Tag Archives: payers

The politics of market access

Market access is wrapped in the politics of health

The attention politicians and stakeholders pay to healthcare and its challenges is a keen driver of the ease or difficulty companies experience with drug regulation and market access and has implications through the development process to discovery. How this manifests itself, in part depends on a country’s investment in research infrastructure, availability of scientific talent, as well as the competency and experience of experts in the various regulators, whether HTA or pricing.

One thing that focuses political minds is a crisis in the healthcare system, whether a cluster of unexplained deaths, systematic abuse of payment systems, medical corruption, poor hospital management, declining outcomes, drug shortages, golly, the list goes on. What drives reform, well as Harold Macmillan is reported to have said, “events, dear boy, events”.

Can market access be sensibly undertaken without consideration of political forces? Those who see market access as simply planning will no doubt be surprised when their plans are overtaken by events.

Politics is the exercise of power

Like bankers who need to know their customers, market access specialists need to have deep knowledge of regulators, and the very functioning of government.

In many countries, payers and governments exert monopsony power; they are often the only game in town when it comes to purchasing anything healthcare, whether doctors, hospitals or medicines. And regulators are monopoly suppliers of regulation, whether pricing, reimbursement decisions or HTA. Together, whether in a coordinated way or not, they set the tone for how market access will actually work, quite independently of the actual demand on the clinical ground for a new medicine or therapy.

Sense-making of political forces is a core competency for market access

Public bodies do not exist in a vacuum, but are determined by how political concepts are implemented by governments. In the main, if you are into archetyping, there are broadly three views of how government decides how a healthcare system will work:

1. some things are properly done by non-publicly owned agents because they have proved competent (pharmacies); but this does not mean they are unregulated;

2. some things are a mix of public (hospitals in some cases) and private entities (doctors and general practitioners);

3. some things are generally accepted to be public responsibilities (public health).

Over time, of course, events dictate how governments interpret these three broad roles, and they will move things from one area to another as a function of reform or policy.

What we are seeing right now is the emergence of new ways for governments to regulate through the availability of big data and data analytics. These show up in indicators of hospital performance for instance, or activity based funding. The ability to measure these accurately is necessary.

With even greater power from the data analytics, e.g. machine learning, we can anticipate greater use of predictive modelling of features of healthcare systems. Two examples stand out for me.

The first is pharmacovigilance. A ‘smart (in artificial intelligence sense) regulator’ could directly monitor for adverse drug reactions; this would alter how regulatory action occurs and impact the speed of decision making on safety.

The second is drug pricing. ‘Smart drug pricing’ would enable reimbursement regulators (pricing authorities) to build predictive analytical pricing models of new medicines performance in clinical use to more precisely model value pricing. This would obviously alter the pricing dynamic.

Other factors come and go in terms of acceptability by governments, most notably the role of contestable markets themselves in healthcare. Some, such as Alain Enthoven, believed that competition in managed markets could be a driver of service quality, improve responsiveness to patients, for instance. The purchaser/provider split, so-called, is one approach to apply what I have called a ‘game theory’ approach to setting service standards and quality. Actually, there are two ways to characterise quality in healthcare. One is through (rigid) specification of standards (the Crosby model) and the other is through continuous quality improvement [CQI] (the Deming model); there are others such as 6-sigma, Lean/Toyota, etc., but they are in my view just variants of these two main ways of understanding quality.

A lower tolerance for risk will lend itself to rigid standard setting with a corresponding impact on how new medicines are viewed, e.g. greater need for evidence prior to adoption, versus a higher tolerance for risk through CQI with e.g. conditional approval.

Risk and salience are key factors in politics and hence in market access

Politicians respond to events that are widely salient, while civil servants are tasked with dealing with ‘technocratic’ or procedural matters. Counterfeit medicines, something I’ve worked on, can be either a risk that can kill people, or a factor in intellectual property and copyright. The former is politics, the latter is technocratic. Focusing on the second means dealing with settled matters and the interpretation of rules and regulations. A health crisis, for instance, lifts the event to wider salience amongst the public and in so doing can constrain the freedom of politicians to act or not, indeed, whether they can ignore the issue or delegate it to civil servants. A crisis also creates a window of opportunity though which new ideas and changes can flow – as they say, you shouldn’t waste a good crisis.

