CASE STUDY

2024

Submitted By -
Ankit Kumar
 About HiLabs Market Potential(TAM)
Market Description Estimated
HiLabs is a health data analysis company that uses AI to
Segment Market Size
clean healthcare data. The company's MCheck platform
Large Employers Large companies often
ingests, cleans, and enriches healthcare information to (1,000+ employees)
provide comprehensive
health plans and invest in
reduce operational costs and improve patient outcomes. advanced tools to $20–30 billion
manage them.

Mid-Sized Mid-sized companies also $5–10 billion

Business Objectives: Employers (100-999
have substantial
healthcare spending but
Empower employers with data-driven insights that employees) may focus on cost-saving
tools.
improve decision-making regarding healthcare provider Insurers managing
Health Insurers & $15–20 billion
networks. Payers
employer-sponsored
plans looking for better
network and cost
management tools.

Company's Vision:
Key Competitiors:
HiLabs was founded by Amit Garg and Neel
Butala in 2014 to detect and correct data
errors in healthcare. The company's mission
is to make healthcare data meaningful

Overview Problem User Persona Features Prioritization wireframe Success Metrics & Pitfalls
 OverView

Assumptions Stakeholders Target Users Breakdown

• The current data The target users are • Analyzes past provider usage
• Pharmaceutical to predict affected employees
ecosystem is HR/Benefits Managers,
Companies and suggest in-network
fragmented and lacks Finance Managers, Network alternatives.
• Healthcare Providers • Estimates cost changes by
standardization Administrators, Third-Party
• Government assessing the financial impact
• Stakeholders require Administrators, and indirectly, of adding or removing
• Patients providers.
real-time access to Employees, all focused on
• IT and Data • Minimizes disruption by
accurate and actionable optimizing healthcare network suggesting provider
Management Teams
data insights. costs, access, and quality. alternatives and notifying
employees in advance.

Overview Problem User Persona Features Prioritization wireframe Success Metrics & Pitfalls
 Problem Statement
Employers face significant challenges in understanding the impact and costs associated
with changing healthcare provider networks. Traditional methods do not capture the
complexity of member-provider relationships and the true extent of disruption. Using
claims data, build an AI tool that would help employers take informed decisions in case of
disruptions without causing trouble to member experience
Target Users Breakdown
Why this problem

This tool, through claims data, helps

It focuses on employer's needs: There is no need to have regulatory
the employer make good decisions in
cost control and employee complexity like CMS compliance, so
provider networks and optimize the
satisfaction, showing clear ROI there is high market demand in this
cost with minimum disruption of
and competitive differentiation. streamlined development process.
employee care.

Overview Problem User Persona Features Prioritization wireframe Success Metrics & Pitfalls
 David Priya Michael
Senior HR Director Benefit Manager Chief Financial Officer

User Story User Story User Story

As an HR Director for 2,500+ employees, Priya, Benefits Manager for 300 Michael, CFO for 1,200 employees, faces
David can rely on AI-based recommendations employees, faces challenges predicting challenges predicting financial impacts,
to choose the best alternative provider which healthcare service changes, meeting managing employee cost uncertainty, and
helps maintain consistent care, manage the wellness needs, and limited usage visibility. lacks predictive tools and seeeks cost control,
impact effectively, and ensure employees She seeks reliable care access and care access, and balanced savings with
receive timely, If a provider is removed. proactive management. satisfaction.

Paint Points Paint Points

• Difficulty anticipating changes for employees
Paint Points
• Hard to consolidate employee-provider data
relying on specific services in one area. • Difficulty in predicting overall financial
across locations.
• Wellness and family care are critical, so any impacts.
• Managing a large, dispersed workforce’s
changes have a major impact. • Unclear effects on out-of-pocket expenses.
varied healthcare requirements.
• Limited access to data on employee service • Hard to foresee and manage potential
• Challenges in predicting and responding to
usage. negative outcomes.
location-specific disruptions.
Goals Goals Goals
• Ensure consistent healthcare access across • Maintain reliable access to key local • Control costs while maintaining access to
all locations. healthcare services. quality care.
• Use data to predict and manage regional • Proactively manage changes to minimize • Use data-driven insights to predict and
impacts effectively. disruptions. mitigate financial impacts.
• Improve communication tailored to location- • Use data insights to communicate changes • Balance cost savings with employee
specific employee concerns. and build employee trust. satisfaction and care accessibility.

