Transform executive decision-making with instant, trusted answers from your data (optional: using Databricks Genie)

Industry: Healthcare

Service: Databricks Genie Implementation

Timeline: 8 weeks

Transform executive decision-making with instant, trusted answers from your data (optional: using Databricks Genie)

Client: Large academic medical center (Northeast US)

March 24, 2026

The Challenge

Large healthcare enterprises deal with enormous volumes of data ranging from patient information to financial performance across diverse business units .

The primary bottleneck in business leadership today is no longer the availability of data, but the overreliance on technical teams to interpret it. This dependency creates significant delays in decision-making even for straightforward questions such as, “What was the contribution margin by service line in the last quarter?”

As a result, the client needed a way to decouple the technical dependency and empower their senior leadership through a self-serve platform that answers their questions instantly, accurately with appropriate governance

What We Built

In an 8-week project, the first phase focused on discovery and foundation building. We worked closely with the client to define the use cases, data architecture design, and build the semantic layer that would power the analytics. This involved understanding not just the technical aspects but also how the finance and operations teams thought about their success metrics and how they were accustomed to seeing them reported.

The second phase was to build and productionalize the product. The beta implementation was iterated and validated on various question types followed by structured user acceptance testing and was then deployed to production.The delivery also included comprehensive documentation and a strategic playbook so the internal team could maintain and evolve the system going forward.

The Product

Databricks Genie Space, a natural language interface that lets business users ask questions in plain English and get accurate, governed answers in seconds was implemented.

For example, a user can ask a question such as "Show me Contribution Margin Ratio by service line for inpatient cases in FY25, broken down by campus" and Genie translates that into a SQL query, runs it, and returns a table or visualization without requiring an analyst .

Ensuring the product performed reliably in a complex and regulated healthcare environment required careful due diligence at every layer of the stack.

Data architecture

A layered data model in Databricks was designed, starting from raw data and building up through curated views that apply consistent exclusions before any data reaches the analytics layer. This ensures every answer Genie returns reflects the same business rules the finance team used in standard reporting.

Semantic layer

On top of the curated data, we built metric views that define exactly how each business concept should be calculated: contribution margin, case mix index, OR hours, length of stay, payor group, service line, and more. Instead of letting the AI guess how these metrics are defined, the metric definitions were directly encoded, along with plain English descriptions and synonyms so that the model understands what users mean when they ask.

Inpatient vs Outpatient

The health system tracks inpatient and outpatient care differently, with different metrics, units, and reporting standards for each. A system to handle both in a single interface while maintaining the correct logic for each, including surgical cases, ED admissions, observation stays, and ambulatory surgery encounters was built.

Guardrails and instructions

Genie was configured with explicit behavioral instructions so it can self-navigate when a question is ambiguous, handle time periods appropriately, and when to communicate it cannot answer rather than guess. In the finance domain , “A wrong number is worse than no number.”

Key Features:

Natural Language Querying

Executives type questions in plain English: "How many cardiac patients were admitted last month?" Genie translates this into optimized SQL and returns results in seconds.

Contextual Understanding

The system understands clinical terminology, relationships between entities, and common analytical patterns. It can handle follow-up questions that build on previous queries.

HIPAA-Compliant Security

Row-level and column-level security ensure that sensitive patient data remains protected. Users access only the information their role permits, with all access logged for compliance auditing.

Real-Time Access

Data updates continuously, giving leadership current visibility into clinical operations without waiting for batch report generation.

Query History & Audit Trail

All queries are logged with user attribution, timestamps, and results—meeting healthcare compliance requirements while enabling teams to learn from each other's analytical approaches.

The Result

The deliverable was a production-ready Genie Space with 95% accuracy and 100% coverage across various question types that is validated through structured user acceptance testing before going live.

Finance and operations leaders are now able to get answers to complex operational questions in seconds, directly from a chat interface, without involving a data analyst for routine queries.

The Genie Space covers a wide range of questions across inpatient and outpatient operations, including:

Volume and encounter trends by facility, department, and service line

Financial performance including revenue, cost, contribution margin, and profit/loss

Operating room metrics such as OR hours, surgical delays, and prep time

Length of stay analysis segmented by patient type, payor, and care setting

Case mix and clinical complexity across campuses and fiscal periods

Delivery included full documentation and a strategic playbook covering recommendations for future phases and guide to scaling the system as their data needs grow.

Why It Works

Delivering a conversational generative AI product that performs reliably in healthcare and finance environments requires more than a good AI model. It requires a clean, well-governed data foundation, a precisely defined semantic layer, and careful configuration of how the model behaves. That is where the most critical work took place. While the Genie interface is what users see, the accuracy they experience is a result of the engineering mastery beneath.

Collaboration with the client played a crucial role. Reaching production-grade accuracy required working closely with their teams to build both document and domain knowledge into the AI system.

Interested in Self-Service Analytics for Your Organization?

Whether you're in healthcare or another highly regulated industry, we can implement analytics platforms that balance accessibility with governance.