For a decision where the stakes were high, we provided the quantitative clarity to maximise decision quality. Our Strategic Digital Twin didn’t just provide a single answer; it revealed the option value of different network designs and investment timings, enabling our client to build a robust and adaptable network by design.
Executive Summary
A global pharmaceutical leader was on the verge of launching a revolutionary, disease-modifying treatment for debilitating neurodegenerative condition. While the new therapy offered unprecedented hope, it also presented a monumental challenge: preparing a national health system for the surge in demand for diagnostics and treatment administration. The existing infrastructure was not equipped to handle the complex patient pathway required, threatening to create significant bottlenecks and delay patient access.
Decision Lab partnered with the client to develop a sophisticated discrete-event simulation model. This powerful decision-support tool allowed stakeholders to visualise the entire patient journey, from initial GP referral to treatment. By modelling various scenarios, the tool identified critical constraints in diagnostic capacity (MRI, PET, CSF tests) and infusion services.
Our collaborative approach enabled the client to quantify the precise impact of increased patient demand, test the effectiveness of service redesigns, and build a data-driven case for investment. The simulation demonstrated that by optimising patient pathways—creating a more antifragile system—the average time-to-diagnosis could be reduced by up to 47%, and time-to-treatment could be cut by 35%. This allowed our client to proactively engage with healthcare providers, armed with clear evidence to guide strategic planning and ensure patients could access this life-changing treatment without delay.
The results allowed our client to proactively engage with healthcare providers, armed with clear evidence to guide strategic planning and ensure patients could access this life-changing treatment without delay.
The Challenge: Preparing for a Paradigm Shift in Neurological Care
The introduction of the first-ever treatments designed to tackle the underlying causes of a progressive neurological condition marked a pivotal moment in medicine. Our client, a pioneer in this field, recognised that the success of their new drug depended not just on its efficacy, but on the healthcare system’s ability to deliver it.
The new treatment required a complex and resource-intensive diagnostic process involving PET scans, MRI scans, and specialist consultations to confirm eligibility. Furthermore, ongoing monitoring was necessary to manage potential side effects. Projections indicated that up to 280,000 patients in England alone could be eligible, placing an unprecedented strain on a system already facing capacity constraints.
The core challenge was to understand and mitigate the risks posed by these new demands. The client needed to:
- Identify and quantify potential bottlenecks in the diagnostic and treatment pathway.
- Forecast the impact of a significant increase in patient referrals on existing resources.
- Develop strategies to optimise patient flow and build a resilient, or antifragile, healthcare ecosystem.
- Communicate these complex challenges to healthcare payers and providers to facilitate proactive service redesign.
Without a clear, evidence-based view of the future, the launch risked being hampered by long waiting lists, delayed diagnoses, and inequitable patient access.
The Solution: A Strategic Partnership in Simulation
Decision Lab fosters strategic partnerships with our clients, helping understand the intricate details of their challenges. In this case, we collaborated closely with the client and their data partners to design and build a bespoke discrete-event simulation model. This wasn’t just about delivering a tool; it was about co-creating a solution to a strategic problem.
Our process involved:
- Deep-Dive Discovery: We held intensive workshops with the client’s clinical and market access teams to map out the complex “as-is” patient pathway and a hypothetical optimised “ideal” pathway.
- Agile Development: Using an agile methodology, we built the simulation in iterative sprints. This allowed for continuous feedback and ensured the model accurately reflected the nuances of the UK healthcare environment.
- Data Integration: The model was populated with robust, real-world data, including primary care activity, hospital episode statistics, and findings from clinical literature, to provide a credible and reliable foundation for analysis.
The resulting decision-support tool, integrated into a user-friendly web platform, empowered the client to:
- Simulate Patient Flow: Model the journey of thousands of patients through the system over a three-year period.
- Test Scenarios: Compare the “as-is” pathway against optimised models, adjusting over 50 variables, including patient numbers, resource availability (e.g., MRI hours per week), and pathway logic.
- Visualise Outcomes: Generate clear, intuitive dashboards and reports that highlighted key metrics such as average time-to-diagnosis, waiting list sizes for specific tests, and total infusion hours required.
This simulation provided a virtual sandbox where different strategies could be tested and their outcomes measured, turning uncertainty into actionable insight.
Results and Business Outcomes: Building a Resilient, Antifragile System
The simulation model delivered immediate and significant value, transforming our client’s conversations with healthcare stakeholders from speculative to strategic. The key business outcome was the ability to build a compelling, data-driven narrative for change.
Key Metrics and Outcomes:
- Quantified Bottlenecks: The model precisely calculated the impact of increased demand. For one scenario, it showed that with a 25% increase in patient referrals, the waiting list for memory assessments would grow by over 200% within three years under the current system.
- Evidence for Optimisation: By simulating an ‘ideal’ pathway that co-located diagnostic services, the model demonstrated a potential 47% reduction in the average time to diagnosis and a 35% reduction in the average time to treatment initiation.
- Strategic Resource Planning: The tool provided clear data on resource utilisation, showing, for example, the exact number of additional weekly MRI hours and infusion clinic appointments needed to meet demand, enabling targeted investment discussions.
By using the simulation, our client helped healthcare systems become more antifragile—not just robust enough to withstand the shock of new demand, but capable of adapting and growing stronger. They could proactively identify pressures and design more efficient, streamlined services. This strategic foresight ensured that the launch of their ground-breaking treatment would be defined by patient benefit, not by system failure, cementing their position as a true partner to the healthcare community.