Why do we collect outcomes? It’s something that we think about all of the time. The obvious answer is that it is a check to make sure that services are effective. That makes sense, we want to reassure ourselves that public money is being used to pay for services that have an impact rather than busy services.

Collecting outcomes and demonstrating impact comes at a cost to delivering services. Does the need to show contract compliance justify the expense and effort of collecting the data you need? Does contract compliance justify the personal data you need to collect from the people that you work with?

On another level are you entirely confident that there is a provable line of causality between the service you provide and the outcomes achieved? That might be easier to demonstrate in purely clinical outcomes, such as hip replacements, but it becomes more and more debatable as you look at services that work in preventative healthcare.

Many of the factors in people’s lives are interdependent meaning that outcomes might be achieved (or not achieved) due to issues well beyond the scope of any single service.
For this reason, I suggest that focusing on outcomes purely as transactional tools misses an opportunity to achieve greater social benefit from data gathering. Moving to a model of neighbourhood metrics where we measure place rather than service provides a much wider benefit.

Outcome collection can work on three levels; the individual level, the neighbourhood level and the service level. For simplicity, I’m defining neighbourhood as a discrete geographical area.

On an individual level, it’s useful to understand the outcomes achieved as part of the process of delivering a service. Within that relationship, there is some benefit in being aware of an individual service contribution to those outcomes but it’s also essential to understand the wider outcomes being achieved by other factors and influences. On a personal level, we need to bear in mind that the individual is already aware of all their outcomes because they are living them.

Service outcomes are essential to support business planning and, as mentioned above, are key transactional metrics in monitoring contracts. Somewhat counterintuitively, the key benefit of service level outcomes is knowing when you are not achieving what you have set out to achieve. If outcomes are not being achieved by outside factors or the intervention, then you can be confident that the service isn’t working as expected. This provides you with useful data to reassess the business model.

Outcomes being achieved on a neighbourhood basis are the most neglected area of data collection yet, I believe, the most powerful level of data to collect. Neighbourhood outcomes are where data is collected from all services and aggregated for a geographical area. That means collecting information on the entire range of issues and outcomes that people present. This form of whole person data collection is in line with the NHS Making Every Contact Count Plus model and our approach to using Risk Maps in assessment and outcome measurement.

To make outcomes work on a neighbourhood level it is essential to have a consistent approach to data gathering.

But why do this? This can be demonstrated in smoking cessation. We know that vaping has had a massive impact on smoking cessation rates. This is a good example of where external factors are providing an outcome that isn’t been captured. In terms of smoking cessation, we capture data from GP systems and smoking cessation services. Both are accountable for the outcomes they provide but equally, both are not necessarily providing interventions on a level that accounts for the change in behaviour. We know that giving up smoking is provides a wholesale improvement yet we measure it as a factor of contract management rather than a societal benefit. This means population-level behaviour changes are happening but are not necessarily taken into account in designing new services.

If you focus on transactional outcomes then you lose the context of the wider society that people live in.

By moving to an approach that ignores the causality of individual services you begin collecting data that is useful in itself. On a daily basis, services are collecting information that describes the places we live in and the needs and aspirations of the people who live in our communities. Releasing that data will improve our public services much more quickly than a narrow focus on whether contracts are functioning within agreed limits.