Another source of political change comes from rights-based challenges to health policy. While often seen as only of relevance within legal rule sets, (i.e. technocratic), they are about citizens’ or patients’ rights and access to treatment and therefore have high public salience. In the US, right now, there is a major political issue around the ‘deferred action’ programme which funds care for immigrants. Hitherto a technocratic issue, it is now widely salient (people in general are learning about it) and politicians (a.k.a. lawmakers) actions are now constrained, perhaps tellingly by moral factors. Other noteworthy historical examples are the Chaoulli case in Quebec and the various cases decided by the European Court of Justice on portability of healthcare benefits in the EU.

Politics and policy options determine how costs and benefits are distributed

Underlying political positioning is how policy implementation distributes the costs of a policy and the benefits of a policy (whether in time, money or resources). This type of analysis, from the work of the political scientists James Q Wilson, is revealing as it lays bear these underlying assumptions and the real world impact of a policy and its implementation. Examples where this approach is useful includes analysing drug rationing (approvals, defining treatment cohorts), price controls, reimbursement (or not) of branded generics, health technology assessments, and more generally the logic of market access. What we learn is where the costs sit and where the benefits sit, and importantly, who pays and who benefits. Such an analysis is profoundly revealing for identifying, for example free-riders, and the NIMBYs, two groups where there are often strong socio-political beliefs arising from political ideology transferred into policy.

Approaching market access purely as a technocratic exercise will under-power the associated solutions for market access initiatives. There is a good reason to know how and in what way the benefits of a new medicine are distributed (to whom and how), and what those costs are, whether measured in money, time, risk or opportunity. These considerations, drawn from the ability to apply relevant political analysis and insight, adds explanatory power and relevance, for instance, market entry strategies, or identifying gaps in evidence.

Real world data and evidence in healthcare: The market access challenge

We are in a new world when it comes to access to and the use of data and evidence. Real world data and evidence takes us from structured studies to the routine delivery of healthcare, actual use of a medicine, and the patient’s actual health status.

What is knowing this worth and to whom?

Real world data is best understood in the context of decision making, or choices and how they are made and the consequences that flow from these decisions. To illustrate:

  1. Patients get the wrong treatment, i.e. they are misdiagnosed. This is a particular issue for patients with rare diseases which experience not just being treated for the wrong condition (i.e. they are in the wrong treatment pathway).
  2. Clinical reasoning may be flawed. The main issue here is medical misdiagnosis, and clinical reasoning itself (backward/forward driven reasoning), and the rules for diagnosis, guidelines, the order items are listed in the differential diagnosis, and behavioural heuristics that impact clinical reasoning. Medical errors are more associated with backward-driven reasoning, using the hypothetico-deductive method; while forward-driven begins with data, with fewer errors. Other reasoning concerns include: doctors are reluctant to make a rare disease diagnosis, called the zebra retreat; inappropriate referral and diagnosing a mimic and sending the patient off to the wrong specialist, not listening to the parents of ill children, and so on.
  3. The treatment is the problem. Even if the treatment is diagnostically correct, the success or failure of that treatment often depends on whether the patient is adherent. It also depends on whether there are adverse drug events which alter patient acceptance of the medicine. Some patients may be non-respondent to the treatment, too.

What does that mean?

Enabling much of this is the use of computational methods, and machine learning, which uses real world data to enable precision medicine, case finding, precision cohort identification and treatable populations.

Regulators currently rely on industry reporting for adverse drug event reporting. RWD could enable regulators to directly monitor the market in real time and identify AD events. This would alter the pharmacovigilance system. In addition, they could gather data on off-label use (for and against) to assess the validity of treatment claims.

RWE may speed regulatory approval as the studies are tightly focused, don’t make expansive product claims and benefits are easier to demonstrate, thereby reducing regulatory risk.

Reimbursement regulators, providers and payers benefit from the potential to improve the quality of care as delivered to patients. This is enhanced by the development of more sophisticated decision support tools built on e.g. computational approaches or embedded in electronic record systems. This includes, for example, ‘red flagging’ tools to improve differential diagnosis, identify mimics, and trigger appropriate clinical suspicion as well as ‘referral filters’ to address inappropriate referrals, and so on. All these improve the value for money equation, and importantly reduce treatment risk, which drives avoidable costs out of the system.

Pharmaceutical companies can use this type of data to inform their drug portfolio development process. This would bring some order to research and development to improve internal priority setting and assessment of research targets in particular to avoid research bias (the impact of behavioural heuristics in R&D decision making for instance). The impact on trials cannot be ignored, use of synthetic control arms, improve precision of trial cohorts to remove the 80% or more of individuals who are not selected for a trial and perhaps save 60% or more of trial costs, and predict trial outcomes.

