HEALTHCARE

Enterprise Revenue Cycle & Encounter Utilization Analytics

A governed analytics solution connecting payer coverage, patient financial responsibility, encounter utilization, and operational workload across a multi-site healthcare environment.

ROLE

Business Intelligence Analyst

TOOLS

Power BI · SQL · Power Query · DAX · Jira · Lucidchart

FOCUS

Requirements · Process Analysis · Data Modeling · UAT

DATA

61K+ Synthetic Healthcare Encounters

Explore Solution

View Artifacts

PROCESS TRANSFORMATION

From fragmented reporting to governed self-service analytics.

BEFORE

Separate departmental reporting
Manual data extraction and reconciliation
Repeated requests for additional analysis
Limited visibility across financial and operational metrics

AFTER

Governed enterprise analytics
Standardized KPI definitions and business rules
Self-service financial and operational analysis
BI escalation only for unresolved exceptions

Current-state reporting relied on separate departmental analysis, manual reconciliation, and repeated reporting requests.

Current-state reporting relied on separate departmental analysis, manual reconciliation, and repeated reporting requests.

ANALYTICS SOLUTION

Explore the healthcare analytics solution.

Interact with the full Power BI solution to explore executive performance, payer and financial exposure, and healthcare operational workload.

Executive Overview

Page 1/3

Payer & Financial Analysis

Page 2/3

Encounter & Operational Analysis

Page 3/3

Interactive portfolio report built with synthetic healthcare data. Use report pages, filters, slicers, and visual interactions to explore the solution.

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BEHIND THE SOLUTION

How the analytics solution was built.

Explore the analytical model, representative DAX measures, and key Power Query transformations behind the reporting solution.

The analytical model is centered on Fact_encounters with patient, payer, provider, organization, and date dimensions to support consistent filtering, historical analysis, and KPI calculation.

Fact_encounters

Dim_patients

Dim_payers

Dim_providers

Dim_organizations

Dim_Date

The analytical model is centered on Fact_encounters with patient, payer, provider, organization, and date dimensions to support consistent filtering, historical analysis, and KPI calculation.

Fact_encounters

Dim_patients

Dim_payers

Dim_providers

Dim_organizations

Dim_Date

data governance & security

Designed with healthcare privacy, access, and data protection requirements in mind.

The production scenario was designed around HIPAA-compliant privacy and security requirements, including minimum necessary data use, role-based access, controlled healthcare data exposure, and auditable access.

data minimization

Direct identifiers not required for approved reporting were excluded from the analytical model, including SSN, driver's license, passport information, and full street address.

identity & Access

Production access requirements included enterprise authentication, MFA, role-based authorization (RBAC), and facility-level data restrictions.

audit & Access lifecycle

Access requirements covered provisioning, role changes, access removal, periodic reviews, and auditable production activity.

safe testing & data handling

Development and testing requirements prioritized synthetic or approved nonproduction data and restricted unnecessary exposure of production PHI.

Portfolio implementation: This case study uses synthetic Synthea data and contains no production PHI. Detailed security architecture and IAM implementation remain responsibilities of the appropriate Security, Privacy, IAM, Compliance, and platform teams.

UAT & issue resolution

When a technically correct calculation produced the wrong business result.

UAT validated business workflows, KPI accuracy, data quality, privacy, and access requirements. One operational test exposed an unusually high inpatient duration KPI and triggered a deeper investigation.

Initial Result

107.24hrs

Average INpatient duration

Investigation

UAT Finding → DEF001 → Root Cause Analysis→ CR001 → Retest

Business rule gap identified & corrected

Validated Result

29.17hrs

Average INpatient duration

UAT Finding

Average inpatient duration
appeared materially higher
than expected even though
the calculation executed
successfully.

UAT Finding

Average inpatient duration
appeared materially higher
than expected even though
the calculation executed
successfully.

root cause | def001

Record-level analysis
identified extreme positive
encounter durations that
were not captured by
the original validation rule.

root cause | def001

Record-level analysis
identified extreme positive
encounter durations that
were not captured by
the original validation rule.

resolution | cr001

Revised duration review
logic introduced Invalid,
Suspicious, and Valid
classifications followed by
regression testing and UAT
retest.

resolution | cr001

Revised duration review
logic introduced Invalid,
Suspicious, and Valid
classifications followed by
regression testing and UAT
retest.

