Utilizing AI to Enhance User Needs Analysis in Healthcare Technology: Stay Ahead of the Competition

Product and Project Managers in the healthcare technology industry face challenges when conducting User Needs Analysis (UNA). The complexity of the healthcare domain can make needs assessment difficult to understand and navigate, especially for those new to the industry. But Generative AI has completely changed the game.

Generative AI simplifies the complexities of healthcare-related User Needs Assessment (UNA), potentially speeding up the project initiation process and improving project outcomes. PMs can now efficiently identify user needs easier than before, even on the most challenging healthcare technology projects.

By unlocking the power of Generative AI for healthcare technology UNA, it is possible to make the project organization more innovative, faster, and more effective during project initiation. If you need help with needs analysis in the healthcare technology domain, I highly recommend trying Generative AI. You’ll be amazed at how much it can streamline the process.

In this article, I will cover 14 ways Generative AI can help to address 14 unique challenges faced by product and project managers in healthcare technology organizations conducting UNA.

They are:

1. Uncovering strategies to improve data collection during User Needs Analysis
2. Identifying patterns and trends in the data
3. Identifying opportunities for personalizing user experiences
4. Generate training and support needs
5. Identify different healthcare professionals with distinct roles and responsibilities
6. Identify complex workflows involving patient care, treatment plans, and interdisciplinary collaboration
7. Explore variations in workflows across healthcare settings
8. Provide an understanding of the need to comply with healthcare-specific regulations, such as HIPAA and GDPR
9. Provide an overview of Industry-specific certifications, such as EHR certification for incentive programs
10. Identifying the nature of patient safety and treatment outcomes depending on data accuracy
11. Expose the need to protect sensitive patient health information from unauthorized access and data breaches
12. Educate the product manager on integration needs with other healthcare systems        
13. Understand unique time constraints encountered by Healthcare providers
14. Understand the needs of healthcare professionals and patients with disabilities

User Needs Analysis is vital in developing healthcare technology. By conducting a thorough UNA, developers and engineers can ensure that the final product addresses various user groups’ unique expectations, ultimately improving patient care and user satisfaction. AI tools like ChatGPT offer promising solutions to some of the unique challenges faced by people conducting these types of investigations for healthcare applications, including streamlining data collection, identifying patterns and trends, personalizing user experiences, enhancing decision-making, and facilitating training and support. By leveraging these AI technologies, healthcare technology can create more efficient, user-friendly, and effective solutions that meet the demands of today’s rapidly evolving healthcare landscape.

Introduction

As an engineering and software professional with thirty years of experience, I’ve seen the evolution of the development processes and the increasing importance of a comprehensive user needs assessment.  Projects that do not conduct a detailed UNA encounter challenges during user acceptance testing, clinical trials, and even during product launches. 

In this blog post, I’ll delve into the concept of User Needs Analysis, its importance to healthcare technology, and how AI tools like ChatGPT can help overcome the unique challenges User Needs Analysis faces in this domain.

What is User Needs Analysis, and Where does it Come From?

User Needs Analysis (UNA) is a systematic process of identifying, understanding, and prioritizing the requirements and expectations of users in the context of a product or service. The concept of UNA is rooted in the broader field of systems engineering, where understanding user requirements is a crucial design aspect. A formal UNA originated from Roger Kaufman for organizational systems and educational technology. This process emphasizes the importance of designing usable and efficient systems for end-users.

Why Is UNA Important When Building Healthcare Technology?

User Needs Analysis is crucial in healthcare technology development, even more critical than in other industries. Healthcare professionals have diverse roles and responsibilities, requiring tailored solutions. For example, physicians need easy access to patient records, while administrators require tools to manage billing and scheduling. A well-executed UNA ensures the technology addresses the unique requirements of each user group.

Secondly, patient safety and treatment outcomes depend on accurate and up-to-date medical information, making it essential for healthcare technology to be reliable, efficient, and user-friendly. A thorough UNA helps identify potential usability issues and areas for improvement, ultimately leading to a better user experience and improved patient care.

Lastly, healthcare organizations must comply with strict regulations like HIPAA and GDPR to ensure data privacy and security (Mense, Urbauer, Sauermann, & Wahl, 2018). A comprehensive UNA can help identify potential security risks and ensure the technology adheres to industry standards and best practices.

