How Analytics Teams Gained 30% Productivity Building CKD Risk Models with AI

Chronic kidney disease affects about 15% of US adults, but early stages often go undetected until significant kidney damage has occurred. That’s the challenge my team and I have been tackling: building a predictive model to flag patients at risk of developing CKD using early indicators from prior hospitalizations, lab results, past diagnoses, and similar clinical factors.

The complexity of this kind of healthcare analytics work is what makes it both fascinating and daunting. You’re looking at many different data sources and indicators. CKD risk involves lab values like creatinine and eGFR trends, comorbidities like diabetes and hypertension, medication patterns, and hospitalization history. Before AI tools became part of my workflow, a project like this meant weeks of reading academic papers, manually coding complex SQL queries, and writing Python scripts from scratch for data transformation.

AI has changed how I approach this work. I’d estimate it boosts my productivity by 30-40%, and the real shift is where I spend my time and mental energy.

Working with Domain Experts Gets Supercharged

I’ve always worked closely with clinical experts on projects like our CKD model. Our director of quality brings deep clinical knowledge about kidney disease progression, while our senior data architect understands the operational challenges of implementing predictive models in clinical workflows. But even with that expertise, we still needed to ground our approach in current research.

This is where AI really helps. Instead of manually searching through PubMed to find the key articles on CKD risk factors, I can quickly get references to the most relevant studies and clinical guidelines. For our CKD project, AI helped me identify the core research around predictive measurements: serum creatinine trends over time, proteinuria markers, blood pressure patterns, and specific comorbidity combinations that increase risk.

AI gives me a starting point, not the final answer. I still run everything past our director of quality to make sure it makes clinical sense, and I validate against my own understanding of how this data actually gets captured in Epic.

The real value is speed. I can move faster from “What should we measure?” to “How do we measure it accurately?” and spend more time on the clinical logic with our domain experts.

Data Transformation in Python Gets Easier

Once we identified the clinical indicators for our CKD model, the technical work began. Our model needed to pull lab results across multiple years, identify diagnosis patterns, and create meaningful features from hospitalization data. That means complex SQL joins across Epic’s lab tables, problem list tables, and encounter data, followed by extensive Python work for feature engineering.

The SQL part used to be the most time-consuming piece. Writing queries like “Every third month starting from the patient’s first elevated creatinine, except when there’s a gap longer than six months” required careful logic and lots of testing. Now I can describe the business requirement and get working SQL about 80% of the time. The other 20% usually needs tweaking for Epic-specific table relationships that AI doesn’t understand.

Python data transformation is where AI really shines. Machine learning models need a lot of feature engineering: calculating rolling averages, creating time-based indicators, handling missing data properly. This work is precise but repetitive. AI can generate most of these transformation scripts reliably, which lets me focus on the clinical logic of what transformations actually make sense for CKD prediction.

Academic Literature Becomes More Accessible

One thing that’s changed how I work with our clinical experts is my ability to quickly reference and cross-check academic materials during our discussions. When our director of quality mentions a specific aspect of CKD progression, I can immediately pull up relevant research and see whether our model approach aligns with current evidence.

This makes our collaboration more productive. Instead of scheduling follow-up meetings to research questions that come up, we can often resolve them in real-time. AI helps me navigate the literature faster and identify the most relevant studies for our specific use case.

The validation framework for our CKD model came together the same way. I could quickly outline industry best practices for healthcare prediction models: train/test splits, cross-validation approaches, appropriate performance metrics, bias testing methods, then adapt that framework for our specific requirements with input from the team.

The Human Expertise Piece Remains Critical

Here’s what AI can’t do: understand the nuances of Epic’s data model or the clinical context of how things actually get documented. It might suggest using problem list diagnoses for diabetes identification, but I know that providers document diabetes in multiple ways including problem lists, encounter diagnoses, medication patterns, lab results. You need both clinical and technical expertise to make those judgment calls about which data sources to trust.

Real Impact on Project Delivery

The productivity gains aren’t theoretical. Our CKD model is in validation now, implemented using Epic’s ML tools, on a timeline that would have been challenging without AI assistance. The time I used to spend on manual coding and literature review now goes toward higher-value work.  This ensures the model makes clinical sense, validating results with our clinical team, and building robust implementation processes.

This means better deliverables. Instead of rushing to meet deadlines with basic functionality, I can spend time on the details that make analytics projects actually useful in clinical practice. Better documentation, more thorough testing, clearer visualizations, more comprehensive validation.

The administrative side improved too. Project updates, scope documents, stakeholder communications all of that goes faster when AI helps with initial drafts. Those time savings add up across every project.

Team Learning and Knowledge Sharing

We’re beginning to share AI insights and best practices more formally across our team. During regular meetings, we’ll discuss how different team members used AI for specific projects, what prompts worked well, which tools performed better for certain tasks. It’s mostly organic right now, but people are getting excited about the possibilities.

What’s interesting is seeing how different team members gravitate toward different applications. Some focus on code generation, others on research synthesis, others on document creation. As we share these approaches, the collective capability of the team grows faster than individual adoption would suggest.

The Future of Healthcare Analytics

We’re still early in AI adoption for healthcare analytics. Over the next couple years, as Epic rolls out more AI-integrated tools, I expect this to change significantly. Features like AI-powered dashboard insights and natural language querying could transform exploratory analysis and executive reporting.

But the need for clinical expertise and data model knowledge isn’t going away. The role is evolving from writing code to writing better prompts, which actually requires deeper understanding of what you’re trying to accomplish and how healthcare data works.

Building Something That Matters

Our CKD identification model represents what’s possible when AI augments clinical and technical expertise rather than trying to replace it. The technology handles routine work: code generation, literature synthesis, data transformation, while human judgment guides the clinical logic and validation approach.

The result is analytics work that’s both faster and more thoughtful. We can tackle more complex projects, deliver higher-quality results, and spend time on the aspects that actually require human insight.  We can understand clinical workflows, validate results against real-world practice, and make sure our models serve patients and providers.

The CKD project will help clinicians identify at-risk patients earlier, potentially preventing progression to end-stage kidney disease. That’s the kind of impact that makes this work worthwhile. AI is helping us get there faster without sacrificing quality, and that’s exactly how it should work.