To succeed in the Heidi interview process, you must demonstrate a holistic understanding of product management, tailored specifically to the unique constraints and opportunities of the digital health space.

Product Sense & User Empathy – You must show a deep appreciation for the daily realities of healthcare workers. Your solutions should prioritize reducing cognitive load, minimizing clicks, and fitting seamlessly into existing clinical workflows.

Technical & System Aptitude – You do not need to write code, but you must be comfortable discussing system architecture, APIs, LLM behavior, and data pipelines. Understanding how healthcare systems exchange data is highly valued.

Execution under Ambiguity – Startups move fast, and priorities can shift rapidly. You need to demonstrate a structured approach to decision-making, clear prioritization frameworks, and a track record of shipping products despite resource constraints.

Mission Alignment – Heidi is dedicated to solving real-world clinical problems. You should be prepared to articulate why you want to work in healthcare technology and how your background aligns with this mission.

The interview loop at Heidi is structured to evaluate your practical product skills, technical competence, and cultural alignment. The process is rigorous but designed to be collaborative, giving you a clear window into how the team operates on a daily basis. You will interact with cross-functional team members, including product leaders, engineers, and clinical experts.

The process typically begins with a conversational screen to align on experience and expectations, followed by deeper dives into your product craft and technical capabilities. A key highlight of the loop is the practical assessment, which simulates real challenges you would face on the job, such as integrating with legacy systems or optimizing AI model outputs.

The timeline above outlines the typical stages of the Heidi interview loop from initial contact to offer. Candidates should use this visualization to pace their preparation, ensuring they dedicate sufficient time to both high-level product strategy and deep technical system design before the onsite rounds. While the exact duration can vary based on team availability, the process is designed to move efficiently.

To excel in your interviews, you must understand the specific competencies evaluated across Heidi's core product domains.

Clinical Workflow & Product Design

This area evaluates your ability to build intuitive, high-adoption applications for healthcare professionals. Clinicians are time-poor and highly sensitive to software friction, meaning your designs must be exceptionally clean and functional.

Be ready to go over:

• Ambient User Experience – Designing interfaces that capture clinical conversations without requiring active screen management.
• Cognitive Load Reduction – Strategies for presenting complex medical data without overwhelming the user.
• Mobile vs. Desktop Workflows – Tailoring product experiences for clinicians who are constantly moving between patient rooms and desktop workstations.
• Advanced concepts (less common) – Voice-to-text optimization UI, offline mode synchronization for rural clinics, and accessibility standards for medical software.

Example questions or scenarios:

• "Design an interface that allows a physician to review, edit, and sign off on an AI-generated clinical note in under thirty seconds."
• "How would you design a notifications system for our Comms product that alerts clinicians to critical patient updates without causing alarm fatigue?"

Integrations & Technical Architecture

Healthcare software does not exist in a vacuum; it must connect with legacy Electronic Health Records (EHR) systems. This evaluation area focuses on your technical literacy and ability to design scalable integration strategies.

Be ready to go over:

• EHR Integration Patterns – Understanding read/write capabilities with systems like Epic, Cerner, and Athenahealth.
• Interoperability Standards – Practical application of FHIR resources, HL7 messaging, and secure data exchange.
• API Design & Platform Strategy – Building developer-friendly APIs that allow external partners to build on top of your core service.
• Advanced concepts (less common) – SMART on FHIR launch frameworks, OAuth2 authentication flows in clinical environments, and handling real-time webhook failures.

Example questions or scenarios:

• "Our integrations product needs to write clinical notes back to a legacy EHR that only supports batch updates every twelve hours. How do you design the user experience to handle this latency?"
• "Walk us through how you would prioritize building out our integration marketplace. Which EHRs or third-party apps do you integrate with first, and why?"

AI & Intelligence Application

As a Product Manager working on Intelligence, you will guide how machine learning and LLMs are applied to clinical data. This requires balancing product utility with safety, accuracy, and cost.

Be ready to go over:

• LLM Prompt Engineering & Tuning – Collaborating with engineering to refine model outputs for medical accuracy.
• Hallucination Mitigation – Product guardrails and review workflows that prevent incorrect clinical information from reaching a patient's permanent record.
• Data Privacy & De-identification – Managing sensitive patient health information (PHI) in compliance with global standards.
• Advanced concepts (less common) – Retrieval-Augmented Generation (RAG) architectures for clinical reference, multi-modal medical inputs, and cost-per-token optimization strategies.

Example questions or scenarios:

• "How do you evaluate the quality of our clinical note generation model? What metrics do you track, and how do you handle edge cases where the model hallucinates?"
• "We want to launch a feature that automatically drafts patient referral letters using historical clinical data. How do you design the feedback loop to continuously improve the model?"