations. If I were breaking into data science today, I’d be looking where the data is abundant, the impact is real, and the hiring demand is strong. And right now, banks and hospitals check all three boxes. If you’re job hunting, have you considered these industries?

AI in healthcare is useless without one thing: Data. Everyone’s talking about AI revolutionizing healthcare. What they’re not talking about? AI is only as good as the data it learns from. Garbage in, garbage out. 🚨 Bad data = Bad AI decisions. 🚨 Fragmented data = Half-baked AI insights. 🚨 Delayed data = AI that reacts too late. The real transformation in healthcare isn’t just AI. It’s how we collect, structure, and use data to make AI actually useful. The Data crisis in Healthcare is real: 🏥 80% of healthcare data is unstructured. 🩺 Medical records are siloed across EHRs, wearables, and provider systems. ⏳ Care teams waste hours manually entering data instead of using it. And here’s what no one admits: AI isn’t the problem. The data mess is. We expect AI to predict patient deterioration, optimize staffing, and reduce hospitalizations. But without clean, real-time data? AI is just guessing. Where AI + Data is quietly changing Healthcare 1️⃣ Real-time patient monitoring → AI predicting sepsis hours before symptoms appear. 📉 31% fewer ICU admissions. 2️⃣ Automated documentation → AI reducing charting time from 50+ minutes to 10-12 minutes. ⚡ More time with patients, less time on admin work. 3️⃣ Predictive analytics → AI flagging at-risk seniors before a crisis hits. 🏥 26% reduction in ER visits. 4️⃣ Smart patient-caregiver matching → AI optimizing schedules and workload balancing. 🤝 Fewer burnout cases, higher patient satisfaction. The future of AI in Healthcare is data-first. At Inferenz, we focus on AI that actually solves the data problem first: 🔹 AI that connects fragmented data—turning scattered records into real-time insights. 🔹 AI that strengthens decision-making—empowering care teams, not replacing them. 🔹 AI that adapts, learns, and evolves—making healthcare more predictive, precise, and personal. Because AI without good data is like medicine without a diagnosis—dangerous and ineffective. The question isn’t whether AI belongs in healthcare. It’s whether we’re ready to fix data so AI can actually work. Let’s build data-first, human-first AI. Gayatri Akhani Yash Thakkar James Gardner Brendon Buthello Kishan Pujara Trupti Thakar Amisha Rodrigues Priyanka Sabharwal Prachi Shah Jalindar Karande Mitul Panchal 🇮🇳 Patrick Kovalik Joe Warbington 📊 Julie Dugum Perulli Chris Mate Ananth Mohan Michael Johnson Marek B. Dustin Wyman, CISSP Rushik Patel #AI #Healthcare #DataMatters #HealthTech #HumanizingAI #PatientCare #Inferenz

You didn’t pursue a career in healthcare informatics just to chase outdated job titles. The world is changing. So are the roles. If you're still searching with 2015 job titles, you’ll miss the 2030 opportunities. Here’s the truth: The next decade will belong to those who understand not just healthcare, but data, automation, and digital systems together. And Healthcare Informatics is at that intersection. Top Hiring Trends for Healthcare Informatics (2024–2025): According to [HIMSS & BLS 2024 projections]: Healthcare Data Analyst roles grew by 18% last year. Clinical Decision Support & AI roles are emerging in major health systems. EHR System Support & Optimization remains the most in-demand skill. Population Health & Value-Based Care roles up by 11% due to Medicaid reforms. Clinical Research Informatics is growing in pharma/biotech. 2025–2035: What Roles Will Dominate? If you’re planning for long-term success, focus on roles that blend: Data + Outcomes AI + Patient Safety Compliance + Digital Health Here are the future-proof titles to track (and skill up for): Next-Gen Healthcare Informatics Roles: Healthcare Data Scientist (Python, SQL, predictive analytics) Clinical AI Analyst (ML models for outcomes + risk prediction) Digital Health Program Manager (mHealth, RPM, app-based care) Value-Based Care Analyst (Population health metrics, QI dashboards) Health Data Governance Specialist (HIPAA, HITECH, compliance) Clinical Informatics Consultant (Epic/Cerner + workflow redesign) Health Equity Data Analyst (DEI metrics, SDoH data) Telehealth Informatics Coordinator (virtual care workflows + UX design) Top Skills to Focus on (2025 and beyond): SQL, Python/R for health data Power BI / Tableau for dashboarding Epic or Cerner EHR optimization Clinical workflow mapping & UI/UX HL7, FHIR, interoperability knowledge Privacy regulations (HIPAA, GDPR) AI/ML foundations for clinical contexts Job Hunting Tip: Don’t search by degree. Search by outcome. Try: “Remote Patient Monitoring + Analyst” | “Epic + Optimization” | “Public Health + Data” These combos will open new doors. Tag a classmate, I’ll help you decode job titles, keywords, and roles that actually work in 2025. We rise faster when we learn together 💙 #HealthInformatics #HealthcareAnalytics #PublicHealthCareers #EntryLevelJobs #InternationalStudent #HealthTech

