Vocal Mind

An AI-guided voice and speech analysis tool that assists mental health practitioners in diagnosis during sessions.

An accurate mental health diagnosis can take weeks up to years to determine. This is due to variability in diagnostics criteria and limitations of subjective tools. Vocal Mind leverages AI to analyze patterns in patients' voices and speech to enable timely, accurate diagnosis of mental health disorders.

Selected as 1 of 9 teams to pitch to Cornell Tech faculty and partners

Skills

User Journey
MVP Validation
AI Models

Team

Team

My Role

Product designer

My Role

Product designer

Collaborators

2 Engineers,

1 Product manager

Collaborators
2 Engineers,
1 Product manager

Timeline

Timeline

4 months
Aug - Dec 2023

4 months
Aug - Dec 2023

Research

Transcarent partnered with Cornell Tech to tackle a challenge: how might tech be leveraged in the healthcare industry?
Transcarent partnered with Cornell Tech to tackle a challenge: how might tech be leveraged in the healthcare industry?

We researched the stakeholder ecosystem, current patient experience, and technological advancements to get a clear picture of the systems within the healthcare industry.

Current user flow for a patient seeking treatment

Combined industry and stakeholder research to visually represent the processes, players, and their positive or negative concerns.

INTERVIEWS

I spearheaded the creation of our interview guides— one for medical providers and the other for people who have sought medical care. It was essential to understand their unique experiences and identify the interconnected issues.

“Providers are interested in generative AI, but the research is very new. AI is already used in diagnostics and pathology, classification tasks.”

“Providers are interested in generative AI, but the research is very new. AI is already used in diagnostics and pathology, classification tasks.”

Medical Student 
at Weill Cornell Medicine

5

Doctors

Doctors

5

Patients

Patients

2

Med Students

Med Students

Practitioner

How do they conduct their medical practices and what do they think about incorporating AI?

Variability in mental health diagnostics criteria and limitations of subjective screening tools can lead to delayed or misdiagnosis.

Patients

How do they handle insurance, find doctors, and go through consultations?

Patients have difficulty articulating their emotions, which slows or prevents timely diagnosis.

Our idea was fueled by the pain points in the diagnosis process that could benefit from technological enhancements and the healthcare industry's current openness to adopting technology.

Our idea was fueled by the pain points in the diagnosis process that could benefit from technological enhancements and the healthcare industry's current openness to adopting technology.

By 2025, 90% of hospitals will use AI-driven technology for remote patient monitoring and early diagnostics. 

HOW MIGHT WE…

improve the mental health diagnosis process by utilizing an AI digital tool?

Development

I collaborated with the engineers and product manager to determine the product's technological feasibility and key desirability through our risky assumption tests.
I collaborated with the engineers and product manager to determine the product's technological feasibility and key desirability through our risky assumption tests.

Risky Assumption #1

Is the way people speak affected by their mental state?

Ten people were enlisted to complete a week-long experiment where they filled out a mood tracker and sent audio recordings about their day. Our questionnaire was modeled on the clinical guidelines for mental health diagnosis. The recordings were interpreted using an existing speech behavior analysis API by Humane AI.

Clinical guideline for mental healthcare diagnostics

Our questionnaire that we sent to participants

By comparing the API's insights to those of the mood tracker, we determined the accuracy of the results.

There was a significant correlation found between the participants’ inputs from the mood tracker and API analysis.

Participant's mood tracker answers

Voice Sentiment Analysis AI API

We identified consistency in the AI’s insights throughout the experiment.

Day 1

“Confident and focused”

Day 2

“Tired, bit stressed, and focused”

Day 3

“Felt confident”

Day 1

“Confident and focused”

Day 2

“Tired, bit stressed, and focused”

Day 3

“Felt confident”

KEY TAKEAWAY

We found there is a useful correlation between the participant’s mental health state and audio recording patterns.

Risky Assumption #2

Do people speak as they normally would when being recorded?

We compared the answers and mannerisms of interviewees while initially speaking unrecorded and after we began recording the conversation. The participants were randomly selected, unaware of the purpose of the research, and did not know ahead of time that they would be asked to be recorded halfway through. 

80% of participants had no significant voice change

Participants spoke in a similar manner with or without recording.

75% of participants felt more comfortable the more they spoke.

Participants took longer pauses as the conversation continued, which we used as an indicator of comfortability.

80% of participants had no significant voice change

Participants spoke in a similar manner with or without recording.

75% of participants felt more comfortable the more they spoke.

Participants took longer pauses as the conversation continued, which we used as an indicator of comfortability.

KEY TAKEAWAY

Even with the conversation being recorded, people can still speak normally. 

The Solution

Vocal Mind, an AI-guided voice and speech analysis tool that assists mental health practitioners in diagnosis during sessions.

User journey of a patient and practitioner leveraging Vocal Mind during their consultation session

Reflection

Each individual's skills and expertise bring value to a
cross-functional team and create a more impactful solution.

Each individual's skills and expertise bring value to a cross-functional team and create a more impactful solution.

Each individual's skills and expertise bring value to a cross-functional team and create a more impactful solution.

Our team created a collaborative environment where each of our unique perspectives was valued throughout the development process. We were able to thoughtfully consider the user's needs, the tech needed to make our solution feasible, and the roadmap for our product launch. This genuine involvement and curiosity led to a meaningful solution, and we were selected as 1 of 9 teams from a cohort of 80 groups to pitch to Cornell Tech faculty and partners.