# Uncovering the Data Dilemma - Building a Strong Data Culture in Healthcare _Date: 28-02-2024 , _Tags:_ #data-science #RWD #AI #culture #ML #healthcare #digital-health #consumer-tech ![[adryhealtharticles.png|200]] **Interview with Christine Doig-Cardet - Chief Product Officer @ IOMED** ## Introduction Healthcare has been notably slower in embracing digital innovations. What are the main obstacles and which lessons can we learn from faster-paced industries? Today, I had the privilege of interviewing Christine Doig-Cardet, Chief Product Officer at IOMED, a startup that has created a Natural Language Processing system capable of standardizing structured and unstructured healthcare data. Before joining IOMED, Christine had a successful career leading data science and product teams at various large consumer tech companies, including Netflix and Google. ![[Christine Doig Cardet Data Science Digital Medicine Beats.png]] ## Hello Christine, thanks for joining the Digital Medicine Beats Newsletter. You have quite a unique experience with product and data science leadership roles for Anaconda, Netflix, and Google. Could you tell us more about your professional path? _I have always had a passion for mathematics. My career began in the field of manufacturing and operations research, where I utilized data and modeling techniques to enhance processes. Over time, I transitioned across industries such as energy, entertainment, productivity, and healthcare._ _I have found immense value in the cross-pollination of knowledge gained from working in diverse sectors, as it has highlighted commonalities in utilizing data and AI effectively. I have transitioned throughout my career from being a data scientist-practitioner to assuming more strategic responsibilities._ _This shift has empowered me to not only implement solutions but also innovate by developing new software, integrating advanced AI models, and exploring novel applications in digital products._ _Additionally, personal circumstances, such as becoming a mother, have influenced my decisions, sparking introspection on what matters most to me outside of work and shaping my evolving interests and industry preferences._ ## You have expertise in both developing technical and consumer-oriented products across several industries. Which aspects of your previous work experiences can be applied to your current position at IOMED, and what do you consider specific to the healthcare industry? _Healthcare data is notably sensitive and necessitates a dedicated emphasis on data privacy, governance, and ethics. While these considerations are relevant across industries, their impact on healthcare is distinct due to the critical nature of the sector._ _Unlike industries that may prioritize rapid innovation at all costs, healthcare demands a more deliberate and cautious approach to safeguard sensitive information and ensure ethical practices. This slower pace also presents opportunities to learn from past mistakes in other industries, facilitating more efficient progress in healthcare._ _The potential impact of AI in healthcare is significantly profound compared to other sectors. We focus a lot on the potential harms of AI and not enough on its benefits, but there are also ethical implications in being too cautious when it comes to bridging AI-based solutions that can change patient outcomes. Time is of the essence for many patients._ _I propose a balanced approach that prioritizes caution without allowing fear to impede innovation, recognizing that there is an opportunity cost involved, particularly in terms of saving lives._ ## There is a lot of buzz around AI in healthcare. In your view, what are the current challenges limiting the adoption of AI-based solutions for healthcare providers and life science organizations? How can these challenges be mitigated? _Healthcare is probably the most complex industry, given the variety and nature of the different stakeholders. To highlight the main stakeholders, we have patients, doctors, research groups, patient advocacy groups, pharmaceutical companies, regulators, hospitals, and policymakers._ _Progressing towards alignment among these diverse stakeholders, each with their own interests and viewpoints is a complex task that requires time to achieve the desired industry transformation._ _Not to mention the data access challenges. While the industry generates vast amounts of data, much of it is either stored in incompatible, siloed systems or unstructured formats like free text or handwritten notes. Improving data accessibility while adhering to data privacy and governance regulations is crucial for enabling data-informed decision-making at all levels of healthcare._ _It has been quite unexpected for me that there isn't as strong of a data-driven culture in the healthcare industry compared to other sectors, considering the scientific nature of the field. While people are well-acquainted with hypothesis testing and conducting studies to validate treatment efficacy are common practices, there seems to be a reluctance to move beyond the established research methodologies and use data for making a wider range of decisions. I found this phenomenon particularly fascinating._ _Conversely, in consumer digital products, we have adopted their scientific approach to hypothesis testing. We have incorporated a mindset of experimentation, borrowed from clinical trials, into digital product innovation. Interestingly, the field that originated clinical trials has not extended this mindset further in the industry._ _To mitigate these challenges and facilitate the adoption of AI in healthcare, efforts should be focused on improving data accessibility, fostering a data-driven culture within the industry, and promoting the use of data for informed decision-making across various healthcare functions._ ## What strategies can be employed to measure the effectiveness of AI-driven digital health products to enhance their acceptance rates? _It's crucial to establish our objective and the problem we aim to solve. I've noticed a tendency to misuse metrics without grasping their underlying purpose. Metrics serve as approximations, not always perfectly aligning with our true objectives. It's essential to differentiate between our goals and the metrics we use to measure them accurately._ _When making data-informed decisions, there is always a degree of uncertainty involved. The level of precision required varies based on the significance and impact of the decision