User Journey Maps

Comprehensive mapping of user experiences before and after the platform implementation, showing the transformation from frustration to empowerment across all personas.

User Personas

Dr. Sarah Chen

Senior Data Scientist

Global Pharma Inc.

Goals

Extract patient journey insights quickly

Generate reliable SQL queries

Validate data before analysis

Pain Points

Weeks spent writing custom queries

Manual data validation processes

Inconsistent data formats across systems

Tech Comfort

Dr. Michael Rodriguez

Clinical Research Director

Rare Disease Institute

Goals

Understand patient treatment pathways

Identify outcome predictors

Support regulatory submissions

Pain Points

Data scattered across multiple systems

Long delays for basic analysis

Difficulty translating data to insights

Tech Comfort

Lisa Thompson

Marketing Manager

Biotech Solutions

Goals

Understand market opportunities

Develop targeted campaigns

Measure market impact

Pain Points

Clinical data too complex to interpret

No business context for insights

Slow turnaround for market analysis

Tech Comfort

Dr. Sarah Chen

Senior Data Scientist

Current State Journey

Discover Need

Actions

Receives request for patient journey analysis
Reviews requirements

Request Data

Actions

Submits IT ticket for data
Negotiates data access permissions

Wait for Analysis

Actions

Follows up on data request
Plans analysis approach

Receive Results

Actions

Downloads raw data files
Validates data quality

Interpret Findings

Actions

Writes custom SQL queries
Cleans and transforms data
Generates visualizations

Take Action

Actions

Presents findings
Answers stakeholder questions

Pain Points

2 hours

Unclear requirements
Multiple stakeholder requests

Pain Points

4 hours

Complex approval process
Data governance delays

Pain Points

1-2 weeks

No visibility into progress
Planning without seeing data

Pain Points

1 day

Inconsistent formats
Missing documentation

Pain Points

2-3 weeks

Time-intensive coding
Data quality issues
Complex joins across systems

Pain Points

2 hours

Limited time for iteration
Stakeholders want more analysis

Future State Journey

Discover Need

Actions

Receives clear requirements in platform
Reviews existing similar analyses

Request Data

Actions

Selects data sources in platform
Applies filters instantly

Wait for Analysis

Actions

Platform processes data in real-time
Reviews preview results

Receive Results

Actions

Views pre-validated, clean dataset
Exports to preferred format

Interpret Findings

Actions

Uses 1-click SQL generation
Leverages built-in visualizations

Take Action

Actions

Shares interactive dashboard
Enables stakeholder self-service

Pain Points

30 minutes

Template requirements
Historical analysis library

Pain Points

10 minutes

Integrated data access
Real-time filtering

Pain Points

2 minutes

Instant processing
Live data preview

Pain Points

5 minutes

HIPAA-compliant validation
Multiple export options

Pain Points

2 hours

Automated SQL generation
Interactive dashboards

Pain Points

30 minutes

Stakeholder-friendly interface
Self-service analytics

Dr. Michael Rodriguez

Clinical Research Director

Current State Journey

Discover Need

Actions

Identifies research question
Defines patient population

Request Data

Actions

Contacts data science team
Explains clinical context

Wait for Analysis

Actions

Continues other research
Periodically follows up

Receive Results

Actions

Reviews data summaries
Validates clinical accuracy

Interpret Findings

Actions

Analyzes clinical outcomes
Compares to literature

Take Action

Actions

Writes research paper
Presents at conferences

Pain Points

2 hours

Scope creep potential

Pain Points

4 hours

Translation between clinical and technical language

Pain Points

1-2 weeks

Research timeline delays
Pressure from leadership

Pain Points

1 day

Technical format difficult to review
Missing clinical context

Pain Points

2-3 weeks

Limited ability to drill down
Static reporting

Pain Points

2 hours

Difficulty reproducing analysis
Limited follow-up questions

Future State Journey

Discover Need

Actions

Uses platform templates for research questions
Defines cohorts visually

Request Data

Actions

Self-service data access
Real-time compliance validation

Wait for Analysis

Actions

Immediate results available
Platform handles data processing

Receive Results

Actions

Views clinical-friendly dashboard
Sees business context automatically

Interpret Findings