The Process of User Needs Analysis

The purpose of UNA  is to answer these questions;

• What exactly is needed for the system, and by whom?
• What are the uncontrollable constraints confining its design?
• What are the gaps in existing solutions that need to be addressed?

The steps in User Needs Analysis typically include the following:

Step 1 – Perform a gap analysis: A SWOT analysis can be conducted at this point in the user needs assessment process.  A SWOT analysis is a strategic planning tool that helps organizations identify their Strengths, Weaknesses, Opportunities, and Threats. It helps evaluate an organization’s internal and external environment, make informed decisions, and develop effective growth and success strategies.

Step 2 – Identifying stakeholders: Determine the various user groups and their roles within the system. In healthcare technology applications, stakeholders may include physicians, nurses, administrators, and patients. Understanding the needs and expectations of each group is essential for designing a system that meets their unique requirements.

Step 3 – Gathering data: Collect user needs, preferences, and expectations through interviews, surveys, observations, or other data collection methods. This may involve observing clinical workflows, conducting focus groups with healthcare professionals, or analyzing existing documentation and records in healthcare settings.

Step 4 – Analyzing data: Analyze the collected data to identify patterns, trends, and insights that can inform the design of the technology. This may involve qualitative analysis methods, such as thematic or grounded theory, or quantitative techniques, such as statistical analysis or data visualization.

Step 5 – Prioritizing needs: Rank user needs based on their importance to the overall system and the impact on user satisfaction and performance. This step helps ensure that the most critical needs are addressed in the design process and that resources are allocated effectively.

Step 6 – Documenting findings: Create a detailed report outlining the conclusions of the User Needs Analysis, which can serve as a reference for future design decisions. This report should include a comprehensive list of identified user needs, priorities, and insights or recommendations from the analysis.

What is the output of a User Needs Analysis?

The output of a User Needs Analysis is typically a comprehensive document detailing the identified user needs, their priorities, and any insights or recommendations derived from the analysis. This document is a foundation for subsequent design decisions and ensures that the final product meets users’ expectations and requirements. In addition to the report, other outputs may include user personas, scenarios, or use cases that illustrate the needs and goals of different user groups.

In some organizations, a User Needs Assessment Report might be a feasibility report, recommendation memo, build or buy decision matrix, or technology readiness report.  In other companies, it serves as a project deliverable and input to the Marketing Requirements Document (MRD) defining what the product is supposed to do.

Here is an example of a User Needs Assessment report.

How To Recognize a Good UNA?

A good UNA should be:

Comprehensive: It should cover all relevant user groups and their needs. In healthcare technology, this means considering the perspectives of physicians, nurses, administrators, patients, and other stakeholders interacting with the system.

Evidence-based: The findings should be grounded in user and stakeholder data. This helps ensure that the identified needs are accurate and relevant rather than being based on assumptions or speculation.

Transparent: The methodology and data sources should be documented, allowing for replication and validation of the findings. This transparency also helps build trust and credibility with stakeholders, who may be more likely to accept and support the design decisions based on the UNA.

Actionable: The insights and recommendations should be specific, relevant, and applicable to the design and development of the system. This means providing clear guidance on addressing identified needs and challenges rather than simply describing the issues in general.

How Can AI Tools Alleviate UNA Challenges Unique to Healthcare Technology Development?

Developing technology for healthcare can pose particular problems when conducting UNA.  Here are some ways it can be uniquely challenging;

• Diverse and complex user roles and workflows in the clinical setting
• Strict regulatory and compliance requirements are mandatory
• High-stakes data accuracy and security needs
• Integration with existing healthcare systems and infrastructure
• Balancing user-friendliness with comprehensive functionality
• Adaptability to evolving healthcare practices and healthcare technological advancements
• Cultural and organizational factors within healthcare settings vary from department to department
• Ensuring accessibility and inclusion for Providers, Administrators, and Patients with diverse needs
• Training and support for healthcare professionals, patients, and administrators can be distinctly different
• Ethical considerations in the design and implementation of the system must be considered

AI tools like ChatGPT can help address challenges faced by Product Managers, Clinical Marketing, and Project Managers when conducting UNA for healthcare technology development. The following lists ways in which generative AI can be applied to help expose and surface unrealized needs;

1. Uncovering strategies to improve data collection during User Needs Analysis:

Generative AI tools can build a data collection strategy that will enable the analysis of unstructured data from interviews, surveys, or other sources.  This can help save time and resources, allowing for a more comprehensive and in-depth UNA.

