While improving mental health access, we designed matchmaking for therapy that helped address individual’s barriers faced in finding a good therapist.
As three designers in a team, we designed and prototyped a mental health matching service platform that helped address individual’s barriers faced in finding a good therapist. From August to December, we identified key issues, interviewed stakeholders, conducted precedent research, and designed a platform targeted toward adults in major urban areas seeking therapy relationships.
The first stage of our design process was observation of our own experiences, the experience of our friends and family, and that of a few social workers we were able to speak to. We established that most people faced issues in accessing metal health treatment, and moved on to interviewing to determine what those specific barriers were. We then, discussed these five participant’s painpoints and pleasure points and coded our interviews.
Later, we developed personas based on these interviews in order to think about what kinds of solution might be appropriate for these concerns. With our personas as a guide, we did precedent research to collect examples that would serve as inspiration for different aspects of our platform. We were able to create a prototype of our platform and perform user testing to decide on aspects of its final design.
We interviewed five participants in major cities both in the United States (Boston metro area, New York City, Columbus) and India (Mumbai). The age range of our participants ranged between 20 and 30 years old of which three were women, one non-binary person and one man. All of the participants were college-educated and had sought mental health treatment for stress, anxiety, depression, or family problems. Out of the five interviews, two were conducted by Ragini, two by Niyati, and one by Dee. All were recorded and then later transcribed into Google Sheets files for coding.
Before beginning the interview process, we developed an interview guide with categories of questions we wanted to ask our participants. We also took care in designing questions to not force people to reveal any sensitive information such as the specific issue they were seeking therapy for or specific topics they discussed in their therapy appointments. We are aware that mental health is a very sensitive subject for a lot of people, so we wanted to be sure that we wouldn’t overstep any boundaries.
A coding scheme was then developed for analyzing the transcripts. Transcripts were largely analyzed by the unit of responses to each question, but particularly long responses were broken into paragraphs and analyzed by the unit of paragraph and then they were anonymized. We met to code two of the five transcripts as a group, then discussing discrepancies to resolve them. Of the remaining three transcripts, one was then coded by Niyati and Ragini, and two were coded by Dee, with some uncertainties resolved by Niyati and Ragini.
Therapy to many of our participants felt like dating. They saw some. Then, they saw others. It was unclear any of these was the right one, so they chose to continue with the ones that seemed to check the right boxes. Value can entail many aspects which include cost, fit and comfort as important goals. To ensure a valuable experience, we plan to ensure optimal matches according to the personal needs of an individual.
Our precedents aimed to address the below three tenants to reduce the fourth in their ethos. Effectiveness is a function of how much time, cost and work you are prepared to put in:
The effectiveness of seeking therapy or seeking a match can’t come at high stress.
We studied around 20 different platforms such as Headspace, Tryframe, Hinge, Bumble to name a few and created a chart (below) to identify an overview of the key features as a reference and map down our next steps.
In an ideal world, every one would find the perfect mental health match to address their needs. However, most of our participants we interviewed in urban settings, many of whom were people of color found this difficult and tested out several options for their mental health treatments.
WellMatch, our proposed platform, creates an improved matching system. You tell us what you need. We give you options and free consultations. No more settling for less. You are in charge of your access. After filling out a survey, users will be provided matches that align with their needs from cost to fit. Each user will receive free consultations to therapists they match with. Users can anonymously share and rank their experiences on their matches after consultations.
We aimed to address two core issues: cultural competency, and choice anxiety by establishing a platform that incorporates a data matching algorithm and therapy sample videos for our users.
Since we garnered both quantitative and qualitative data during the research stage, we have included our findings from both primary and secondary research in our feedback grid when testing our prototype for effectiveness.
Matchmaking is affected by gender, age and income and while they may not necessarily predict a therapeutic alliance, these factors play a significant role in users' decisions to even choose a therapist to meet. We found that utilizing customization by providing enhanced navigation and algorithm elements are popular as people were comfortable with them. Multiple choice surveys as a tool is effective only if the necessary questions and answers are in an easy format with a variety of options.
While I was working on this project I acquired the knowledge of a trail of layers of research before and even after a problem has been defined. I could proudly say our research process was thorough with very insightful meta-data that we could rely on, at every step of reaching the end goal. Two of my biggest contribution in this project was designing the presentation in a cohesive manner and the ideation for matching therapists as per the users need. Since I have had professional design work experience in the past, I also helped my team with understanding the importance of layouts, button placements (how user behavior patterns work), and following a consistent visual language.