Actions

Explores interactive patient journeys
Compares cohorts dynamically

Take Action

Actions

Generates publication-ready outputs
Shares with research team

Pain Points

30 minutes

Clinical research templates
Visual cohort builder

Pain Points

10 minutes

Direct data access
Automatic compliance checks

Pain Points

2 minutes

Real-time processing
No waiting period

Pain Points

5 minutes

Clinical terminology
Contextual insights

Pain Points

2 hours

Interactive exploration
Dynamic comparisons

Pain Points

30 minutes

Publication templates
Easy collaboration

Lisa Thompson

Marketing Manager

Current State Journey

Discover Need

Actions

Needs market sizing data
Wants patient journey insights

Request Data

Actions

Emails clinical team
Attends cross-functional meeting

Wait for Analysis

Actions

Plans campaign around estimated timeline
Updates leadership

Receive Results

Actions

Receives technical report
Struggles to understand findings

Interpret Findings

Actions

Asks for explanation
Tries to extract key insights

Take Action

Actions

Creates simplified summary
Presents to leadership

Pain Points

1 hour

Vague requirements
Unclear what's possible

Pain Points

30 minutes

Complex process
Multiple stakeholders involved

Pain Points

3-4 weeks

Campaign delays
No progress visibility
Leadership pressure

Pain Points

2 hours

Too technical
No business implications
Tables of numbers

Pain Points

1 day

Medical terminology
Statistical complexity
No market context

Pain Points

Ongoing

Uncertain about accuracy
Limited actionable insights

Future State Journey

Discover Need

Actions

Uses platform templates for research questions
Defines cohorts visually

Request Data

Actions

Self-service data access
Real-time compliance validation

Wait for Analysis

Actions

Immediate results available
Platform handles data processing

Receive Results

Actions

Views clinical-friendly dashboard
Sees business context automatically

Interpret Findings

Actions

Explores interactive patient journeys
Compares cohorts dynamically

Take Action

Actions

Generates publication-ready outputs
Shares with research team

Pain Points

20 minutes

Clinical research templates
Visual cohort builder

Pain Points

5 minutes

Direct data access
Automatic compliance checks

Pain Points

1 minute

Real-time processing
No waiting period

Pain Points

10 minutes

Clinical terminology
Contextual insights

Pain Points

30 minutes

Interactive exploration
Dynamic comparisons

Pain Points

15 minutes

Publication templates
Easy collaboration

User Journey Flow Diagram

Visual flow diagram showing the complete user journey transformation from current pain points to streamlined future state workflows.

Pharma Researcher Platform Flow

Clinical research scientist analyzing patient outcomes and drug efficacy

Analytics Dashboard

overview

Outcome Analysis

insights

Session Time

35m

Patient Search

discovery

Export Findings

reporting

Primary Goals

Identify patient cohorts

Cohort Definition

discovery

Key Features

Patient JourneyCohort Analysis

Patient Journey

insights

Use Cases

Rare disease studies

Data Analyst Platform Flow

Healthcare data analyst creating reports and visualizations for stakeholders

Analytics Dashboard

overview

SQL Export

reporting

Data Exploration

discovery

Visualization Builder

reporting

Report Generation

reporting

Session Time

42m

Primary Goals

Create data visualizations

Key Features

Dashboard

Data Mapping

reporting

Use Cases

Executive dashboards

Clinical Researcher Platform Flow

Medical professional studying real-world evidence and treatment outcomes

Analytics Dashboard

overview

Clinical Insights

insights

Patient Cohorts

discovery

Treatment Timelines

insights

Evidence Package

reporting

Session Time

31m

Primary Goals

Understand treatment patterns

Key Features

Patient Journey

Cohort Analysis

Outcome Tracking

insights

Use Cases

Treatment effectiveness

Regulatory Analyst Platform Flow

Compliance specialist preparing submissions and regulatory documentation

Analytics Dashboard

overview

Regulatory Export

reporting

Compliance Check

governance

Audit Trail

governance

Population Validation

governance

Submission Package

reporting

Session Time

44m

Primary Goals

Ensure data compliance

Key Features

Data Validation

Audit Trail

Use Cases

FDA submissions

Technical Workflows & Data Processing

Behind-the-scenes technical workflows powering the user experience transformation