2. Identifying patterns and trends in the data:

Specialized generative AI models can analyze large volumes of data to uncover patterns and trends that may be difficult for humans to discern, leading to more accurate and insightful User Needs Analysis. For example, AI tools can identify subtle correlations between user needs and specific demographic factors, such as age or job role.

3. Identifying opportunities for personalizing user experiences:

Large Language Models (LLMS) such as ChatGPT can assist clinical marketing in identifying potential user preferences and suggest factors to consider when assessing a user’s needs. It can provide insights into general preferences and factors commonly necessary to users based on their knowledge of various topics and domains. However, LLM suggestions are based on pre-existing knowledge and may not account for a project’s specific context or unique requirements.

To better define user preferences for your user needs assessment, it’s recommended to use LLM suggestions as a starting point and then conduct primary research, such as interviews, surveys, or focus groups, to gather insights directly from your target user population. This will help you obtain a more accurate and comprehensive understanding of your users’ preferences and needs tailored to your project.  This can help address the diverse needs of healthcare professionals, ensuring that the technology is usable and effective for all user groups.

4. Generate training and support needs:

AI-generated content is a starting point that can be refined and tailored to meet the specific needs of the healthcare professionals being trained.  To create personalized training materials, you can provide the AI with relevant contexts, such as specific user roles, system features, and desired-to-learn outcomes.

This helps define the training and support needs of the system to be created.

5. Identify different healthcare professionals with distinct roles and responsibilities: 

AI tools can provide an overview of other healthcare professionals with specific roles and responsibilities for novice or blank-page technical product managers or project managers. 

Just keep in mind the AI’s understanding is based on the knowledge it was trained on, and it may not cover the latest developments or changes in healthcare roles. Here is a general list of healthcare professionals with distinct roles and responsibilities.

6. Identify complex workflows involving patient care, treatment plans, and interdisciplinary collaboration:

Specialized AI algorithms can analyze large volumes of data to uncover patterns and trends, helping to optimize workflows, enhance decision-making, and facilitate collaboration between healthcare professionals.

LLMs, on the other hand, can help you understand complex workflows involving patient care, treatment plans, and interdisciplinary collaboration by providing information and guidance. However, the LLM’s knowledge will be limited to the training data available, and healthcare professionals must verify all information.

7. Explore variations in workflows across healthcare settings:

Different healthcare settings may have unique workflows depending on factors such as the type of facility, available resources, patient population, and local practices.  LLMs can provide general information on variations in workflows across healthcare settings (limited to the data it has been trained on)

8. Provide an understanding of the need to comply with healthcare-specific regulations, such as HIPAA and GDPR:  

LLMs can provide general information about the importance of complying with healthcare-specific regulations, such as HIPAA (Health Insurance Portability and Accountability Act) in the United States and GDPR (General Data Protection Regulation) in the European Union. Healthcare organizations and professionals must adhere to these regulations to ensure patient privacy, data security, and compliance with legal and ethical requirements.

9. Provide an overview of Industry-specific certifications, such as EHR certification for incentive programs:  

LLMs can provide an overview of industry-specific certifications by summarizing key aspects, including their objectives, requirements, and benefits.

10. Identifying the nature of patient safety and treatment outcomes depending on data accuracy:

LLMs can help you understand user needs related to patient safety by providing information, suggestions, and insights.

Information and guidance: You can ask the generative AI for information about patient safety concepts, best practices, and guidelines. Based on its training data, it can provide relevant and general information to help you understand the importance of patient safety.  For example, you could ask: “What are the key components of a successful patient safety program?”

Identifying potential areas of concern: By asking the generative AI about common patient safety issues, you can gain insights into areas requiring attention in your context. For example, ask: “What are some common causes of medication errors in hospitals?” It can provide information on potential areas of concern, such as medication errors, hospital-acquired infections, and communication breakdowns. This information can serve as a starting point for understanding user needs and exploring strategies to address these issues.

