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How to Prepare a User Interview Questionnaire?

5 mins 10 secs read

The User Interview is a qualitative, non-binary, open-ended process that is a crucial contextual inquiry research method. And creating a good questionnaire is like sharpening your weapon to make the best impact.

A well-prepared, well-rehearsed user questionnaire is a must for an insightful interview.

Why make a questionnaire before a User Interview?

To avoid Response Biases*!

Seven-eight response biases influence user interviews greatly; understanding the user persona objectively is crucial for good design decisions. A thorough understanding of the response biases will help you use them or avoid them to get functional responses from the user.

*Note: There will be an upcoming blog on Response Biases and how to use or avoid them in research. Keep following Mantra Labs’s blog posts.

To make interviews more relevant to the research goals:

Planning an interview questionnaire keeps the facilitator on track during interviews. What does this mean? Multiple probing questions are asked during any user interview to gain more information and a deeper understanding of the participant’s behavior. Due to the probing, three situations occur.

1. The facilitator gets carried away in probing and deviates from the central questions under the influence of the participant’s response.

2. The facilitator asks questions spontaneously under the influence of their biases that might not be relevant to the research goals.

3. There is a chance of losing track of interview goals and shifting off-topic. Sometimes it’s a good idea to check all the possibilities that could influence the user’s behavior. However, there should be a clear line at which to stop and come back to the main questions.

To make sure questions are open-ended and revised/reframed before interviews:

“Details are in the story,” and Open-ended questions allow participants to tell the story of their experience. Since participants’ responses should not be influenced or bound to respond in a certain way, it is crucial to plan open-ended questions.

E.g., a pet product company is researching the possibility of their product entering the market and developing some pet grooming products for millennial pet owners. Let’s look at some 

questions to understand how open-ended they are.

Q 1: Would you use shampoo to make your dog smell better?

Q 1.1: What shampoo would you use to make your dog smell better?

Problem with these questions: 

Q 1: It’s a Yes/No question that will not give any qualitative insights.

       This question has little scope to know all the products the participant uses for grooming the dog.

Q 1.1: This is better than Q.1 because it has the scope of answering about shampoo products, but more is needed to know every detail of the pet cleaning-care habits of the user.

This can be a follow-up question to dig deeper, but not the main question.

What would be an excellent open-ended question?

Q. 1: What does your pet grooming process look like, and what products do you use while grooming?

Now this question has scope for the participant to tell the story of their pet’s grooming, including their process, the product they use, and how everything impacts their pet’s grooming pattern.

To avoid repetitive/similar questions:

If the questions still need to be pre-prepared and revised, the facilitator often asks questions similar to prior questions. This scenario causes a waste of time, sometimes irritating the participants as well.

To limit the number of questions and make them effective per the interview time limit.

  1. The time taken in an interview significantly impacts the qualitative information received from the participants. 40-45 minutes is the sweet spot for an interview. More than that, it starts becoming too much for the participant. If the facilitator continues to engage, the participant might get disengaged and be in a hurry to finish the interview.
  2. If it takes less than 40 minutes, you might not get a deep understanding of the question due to needing more probing in the response received from the participant.

How to make a good User Interview questionnaire?

Now that we know why, a good questionnaire is crucial before the research interview. There are some parameters and ways to write a good questionnaire.

Defining the business goal and user goal:

Keep stakeholder analysis as the first priority before writing the questionnaire. It’s crucial to know the business goals of the critical stakeholders to lay the foundation for your research interview. Also, try to understand and write the User Goal according to key stakeholders and their target personas.