being made. It's crucial to consider the consequences of the decision, the context in which it will be implemented, and the objectives you aim to achieve. By taking these factors into account, you can determine the appropriate processes, metrics, and approaches to use._ _Factors like company size and available resources play a significant role in determining the most appropriate approach. Another aspect to take into account is the potential ambiguity present in measurements, particularly when conveying metrics to external stakeholders. Embracing uncertainty is crucial in the process of making data-informed decisions._ _For instance, when I was working in product at Google or Netflix we had highly skilled product analytics teams and robust infrastructure for experimenting and computing various metrics. Conversely, the situation is vastly different at a startup, where there is no product data analyst on the team or in-house tailored build data platform. It is important to consider the context of the company and determine the most suitable approach, but ultimately, you can adopt data culture and tailor your approach to whatever your context is._ ## How can organizations foster open communication and collaboration to bridge knowledge gaps and ensure effective data-driven decision-making? _We tend to be confined within our familiar roles, routines, and language, forming tight-knit groups where assumptions are easily shared and understood. However, when collaborating with individuals from different parts of the organization or external partners, our limited understanding and incorrect assumptions can hinder effective communication and hinder our ability to achieve shared goals._ _And since we are hesitant to engage in open and honest discussions regarding the information gaps, our lack of knowledge, or the assumptions we bring to the conversation, we fail to address these important aspects._ _We face miscommunication scenarios where the meaning I assign to a data point may differ from the interpretation you derive from it. This disparity often results from discussing these data points using varying terminology within our respective fields._ _I believe the critical piece lies not in technology or processes, but in collectively defining concepts with clarity and explicitness. Often, what we assume to be a shared understanding may not be as clear-cut as we think._ _And suddenly, I realize why this isn't feasible. I now comprehend the reason behind this limitation, as I had been basing my understanding on assumptions about what you were seeking from this data point or what was accessible in your database. It all boils down to clear, open, and honest communication, being unafraid to inquire about the unknown, or challenging the assumptions we hold._ ## What are the potential challenges and best practices for validating and testing ML models in the real world? _I observed that while there is a wealth of resources on testing algorithmic outcomes offline, there is limited guidance on validating results online, in production._ _I was surprised to see that many startups or AI teams tend to deploy a single model in production without thorough validation. They make improvements offline and promptly deploy them without double-checking._ _Based on my experience at Netflix and Google, where models are tested in a live environment, it's not as simple as blindly trusting your offline experiments to deploy a new or improved model._ _Real-world implementation can lead to unexpected results, highlighting the importance of comprehensive testing and validation of models in an online environment. I suggest readers delve deeper into this area to improve their understanding of ensuring model effectiveness in real-world applications._ ## Is there any point that you think would be interesting for data scientists, product managers, or strategic leaders to know? _When it comes to giving advice, I believe it's crucial to emphasize the importance of industry expertise alongside technical skills. Understanding the context of an industry is just as vital as mastering technical knowledge._ _I encourage everyone to engage with experts and users, delving deep into their perspectives to broaden understanding._ _Follow your curiosity and explore niche intersections, such as AI in healthcare, where opportunities abound due to the limited presence of experts._ _I suggest everyone maintain a realistic perspective during this AI hype period, manage expectations, and advocate for education on the technology's capabilities and limitations._ _By approaching AI with humility, we can foster a more realistic and accepting environment for its adoption._ _Personally, as a newcomer to the healthcare sector, I am eager to witness the impact that AI and innovative products can bring to the industry._ ## Key Takeaways - **On the Barriers to AI Adoption in Healthcare**: Challenges such as limited data access, multistakeholder environment, interoperability issues, and a lack of data-driven culture in healthcare despite its inherently scientific foundation, are the key factors that hinder the widespread implementation of AI. - **On building Effective Product Measurement Strategies**: Understanding the purpose behind metrics, embracing ambiguity, and aligning data analysis with organizational context is crucial for measuring the effectiveness of AI-driven products accurately - **On Communicating Data Effectively**: clear, open, and honest communication is essential for bridging knowledge gaps and ensuring effective data-driven decision-making across diverse stakeholders and roles. Mutual understanding is enhanced by confirming assumptions, as individuals may rely on various professional lexicons - **On Real-world Validation of ML Models**: comprehensive testing and validation in online settings is paramount to ensure model effectiveness in practical applications - **On Career Advice**: industry expertise is as important as technical skills. Furthermore, continuous engagement with experts and users is key to building empathy. Finally, product data managers and data scientists should manage expectations during the AI hype period, and approach AI adoption with humility. --- _This article is a repost of the original Digital Medicine Beats Newsletter. Subscribe to Digital Medicine Bests to receive new content._ <iframe src="https://digitalmedicinebeats.substack.com/embed" style="min-width:100%;height:300px"></iframe> --- _Copyright © 2024 - Adriano Fontanari_