#FutureReadyHealthcare
Introduction
The landscape of life sciences user experiences (UX) is undergoing a paradigm shift with the integration of Artificial Intelligence (AI). This whitepaper explores the challenges of traditional UX in the industry and how AI can revolutionize UX for patients, healthcare professionals (HCPs), and researchers. We will delve into specific applications of AI in UX design, emphasizing the importance of a human-centered approach that prioritizes ethics, empathy, and user needs.
Finally, we will look into the future, exploring the potential of AI to personalize care, streamline workflows, and accelerate advancements in life sciences and how true design systems could help exponentially improve the counterproductive, isolated efforts, leading to disparate experiences, that most organizations are plagued by in their efforts to introduce their digital properties to market faster.
The current landscape of traditional life sciences UX: A maze of complexity and missed opportunities
Traditional life sciences UX faces significant challenges that hinder effective communication and engagement with both patients and HCPs. Let us delve deeper into these challenges, supported by data points:
Information overload and complexity
A study by the US Department of Education found that 36% of adult Americans have only basic or below basic health literacy skills, and only about 12% of adults in the United States possess adequate health literacy skills to understand complex medical information.
Unfortunately, we also found that there is no definitive statistic on the global percentage of people with adequate health literacy skills. There are variations by region wherein studies show significant differences between developed and developing countries. Some regions such as South and South-West Asia have high illiteracy rates, and this obviously impacts health literacy as well. There are measurement challenges where accurately measuring health literacy is a challenge. Different studies use a large assortment of methods, which makes it difficult to directly compare results.
However, we do know it is a widespread issue. Here are some insights that we can consider:
Estimates suggest limited health literacy in a sizable portion (potentially between 25% and 75%) of adults in some European countries. This implies that the majority of patients struggle to navigate technical jargon often found on pharma websites and clinical trial information.
A survey by the Pew Research Center revealed that 37% to 69% of internet users find health information online. Many users report difficulty in finding trustworthy health information online. This highlights the challenge of presenting complex medical information in a clear, concise, and trustworthy manner.
Lack of personalization
A study published in the Journal of Medical Internet Research found that 80% of patients desire personalized health information tailored to their specific conditions and needs. Traditional UX often fails to cater to this diverse range of needs, leading to user frustration and a sense of information overload.
A study by Accenture indicates that 91% of HCPs believe that patients expect a personalized healthcare experience. However, traditional life sciences UX often presents the same information to all users, neglecting the specific needs of different HCP specialties and practice settings.
Limited considerations for accessibility
The World Health Organization (WHO) estimates that more than 1 billion people globally experience some form of disability. Traditional life sciences UX often fails to consider accessibility guidelines, making it difficult for users with disabilities to navigate websites and access crucial health information. This creates a barrier to informed decision-making and participation in healthcare initiatives.
The consequences of poor design
These challenges result in missed opportunities for both patients and HCPs.
Patient challenges
Poor UX design can lead to confusion about treatment options, medication nonadherence, and delayed diagnoses, negatively impacting the health outcomes of patients.
HCP efficiency
Inefficient interfaces hinder the access of HCPs to relevant information and reduce productivity, affecting their ability to provide optimal care.
Adoption and costs
Resistance to adopting new technologies because of poor UX design can stall innovation and increase healthcare costs through inefficiencies and errors.
Trust and compliance
Negative digital experiences erode patient trust and satisfaction, whereas failing to meet regulatory standards can expose organizations to legal risks and penalties.
The transformative power of AI: A user-centric revolution
The limitations of traditional life sciences UX are paving the way for a user-centric revolution powered by AI. Here is how AI is transforming the landscape and influencing UX across llife sciences.
Personalized UX
A study found that 75% of patients are more likely to choose a healthcare provider that offers personalized experiences. AI-powered chatbots can address this need by answering patient queries in a personalized manner, considering factors such as demographics, medical history, and language preference. For example, an AI chatbot for a pharmaceutical company could answer a patient’s queries about a medication in a way that is tailored to their age and level of health literacy.
A report by McKinsey & Company suggests that AI-powered recommendation engines can increase click-through rates by up to 300%. This translates to patients receiving more relevant information about treatment options and potential clinical trials, leading to more informed healthcare decisions.
Streamlined workflows and content creation
A study by McKinsey estimates that AI can automate up to 60% to 70% of repetitive tasks across industries, including life sciences. This frees up valuable time for UX design teams, allowing them to focus on creative problem-solving, user research, and developing innovative user interfaces.
AI-powered content creation can reduce content creation time exponentially. This allows pharmaceutical companies to generate personalized and up-to-date clinical trial information more efficiently, reaching a wider range of potential participants.