Doing secondary research and gaining more information about the products, services, and business: 

It helps the facilitator make questions more relevant to the topic. The planning of the questions would be about the product and the service, which would help to find impactful insights. Here are some matters to focus on while doing secondary research:

  •  Analytics of the website (whatever possible, Google Analytics, Hotjar, Similarweb), target personas, user journey, pain points, what are your most trafficked pages, which site pages rank high in SERPs, visits from organic sources, traffic referrals from other sites and channels, Traffic from direct URL into the search bar, Devices used by the traffic ETC.)
  • Top performing keywords
  • How long do people typically spend on your website?
  • Page load time of the site?
  • Competitors analysis
  • User Personas
  • Geolocations of major user bases
  • Industry trends
  • The existing research paper regards the context
  • Existing customer journey map (If any)

Write the User Interview objective and key result properly:

This is the foundation of any research. Defining the OKR of the interview will keep your approach more constructive. The researcher should define why they are interviewing and what they want to achieve. Later, the questions should be formed in order to get the relevant information with regard to the interview objective.

The questions need to be framed in four categories that go in order one after another during the interview:

Intro Question and Ice Breakers

* Hi, How does your day/week go in Mantra Labs?

* Could you tell us about your role, followed by what your team does at Mantra Labs?

* How do you plan for your certain work to achieve your ‘X’ target?

Topic Specific Questions

* How do clients approach you and vice-versa? 

* Could you describe a couple of scenarios where you failed to perform the task and how it happened?

* How do you overcome obstacles when performing such tasks?

Opportunity specific questions

* What is making a good impact on existing clients during the project?

Opinionated questions

* In your opinion, where would you suggest this service should improve?

Conclusion

User Interview Research in UX is crucial to making informed decisions to solve complicated problems for any product or service. As it is essential, it needs to be understood. 

What does it mean? How should it be done? Also, when should it be done? But above all, the biggest problem with interviewers is a need for more technical and experiential knowledge of how to prepare Research Interview Questionnaires.

To do that, Interviews need to understand fundamental response biases, which can destroy the creation of the questionnaire and interview insights. Do thorough secondary research about the matter. Next, review the business goals and write the interview research goals and OKRs. And finally, have a structured questionnaire covering all types of questions. 

About Author,

Vijendra is currently working as a Sr. UX Designer at Mantra Labs. He is passionate about UXR and Product Design.

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Conversational UI in Healthcare: Enhancing Patient Interaction with Chatbots

As healthcare becomes more patient-centric, the demand for efficient and personalized care continues to grow. One of the key technologies that have gained traction in this domain is Conversational UI (CUI) — a user interface where interactions occur through natural language, often with the help of chatbots. For developers, building a robust CUI in healthcare requires a balance of technical proficiency, understanding of the healthcare landscape, and empathy toward patient needs. Let’s explore how CUI can improve patient interactions through chatbots and what developers should consider during implementation.

Why Conversational UI is Gaining Popularity in Healthcare

From scheduling appointments to answering medical queries, healthcare chatbots have become vital tools for enhancing patient engagement and streamlining healthcare workflows. Conversational UIs enable these chatbots to interact with patients naturally, making them accessible even to non-tech-savvy users. By incorporating AI and NLP (Natural Language Processing), chatbots can now simulate human-like conversations, ensuring patients receive timely, relevant responses. 

Image credit: https://www.analytixlabs.co.in/blog/ai-chatbots-in-healthcare/ 

Key Areas Where Chatbots Are Revolutionizing Healthcare

  1. Appointment Scheduling and Reminders – Chatbots can automatically schedule appointments based on patient availability and send reminders before the visit, reducing no-show rates. For developers, this feature requires integration with hospital management systems (HMS) and calendar APIs. The challenge lies in ensuring secure and real-time data transfer while adhering to healthcare compliance standards like HIPAA.
  1. Medical Query Resolution– Chatbots equipped with NLP can answer common patient questions related to symptoms, medications, and treatment plans. This reduces the burden on healthcare providers, allowing them to focus on more critical tasks. Developers working on this feature need to consider integrating medical databases, such as SNOMED CT or ICD-10, for accurate and up-to-date information.
  1. Patient Monitoring and Follow-ups – Post-discharge, chatbots can monitor a patient’s condition by regularly asking for health updates (e.g., vital signs or medication adherence). Developers can integrate IoT devices, such as wearable health monitors, with chatbot platforms to collect real-time data, providing healthcare professionals with actionable insights.
  1. Mental Health Support – Chatbots have shown promise in offering mental health support by providing patients with an outlet to discuss their feelings and receive advice. Building these chatbots involves training them on therapeutic conversational frameworks like Cognitive Behavioral Therapy (CBT), ensuring they offer relevant advice while recognizing when a human intervention is required.

