Real World Modeling
Visualising and mapping data for the Real World.
Simulation by Health iQ
Health iQ's simulation models are built using a modelling technique called `discrete event simulation' (DES) which codifies the behaviour of a complex real-world system as an ordered sequence of well-defined events. These touchpoints are outlined in a clear pathway which is easy to follow and understand and represents the basis of the model.
The transparent pathway together with the use of real-world data helps to overcome a defensive attitude towards change and can function as a conversation-opener. DES is ideal for simulating health care systems, treatment pathways or health economies as it allows investigation of the real-world impact of a multitude of changes such as medication or device choice, service arrangements, resource use, etc.
The `game based learning' approach allows for greater flexibility than other traditional modelling techniques and is great for communicating a story and conveying a compelling case for change.
Our simulation models incorporate our own unique 2D isometric designs which give them even greater impact.
Health iQ Simulation, brings real world data to life
Demonstrating the value
of your intervention or
service change.
A Simulation model by Health iQ can cover a multitude
of treatment effects and downstream service efficiencies from
of your intervention or
service change.
A Simulation model by Health iQ can cover a multitude
of treatment effects and downstream service efficiencies from
New pharmacological treatment delaying time to more complex intervention
Shorter infusion time leading to freeing of capacity
New medical device reducing LOS in hospital leading to freeing of capacity
New drug is better tolerated and avoids a presentation to GP or hospital due to adverse events
Patients reach a stable controlled disease level much quicker which reduces the resource use burden.
Health iQ’s RWE-Powered Global Value Dossier
A Global Value Dossier (GVD) presents a summary of your product value, supported by the most robust and compelling evidence. Traditionally these are primarily based on Systematic Literature Review (SLR), or gathering existing published evidence in the form of previous research and clinical trials. The problem with this approach is that it often doesn’t reflect real-world practice and is out of date in terms of reflecting current treatment pathways.
Unlike many providers of GVDs, Health iQ are specialists in RWE-based research and are able to access longitudinal patient data from across the world.
We house licensed datasets such as HES and CPRD, which provide national coverage of both the primary care and secondary care pathways. We can map out treatment pathways from actual patient data and build cost-effectiveness analysis which incorporates the full cost burden of a condition, including all the co-morbidities and side effects. This allows us to provide RWE-powered dossiers which are more precise, focused and impactful than those relying only on summarising existing research through systematic literature review (SLR).
We access data based on what is most appropriate to support your product's value story. We then conduct new research focused around the key value messages you want to convey, so that the evidence we produce and incorporate into your dossier is built entirely around the need of your product. This creates a far more compelling output than one based on SLR only, which are often general, miss specific data points which do not already exist in the public domain and are not necessarily based on current practice.
As well as delivering quality RWE-supported GVDs, we can also translate the main template into customised local dossiers to suit specific geographies which used to support local engagement with payers, or to inform HTA or formulary submissions.
These will also be supported with data from the local region, again making the dossier relevant, current and focused.
RWE typically included in a Value Dossier (either at Global or Local level):
• Disease epidemiology.
• Burden of illness.
• Treatment pathways.
• Burden of unmet need.
• Summary of supporting clinical research (RCT, PASS etc.)
• Humanistic QOL data.
• Economic studies (Cost-effectiveness analysis, budget impact modelling).
