Needs-based resource allocation - a handy tool with practical insights (guest blog 2/4)
Dr. Eliana Jimenez Soto, experienced Health Economist
The Beacon Strategies team would like to welcome back Dr Eliana Jimenez Soto as a guest blogger for her second instalment of the ‘needs-based resource allocation’ series. Eliana is a health economist with over 15 years of expertise in health financing, data analytics and M&E. From a PHN perspective, Eliana has held roles as a Senior Technical Advisor to a number of PHNs, providing technical advice on outcomes-based commissioning financing and the overall use of evidence to improve program performance and outcomes. Eliana has also been a consultant on health economics on equity-based financing to WHO and UNICEF. Her international assignments include the development of complex financing costing and epidemiological models to improve service delivery and evaluate outcomes for disadvantaged populations.
In the real world of service delivery, economic principles are problem-solving tools that use data and modelling to support both strategic and programmatic decisions. They should bring about ‘aha moments’ with insights that managers and program staff can put into practice to achieve their intended vision and goals.
In our previous blog, we started discussing how we used needs-based allocation to support our client moving towards a more equitable and transparent outcomes-based commissioning of services. Here we want to briefly discuss the work we undertook and some of those key insights.
We knew our task was not to tell the client the percentages of funding to be allocated to individual services, but rather to build a resource allocation modelling tool that they could easily use during upcoming funding negotiations.
For these purposes we built a basic Excel tool with infographics and a video tutorial that the client could use to:
Visualise key patterns of disadvantage/need
Rank service target populations according to their level of disadvantage
Compare how much current allocation deviated from an ‘equity funding benchmark’.
The first ‘aha’ moment for the client came when we started looking at the vast amounts of data available and showed how some of the ‘usual indicators’ were masking and even distorting patterns of inequality in the target population.
After carefully sorting indicators, a basic heat table with selected variables was produced and incorporated into the tool. By focusing on the ‘right indicators’ very clear and sometimes unexpected patterns of disadvantage and population rankings emerged, which was a powerful insight from this exercise. Most of these indicators were used in the final modelling of our equity funding benchmark.
An equity funding benchmark shows how much funding should be allocated to providers after accounting for the size of their target population and their relative levels of disadvantage. To model our ‘equity funding benchmark’, weighted capitation was used.
Weighted capitation has many flavours and is currently being used in many countries including the UK and the US to fund health care services.
In a nutshell, weighted capitation accounts for the fact that population size is an important driver of funding needs, but it is not the only one. Other factors such as social determinants of health, demographics and prevalence will have an impact on the relative need of services and so on funding requirements.
Our client service providers were operating across relatively well-defined geographical locations. For each location, we estimated their per capita funding (i.e. based on the size of the target population), which was then weighted/adjusted by the observed levels of economic disadvantage, health status, and hospitalisations for key conditions.
The cost of delivering services also varied substantially across locations and it was primarily driven by the availability of specialist services and transport costs. Indicators of remoteness and local shortages of specialist services were also incorporated as weights/adjusting factors.
Our final resource allocation modelling compared current funding allocation vs per capita (i.e. funding allocation based on population size without any adjustment by disadvantage/higher costings) vs. our equity funding benchmark (i.e. per capita adjusted by disadvantage and cost drivers).
In all these years working on this sort of modelling exercises, I have seen that even people who usually say ‘I do not have a head for numbers’ find this sort of comparisons compelling, particularly when they involve something as tangible as dollars. So, the inevitable ‘aha’ moment usually comes when people realise how close or far their current funding is from that equity benchmark.
We all know that this type of ‘hard core’ evidence is just one of the many factors with an impact on how decisions are made. Less tangible, but critical knowledge such as differences in the local capacity as well as management of community and stakeholder relationships are also important considerations before making decisions on future funding. However, in the spirit of transparency and to give voice to all communities and stakeholders, especially the quiet ones who are usually underfunded, this type of evidence should be brought to the table.
Evidence-based modelling exercises, such as this one, are also likely to bring many other insights as other important issues start coming to the surface. Organisations might realise that they are funding services in an inequitable manner, collecting the wrong data while neglecting information that is truly needed, building infographics that are not that insightful, or paying too much/too little while not understanding what drives differences in costings across providers. Some would say that ignorance is bliss, but for others, knowledge is power and so these ‘aha’ moments are just priceless.
Buckle up as Eliana’ takes you on a road trip through equitable resource allocation to outcomes-based commissioning in her next blog.