Empathy through AI
A study by Forrester Research found that 74% of customers expect companies’ CX to understand their emotions and take them into account. Sentiment analysis powered by AI can analyze user feedback from surveys, chat conversations, and social media platforms to identify user emotions and areas of concern. This allows pharmaceutical companies and healthcare institutions to design UX that is sensitive to user anxieties and frustrations, leading to a more empathetic UX.
A study published in the Journal of Medical Internet Research and the National Library of Medicine suggests that AI-powered chatbots can provide emotional support and address mental health concerns with a high degree of accuracy. This opens doors for pharmaceutical companies to develop AI-powered chatbots that offer medication adherence support and manage potential side effects, catering to the emotional well-being of patients.
These data points highlight the transformative power of AI in user-centered life sciences UX design. By personalizing experiences, streamlining workflows, and incorporating empathy through AI, pharmaceutical companies and healthcare institutions can create a future where patients and HCPs are empowered with the information and support, that helps them to make informed healthcare decisions.
Specific applications of AI in life sciences UX design: Putting theory into practice
Let us delve into concrete examples of how AI is shaping life sciences UX, backed by relevant data points:
AI-powered content creation
A study by Deloitte suggests that 77% of patients prefer/express greater satisfaction to access and track (72%) healthcare information online/have virtual visits. However, complex medical jargon often hinders understanding.
Only 2% of all patients had online access to the patient records kept by their general practitioner (GP), and 8% for medical specialists. Among people with a chronic condition, these proportions were 4% and 7%. New research shows that 57% to 80% of patients prefer telehealth when obtaining care. AI can analyze vast amounts of medical data to generate clear, concise, and patient-friendly summaries.
A report by Accenture indicates that a quarter of healthcare consumers (23%) say that reliable and secure digital tools that help them to understand their health habits would motivate them to take a more active role in managing their health. A total of 83% of patients would be more likely to choose a healthcare provider that communicates information and educational materials tailored to their specific needs between visits.
AI can personalize content such as medication information sheets, clinical trial descriptions, and disease education materials based on factors such as age, literacy level, and medical history. This improves comprehension and empowers patients to make informed decisions.
Chatbots that foster connection
A study by Persuasion Nation and Juniper Research estimates that by 2024, healthcare chatbots will handle more than 80% of routine inquiries as more customers give preference to using self-service options before contacting a representative.
AI-powered chatbots can provide real-time support for patients, addressing basic medication inquiries (eg, side effects and dosage information) and offering medication adherence reminders.
Studies suggest that more patients are interested in using AI-powered chatbots for mental health support. On the other hand, a sizable number of users drop out during consultations with health chatbots owing to poor experiences.
Psychological barriers play their own parts also, where surveys show that ~40% of respondents express unwillingness to even interact with chatbots for healthcare requirements and many specialists express concern about the inherent limitations relating to potential discriminatory bias, explainability, and safety hazards of AI in the medical space. One survey found that more than 80% of professional physicians believe that health chatbots are unable to comprehend human emotions and represent the risk of misleading treatment by providing patients with inaccurate diagnostic recommendations.
As we navigate this space, pharmaceutical companies can leverage AI to develop chatbots that offer better emotional support and address potential side effects associated with medications, promoting patient well-being by creating informed, cohesive, and empowered UX.
Machine learning for navigation optimization
A study by the Nielsen Norman Group UX found that users abandon websites with poor navigation within 10 to 20 seconds. Machine-learning algorithms can analyze user behavior data on pharmaceutical websites and mobile apps. These data can then be used to optimize navigation, search functionality, and content placement, resulting in more intuitive and user-friendly experiences that keep users engaged.
A reportby McKinsey & Company suggests more than three quarters of consumers—more than 71%—expect companies to deliver personalized interactions and up to 76% of customers get frustrated when that does not happen. Seventy-six percentage of customers purchase more from brands that personalize their digital experiences, 78% of consumers are more likely to recommend friends and family to companies that personalize, and 78% are more likely to revisit/repurchase based on such interactions. Personalized UX based on user behavior data can increase website conversion rates multifold. By personalizing navigation based on user needs (eg, HCPs vs patients), AI can ensure users find the information they need quickly and efficiently.
AI-powered clinical trial matching
A study by the National Cancer Institute found that only 5% of eligible patients participate in clinical trials.
Matching patients to suitable clinical trials can be a complex process. AI algorithms can analyze patient medical records and preferences to identify relevant clinical trials, increasing awareness and participation in research.
These examples display how AI is transforming life sciences UX, offering a glimpse into opportunities which, when built upon, could underscore what the future might look like where UX are personalized, user-friendly, and empower patients and HCPs to make informed decisions about their health and well-being.
The human-centered approach: AI as a partner, not a replacement
Although AI holds immense potential, it is crucial to remember that it is a tool, not a substitute for human-centered design principles. Here’s why a balanced approach is essential:
Maintaining an ethical framework
Data privacy and security are paramount. User data must be collected and used ethically, with clear consent and transparency. Regulations around data governance specific to AI in life sciences UX need to be established to ensure user trust.
