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Transforming Telemedicine Through User-Centered Research

Project Overview

When COVID-19 forced the rapid adoption of telemedicine tools, healthcare providers struggled with platforms that weren't designed for real-world medical consultations. I found that 82% of doctors reported spending valuable appointment time troubleshooting basic technical issues instead of focusing on patient care.

My Role: Lead Analyst

I transformed raw survey data from 114 medical providers into actionable design solutions that directly addressed critical pain points, leading to follow-up research and academic recognition.​

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Tools Used: Microsoft Excel, Miro, Figma

The Team

I worked with a wonderful cross-functional team:

  • Krzysztof Z. Gajos, Ph.D., Harvard University

  • Anoopum Gupta, M.D., Ph.D., Mass General Hospital

  • Vineet Pandey, Ph.D., Harvard University

  • Maia Jacobs, Ph.D., Northwestern University

Research Process and Approach

Over 100 doctors completed a survey with 35 questions about their role, attitudes towards telemedicine, and use of telemedicine technologies. The questions were a mix of multiple choice, Likert scales, and open-ended responses. I was brought in to analyze the data and then communicate the findings and provide design recommendations.

 

Before diving into data analysis, it was crucial to establish a foundational understanding of the project's context and the data itself. I researched the current state of telemedicine and familiarized myself with the survey questionnaire. Then, I familiarized myself with the survey questionnaire, which allowed me to more efficiently analyze and understand the data. 

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Analyzing the data

Once I had a grasp on the project and the research questions, I dove into analysis. I started by cleaning the data. A few things I did to clean the data was remove incomplete responses, respondents who didn't meet our criteria, and respondents who "straightline."

​Next, I analyzed the quantitative data.

I used Excel functions to run descriptive statistics (mean, range, and standard deviation) of Likert-scale questions and count the frequency of answers. I made tables and graphed answers to help me understand the meaning of the data and answer our research questions.

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Qualitative Data Analysis

Then, I analyzed the qualitative data. I first coded the responses by writing the main theme of each responses. After a first round of coding, I grouped the codes by themes using post-it notes in Miro to identify the high-level findings. I discussed these themes with the research team and a medical provider to get their thoughts and feedback. I iterated on the themes and then coded the data with these themes. The output of the qualitative analysis was an organized spreadsheet that described the participant's actions, perceptions, challenges, and needs.

Communicating the findings

Once the quantitive and qualitative data was analyzed, I looked at the findings of all the data together to identify the key insights. I synthesized the main challenges and workarounds that doctors were using. â€‹I then turned to other work to see how existing systems and studies were addressing these challenges. I incorporated existing strategies into my design recommendations and highlighted opportunities for novel technologies. I presented these findings with actionable next steps to the broader research team. 

Key Insights

My quantitative analysis found that providers' enthusiasm for telemedicine increased after the rapid implementation of telemedicine due to the pandemic.

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However, enthusiasm for telemedicine was still low. Why? I dug into the qualitative data to find out more.​​

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My analysis of the qualitative data revealed:

82% of providers spend billable time on patient technical support, decreasing satisfaction and representing pure revenue loss.​

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Providers would have to take the time to help patients setup their computer and environment or adjust their setup during the appointment. 

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For example, one provider would tell patients to:

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My Recommendation:
Real-Time Environment Optimization

Based on user workflows and pain points, I designed a Pre-Appointment System Check that:

  • Automatically validates audio, video quality, and lighting before consultations begin.

  • Provides real-time feedback and guided troubleshooting for patients.

  • Routes technical issues to support staff, freeing doctors to focus on medical care.

  • Integrates the proven workarounds doctors were already using.

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Impact

  • Findings laid the groundwork for follow-up research and technical evaluation.

  • Identified opportunity to reduce 15-20% time waste per appointment.

  • Developed wireframes for an automated system check tool to resolve technical issues pre-appointment.

  • Contributed to evidence base for telemedicine design best practices.

  • Presented work at 2022 International Society for Research on Internet Interventions to 300+ global attendees.

Lessons Learned

  • User workarounds are innovation goldmines: The most effective design solutions often already exist in user-created workflows.

  • Context is everything: Understanding the high-pressure medical environment was crucial for creating viable solutions.

  • Cross-functional validation matters: Medical professional input transformed good insights into clinically relevant recommendations.

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