The evidence base for dossier submissions can be evidence informed with respect to the size of the treatable population, and patient response to treatment, reducing payer risk which manifests itself in refusing to reimburse.

The table suggests just a few changes from current market access to data-driven RWE market access.

Needless to say, this alters the underlying assumptions of pharmacoeconomics, medicines pricing and positioning.

I’ve summarised just a few points in the table below, to distinguish between what today could be called “Push market access”, a sales driven approach to placement, to a “RWD/RWD market access” with reduced risk and improved opportunities for demonstrating product value.

Stakeholder push’ Market Access RWD/RWE Market Access



Patient Risk of non-beneficial treatment Precise patient treatment cohorts

Risk of mis-/missed diagnosis, medical error Precision diagnosis with decision support tools



Clinician Uncertainty of benefits of treatment and the ‘halo’ of uncertainty inherent in clinical decision making Precision patient identification releases benefits through treatment targetting



Payers Pay for uncertain benefits Pay only for responders

Pay for treatment to non-responders Precision medicine to demarcate treatable population

Pay for non-adherent treatments Pay only for adherence, and risk reduction of non-adherence

Risk averse for uncertain treatable populations Risk managed for an evidence treatable population



Pharma industry Weak evidence for size of treatable population, with a “price per pill” Precision patient cohorts defines treatable population with cohort pricing

Missing Phase 4 evidence Good quality Phase 4 evidence

Risk of non-adherence, and non-responders Reduce risk through precision case finding

Missed patients Find the true treatable population

Drives costs into the healthcare system Removes costs from the healthcare system

Payer decision making

The relevance of value in establishing the positioning of medicines is the new normal for pharmaceutical marketing. Pharmaceutical companies have customers who are highly constrained by whether healthcare system funding is sustainable long term. Remember, payers think epidemiologically and in multiple years of costed care so industry needs to assess how that can be understood for product value. The pharmaceutical industry is constrained by its ability to generate revenues from medicines sales to cover the costs of research and development.

These two collide in the decision making process to adopt, or not, a medicine. The payers broadly have to balance the sustainability of their budgets with a potentially innovative medicine that will improve care outcomes. The pharmaceutical companies have to construct the value case to demonstrate these care outcomes. That probably means at least two things among many;

  1. Stop pricing drugs by the pill or pack, and start pricing valued outcomes for a defined set of patients over a number of treatment years, and
  2. Forget about trying to ‘time’ the market for product launch. The right time is set by payer budget cycles and their drug investment and disinvestment decisions. And, oh yes, the evidence.

By the way, my approach does differ from the journey model of Ed Schoonveld in important respects, by identifying the structured, and gated, decision processes involved; that why medicines aren’t sold, but bought.

Let’s first look at the colliding priorities. The diagram shows that payers are concerned with the value of a medicine in minimising treatment risk for the treated population. A company is seeking the value of the medicine by maximising the size of the treatment population that they believe benefits. As you grow the treatable population beyond the evidence, risk rises; for payers, reducing that risk is addressed through evidence.

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This is a collision of notions of ‘uncertainty’ in decision making and folks on the industry side should be used to requests for more evidence and novel access arrangements such as conditional reimbursement with evidence generation, and so on. As in any model of competing interests seeking a common price, the intersection of these two notions of uncertainty is defined by a price at which both parties will agree the price pays for the uncertainty it quantifies (i.e. it quantifies uncertainty in a certain way). The intersect quantifies risk, and sets the size of the treatment population that can benefit for that price.

The resulting curve may be thought of the ‘community effectiveness curve‘ depicting the optimal balancing of risk for the treatment community and a proxy for price agreement along that curve. This, by the way, is a better way to identify price corridors for people who still think that way.

This structured process is what this article is about.

Here is the gated decision process for payer decision making. While payers may not formally see themselves going through this in a linear way, they are thinking these thoughts, in this order.

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Gated Payer Decision Making for Market Entry of New Medicines

From the payer perspective, information needs to be specific to the decision gate and having the wrong information at the wrong time (e.g. the right information at the wrong gate) will just frustrate folks and probably irritate decision makers.

The diagram is read left to right, and a ‘yes’ answer to a question is needed in order to move through the gate. Getting a ‘no’ means the information supplied failed to make the case.

