Himanshu D.

Imagine healthcare with no shared transaction standards. No HIPAA. No X12. No FHIR. No common baseline. Even with modern JSON APIs, things would start to fall apart fast: • Every payer would design their APIs differently • Quality would vary wildly • Developer experience would be inconsistent at best Interoperability wouldn’t just be hard — it wouldn’t exist. In this clip, Stedi CEO Zack Kanter and Brendan Keeler, Interoperability Practice Lead at HTD Health and author of the Health API Guy Substack, talk through why standards – even if imperfect – matter. Without them, most healthcare integrations and related innovations simply wouldn’t happen.

15

1 Comment

📊Data gaps cost time, money, and trust. 📃The Snowflake Blueprint for Modern Health Care & Life Sciences shares the approach B EYE consultants recommend: unite EHR, imaging and claims data on one governed Snowflake platform so trials start sooner, audit evidence is ready on demand, and analysts focus on discovery instead of cleanup. ➡️ Get the PDF here: https://lnkd.in/dFfg4hzc #HealthcareData #Snowflake #ClinicalTrials #DataGovernance  

6

🤝 Gravity is now a Databricks Built on Validated Partner, recognizing Gravity's production use of Databricks SQL, Managed MLflow, and Delta Lake at enterprise scale. What does this mean for healthcare organizations? Less time wrangling data. More time building AI that actually moves the needle. 💡 Gravity brings pre-built clinical and payer data models, industry-standard ontologies, and proprietary ML models and now, with Databricks powering the ML and AI infrastructure, your data science teams can start at insight and agent development, not ground zero. From pipeline to deployment. From data to outcomes. That's the future we're building together. 🔗 Explore more about our partnership: https://lnkd.in/g-rGRMBp #HealthcareAI #AIInfrastructure #HealthTech Innovaccer

29

Adopting Claude Code in the Enterprise: -----Lessons and Best Practices---- For enterprises considering adopting Claude, the opportunity is significant but so are the responsibilities. Implementing it well requires thinking beyond the tool itself & focusing on governance, architecture, security, & developer enablement. Why Enterprises Are Looking at Claude? Because it combines strong reasoning capabilities with a large context window, allowing it to: Understand complex repositories Plan multi-file refactors Assist with debugging & architecture reviews; and Generate tests & documentation automatically In practice, this means developers can spend less time on repetitive tasks & more time on design, architecture, & innovation. Organizations that adopt it successfully are seeing improvements in: Developer velocity Code quality & documentation Faster onboarding for new engineers Reduced technical debt But unlocking these benefits requires a thoughtful deployment model. 1. Start with Clear Governance AI-assisted development should never bypass existing engineering discipline. Enterprises should define: AI usage policies Code review requirements; and Ownership & accountability for AI-generated code A simple rule I’ve seen work well: AI to propose code, humans to approve it. 2. Cybersecurity Embedded from Day One Data Protection Prevent sensitive code or credentials from being exposed in prompts Implement secure API gateways and monitoring Secure Model Access Use enterprise authentication (IAM integration) Role-based access for development environments Auditability Log AI interactions for compliance Maintain traceability for generated code Dependency & Vulnerability Scanning Automatically scan AI-generated code Integrate with existing SAST/DAST pipelines Without these safeguards, AI coding tools can unintentionally introduce data leakage risks or insecure code patterns. 3. Define the Right Architecture A typical Claude enterprise architecture includes: Developer Environment → Claude Code Interface → Secure AI Gateway → Model API → Enterprise Code Repositories → CI/CD Pipeline → Security & Compliance Monitoring 4. Invest in Developer Skills The most successful teams focus on: Prompt engineering for developers Knowing how to ask the right questions. System thinking AI accelerates coding architecture decisions become even more important. Competitive Advantage of Claude Code 1. Faster Software Delivery Teams can iterate faster & reduce development cycles significantly. 2. Reduced Technical Debt AI can identify outdated patterns & suggest improvements. Where This Is Heading The most forward-thinking companies are moving toward a model where AI becomes a standard layer in the software development stack. In the same way that: Git transformed version control CI/CD transformed deployment Real competitive advantage won’t come from simply adopting AI, it will come from how well organizations integrate it into their culture. #claudecode

20

Curious how your Databricks data estate is really performing? Download a free sample of Unravel's Databricks Health Data Estate Report and see the kind of deep insights you can unlock: 📈 Performance insights to speed up jobs and workflows 📈 Productivity boosts with top improvement areas—no log diving required 📈 Savings projections for clusters and pipelines 📈 SLA attainment metrics to reduce pipeline delays 📈 Job health visibility to fix frequent failures fast Start uncovering hidden opportunities for efficiency: https://lnkd.in/gJ7qu4Nu #Databricks #CloudFinOps #DataObservability #PerformanceTuning #DataEfficiency #UnravelData

5

Automate. Accelerate. Analyze. Data teams spend too much time on manual coding and complex workflows. That’s why we created “WhereScape + Databricks: Automate Your Data Infrastructure and Analytics.” In this free white paper, discover how to: ✔ Cut data pipeline development from months to days ✔ Automate documentation and governance with full lineage ✔ Deploy Data Vault 2.0 on Databricks seamlessly ✔ Leverage Delta Lake, Unity Catalog, and the Medallion Architecture with automation Build smarter, not harder. Download now and transform how you deliver analytics: https://ow.ly/csY950WxoVx #Databricks #DataAutomation #DataEngineering #DataVault #Lakehouse #ETL

7