Summer 2026 CHE Research Summaries
Posted on Tuesday 1 September 2026
In Do longer waits for heart surgery worsen patient health? researchers address NHS waiting times, now a familiar topic of public debate and a major policy concern in many countries. One key question they addressed is whether waiting longer, especially for urgent care, means that health may deteriorate and ultimately result in poorer health outcomes.
It examined how delays affect survival and recovery rates for two common coronary heart disease procedures in England before and during the COVID-19 pandemic: bypass surgery and angioplasty. The results showed that waiting lists are not just an administrative issue. For some patients, they pose a real clinical risk. Policymakers should consider that when waiting times become very long, they can cause real harm, especially for those awaiting bypass surgery and that investing in mechanisms that cut these delays may not only improve the patient experience but can also improve health and save lives.
In Can healthcare be improved by tailoring payment to outcomes? researchers investigated the longstanding idea that healthcare providers should be rewarded not just for the treatment they give, but for how successful it is. Recognising the intuitive appeal, they addressed questions about the practicalities of implementing such a system: Can we measure how successful treatments are? How could we base payments to providers on those measures? How large would the extra payment need to be? Would the cost be justified?
They explored whether outcome-based payments could improve healthcare delivery and whether the benefits justify the costs. Using Patient Reported Outcome Measures (PROMs) and data from hip and knee replacements in the NHS as an illustration, the research examined how bonus payments might incentivise better patient outcomes. The findings suggest that relatively modest bonus payments could potentially improve pain and mobility outcomes and would remain cost-effective across a broad range of circumstances.
In Assessing pricing policies for medicines which treat more than one condition researchers looked into the prices the NHS pays for new medicines and whether they reflect the benefits they provide to patients. An important question addressed was whether the NHS should pay different prices for a medicine when it is used to treat different health conditions (multi-indication drugs) or pay a single price regardless of how it is used, even if the potential health gains vary between conditions.
The research compared different approaches to pricing these 'multi-indication' medicines. Using a model applied to two case-study drugs, they examined the long-term effects of each approach on patient access to treatment, health outcomes, NHS medicines spending, and incentives for pharmaceutical innovation. They found that charging different prices for the same medicine across different conditions could improve patient access to treatment. However, it would also lead to substantially higher NHS spending on medicines.
In How big is the health inequality impact of a new treatment, and how much is it worth? researchers introduced two simple metrics for distributional cost-effectiveness analysis (DCEA). The first measured the change in predicted health gap, which quantifies the size of the health inequality impact. It showed how much an intervention changes health inequality between the least and most disadvantaged groups, expressed either in QALYs or as a proportion of health opportunity cost. The second measure was the inequality-adjusted ICER and the corresponding Health Inequality Modifier (HIM), which quantifies the value of the health inequality impact. It showed how much more (or less) generous a cost-effectiveness threshold would become after factoring in the health inequality impact, given a high, medium or low degree of concern for reducing social inequality in health.
Applying these methods to 1,336 diseases in England showed that the size of an intervention's impact on health inequality depends not only on who gains and loses, but also on how cost-effective the intervention is. Also, most health inequality modifiers are modest, compared with existing modifiers for disease severity and highly specialised technologies, even with a high degree of concern for reducing social inequality in health.
In Beyond fiscal space: what does state capacity mean for health financing? researchers proposed that the traditional framing of universal health coverage only as a financial problem defined by how much governments are willing to spend on healthcare is incomplete.
Analysing data from 141 countries over two decades, the research found that stronger state capacity —the ability of governments and associated agencies to implement and execute policies effectively and efficiently—was associated with higher public investment in health and better financial protection, including lower out-of-pocket costs. These effects were strongest in low- and middle-income countries. The findings re-emphasised that progress towards UHC is not only a fiscal challenge but also a system-wide governance challenge. Decision-makers need to pay attention to key aspects of state capacity if they want to ensure that financial resources for healthcare translate into better population health outcomes.