With analytics being a key part in directing our COVID response, managing health systems and underpinning population health management, Andi Orlowski, Director of the MLCSU-hosted Health Economics Unit, explains why using local analysts can help get the best from the data.
As NHS analysts we can do some pretty awesome stuff with data. We do plenty already, but I’m convinced we can do more. The NHS doesn’t get as much as it could from its analyst workforce, so how could we improve this? How could analysts build on what they currently do and add even more value to decision makers?
Analysts add value in so many ways
Through work on variation and gap analysis we help health systems improve the quality and safety of the care pathways and services provided. By looking at allocative efficiency we help ensure that pathways and interventions used are efficient and direct resources to have the best effect.
We use qualitative, quantitative and mixed methods approaches to evaluate an entire care pathway or a single intervention to see if the outcomes are what the system expected. We help uncover health inequalities and show where parts of our population may need additional and different resources to access care and interventions.
We do performance management and operational monitoring. This may not seem that glamorous. But when dealing with billion pound organisations, thousands of employees and what feels like a near infinite number of services and products, NHS analysts for a single trust are handling ‘business’ far larger than most private company analysts.
We also use data to help direct care for individuals and populations: finding those at risk of a negative outcome or where there are potential gaps in care and help the system intervene in the right way at the appropriate time to change that outcome.
Through local knowledge, we also help avoid some of the dangers and downsides of analytics…
Like any powerful tool, analytics contains risks as well as benefits. By combining local and technical knowledge, analysts can help decision-makers to navigate these waters.
Analysts are essential to promoting transparency. For example, if decision-makers can’t see how an algorithm has produced a result, they won’t be able to know if the algorithm is biased, which makes it difficult to fully trust the technology when making clinical decisions. To keep bias out of artificial intelligence tools, developers and vendors should be transparent about their methodologies, capabilities, limitations, and data sources. Analysts then need to explain the dataset these answers came from and what level of accuracy can be expected.
Data are rarely complete. Assuming they are can be dangerous. So when we look at the data we need to be mindful of how this is sourced. Missing and poorly characterised populations may lead us to make decisions that favour the ‘better represented’ populations and disproportionately over fund areas with increased activity and drive inequalities. Again, local analysts are fundamental to this.
Using data to help direct care for individuals and populations will only work if we truly understand the people we are looking to support. Local analysts who understand the demographics of the area are best placed to consider if the data completely and accurately captures those in the population.
So how do we get more from what we have?
I’m firmly convinced that local analysts, with experience of their neighbourhoods, are essential for making the best use of data. They can spot the aberrations, then further interrogate the data to see if it is telling the ‘truth’. Decision makers and analysts understanding their populations and patients is key to turning analysis into action and integrating systems.
I believe that analysts should not be isolated or hidden away. We should be working within mixed teams alongside clinicians, managers, researchers, software engineers and outstanding communicators.
We should also be better networked as a (mixed) profession. We should always have ready access to technical and professional support. This is why I’m so committed to the mission of organisations such as the Health Economics Unit and The Strategy Unit. We are working to support and develop analysts. We want to grow capacity and capability within and for local systems.
Similarly, the brilliant Association of Professional Healthcare Analysts (AphA) is always on hand to provide support, networking opportunities and everyday practical resources for members.
Finally, the development of the Midlands Decision Support Centre (DSC) is incredibly exciting. Its purpose is to provide an analytical network. It provides specialist support, reduces duplication by joining up systems with shared interests, builds analytical and problem-solving capacity across the region, and importantly connects analysis to decision-making. Let’s see how this plays out, but my bet is that this is a model for other regions to learn from.
Data analysts are the foundation to creating a health and care system fit for the 21st century, contributing to people receiving better care and better health outcomes in a sustainable system. We do so much already, but I know we have much more to offer.
Find out more
For other viewpoints on better use of data in the NHS read the paper Bringing NHS data analysis into the 21st century.
And if you want to chat about analytical collaborations, data, AphA or the DSC then please contact Andi Orlowski, Director of the Health Economics Unit via firstname.lastname@example.org.