Harnessing economics

Despite a recent promised £20bn boost, the NHS continues to face significant financial challenges. The need to maximise the impact of every penny has contributed to a rise in the demand for specialist input from health economists to directly support commissioning.

Health economists use advanced forecasting and modelling techniques with NHS data sets to explore cause-and-effect relationships, predict future demand and account for uncertainty in business proposals.

The convergence with finance is clear; delivering the best outcomes for patients in an environment of conflicting priorities and finite resource is the business of health economists and finance experts working collaboratively.

Most NHS business cases will make the case for change by contrasting a modelled ‘do nothing scenario’ with the monetary effect resulting from an intervention.

Baseline data is projected over time, using a set of assumptions to determine expected outputs in a number of scenarios. In other words, no randomness is incorporated.

Health economics expertise can strengthen a business case by incorporating estimates from critically appraised health literature and making the models randomly generated.

A probability curve showing outcomes and their likelihood can be plotted to account for expected outputs under multiple scenarios. These enhanced financial models will reflect a truer picture of plausible outcomes derived from implementing a new initiative or model of care. And, once the initiative is in place, health economists can use advanced modelling to isolate its effects and assess its impact.        

The health economist’s toolbox is broad; they use advanced analytics techniques, such as econometric modelling, accounting for factors that aren’t traditionally incorporated into business planning within the NHS. Clinical engagement also plays a significant role, particularly when planning new services.

For example, Arden and Greater East Midlands Commissioning Support Unit supported the development of a business case to fund a clinic for cascade testing to detect familial hypercholesterolaemia, a rare genetic disorder. By working closely with clinicians using multiple data flows, and reviewing health economic literature to incorporate likely estimates into the model assumptions, we were able to compute expected patient outcomes.

This enabled us to confidently identify that nine lives would be saved within five years and 116 adverse events (angina, stroke and so on) would be avoided if the clinic were set up.

Contract analysis

Health economics has multiple applications. We are often asked to produce impartial evidence during contractual disputes or clarify whether sudden changes fall within tolerance limits. Our methods follow scientific enquiry principles, and results are framed as such.  

We recently conducted econometric modelling using NHS observational data to examine the effect of health, demography and other relevant factors on the A&E conversion rates in a sustainability and transformation partnership. We computed the marginal effects of having an attendance in a particular acute trust and found that high levels of deprivation alone did not account for higher A&E attendances than neighbouring trusts, as had been suggested.

We use intervention analysis to assess the effect of an unexpected or sudden external factor (such as a change in coding practices pushing up prices) to assess if observed changes are within expectation in trends based on a single attribute. Although often perceived as complex, this mathematically driven analysis gives precise results and is very effective.   

Preventative care

The NHS long-term plan sets out ambitions to use population health analytics to develop more targeted, preventative health interventions, which will be vital to the sustainability of the NHS.

Realistically, commissioners must accept they are unlikely to see the true value of preventative healthcare for at least five to 10 years – and often much later in terms of long-term outcomes, such as healthy life expectancy. This means it is even more important that decisions are robust and that clear expectations are set out for when return on investment can realistically be delivered.

Population health management brings imperatives into commissioning: moving towards value-based commissioning; understanding how the determinants of health impact on health outcomes with the aim of reducing health inequalities; and using integrated data sets that capture the care continuum for planning purposes.

The measurement and interpretation of health outcomes and their association with the determinants of health, the analysis of healthcare consumption by different populations and its complex association all are topics studied by health economists. 

Commissioning organisations increasingly realise health economics need no longer be confined to academia and has practical applications in the planning process. As economists, we have a role in bridging the gap between research and commissioning, deploying an analytic toolkit to support some of the NHS’s most ambitious programmes.

The interest in health economics is growing, with integrated care systems requiring the analysis of much broader data sets, so we can start to segment the population, planning services around groups with similar needs. As the challenges become more complex and demand grows, the benefits of a more scientific approach to analysing data and modelling potential outcomes will become clear.

Ana Ohde is senior health economist at NHS Arden and Greater East Midlands Commissioning Support Unit