Life sciences problems worth hacking

[#1]

Life sciences has never lacked difficult problems. What is changing is our ability to experiment with them. AI, simulation, synthetic data and advanced analytics are creating new possibilities across clinical research, manufacturing, supply chains, patient engagement and workforce planning. But knowing that a technology exists is very different from knowing whether it can solve a problem that matters.

That is where a hackathon can be useful. A good hackathon does not begin with “What can we build with AI?” It starts with a genuine business problem and gives a multidisciplinary team permission to explore it. The objective is not necessarily to produce something ready for deployment. It is to make an idea tangible enough to learn from it. Across the life sciences ecosystem, ten challenges stand out as particularly interesting candidates.

Can we make AI governance tangible?

As AI becomes more embedded in drug development, governance moves from being an abstract principle to an operational requirement. Organisations need to understand how models reached their conclusions, what data influenced them, how performance changes over time and whether they can produce the evidence a regulator or internal risk team might require.

That makes AI governance an intriguing hackathon problem. A team could take one AI-driven clinical decision and explore what an AI Governance Assistant might look like. Could it track model lineage? Detect bias or drift? Capture evidence as the model operates? Could it generate an audit-ready explanation of what happened? The prototype does not need to solve AI governance. Its value comes from making the challenge concrete. Clinical, data, regulatory and risk colleagues can explore together what “responsible AI” actually requires when it moves from principle to practice.

Can we find the right patients faster?

Patient recruitment remains a persistent bottleneck in clinical trials. Yet the problem involves much more than finding people who meet eligibility criteria. Geography, travel burden, likelihood of enrolment, and recruitment choices can ultimately affect timelines and trial outcomes.

A hackathon creates an opportunity to bring these factors together. A team might prototype an intelligence engine around a particular recruitment problem. It could identify potential populations, estimate travel burden, predict enrolment likelihood or compare different recruitment strategies. The interesting question is not whether the prototype could immediately transform recruitment. It is whether bringing previously fragmented information together reveals a better way of approaching the problem.

Can we rehearse a clinical trial decision?

Some of the most important decisions in life sciences are expensive to reverse. Trial design provides a good example. Protocol assumptions, patient populations, dropout rates and other variables can have consequences that only become apparent much later. Digital twins introduce an intriguing possibility: what if some of those decisions could be explored virtually before resources were committed in the real world?

That is almost tailor-made for a hackathon. Choose one important “what if?” Could a team model the consequences of changing a protocol assumption? Could it simulate dropout under different conditions? Could it show how one decision might affect costs or timelines? The purpose is not to build a complete virtual trial. It is to discover whether simulation could make one consequential decision better informed.

Can synthetic data remove an innovation barrier?

Life sciences organisations live with a fundamental tension. Researchers want access to more data. Patients, regulators and organisations rightly expect sensitive information to remain protected. Synthetic data offers one possible route through that tension. It could allow teams to experiment with data that preserves important statistical characteristics without exposing the underlying sensitive information.

A hackathon could turn that broad possibility into a practical test. Take a dataset that is difficult to make available. Generate a synthetic alternative. Then ask the questions that really matter. Is it useful enough? How does its quality compare with real data? What privacy risks remain? What could researchers now attempt that was previously difficult? That makes the hack itself an experiment about experimentation: can we create safer conditions in which more people can explore data-driven ideas?

Can we see a supply disruption earlier?

Supply chain resilience has become a strategic issue across the life sciences. Geopolitical tensions, tariffs and regulatory changes add new uncertainty to already complex networks.

The obvious ambition is to predict everything.

A better hackathon challenge is much narrower. Choose one vulnerable supply chain. Map the critical dependencies. Identify a handful of meaningful early-warning signals. Bring in inventory information. Then explore whether a prototype could detect emerging disruption, forecast a shortage or help a manager compare alternative responses. The real test is whether the team can move the decision upstream. Instead of discovering a problem after it has happened, can we see enough evidence early enough to do something about it?

That is a much more manageable innovation question.

Can manufacturing data spot trouble before we can?

Life sciences manufacturing environments generate substantial amounts of operational data. Yet quality problems can remain difficult to detect until relatively late in the process. This creates another strong hackathon candidate.