Key Considerations for Developers

1. Natural Language Processing (NLP) and AI Training

NLP plays a pivotal role in enabling chatbots to understand and process patient queries effectively. Developers must focus on the following:

Training Data: Start by gathering extensive datasets that include real-life medical queries and patient conversations. This ensures that the chatbot can recognize various intents and respond appropriately.

Multi-language Support: Healthcare is global, so building multi-lingual capabilities is critical. Using tools like Google’s BERT or Microsoft’s Turing-NLG models can help chatbots understand context in different languages.

Contextual Understanding: The chatbot must not just respond to individual queries but also maintain the context across the conversation. Developers can use contextual models that preserve the state of the conversation, ensuring personalized patient interactions.

2. Security and Compliance

Healthcare chatbots handle sensitive patient information, making security a top priority. Developers must ensure compliance with regulations such as HIPAA (Health Insurance Portability and Accountability Act) in the U.S. and GDPR (General Data Protection Regulation) in Europe. Key practices include:

  • Data Encryption: All communication between the chatbot and the server must be encrypted using protocols like TLS (Transport Layer Security).
  • Authentication Mechanisms: Implement two-factor authentication (2FA) to verify patient identity, especially for sensitive tasks like accessing medical records.
  • Anonymization: To avoid accidental data breaches, ensure that the chatbot anonymizes data where possible.

3. Seamless Integration with EHR Systems

For chatbots to be truly effective in healthcare, they must integrate seamlessly with Electronic Health Record (EHR) systems. This requires a deep understanding of healthcare APIs like FHIR (Fast Healthcare Interoperability Resources) or HL7. Developers should aim to:

  • Enable Real-time Updates: Ensure that chatbot interactions (e.g., new appointment schedules, and symptom checks) are instantly reflected in the patient’s EHR.
  • Avoid Data Silos: Ensure that all systems (EHR, chatbot, scheduling system) can communicate with each other, eliminating data silos that can lead to fragmented patient information.

4. Scalability and Performance Optimization

In healthcare, downtime can be critical. Developers need to ensure that chatbots are scalable and capable of handling thousands of patient interactions simultaneously. Using cloud-based platforms (AWS, Google Cloud) that offer auto-scaling capabilities can help. Additionally, performance optimization can be achieved by:

  • Caching Responses: Store frequently used responses (such as FAQs) in memory to speed up interaction times.
  • Load Balancing: Implement load balancers to distribute incoming queries across servers, ensuring no single server is overwhelmed.

Tools and Platforms for Building Healthcare Chatbots

Several tools and platforms can aid developers in building healthcare chatbots with conversational UIs:

  1. Dialogflow (Google): Offers pre-built healthcare intents and integrates with Google Cloud’s healthcare APIs.
  2. Microsoft Bot Framework: A scalable platform that integrates with Azure services and offers AI-driven insights.
  3. Rasa: An open-source NLP tool that provides flexibility in creating highly customized healthcare bots.

Conclusion

Conversational UI in healthcare is transforming patient care by offering real-time, scalable, and personalized interactions through chatbots. However, for developers, building these systems goes beyond programming chatbots — it involves understanding the unique challenges of healthcare, from regulatory compliance to seamless integration with hospital systems. By focusing on NLP capabilities, ensuring security and privacy, and integrating with existing healthcare infrastructure, developers can create chatbots that not only enhance patient interaction but also alleviate the burden on healthcare providers.

References

  1. NLP in Healthcare: Opportunities and Challenges
  2. HIPAA Compliance for Chatbots

About the Author:

Shristi is a creative professional with a passion for visual storytelling. She recently transitioned from the world of video and motion graphics to the exciting field of product design at Mantra Labs. When she’s not designing, she enjoys watching movies, traveling, and sharing her experiences through vlogs.

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