More patients believe it is important for AI in healthcare to be unbiased. More than five-sixths of health executives (86%) have not yet invested in the capabilities to verify data sources across their most critical systems.
AI algorithms must be rigorously tested for bias to ensure fair and equitable outcomes for all users. This includes considering factors such as race, ethnicity, and socio-economic background when developing and deploying AI-powered solutions in life sciences UX.
Explainability of AI-driven recommendations
A study published by the National Institute of Health (NIH) found that a lack of transparency in medical AI algorithms can lead to decreased trust. It Is essential to understand the rationale behind AI-driven recommendations, especially for HCPs making critical treatment decisions. This can involve providing explanations for AI suggestions and offering options for human override when necessary.
A report by the WHO emphasizes the importance of human oversight in AI-powered healthcare solutions. Human expertise remains crucial in interpreting AI outputs and ensuring they align with medical best practices.
Empathy and emotional intelligence
A study by the American Medical Association found that empathy is a core competency for physicians; in fact, 65% of patient satisfaction has been attributed to physician empathy. AI lacks the human capacity for empathy and the ability to nurture a growing sense of emotional intelligence. It is still in a fledgling stage where it simulates EQ rather than actively emulating it.
This means HCPs and UX designers must leverage their empathy to interpret user data and ensure that AI-powered solutions are emotionally resonant and supportive. This could involve designing AI chatbots that use empathetic language and offer emotional support to patients navigating challenging diagnoses or treatment regimens.
A report by Forrester Research suggests that customers who have a great CX with a company’s online and offline properties feel that a company understands their emotions are more likely to be loyal brand advocates in the future. By combining AI capabilities with human empathy, pharmaceutical companies can create a more holistic and supportive UX for patients.
The inflection point: Balancing AI with human expertise
The integration of AI in life sciences UX represents a significant inflection point. By prioritizing ethics, transparency, and human oversight, we can ensure that AI complements existing human expertise and fosters a future where
Patient challenges
Patients can receive personalized, better-designed, and empathy-driven support throughout their healthcare journey.
HCP efficiency
HCPs can access AI-powered tools that can help streamline workflows and enhance/augment their decision-making process.
HCP efficiency
Researchers can leverage AI to accelerate scientific discovery and develop personalized treatment options.
The future of life sciences UX where we partner with AI: A glimpse into what tomorrow could look like
The future of life sciences UX powered by human empathy and augmented by AI is brimming with exciting possibilities:
AI-powered virtual assistants
Imagine an AI-powered virtual assistant that seamlessly integrates with the healthcare ecosystem of patients. These virtual assistants could provide medication reminders, manage appointments, and even offer personalized coaching for a healthier lifestyle while flooding back pertinent, invaluable patient data to digital health systems driving innovation possibilities like never before.outcomes of patients.
AI-driven drug discovery platforms
AI can accelerate drug discovery by analyzing vast datasets of genetic information, protein structures, patient data and more. These could lead to the identification of new drug targets, the development and evolution of personalized therapies, and ultimately, faster breakthroughs in the fight against diseases across therapy areas.
Augmented reality (AR) and virtual reality (VR) applications
AI can power AR and VR applications that enhance patient education and empower HCPs and surgeons during surgical procedures. Imagine a future where patients can visualize and learn about their condition through interactive AR experiences or where surgeons can utilize AI-powered AR/ VR, as well as mixed reality (XR) and extended reality (XR) simulations to plan and conduct complex surgeries with greater precision.
Challenges and opportunities: Navigating the evolving landscape
Although the future seems bright, challenges remain. Here are a few key considerations:
Regulation and standards
Guidelines and regulations need to evolve to address the integration of AI into life sciences UX. Clear frameworks are essential to ensure data security, patient privacy, and the ethical and responsible development of AI-powered solutions.
The human factor
It is crucial to address potential job displacement anxieties and ensure that AI complements existing skillsets in the life sciences UX workforce. Helping with upskilling and retraining programs will be critical to ensure a smooth transition and foster a future in which human beings and AI work together.
Bridging the digital divide
Not everyone has equal access to technology. Strategies need to be developed to bridge the digital divide and ensure everyone can benefit from AI-powered advancements in life sciences UX.
Why current design systems in life sciences UX fall short: A missed opportunity for AI integration
The promise of design systems in pharmaceutical, healthcare, and life sciences UX is undeniable. However, current efforts often fall short because of a lack of thorough context and the practical learnings that underpin comprehensive and successful design system development. This disconnect hinders the true potential of design systems and their constructive collaboration with AI in this critical domain.
