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2025 challenges — Zurich
What if every cryptic medical claim could instantly explain itself in plain human language. No ChatGPT, no Google search, no confusion, no angry calls?
Problem
Swiss medical billing is a jungle of codes: TARMED for outpatient services, SwissDRG for inpatient care, pharmacy tariff codes, and more. For customers, these bills are incomprehensible. For insurers, each misunderstanding means more calls, more complaints, and less trust. There is no simple, automated way today to translate these codes into plain language with examples.
Why hack?
Because you can demystify healthcare and help prevent fraud. The impact is huge: more transparency, fewer disputes, and more trust. This is a problem every insured person faces at least once a year.
Can you build an AI that turns raw claims data into clear, visual treatment paths, revealing how patients actually move through the healthcare system?
Problem
Claims data hides valuable insights: sequences of consultations, consultation dependencies, diagnostics, therapies, reha, in- and outpatient services, that make up patient treatment journeys. These patterns are rarely mapped at scale, which means missed chances to understand patient flow, regional differences, and cost drivers. An AI could extract and visualise these paths to support consultation dependencies, treatment side effects, planning, and policy.
Why hack?
Because you can turn hidden data into visible stories, helping stakeholders understand treatment and care patterns, visualize treatment dependencies and uncover opportunities for system improvements.
Can you build an AI assistant that turns complex, boring insurance data into a crystal-clear, personalized story explaining exactly why someone’s health insurance premium has changed?
Problem
Every autumn, customers receive their new health insurance premiums and often feel blindsided. Price changes seem arbitrary, even unfair. In reality, they are the result of a mix of public and personal factors: regional healthcare cost increases, age group changes or insurance model dependencies. All of this is traceable but buried in datasets, laws, and reports. Sales and service teams spend enormous time explaining these factors, often repeating the same stories over and over.
Why hack?
Because you can turn frustration into understanding, acceptance and even trust. This is a real-world pain point with direct customer impact: get it right, and you’ll keep customers better informed and save thousands of hours for service teams.