Start with one recurring quality problem. Ask experienced manufacturing and quality colleagues which signals tend to precede it. Then give data and technology teams the challenge of finding those signals earlier. Could a prototype detect unusual patterns? Estimate the likelihood of failure? Help investigate root causes? Could it also capture evidence that would support subsequent quality and regulatory processes? The important element is the combination of perspectives. The people who understand the process know what matters. The people who understand the data may discover patterns that are difficult for humans to see. A hackathon gives them a reason to investigate the problem together.

Can real-world data answer one important question?

Clinical trials tell us a great deal about treatments. They cannot tell us everything about what happens when those treatments enter the complexity of everyday healthcare. That is why real-world evidence has become increasingly important for understanding outcomes, demonstrating value and supporting stakeholder discussions. But “let's do something with real-world evidence” is far too broad for useful experimentation.

A better hack begins with one unanswered question. Could claims, electronic health records or outcomes data help answer it? What needs to be connected? What analytical approach could be tested? And, crucially, who would make a different decision if the evidence became available? The resulting prototype might be modest. But it can demonstrate the bridge between disparate real-world data and a decision somebody genuinely needs to make.

Can we create a richer picture of the patient?

Precision medicine increasingly draws upon multiple forms of information: genomic, clinical, wearable and behavioural data among them. The difficulty is that the patient may be unified in the real world while their data is anything but. That fragmentation creates an interesting hackathon challenge.

Rather than attempting to “solve personalised medicine”, choose one treatment decision. Explore what happens if a team can bring together several relevant sources of patient information. Could the prototype identify useful segments? Highlight risk factors? Predict different responses? The exercise can reveal something larger than the performance of an algorithm. It can show which information is actually valuable, which connections are missing and what clinicians or researchers would need before trusting a richer decision-support capability.

Can we predict a skills shortage before it arrives?

AI is changing life sciences work as well as life sciences products and processes. Organisations already face shortages in areas including data science, AI and clinical expertise. Meanwhile, established roles are changing, and entirely new combinations of skills are emerging. That makes workforce planning another potential domain for a hackathon.

Pick one critical workforce. Look at the skills it has today, the work that is changing and the capabilities likely to become more important. Could a prototype forecast gaps? Identify adjacent skills? Model different hiring, development or redeployment scenarios? This also broadens the idea of what belongs in a technology hackathon. Not every worthwhile use case needs to come from R&D, manufacturing or commercial operations. Some of the most consequential applications of AI may involve helping organisations prepare their own people for the changes AI creates.

Can we reconnect a fragmented patient journey?

Healthcare and life sciences are becoming increasingly connected around the patient. Yet inside organisations, information about that patient can remain distributed across clinical, medical, commercial, digital and patient-service environments. A hackathon cannot integrate an entire patient ecosystem. It can, however, expose what becomes possible when a few important fragments are connected.

Take one point in the patient journey where it is currently difficult to see. Could a team combine relevant information to understand what happens before and after it? Could it identify an engagement opportunity or detect somebody at risk of poor adherence?

The prototype becomes a way of making fragmentation visible. Sometimes that is the most valuable outcome of experimentation. Teams discover that the problem they thought they needed to solve is not actually the most important one.

A safe place to learn by doing

There is a bigger reason these ten challenges belong in a hackathon. A question I increasingly hear from business leaders is: how do we empower teams to innovate responsibly? Organisations want people to experiment with AI and emerging technologies. But they also operate in the real world, with customers, patients, regulators, production systems and demanding day jobs. Innovation cannot simply mean removing the guardrails. A hackathon creates an unusual middle ground.

It gives people a bounded environment in which they can play with technology, test assumptions and discover what might be possible without immediately carrying all the risks and constraints of production. For business and data leaders, it provides a way to spot early signals. Which ideas have substance? Which deserve further investment? Which looked promising until people actually tried them?

For managers, it provides a practical way to create space for experimentation without allowing innovation to overwhelm everyday work. A challenge can be defined. Resources can be bounded. Teams can be assembled. Results can be reviewed. And for the people doing the work, it creates something even more valuable: hands-on learning.

People learn how to frame a problem rather than simply talk about technology. They learn rapid prototyping. They work across functions that may rarely solve problems together. They discover what the data can and cannot support. They encounter governance and practical constraints early. And they learn to explain what they have built, why it matters and what would need to happen next.

A successful hack is not necessarily the prototype that wins a prize or immediately goes into production. It may be the experiment that changes how people understand a problem. Taken together, these ten life sciences challenges are therefore not predictions about what organisations should build. They are invitations to investigate.