The following is a quick tour of the underlying logic. By the way, I call this a gated process as there are criteria for satisfying the conditions for passing through the gate; it is, I believe, unhelpful to decision making to characterise them as hurdles, as this suggests they are imposed to make life difficult. They are, actually, simply the structure of decision making.

Looking at this from a behavioural perspective, i.e. psychology informing decision making, each gate means this:

  • To get through the first gate, the payer is confronted with existing treatment options and asks why do I need another, or why change? Unfamiliarity may also be at work, with novel treatment benefits that lack comparators. Evidence of unmet need might be helpful along with good epidemiology to demonstrate the possibility of better outcomes.
  • Satisfied that a new therapy may be warranted, there is the question of risk and benefit compared to current treatment. While a new therapy might be indicated (yours?), the associated risk may be unacceptable compared to not using it. The benefits really do have to hold under increased uncertainty for a payer to agree to increased treatment risk. I suggest this is where discussion of standards of care begin to be quantified, having been introduced at the first gate. Payers often are not as aware as they should be on the current standards of care evidence in misdiagnosis, medical error and patient dissatisfaction.
  • Then having agreed that this uncertainty and its associated risk are acceptable, we are confronted with the cost and efficacy issue. Now we are beginning to price that risk. Good analysis of the costs of care and mis-care are useful, again because payers are not often aware of whole system costs (i.e. the costs of a treatment pathway) either because they are using using a fee schedule linked to DRG type classification or haven’t proofed their capitation models.
  • Success in pricing that risk moves to the question of the medicine in the context of total treatment costs and whether the treatment costs themselves for the patient population can be managed or will the scaling of the costs overwhelm the system for this treatment population versus all other options. Companies may see themselves as just suppliers of medicines for a price, and not a partner in the total system. But understanding the cost drivers along the whole treatment pathway, not just the costs a new medicine may drive, becomes an important element in final value pricing. If you have a medicine that reduces associated costs, or avoids certain costs (think the Triple Aim, here), then the determinants of value are much clearer. It may be that a biomarker is a value-add from one perspective but only if it reduces medical error and misdiagnosis, without increasing costs, so precision patient identification becomes important. If you’ve got this far, though, you’ll have already shown you can demarcate the treatment population, including the responder subset with a degree of precision.
  • Finally, the payer thinks about the future and whether there will be new medicines coming along that might address the same treatment population, alter risk differently, improve outcomes, avoid costs, with better patient adherence, and so on. Given, broadly, a medicine is alone in its treatment class for months, rather than years, payers may choose to delay decision making or consider options you’ve ignored that may trade off future costs and present priorities. This may be where a payer will be thinking disinvestment or product substitution and the determinants of that are critical in this final phase. Here’s a scenario: Why might a particular medicine not be a preferred medicine on a hospital formulary? The answer is simple: don’t have production problems where supply cannot be guaranteed. The lesson is that this is where the long game gets played out.

For those of you who read Kahneman’s “Thinking Fast and Slow”, or similar, there are decisional heuristics at work here. And across that gated process, you are contending not just with highly structured evidence informed quantitative information, but also how humans can be influenced by how human’s think they think. This has a raft of factors such as confirmation bias, hyperbolic discounting, choice overload, loss aversion, endowment effect, anchoring, mental accounting and social proof. It will pay to be attentive to when you present what information and the frame of mind decision makers are in. The reason this is important is that that regulators and payers in different countries, hospitals or regions can make different decisions from the same evidence, so something else is going on.

And so, a comment on pricing. To short-circuit this challenging gated process, it is common simply to cut the price, i.e. discount. Discounting is a quick win trick that only works if payers are trying to reduce present costs, which they all are. However, payers with their eye on the future are more likely to be interested in pricing arrangements that address uncertainty over time and so will be amendable to arrangements such as coverage with evidence development or outcomes guarantee. If they are focused on whole system issues, they will be interested in care pathway (cohort/whole system) pricing for instance. If, though, the future costs are a priority, think about capitation arrangements, or simple price/volume but be mindful that this last is like selling products door-to-door in the 1950’s.

I happen to think care pathway pricing of carefully demarcated patient populations with costs taken over say 5 years is a better pricing model for both parties. Value can be demonstrated on both sides along with evidence of such things as improved adherence (to reduce waste by non-responders) or diagnostic decision support aids to address misdiagnosis and sources of medical error or reduce time to the correct diagnosis, in the case of rare diseases for instance.

This article is designed to emphasise product value determination under conditions of uncertainty to arrive at a sustainable long-term relationship.