4 Invalid Records | 64 Suspicious Records | 61,459 Total Encounters

Records remained available for traceability but were excluded from approved duration-based KPIs when they failed the revised review rules.

DEF001 Closed · CR001 Implemented · Regression Passed · UAT Retest Passed

-DEF001 Closed

-CR001 Implemented

-Regression Passed

-UAT Retest Passed

DEF001 Closed · CR001 Implemented · Regression Passed · UAT Retest Passed

project artifacts

Explore the supporting documentation
behind the solution.

Review the requirements, process analysis, Agile delivery, testing, and issue-resolution artifacts created throughout the project.

Requirements & Process Analysis

Requirements Discovery & Elicitation Summary

View PDF ↗

Business Requirements Document

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Functional Requirements Document

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Business Rules & KPI Definition Catalog

View PDF ↗

Data Requirements & Logical Mapping

View PDF ↗

Process Analysis & Gap Assessment

View PDF ↗

Project Summary

Project Case Study

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Agile Delivery & Traceability

Project Backlog, User Stories & Acceptance Criteria

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Requirements Traceability Matrix

View PDF ↗

Testing & Validation

UAT Plan & Business Test Cases

View PDF ↗

Regression Testing & UAT Retest Results

View PDF ↗

Issue Resolution & Change Management

DEF001 Jira Defect & Root Cause Analysis

View PDF ↗

CR001 Business Rule Change & Impact Assessment

View PDF ↗

← Back to Projects

Enterprise Revenue Cycle & Encounter Utilization Analytics

From fragmented reporting to governed self-service analytics.

BEFORE

AFTER

Explore the healthcare analytics solution.

How the analytics solution was built.

How the analytics solution was built.

Designed with healthcare privacy, access, and data protection requirements in mind.

Designed with healthcare privacy, access, and data protection requirements in mind.

data minimization

Direct identifiers not required for approved reporting were excluded from the analytical model, including SSN, driver's license, passport information, and full street address.

identity & Access

Production access requirements included enterprise authentication, MFA, role-based authorization (RBAC), and facility-level data restrictions.

audit & Access lifecycle

Access requirements covered provisioning, role changes, access removal, periodic reviews, and auditable production activity.

safe testing & data handling

Development and testing requirements prioritized synthetic or approved nonproduction data and restricted unnecessary exposure of production PHI.

When a technically correct calculation produced the wrong business result.

When a technically correct calculation produced the wrong business result.

Initial Result

Average INpatient duration

Investigation

Business rule gap identified & corrected

Validated Result

Average INpatient duration

UAT Finding

Average inpatient duration appeared materially higherthan expected even though the calculation executed successfully.

UAT Finding

Average inpatient duration appeared materially higherthan expected even though the calculation executed successfully.

root cause | def001

Record-level analysis identified extreme positive encounter durations that were not captured by the original validation rule.

root cause | def001

Record-level analysis identified extreme positive encounter durations that were not captured by the original validation rule.

resolution | cr001

Revised duration review logic introduced Invalid, Suspicious, and Valid classifications followed by regression testing and UAT retest.

resolution | cr001

Revised duration review logic introduced Invalid, Suspicious, and Valid classifications followed by regression testing and UAT retest.

Explore the supporting documentation behind the solution.

Explore the supporting documentation behind the solution.

Average inpatient duration
appeared materially higher
than expected even though
the calculation executed
successfully.

Average inpatient duration
appeared materially higher
than expected even though
the calculation executed
successfully.

Record-level analysis
identified extreme positive
encounter durations that
were not captured by
the original validation rule.

Record-level analysis
identified extreme positive
encounter durations that
were not captured by
the original validation rule.

Revised duration review
logic introduced Invalid,
Suspicious, and Valid
classifications followed by
regression testing and UAT
retest.

Revised duration review
logic introduced Invalid,
Suspicious, and Valid
classifications followed by
regression testing and UAT
retest.

Explore the supporting documentation
behind the solution.

Explore the supporting documentation
behind the solution.