Design management, Project Oversight
Spreadsheets, Talkdesk, Figjam, Figma
Honor, 2023
Advised Product Design Lead in reimagining client onboarding experience, as well as coaching and critiquing the first phase of designs.
Org background
Honor is revolutionizing the home care industry by building a platform that connects professional caregivers with families looking for quality home care.
Project context
From an initial sales call to the in-home care assessment to ongoing care management, Honor collects detailed care information to build a personalized Care Plan for their clients.
Care Plans are critical part to every new Start-of-Care. They ensure we can quickly match clients with the best caregivers for their needs and deliver high quality care in the home from day 1.
KPIs and Problem Alignment
The initial phase of a clients journey is a critical time when we see higher churn risk. There are several key experience defects that cross-functional teams are oriented around, to reduce churn during that phase.
The project team focused on improving Speed to Start-of-Care honed in on the goal to significantly reduce the time and effort needed to produce Care Plans, without decreasing quality.
Vision & Solution definition
In collaboration with our CPO, I had earlier set an overall UX vision of being more technology-led and human-centered in our product approach. With this in mind, the Design Lead proposed a project vision for Care Plans that leveraged automation and LLMs for transcription and content generation, while refocusing the human conversation on highest value data earlier in the funnel.
Together, the Lead and I aligned with Product Leadership on the project vision. From there Design collaborated with their project team on scoping out MVP requirements and refining the design direction.
What we built
As part of this launch, the team introduced:
LLMs to auto-generate care plan tasks to increase caregiver preparedness
Intuitive UI for ops members to review, accept/reject, or modify AI generated content
Design decisions I collaborated on, ensuring our end product met our design quality bar:
How to build clarity and trust with our ops member users on what the system-generated and why
Prompt engineering to ensure high quality user-facing content was generated
In Fall 2023, the team launched our automated system that creates personalized Care Plans quickly, improving caregiver preparedness and cutting plan creation time.
By including companionship tasks in 90% of plans, Honor enhanced care quality and set a new standard in personalized care through technology.
Decrease in time to set up care plans From 120 minutes to 5 minutes on average
Decrease in inbound communications from Care Pros and Families regarding changes to the care plan
Project leadership & Design management
Spreadsheets, Talkdesk, Figjam, Figma
Honor, 2023
Managed and supported solo Product Designer in manual pilot & initial product launches, filling in gaps in PM, Ops, and Design to successfully test and iterate a new staffing model.
Project context
When signing up for home care, customers are really looking for one thing: high quality caregivers who show up reliably. This is clear in the data as well. The two highest indicators of client churn are negative in-home experiences and caregiver swaps in the first two weeks of care.
Staffing at scale is incredibly challenging:
Matchmaking against shifts is an O(n^m) problem, where humans become increasingly inefficient when they no longer know the bios and latest updates of every client and caregiver.
Clients have high expectations on the quality of the service that span up to 24/7 presence in their home, it is more comparable to finding a romantic partner than getting an Uber or Doordash.
Client A
Problem Alignment and Opportunity
The problem: Honor's solution up to this point was a human staffer with the aid of advanced matchmaking and communication tools. The staffer still largely chose which caregivers to contact and to put in the home, and the technology just made their job easier. As we scaled, match quality and operational efficiency worsened.
The opportunity: while it's an unsolved problem for an algorithm to rival the matchmaking capabilities of a full-time staffer, this is offset by it's ability to optimize matches at scale, meaning that more clients more often get the caregiver that best aligns with their schedule and skillset needs.
Vision & Solution definition
As the most tenured person on the staffing problem and working with a relatively new team, I took lead in identifying which elements in the solution space had been previously attempted and why they were or weren't successful to keep the team focused on more novel approaches.
From there, the Design Lead proposed a product vision that moved a staffer over to a fully optimized system in phases. To start, the team would build an advanced ML algorithm that would provide staffing recommendations across an entire market, and the staffer would maintain oversight and override control.
To bring together this vision, Design played two key roles:
Design was heavily involved in the match-making algorithm to identify data elements to prioritize in our experimenting with in our models, and help the data team understand the algorithm's shortcomings during development.
Design acted as a staffer for several weeks to figure out the workflow for interacting with the model and start designing the tools that would best support the future staffing processes.
HMW build ops tools that center around algorithmic recommendations
What we built
One of the existential challenges of moving to an algorithm solution is that there still had to be a human conversation with a caregiver. Caregivers largely act like a freelancer between work, and rarely accept a commitment to take on a new client ongoing.
So in addition to designing a market-level dashboard with entry points to easily update standard algorithm inputs, Design was challenged to figure out how to introduce outreach tasks to staffers, whose outcomes would also feed back into the algorithm.
Our Design Lead presented a dynamic work-queue for outreach that prioritized and differentiated clients by the difficulty of restaffing, ensuring conversations were occurring in the most logical order. Additionally if the plan received enough declines, re-planning was automatically suggested.
Market view of all caregivers (aka Care Pros) used to determine supply health
Market view of all clients used to get a sense of regional demand
Select shifts to include in a market plan. Some are included by default like unstaffed shifts, or suboptimal matches etc.
Generate and review a draft plan
Outreach tracking
Outreach tracking early iterations, before splitting queue and detail views.
In Fall 2023, the team launched a manual pilot with just the algorithm running side-by-side to normal ops processes and proved higher quality schedules across the market.
In Spring 2024, the team launched Market Planner to incorporate the algorithm into ops daily process and reinvent our staffing practice from the ground up.
In Summer 2024 Market Planner was rolled out to all markets.
Decrease in time to staff open shifts. Staffers used to spend majority of their time staffing, now they spend that time with higher touch outreach to critical caregiver matches.
Higher job acceptance rate for shifts recommended by algorithm and delivered via new outreach process.
Product Design and UX Research
Figma, Framer, Midjourney, and Photoshop
Family Sage, 2024
Partnered with technical founder/CEO to design & build E2E mobile experience, marketing website, and social ads for beta launch in 3 months
Background
Family Sage was founded in 2024 to address the lack of infrastructure and tools for families who care for a loved one, and the anxiety and depression that can come with it.
Family Sage offers a product specifically built for caregivers to foster connection, share insights, and provide professional support to increase success and balance as a caregiver.
Problem definition
Community is a place within the Family Sage platform for caregivers to meet, chat, learn and share — the core product experience.
You can initially think of Community as a dedicated slack or discord channel, with features to promote additional engagement and foster stronger connection amongst users.
User needs
After conducting a handful of interviews with caregivers and aging professionals, and observing Facebook community group behaviors, I established 3 primary user groups by caregiving experience, needs and motivations, and community engagement type.
These user segments were key to ensuring our MVP would provide value and drive engagement for each user segment when joining.
Consumers
Observe & listen
Ask questions
Gain insights & resources
Pick up techniques & skills
Connect & relate to others
Answer questions
Share insights & resources
Expand techniques & skills
Support others
Celebrate milestones
Creators
Answer questions
Provide insights & resources
Run sessions
Offer additional services
Solution definition
The base features of community are likely not that compelling as a standalone app but necessary functionality for our community.
To meet our rapid timeline we leveraged a 3P library called Steam which already had a lot of the core features we needed, unlocking more time for the custom features that would differentiate our product in the market.
Core functionality:
Compose and post messages, text and image
Emoji reactions
Threaded reply
Content moderation