The pitfalls of incompleteness
Limited context
Many “design systems” are in fact no more than style guides that focus solely on visual elements, such as colors, fonts, and logos or standalone referenceable component libraries, which can be visually interpreted by individual creative teams within a client ecosystem. While visual consistency is important, a true design system delves and enables the company to go far deeper, by way of adding efficiencies and guardrails.
It considers user needs, workflows, and content strategy within the life sciences UX ecosystem.
Data points are crucial here, for instance, a study by CSG that Commissioned Forrester Research to survey global CX leaders about their CX experiences and challenges. The survey observed that 73% of healthcare CX leaders find it challenging to keep PX consistent across channels. While a total of 72% of healthcare CX leaders want to deliver more consistent PX, 64% have increased focus on patient journeys, and 100% of them have experienced harmful consequences due to poor PX.
Patients, in turn, feel frustrated when encountering inconsistent information across different healthcare touchpoints. A comprehensive design system, informed by such data, would ensure consistent messaging and information architecture across websites, mobile apps, and printed materials, reducing user frustration and focusing on ways to improve the overall healthcare experience. Optimized content creation with AI and integration with design systems A robust design system establishes clear guidelines for content structure, tone, and brand voice. This provides a foundation for AI-powered content creation tools to generate accurate, consistent, and user-friendly information for patients and HCPs.
Neglecting learnings
Building a design system is a long-term and iterative process. True design systems, as advocated by UX experts, are living entities that evolve based on user feedback and data analysis. A study by Nielsen Norman Group suggests that user testing throughout the design system development process can identify and rectify usability issues early on, leading to a more effective system. Current efforts often lack this continuous learning loop, leading to overly simplistic and inadequate “design systems” that are static and do not adapt to the ever-changing needs of life sciences UX.
The value proposition of a robust design system
When paired with the power of AI, a well-crafted design system can revolutionize pharmaceutical, healthcare, and life sciences UX by
Optimized content creation with AI and integration with design systems
A robust design system establishes clear guidelines for content structure, tone, and brand voice. This provides a foundation for AI-powered content creation tools to generate accurate, consistent, and user-friendly information for patients and HCPs.
A study by Gartner predicts that by 2025, AI will be producing 10% of all data (currently <1%) and 20% of all test data for consumer-facing use cases. Eighty percent of customer service organizations are expected to apply GenAI technology in some form or the other to elevate CX and productivity. Most of this effort is expected toward augmenting content creation, and Conversational User Interfaces will thrive like never before.
Robust, farsighted, and holistically built design systems can help ensure these efforts around AI-augmented content creation, which seamlessly integrate into digital properties, boosting efficiencies and providing them with numerous benefits that are carefully planned, and farsighted AI-augmented UX solutions can support Customer and Patient Experience efforts of Life Sciences organizations.
Personalization at scale
Design systems define reusable components that can be dynamically adapted to meet individual user needs. AI can leverage these frameworks to further help personalize UX at scale. For instance, AI-powered chatbots could tailor their communication style based on the age, language preference, and health literacy levels of users, all within the established framework of design systems.
Ensuring consistency for AI-driven experiences
As AI plays a more prominent role in pharmaceutical, healthcare, and life sciences UX, consistent design systems help ensure a seamless experience across different touchpoints. This includes AI-powered chatbots, virtual assistants, and any future AI-driven functionalities. A study by McKinsey & Company found that customer experience consistency across channels can lead to up to a 7% increase in sales, up to 2% elevation in profitability, and up to 10% increase in shareholder returns. In life sciences UX, consistency translates to improved patient trust and adherence to treatment plans.
A call to action for major life sciences players: Building future-proof life sciences user experiences
Moving forward, life sciences organizations must invest in building the kind of robust design systems our teams specialize in. Ones that prioritize
Comprehensive user research
Understanding user needs, workflows, and pain points is the cornerstone of a successful design system.
Flexibility
Design systems need to be adaptable to accommodate the ever-evolving landscape of life sciences UX and the integration of AI technologies.
Data-driven decisions
Leverage data analytics to continuously optimize and improve the design system based on user behavior and feedback.
Considerations for accessibility inclusions
Catering to a diverse, differently abled audience, needs to be considered.
Conclusion: The time to action and stay relevant is now
The integration of AI in life sciences UX represents a pivotal shift with the potential to revolutionize the healthcare experience for patients, HCPs, and researchers. By embracing a truly focused, well-synchronized, human-centered approach, prioritizing ethics, and fostering collaboration between humans and AI, we can work with your teams to unlock a future of personalized medicine, streamlined workflows, and accelerated scientific breakthroughs.
This whitepaper serves as a call to action for all life sciences organizations and UX and professionals. Fostering collaborations and working together is essential when exploring possibilities and navigating challenges, to design a future where AI empowers us in our efforts to create a healthier world for all. Talk to us to learn how you can leverage AI to uplift design strategies at your life sciences organization.
Author
Vittal Iyer