Overview Problem User Persona Features Prioritization wireframe Success Metrics & Pitfalls
 Provider Match Assist ProviderImpact
Provider Match Analytics
Assist Smart Provider
Provider ImpactSelection
Analytics
When an insurance company removes a This feature helps employers understand the
The model allows employers to customize
provider, this feature helps employers find impact of provider changes, allowing for
and filter provider options based on
alternatives, ensuring minimal disruption proactive adjustments to ensure seamless
specific preferences like price and
and continuity of care for employees. care and employee satisfaction.
network needs, recommending the best
• Identifies comparable providers to • Analyze provider quality,
alternatives.
reduce the impact of changes. satisfaction, visit frequency, and the
• Analyze provider costs, quality, and
• Uses advanced algorithms to tailor number of employees associated to
network fit to suggest affordable, high-
suggestions based on employee gauge importance.
quality network options.
needs. • Track employee visits and assess
• Interactive dashboards let employers
• Ensures similar care levels and potential impact if a provider is
filter by price, and location, offering
satisfaction through carefully matched removed.
clear recommendations.
options. • Offer customizable dashboards for
actionable, data-driven insights

Data Sources and Data Process and Compliance and Testing and
AI/ML Modeling User Interface
Ingestion Transform Security Monitoring

Develop predictive Implement

Technology Collect and
Ensure data privacy models for cost
Dashboard with
insights on cost
HIPAA/GDPR
Set up CI/CD for
standardize claims
Walkthrough data, provider
and compliance
with HIPAA/GDPR
estimation,
disruption analysis,
impact, provider
compliance
measures and
automated testing
information, and utilization, and and deployment.
standards. and provider maintain an audit
public health data. alternative options.
recommendations. trail.

Overview Problem User Persona Features Prioritization wireframe Success Metrics & Pitfalls
 Solution Prioritisation
RICE FRAMEWORK

Features Reach Impact Effort Score

Provider Match
8 8 7 73 1
Assist

Provider Impact 2
Analytics
7 7 6 65

Smart Provider
Selection
6 7 5 58 3

Overview Problem User Persona Features Prioritization wireframe Success Metrics & Pitfalls
Solution Wireframing Provider Impact Analytics
Provider Match Assist & Smart Provider Selection

Dashboard

Oops! No Search Resuts

Here are some alternate curated for you :
Dr Ankit Shukla
Dr Lakshya Gupta

Dr Anurag Ghandhi
Detailed Analytics
Dr. Joe Anderson

Employers can view

provider analytics,
If a provider is removed, users By clicking on this filter,
including employee
searching for them will users can now customize
usage, visit
automatically see a well-matched their search of providers
frequency, and
alternative based on their health based on preferred
performance,
history, ensuring they face no location, price range, and
enabling informed
disruption in care or access to more
decisions. Provider Impact Analytics
services.
 Success Metrics Common Pitfalls & Future Enhancement
North Star Metric Data Accuracy & Quality
Employee Satisfaction Rate: • Pitfall-Outdated or incomplete claims data can lead to incorrect analytics and
poor decision-making
Number of Employees Satisfied with Alternative Providers • Solution-Regularly update and validate data using multiple data sources to
Total Employees Affected by Provider Removal ensure accuracy.

Matching & Recommendations

• Pitfall-Difficulty finding providers that fully align with employees' past
Cost Efficiency Rate:
preferences (e.g., location, cost, specialization).
Total Cost Savings from Recommended Providers • Solution-Offer the best-available alternatives and provide detailed comparisons
Total Network Cost Prior to Changes to aid informed decisions.

Filtering & Customization

Dashboard Engagement Rate: • Pitfall-Too many filtering options may lead to overly narrow or less relevant
recommendations.
Number of Active Dashboard Users (HR/Employers) in a month
• Solution-Provide balanced default filters with flexible customization.
Total Employers with Access
User Behavior & Preferences
• Pitfall-Employee needs and preferences can shift over time, making historical
Recommendation Utilization Rate claimed data less reliable.
Number of AI-Based Recommendations Adopted by employers • Solution-Continuously update analytics with recent data and feedback to
Total employers Recommended capture evolving behaviors.

Overview Problem User Persona Features Prioritization wireframe Success Metrics & Pitfalls
THANK YOU :)