Reagens

Diseases

Doctors

Patients

Drugs

Diseases

Reagens

Doctors

Patients

Drugs

Doctors

Diseases

Patients

Drugs

Reagens

Patients

Diseases

Doctors

Drugs

Reagens

Drugs

Doctors

Diseases

Patients

Reagens

Manufacturer

Distributor

Electronic helth

records

Treatment

guidelines

Extract date

Identify clinical

Decision points

dentify variables

from guidelines

Identify variables

from Data

Select salient

variables

Training Data Set

Treatment

guidelines

Scoring Data set

Identify precision cohrt

Index Patient

Select patients with the same guideline

variables as the index patient

Compote patient

similarity scores

Rank and select the most

similar patients

Precision cohort

Analyze treatment options

Personalized

treatment options

Compote outcome statistics for each treatment option

Group patients by observed treatment option

Search Treatments

List of treatments

top concerns

Find a clinic

Show all services

L1, L2, L3, L4

List of treatments

Details of treatments

Cost calculator

Photos/ Videos

Description

Process

Befour treatment
During treatment
After Treatment

Location Based

Parient profile

-Name

-mobile numaber

-Email ID

-Gender & Age

-Profession

Parients History

Area of treatment

-Treatment goal

-Preference

-List of treatment

-Enter location

About Clinic

Treatment Details

List of Doctors

List of other Treatmen

Data Architecture

Multi-source data integration

Real-time data processing

OMOP CDM standardization

Automated data validation

Secure data governance

Processing Engine

ML-powered similarity scoring

Precision cohort identification

Treatment outcome prediction

Automated SQL generation

Cloud-scale computation

User Interface Layer

nteractive visualizations

Drag-and-drop cohort building

Real-time collaboration

Export & sharing tools

Mobile-responsive design

Technical Process Flow Mapping

Data Ingestion Layer

EHR data extraction
Claims data processing
Treatment guidelines integration

Analysis Engine

Variable identification
Similarity model training
Cohort precision scoring

Output Generation

Treatment recommendations
Outcome statistics
Business intelligence reports

Current State

Discover Need 2 hours

Unclear requirements

70% satisfaction • neutral

Request Data 4 hours

Complex approval process

30% satisfaction • frustrated

Wait for Processing 2-4weeks

No visibility into progress

20% satisfaction • anxious

Receive Results 1 day

Inconsistent data formats

40% satisfaction • concerned

Interpret Findings 2-3 weeks

Time-intensive coding

50% satisfaction • stressed

Take Action 2 hours

Limited iteration time

75% satisfaction • relieved

Future State

Discover Need 20 minutes

Template-driven requirements

90% satisfaction • confident

Request Data 5 minutes

Self-service data access

95% satisfaction • empowered

Wait for Processing 1-2 minutes

Real-time processing

95% satisfaction • amazed

Receive Results 5 minutes

Pre-validated clean data

90% satisfaction • satisfied

Interpret Findings 30 minutes

1-click SQL generation

95% satisfaction • empowered

Take Action 15 minutes

Self-service analytics

95% satisfaction • accomplished

Key Competitors in Patient Analytics

Competitor

Core Patient-Analytics Capabilities

What They Do Better / Differently vs Axtria

Weaknesses or Gaps Relative to Axtria

IQVIA

Massive real-world data (claims + EHR + patient behaviour), patient journey / forecasting products, strong regulatory & global reach. IQVIA+2Veeva Systems+2

Better scale: larger data sources, global coverage; deeper in forecasting & predictive analytics; trusted reference in RWE.

May have less flexibility/customization; higher complexity & cost; possibly slower in agile UX/design; less “low-code/no-code” user-facing cohort tools (depending on product).

Veeva Systems

Pharma commercial cloud + patient data privacy, analytics & engagement (especially via Crossix acquisition), good commercial-user facing tools. Veeva Systems+2Wikipedia+2

Strong in integrating patient data with commercial tools (CRMs, marketing segmentation); better in “engagement + privacy safe analytics” especially for US markets.

Perhaps less depth in advanced journey / cohort tools or EHR/claims heavy RWD; may lag in features like real-time cohort size/quality feedback or agile cohort builders if compared to more specialized startups.

IntegriChain

Specialty pharmacy analytics, patient access, therapy initiation/adherence match, detailed field reimbursement (FRM) analytics, monitoring patient service utilization. IntegriChain

Very strong focus on specialty pharma / rare / high cost therapies; good at operational analytics (patient start / refill gaps / payer friction).

