Since the pandemic, we have seen increased mobile solutions focused on patient convenience, simplifying some interactions with health organizations. Today, patients are used to scheduling appointments, virtual consultations, or even hospital wayfinding from their phones. While this has been a step toward patient satisfaction, it is far from what patients expect when considering a mobile companion in their health journey.

The real problem? Today, patients continue to be passive agents in their health journey. Many studies highlight how healthcare providers can significantly improve satisfaction with patient-centered care solutions that empower individuals to manage their conditions. So, as healthcare organizations consider the next level of innovation, the focus must shift towards productizing patient outcomes—strategically developing healthcare solutions for specific, measurable, and beneficial results—while harnessing the power of Responsible AI. This approach not only boosts patient satisfaction but also amplifies engagement and adherence to treatment plans and, ultimately, improves health outcomes while cutting costs.

Productizing patient outcomes with responsible AI
Productizing patient outcomes isn't just about technology; it's about recognizing that every patient has unique needs, anxieties, and aspirations. So, the journey to productized patient outcomes starts by obsessing about patient understanding and defining health outcome OKRs and KPIs for our mobile solutions.

Then, there is the co-creation consideration. In our new era of AI, more than ever before, it is critical to ensure patient representation from diverse backgrounds as we conceptualize, design, and build our AI solutions to ensure unbiased and fairness in recommendations and decision-making. So, as we consider building Responsible AI mobile solutions, we must balance personalization of care and patient empowerment with ethical AI considerations to ensure patient fairness, privacy, and trust.

To strike this balance, there are five Responsible AI (RAI) Product Pillars product leaders must follow:

1. Privacy protection: While this is not new in healthcare, imagine the data we want to collect and use for an osteoarthritis patient with severe knee pain. In addition to demographic data, medical history, and diagnosis data, we want to gather symptom data from diaries, pain level information from trackers, activity levels, and mobility data from wearable devices, weather patterns and how that is impacting pain, any geo and socioeconomic status to understand and personalize this type of patient's health plans, exercise programs, and lifestyle modifications. So, considering the mountains of data we need to collect, store, and use for our deeply diverse and unbiased AI recommendations; we will need to implement very robust data governance, policies, and procedures, continue to encrypt sensitive data, and now create mechanisms to comply with the AI healthcare Executive order.

2. Trust and user empowerment: There are a couple of components to consider under this pillar:

• Data sharing: We have accustomed patients to sharing their data carefully. However, Patient data sharing is pivotal in AI healthcare solutions, and its importance cannot be overstated. In the case of our osteoarthritis patient, imagine real-time monitoring for any post-surgery complications and being able to anticipate risks while promptly modifying medications or recovery plans. Therefore, we need to empower patients with knowledge and tools about privacy preferences and data-sharing practices so that they can make informed decisions. As we create our mobile solutions, we need to develop patient control mechanisms to set their health goals, voice concerns, and choose their desired level of AI support. As we gain their trust, patients will confidently increase their level of personalization in their health journey.


• Human expertise: For many, AI is an enigmatic black box, so we must ensure patients understand how we have integrated human expertise and clinical judgment into AI-powered processes. Imagine a health recommendation from AI where you can see how doctors have validated that recommendation for patients with a similar background. It would be like when customers purchase an item from Amazon, and they get a level of confidence in the product they are considering based on the customer reviews and the number of people who purchased the product.

3. Fairness and bias mitigation: Like our osteoarthritis patient data, we must collect data representing various demographic groups, ages, ethnicities, socioeconomic status, and geographical locations. So, as we develop AI models, it's crucial to train, detect, mitigate, and test our models and implement corrective measures to address disparities and ensure fairness in decision-making.

4. Ethical design and governance: As organizations dive into AI innovation, they must form an AI Ethics Committee and Review Board to:

• Define AI ethical design principles and adhere to those in developing and deploying AI-driven digital products.
• Oversee the ethical implications of AI-driven initiatives and digital product development by engaging diverse stakeholders, including clinicians, ethicists, and patient advocates, to evaluate ethical considerations and guide decision-making processes.
• Ensure AI-powered tools are accessible and usable for all patients, regardless of age, disability, or technical skills.
• Develop regulatory frameworks to serve as essential guardrails to ensure the responsible development, deployment, and use of AI technologies in healthcare settings.
• Look after risk management protocols, validation requirements, data protection, and data security.
• Build adverse event reporting to identify safety concerns.
• Provide regulatory clarity, certainty, and guidance to stakeholders involved in developing and adopting AI-driven healthcare technologies and regulatory standards to promote interoperability and compatibility among AI-driven healthcare systems, devices, and data sources.
• Implement effective feedback loops by embracing diverse feedback mechanisms, defining actionable insights from the data, and contributing to the continual improvement of AI solutions.

5. Transparency and explainability: It will be crucial for healthcare organizations to foster a culture that values transparency and accountability and prioritizes user trust in product development and patient interactions. Many patients will not initially trust medical recommendations from AI. So, as we design our intuitive interfaces, we need to incorporate Explainable AI (XAI) techniques to delineate AI-driven recommendations and diagnoses while transparently disclosing tech limitations and risks.

"Transparency is the currency of trust in the age of AI."

The time for passive healthcare experiences is over. By embracing responsible AI and productizing patient outcomes, we can usher in a new era of healthcare excellence, where every individual's needs and aspirations are met with precision and care.

The future of healthcare is collaborative, empowered, and driven by responsible innovation. Together, we can build AI-powered tools that heal and empower individuals to thrive. Let's put patients at the center, not the periphery or care.

Learn more about Avanade’s digital health solutions and AI.