Powerful, Engaging & Data-Driven I had the privilege of speaking at 𝐑𝐨𝐜𝐡𝐞 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐃𝐚𝐲𝐬 𝐐𝐚𝐭𝐚𝐫 𝟐𝟎𝟐𝟓 on AI in Diabetes & the Future of Data-Driven Healthcare — and the energy in the room was extraordinary. A full hall, thoughtful questions, deep discussions, and a clear realization among clinicians and leaders: AI is no longer the future — it is already transforming the present. ⸻ ⬛ Demystifying AI: Healthcare’s Data Problem One of the biggest barriers in healthcare isn’t a lack of intelligence — it’s a lack of clean, structured, usable data. Our EHR systems today generate nearly 50 petabytes of data (📌 1 petabyte = 1 million gigabytes) yet we barely use 3% of it. The remaining 97% is noise, fragmentation, incompatible formats, and unstructured text — essentially wasted potential. Meanwhile… ⸻ ⬛ The Cloud Has Changed Everything Cloud infrastructure has given us computing power beyond imagination: • ➤ Compute power doubles every ~14–15 months • ➤ Storage capacity doubles every ~10 months • ➤ Global cloud industry: $650B today → $2.3T by 2030 This enormous growth means AI can now analyze healthcare data at speeds and scales that were impossible just a few years ago. But computing power is not our bottleneck — clean data is. ⸻ ⬛ Enter 𝐈𝐨𝐓: 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞’𝐬 𝐌𝐨𝐬𝐭 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐯𝐞 𝐃𝐚𝐭𝐚 𝐒𝐨𝐮𝐫𝐜𝐞 The Internet of Things (IoT) is already a $2.5 trillion industry, and will at least triple by 2030. And the number of connected devices is exploding: • ➤ 21 billion IoT devices today → 39 billion by 2030 Here is the surprising part: Nearly 25% of all IoT devices are used in healthcare. And one of the most impactful examples? Continuous Glucose Monitoring (CGM). A single CGM sensor gives: • 300–1500+ datapoints per day per patient • continuous, structured, timestamped glucose signals • no dependence on messy EHR systems • high patient acceptance • incredibly rich physiological behavior patterns • a goldmine for AI These devices quietly collect the purest, cleanest time-series data in healthcare — and that is why AI thrives in diabetes management. CGM doesn’t just capture numbers. It captures: • behavior • hormonal patterns • stress signatures • metabolic rhythms • sleep impact • recovery curves • long-range instability • phenotypes of glucose behavior All without a single manual input. ⸻ ⬛ Why the Audience Was Mesmerized Once clinicians saw how 𝐀𝐈 𝐜𝐚𝐧 𝐞𝐱𝐭𝐫𝐚𝐜𝐭 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐥𝐚𝐲𝐞𝐫𝐬 𝐨𝐟 𝐦𝐞𝐚𝐧𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐬𝐢𝐦𝐩𝐥𝐞 “𝐠𝐥𝐮𝐜𝐨𝐬𝐞 𝐜𝐮𝐫𝐯𝐞” — from immediate slopes… to shape patterns… to long-range metabolic memory… the room suddenly understood: 𝐂𝐆𝐌 + 𝐀𝐈 𝐢𝐬 𝐭𝐡𝐞 𝐜𝐥𝐞𝐚𝐫𝐞𝐬𝐭 𝐩𝐚𝐭𝐡𝐰𝐚𝐲 𝐭𝐨 𝐩𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐞𝐝 𝐦𝐞𝐝𝐢𝐜𝐢𝐧𝐞 we have today. #AI #DiabetesCare #CGM #IoT #CloudComputing #BigData #DigitalHealth #HealthcareInnovation #TimeSeriesAI #Endocrinology #Roche #Qatar2025 #MetabolicHealth

