Project Overview

Pathogenix is a web application which allows oncology pathologists to create a report for cancer patients. It helps them deep dive into patient cases to find details of cancer causing mutated genes, and add those details in a report.

What was the problem?

Who are the users?

These people work in cancer laboratories and hospitals

Business & User Goals

• Reduce turnaround time of case review

• Improve consistency and accuracy in genetic interpretation

• Enable faster case interpretation & sign out

The Process

Design is not a linear process, hence:

Impacts

• 40% reduction in turnaround time for case reviews

• 60% fewer errors flagged during sign-off

• 100% adoption by pathologist teams within 2 weeks

• Pathologists reported feeling more "in control" of their workflow

Understanding the background

A sneak peak into the non-digital process:

In a nutshell:

Discovery & Research

Research document:

Interview Framework:

User Interviews :

Personas:

Hypothesis:

1. Users need a platform to understand the status of patient cases, which they need to work on

2. Manually adding the significances is painful, hence they need a way which can be more helpful

3. The users need to understand critical details of a gene upfront

4. They need confidence about how significances of a gene is being added in other similar cases

Service blueprint :

Stages:

Actions:

Touchpoints:

Painpoints:

Opportunities:

Scope definition:

Redefined Brief:

In the current scenario, there is no solid way or defined process through which pathologists can create a report which would have helpful genomic insights.

The newly designed application should help the pathologists to:

1. Get cases assigned to pathologists

2. Get a case completion status

3. Write down their own medical interpretations

4. Get detailed genomic insights and references per case

5. Add desired genes into report and mark those as reviewed

6. Add own interpretation of genes and drugs

7. Sign off report and mark as completed

Userflow & Information Architecture:

Key Screens:

1. Login Screen

2. Cases screen

3. Individual case page

4. Finalise report page

5. Case sign-out page

Impacts & Metrics:

Below tasks were identified for usability testing:

1. Login and determine which case needs more attention

2. Identify the classification of genes for a particular case

3. Identify system interpretations for a given Gene name

4. Add your own interpretations for any Gene

5. Preview report while signout

6. Add your own interpretations and signout the report

Further deep dive revealed interesting numbers:

What worked well:

• Co-designing with users led to deep trust and faster iterations

• Service design methods helped uncover system-wide blockers

What could be better:

• Earlier engagement with legal/compliance would have saved rework

• Could have added voice notes or annotation features for interpretation faster