In a recent White paper to coincide with ISPOR Amsterdam’s theme, we looked at various aspects of health care policy in Europe, and factors that may influence changes in approach in the future. One of the areas we touched upon was the greater knowledge that patients have about their conditions, and the consequences on health care systems as a result.
When we talk about MA, we usually look at it in terms of Pharma, Payers and Prescribers. Pharma for the new therapeutic innovations; Payers from an economic viewpoint; Prescribers as the last gatekeeper to a therapy’s use. But hang on a minute – haven’t we stopped a little early here? Right at the end of the chain and dependent upon all the previous groups mentioned making the ‘right’ decisions, Patients don’t always appear to be considered within this process at all. Odd really, when you think about it, isn’t it?
The growth of the internet has undoubtedly made fundamental changes to people’s lives over the last 20 years. It has become a provider of information to the general public on a scale that would have been previously unthinkable. For some patients this means more “self-diagnosing.” For others it means a greater knowledge about all treatment alternatives – whether orthodox or otherwise. Information available to patients on the web could be viewed as both a positive and negative development, but it cannot nor should not be ignored. Of course, there is the risk of exposure to potential misinformation or the possibility of information being misused or misinterpreted. Simply Google any particular condition (MS for example) and you will be bombarded by information, societies, patient groups, treatments, scientific papers - the list goes on and on. Given the incentives for those with debilitating illness to improve their quality of life, it’s hardly surprising that people are willing to spend hours trawling through this kind of stuff. It’s surely indisputable that patients have never before had the potential to be better informed.
I listened with some interest a couple of weeks ago to a radio documentary about this very subject. Examples of how desperate patients with chronic or terminal illnesses managed to push some of the boundaries of the usual medical protocols in an effort to try new treatments. Some may argue this approach is somewhat reckless – in fact, one of the reasons that such stringent approvals are sought for new drugs is to ensure patient safety – but the clear message that came across from those featured in the programme was that they made a personal, informed choice about their own treatment.
Next time, how will the rise of Patient Power impact on Payers and Pharma in the future?
This week in the final section of our white paper, we take a look at whether the current hype surrounding Big Data has resulted in Pharma moving its attention away from the wealth of smaller-scale data that is already being generated – and as a result, whether this existing ‘Little Data’ is actually being used effectively and to its full potential.
The current chatter in Pharma is about how Big Data should be used. We have shed some light on how this might be done in a Payer context, but the question remains; is Big Data the most important data question right now?
Enter – Little Data. Little Data is often forgotten in all the noise about its younger, bigger brother, and is possibly a misnomer, because Little Data (in our world at least) is normal data. It’s a budget impact analysis or a cost effectiveness model; it might be a clinical paper or primary data received from Payer ad boards, but it’s data all the same, and the central premise of this white paper is that prior to spending millions investing in Big Data, Pharma may actually be better served developing effective approaches for the development and leverage of its existing ‘Little Data’.
Let’s use an obvious example: information gathered during Payer ad boards. Suppose you manage to recruit the right Payers providing the required coverage and current experience, how do you then manage the data that you gather and leverage it to maximise ROI?
By designing discussions around the disease area and using technology to gather responses, health economists can interrogate ad board data to understand formulary trends across regions, disease management preferences, and ultimately develop market access information that advises the critical Phase III clinical trials and commercialisation decisions.
As is the case with all forms of data, the devil is in the detail and so the most important aspect to Little Data is leveraging the insights you collect. By using the approach mentioned above, responses can be shared with internal stakeholders and applied across relevant geographies. It also enables further ad boards to be designed to follow up on interesting trends that appear, and share them also.
But more importantly than that, how does this information change the business model?
Based upon a comprehensive database of market access Payer insights, decisions on market access strategy can be made quickly and efficiently. So the data tells you it is a competitive market and you don’t have greater efficacy for all patients, but what it also tells you is that you have superior efficacy in a particular patient sub-population due, say, to delivery mechanism; and therefore Payers will be more likely to reimburse. This focuses your activity and approach and limits wasted time and effort.
Or suppose your feedback suggests that your initial reimbursement targets are unrealistic due to a lack of credible evidence? With a potent data interrogator, you can discern this and make changes either to the TPP or the evidence portfolio. This might sound obvious, and initially one may assume that Pharma is already doing this, but experience indicates that there is no widely used mechanism to utilise the data that pharma is already generating and feed the insights back into the development pipeline.
To conclude, there is much enthusiasm and energy being invested in Big Data, and previous sections of this white paper have focused on the potential uses of it. But, more importantly it appears that Pharma can lack a coherent approach that utilises all the Little Data it generates. With an approach driven by technology, however, this may be more achievable; and with a much higher ROI than Big Data programmes, this is an option Pharma could exploit now.
With a background in solution and application sales, Mervyn has valuable experience of working with and managing ‘Big Data’ across a number of industries.
Jonathan has a wealth of blue-chip business experience, and is an accomplished Management Consultant, building partnerships with industry leaders across a range of sectors to formulate and execute strategy.
Big Data requires interpretation or translation into something meaningful to those who use it – which then has to be communicated effectively. Without this aspect, Big Data loses a huge amount of its value and impact, falling into the same chasm as any poorly communicated data set does, whether big or small. This week, we take a look at the importance of communicating Big Data stories effectively.
Communicating complex value material is the key to successful distribution and uptake of a new asset. Not only short term, but long term performance and benefit must be relayed in a comprehensive yet user-friendly manner. And Big Data is the key component of a truly meaningful long-term picture.
So, you ask, how do you communicate Big Data strategy to Payers and other stakeholders? The conceptual value of Big Data is scary to most people. What does Big Data mean? How should we use it to actually help inform our decision processes leading to optimal outcomes in our environment?
Yes, Big Data can paint a large picture with a wide brush, but the real value lies in the refined strokes that detail the picture’s beauty. Payers and others are quite open to reviewing the big picture but they need to know how the targeted findings within Big Data will influence their part of the world. How can sub-themes from the overall story be refined to expose true unmet needs within their environment? Yes, on a national level, large data models and output may expose unique and beneficial trends, but it is the local Payer that is left out as their budgetary and treatment needs are not directly addressed. They need to know how the asset will impact their smaller patient population and their limited and often restrictive budget.
Losing the trees amongst the forest can be a risk; frustrated Payers will lose confidence in value stories not tailored to their specific environment, and assets will fail to achieve desired uptake.
The need is to take Big Data and refine the output and approach to target it to make it more relevant for those who have great influence – local Payers. Why not let the Payer define their key metrics? Let them tell you how to refine the Big Data story. The key is to build applications which allow Payers to build/define their treatment environment and see how your asset will improve the current standard of care. Going one step further, this story needs be saved and collated across regions and Payer environments helping you create your own “Big Data” bank of Payer preferences and practices.
Duncan has extensive experience in market access, health economics and outcomes research, across a number of treatment areas and reimbursement environments.
With a background in sales and consulting, Chris prides himself on his ability to bring a fresh approach to solving sometimes long-standing complex and highly problematic MA challenges.