Might have less breadth in whole journey (diagnosis → treatment switch → outcomes) or less in predictive modeling; UX/user-facing cohort building might be more limited; integration with broad EHR or claims data may be weaker.

Sisense

Visualization / dashboarding + embedding of healthcare & pharma data; tools for real-time recommendations (e.g. risk, readmissions) via EHR/EMR sources. Sisense

Great at front-end visualizations; flexibility for clients who want to build dashboards & self serve analytics; strong at turning operational data into UI insights.

Not always specialized for pharma / life sciences; may require more work to connect RWD, claims, regulatory context; less therapy-area templates or lineage; likely lower domain-specific capabilities than Axtria/IQVIA.

WNS Analytics

Builds custom analytics platforms/applications; works in global life sciences & healthcare; able to integrate varied sources for insights. WNS

Strength in customization and building scalable ecosystems; ability to handle diverse/regional data; cost advantage perhaps.

Less brand recognition or existing “plug & play” pharma-standard modules; possibly slower development of new analytics features; UX might be less polished in highly specialized patient journey or cohort tools.

Straive

Pharma & regulatory data curation; AI/ML, generative AI, domain & operations; processing complex healthcare-related data. straive.com

Good for complex data ingestion, handling regulatory / compliance pipelines; strong in AI/ML support for insights.

May lack in out-of-the-box cohort / journey visualization features; possibly less in commercial-user simplicity; maybe less optimized for rare disease cohort building.

Gaps & Opportunities for Axtria (Based on Competitor Landscape)

Speed & Usability for Non-Technical Users

Many competitor tools are still heavy, requiring technical teams. There’s room for Axtria to lead with low-code / no-code cohort builders, real-time feedback, cohort previews.

Specialization in Rare Diseases / Small Cohorts

Rare disease cohorts have special challenges: small numbers, censored data, delayed diagnoses. A module tailored for rare disease patient journeys could differentiate.

Stronger Visual Journey & Predictive Insights

Competitors like IQVIA already strong here, but there’s always demand for more predictive switches, adherence forecasting, drop-off points, and visualization that communicates across stakeholders.

Templates & Reusable Assets

Having therapy-area templates, standardized cohort definition libraries, reference datasets helps with speed and consistency. If Axtria builds a central repository or marketplace of such templates, that’s a plus.

Integration & Interoperability

The IQVIA-Veeva news shows importance of data interoperability / master data / third-party access rights. Axtria could lean into enabling seamless integrations (EHR, claims, pharma commercial systems) and providing transparency in data sources.

Privacy & Compliance, Especially for Global Markets

Handling GDPR, HIPAA, local data laws, identity resolution, de-identification are key. Competitors highlight privacy safe analytics. Axtria should ensure strong messaging and tools here.

AI / ML Assistance

Auto-recommend filters, anomaly detection, progression path suggestions, cohort quality scoring. Competitors are moving into embedding that. It’s an opportunity.

Sample Competitive Comparison (Feature-by-Feature)

Features

Global RWD / Claims + EHR coverage

Cohort Builder (drag & drop / non-technical)

Axtria

✔️ Strong, but may need more vendor diversity

Medium strength (consulting + tech heavy)

Strong / needs further UX polish

IQVIA

✔️ Very extensive

Good in some products; many technical parts

✔️ Very good

Template Library / Reuse

Some capabilities; opportunity to expand

✔️ Likely strong

Real-time size / cohort quality feedback

An opportunity for differentiation

Varies; some strengths

AI/ML Assistance (anomaly detection, recommendations)

Emerging / opportunity

✔️ Strong in RWE & predictive reports

Veeva

Moderate

Some user-facing analytics; typically integrated with commercial tools

Growing, especially for commercial/marketing insights

✔️ Some commercial templates & segments

Some near real-time within commercial tools

Growing, especially with cross-platform partnerships (IQVIA-Veeva)

IntegriChain

More focused on specialty / pharmacy / operational data

Operational dashboards, but less “journey visualization”

Moderate; strong in initiation/adherence but less end-to-end journey & predictive path

Focused templates (e.g., specialty therapy) but less breadth

Less real-time; more retrospective analytics

More basic ML in operational workflows; less in advanced patient journey forecasting