11. Expose the need to protect sensitive patient health information from unauthorized access and data breaches:  

Understanding relevant security regulations, explaining best security practices for healthcare organizations that can be incorporated into new products, and offering insights into best practices for maintaining data security and patient privacy, such as implementing strong access controls, encrypting data, and regularly monitoring and auditing system activity.  It can suggest technical needs around protecting sensitive patient information too.

12. Educate the product manager on integration needs with other healthcare systems:

A generative AI can provide insights and information to help educate a Product Manager on integration needs by explaining interoperability which is the ability of different healthcare systems to exchange and use information seamlessly, enabling better communication, collaboration, and decision-making.

In addition, it can highlight relevant standards and frameworks widely used such as data exchange standards (e.g., HL7, FHIR) and frameworks (e.g., IHE) that facilitate integration and interoperability among healthcare systems.

The AI can also help to identify common integration challenges, such as data security, privacy concerns, and system compatibility, and provide suggestions for addressing these challenges.  It can also suggest integration strategies and techniques, including APIs, web services, or middleware solutions, to help the Product Manager identify suitable options for their specific needs.

13. Understand unique time constraints encountered by Healthcare providers:

By providing information and insights, generative AI can help product managers better understand the unique time constraints experienced by healthcare providers.

Things such as;

• Provide an overview of various healthcare providers’ roles, such as physicians, nurses, and allied health professionals, and discuss their responsibilities and challenges in managing their time.
• Outline typical time constraints healthcare providers face, such as high patient volumes, administrative tasks, and the need for continuous learning and professional development.
• Recognize factors contributing to time constraints in healthcare, such as complex patient cases, documentation requirements, and coordination with other care team members.
• Provide information on how time constraints can affect healthcare providers, including the potential for increased stress, burnout, and reduced quality of care.
• Suggest strategies that healthcare providers and organizations can implement to address time constraints, such as streamlining workflows, using technology to improve efficiency, and prioritizing tasks.

14. Understand the needs of healthcare professionals and patients with disabilities:

Generative AI can help by;

• Increasing awareness by offering information on different types of disabilities and specific requirements that healthcare professionals might have, such as physical, sensory, cognitive, or psychological disabilities.  
• Provide general information on relevant laws and regulations, such as the Americans with Disabilities Act (ADA) in the United States, which aims to ensure equal opportunities and accommodations for individuals with disabilities.  
• Identify potential barriers: ChatGPT can help you recognize potential barriers that healthcare professionals with disabilities may face in their work environment, such as inaccessible facilities, lack of adaptive equipment, or communication challenges.
• Suggest accommodations and modifications that can be made to support healthcare professionals with disabilities, such as providing assistive technology, modifying workstations, or adjusting work schedules.
• Direct you to resources, case studies, or best practices that can help you better understand and address the needs of healthcare professionals with disabilities or specific requirements.

Conclusion

In conclusion, the importance of conducting a detailed and accurate User Needs Analysis (UNA) when developing healthcare technology must be recognized. Running a comprehensive UNA ensures the developed technology aligns with the needs and expectations of diverse stakeholders, ultimately leading to improved user satisfaction and better patient outcomes. The process involves several crucial steps, including gap analysis, stakeholder identification, data collection, data analysis, needs prioritization, and documentation of findings.

Generative AI tools like ChatGPT can be vital in addressing the unique challenges healthcare applications face during UNA. By using AI to build a strategy to understand patterns and trends better, find personalization needs, identify training and support requirements, and gain insights into complex workflows, AI tools can enhance the quality of UNA while saving time and resources. Additionally, AI tools can help address specific challenges in healthcare, such as learning about compliance with regulations, integrating with existing systems, and managing the needs of diverse healthcare professionals and patients.

However, it is essential to remember that AI-generated suggestions should be used as a starting point and combined with primary research to ensure accurate, context-specific findings. By leveraging the capabilities of AI tools and conducting a thorough UNA, healthcare system developers can build applications tailored to user needs, ensuring optimal performance and delivering a positive impact on patient care.