AI and Machine Learning in Health Care: Best Practices and Pitfalls   The book Artificial Intelligence and Machine Learning in Health Care and Medical Sciences edited by Gyorgy J. Simon and Constantin Aliferis is a scholarly and practical resource that addresses the opportunities and challenges of applying AI/ML in medicine. It emphasizes rigorous development, evaluation, and ethical practices to ensure trust and reliability in clinical and organizational applications.   📘 Why This Book Matters AI and ML are revolutionizing healthcare—from diagnostics and treatment planning to operational efficiency. Yet, without best practices, these technologies risk bias, overfitting, and unsafe deployment. This book provides a structured framework for applying AI responsibly, ensuring that innovation translates into improved patient outcomes.   📑 Key Content Covered Foundations of AI/ML Systems: Core principles and operating characteristics. Major ML Methods in Healthcare: Appraisal of algorithms applicable to medical sciences. Causal ML Foundations: Understanding cause‑effect relationships in biomedical data. Rigorous Development & Lifecycle: Building clinical‑grade AI/ML models. Data Design & Preparation: Ensuring quality, transforms, and management. Evaluation & Pitfalls: Addressing overfitting, underfitting, and model overconfidence. Human + Machine Collaboration: Moving from competition to synergy. Lessons from Failures & Successes: Historical insights and enduring challenges. Risk Management: Diagnosing and managing errors in clinical AI/ML applications.   💡 Key Benefits Trustworthy AI: Frameworks for safe, reliable, and ethical deployment. Comprehensive Coverage: From foundations to advanced pitfalls and solutions. Practical Relevance: Case‑based lessons for healthcare professionals. Future‑Ready Insight: Guidance on enduring problems and emerging opportunities.   👥 Who Should Read It Healthcare Professionals & Clinicians: To understand AI’s role in patient care. Medical Researchers: To apply rigorous methods in biomedical AI studies. Data Scientists in Health Tech: To design and evaluate trustworthy models. Policy Makers & Regulators: To ensure safe and ethical AI adoption in healthcare.   🌍 The Professional Edge This book is more than a technical manual—it is a guide to responsible innovation in healthcare. By mastering its insights, professionals can harness AI/ML to improve outcomes while avoiding pitfalls that compromise safety and trust. 🔖 Hashtags #AIHealthcare #MachineLearning #HealthInformatics #MedicalInnovation #EthicalAI #ClinicalAI #ProfessionalDevelopment #TrustworthyAI #PatientCare

Pablo Moreno Franco, intensive care physician at Mayo Clinic in Jacksonville, United States, can explain where AI in healthcare is headed. “It will evolve from specific and limited tools toward more integrated and context-aware systems that support clinicians in real time. We are already starting to see the shift from isolated predictive models to integrated collaborative technologies that help monitor patients, guide decisions, and personalize care (especially in data-rich environments like intensive care). But perhaps the most important aspect is the cultural shift it will bring in how clinicians and developers interact with these tools,” he notes. At institutions like Mayo Clinic, where AI is already a tangible reality, “healthcare professionals are being trained not only to use AI, but to understand its limitations, interpret its results, and participate critically in its development. More and more training programs are incorporating digital literacy, AI ethics and equity, and collaborative experiences like datathons, where clinicians and data scientists tackle real-world problems together. The goal is not to turn every physician into a programmer, but to empower them as informed collaborators in developing reliable, human-centered AI systems,” Moreno Franco summarizes. Regarding its impact on the organization of healthcare centers, the expert believes that “it will help improve efficiency and consistency in areas such as medical imaging, triage, early warning systems, and administrative workflows.” This would expand the ability to analyze large sets of clinical data, allowing for better public health decision-making. Pablo Moreno Franco, médico de cuidados intensivos de Mayo Clinic, en Jacksonville, Estados Unidos, puede relatar hacia dónde va la IA aplicada en la salud. En instituciones como Mayo Clinic, donde la IA es una realidad certera, “los profesionales de la salud están siendo formados no solo para usar la IA, sino para comprender sus limitaciones, interpretar sus resultados y participar críticamente en su desarrollo. Cada vez más programas de formación incluyen alfabetización digital, ética y equidad en IA, y experiencias colaborativas tipo datathon, donde clínicos y científicos de datos abordan juntos problemas reales. La visión no es convertir a cada médico en programador, sino empoderarlos como colaboradores informados en el desarrollo de sistemas de IA confiables y centrados en las personas”, resume Moreno Franco. Sobre el aporte en la organización de los centros de salud, el experto piensa que “ayudará a mejorar la eficiencia y la consistencia en áreas como la imagenología médica, el triaje, los sistemas de alerta temprana y los flujos administrativos”. Eso ampliaría la capacidad de analizar grandes conjuntos de datos clínicos, permitiendo mejorar las decisiones en salud pública. https://lnkd.in/e6tYchUB

Using Data Effectively in the Industry: Lessons from My Experience Data is more than just raw information—it’s the foundation for decision-making. But how we handle data, from quality assessment to modelling and stakeholder engagement, determines its impact. As a data scientist with a strong health research background, here are key lessons from my one-year industry experience: ·        Define the right data - Identify the data that aligns with the problem we’re trying to solve. ·        Assess data quality – Ensure completeness, accuracy, and consistency. ·        Explore the data – Conduct exploratory data analysis (EDA) to uncover patterns and insights. ·        Apply appropriate model – Use statistical or machine learning models best suited for the task. ·        Engage stakeholders early – Discuss intermediate results to align expectations. ·        Refine based on feedback – Incorporate stakeholder input to improve relevance. ·        Deliver actionable insights – Publish results and advocate for data-driven decision-making. Bridging the gap between data science and industry needs requires technical skills and collaboration – open for new challenge, keep learning and networking. The key is not just building models but making sure they drive real-world impact. What’s your experience with using data for decision-making? Let’s discuss!