Serena Labs
Pedro Villa

Co-Founder & CTO

Pedro Villa

Co-founder of Serena Labs. Holds an MSc in Economics from Fundação Getulio Vargas (FGV/EESP). Also founder of Anouk Partners, a firm specialized in data, applied econometrics and digital transformation. Previously founder of multiple healthcare ventures in Brazil with successful exit, and over 15 years of executive experience as CEO, CFO and CIO of large companies.

Pedro Villa is an economist, lawyer and digital transformation specialist. He holds an MSc in Economics from Fundação Getulio Vargas (FGV/EESP) and a law degree from Universidade Católica do Salvador. He holds both Brazilian and Spanish nationality, which has shaped his perspective on healthcare markets across Latin America and Europe.

His Master's research evaluated the causal impact of digitalizing Brazil's Primary Health Care through the adoption of the Citizen's Electronic Health Record (PEC) within the e-SUS APS framework. Using a longitudinal panel of over 300,000 annual observations (2014–2024) and Difference-in-Differences estimators including Sun & Abraham (2021) and Callaway & Sant'Anna (2021).

Pedro operates at the intersection of data analytics, applied econometrics and business strategy, with a focus on healthcare and regulated industries. He brings over 15 years of executive experience as CEO, CFO and CIO of large Brazilian industrial groups, including Katoen Natie Brasil, DPaschoal Group and Fortbras Autopeças (an Advent portfolio company). He was also founder of multiple healthcare ventures in Brazil with successful exit.

Pedro is the founder of Anouk Partners, a consulting firm specialized in digital transformation with emphasis on data and econometric analysis. He is also Co-founder of Serena Labs, an Intelligent Customer Engagement platform headquartered at the Barcelona Health Hub, building specialized AI agents for the European healthcare market.

His research interests include applied econometrics in health markets, causal inference methods, digitalization of public health systems, and data-driven value creation in healthcare organizations.

Insights

product

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A Three-Layer Blueprint for Insurance Enrollment Software

Most digital insurance platforms force a binary: chatbot or form. Both lose. Enrollment isn't one task, it's three, and each maps to a different optimal interface. Here's the three-layer blueprint that allocates conversational AI, a structured wizard, and async recovery across the journey, with the evidence behind each choice.

article · 9 min

research

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Beyond LLMs: Why Structure Beats Technology in Healthcare Interfaces

GPT-5 won't fix what GPT-4 broke. In a controlled test, the same language model hit 63% task completion with structured grounding and just 23% without it. The variable that drives healthcare interface effectiveness isn't the model. It's dialogue structure. Here's the evidence, a four-type taxonomy, and what it means for build-vs-buy decisions.

article · 8 min

product

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Your Customers Don't Know What a Deductible Is. Your Interface Is Making It Worse

The premise underneath most digital health insurance enrollment flows is that customers understand the product. They know what a deductible is. They understand what a coinsurance rate means. They can compare two plans on out-of-pocket maximums while also accounting for network restrictions, premium differences, and expected utilization. But the evidence on what insured consumers actually understand suggests that this premise is wrong.

article · 8 min

market

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The $36 Billion Question: What the Conversational AI in Healthcare Hype Misses

A 4.4× expansion of the global healthcare conversational AI market in nine years (Gartner, 2024). The slope is steep, the trajectory inevitable, the implicit message clear: be in this market, or be left behind. The pattern across the most rigorous syntheses available is consistent: aggregate effectiveness of conversational AI in healthcare is closer to *null* than to the order-of-magnitude productivity gains implied by the market projections.

analysis · 7 min

research

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The Preference–Performance Paradox: Why Your Chatbot's NPS Is Lying to You

70% prefer the chatbot. Chatbots take 73% longer to produce the same output. Same task, same participants, opposite metrics. If NPS is your healthcare interface KPI, you are systematically optimizing for the wrong thing.

analysis · 5 min

market

en

Beyond AI Chatbot Hype: An Evidence-Based Framework for Healthcare Customer Engagement

Healthcare conversational AI is projected to grow from $8.2B to $36B by 2032. The most rigorous meta-analysis (N>55,000) finds null effect. A contingency framework for what works. This piece introduces a contingency framework that predicts when conversational AI works, when it doesn't, and why dialogue structure, not the underlying language model, is the decisive factor for high-complexity tasks like insurance enrollment.

analysis · 10 min

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