While high quality data is helpful to get the best out of using Star, the beauty of the approach is that it enables the stakeholders to fill the data gaps by using their combined experience and perspectives to make a ‘good enough’ approximation. This is the basis for the ‘requisite model’ that the LSE researchers have extensively tested to show that such modelling is effective.
Before a stakeholder workshop, the sources of available data need to be identified and data on the following collated for each intervention to be discussed:
- the numbers of people with the relevant condition in the local population
- the number who could benefit from the intervention
- the cost of providing the intervention for these people.
You will also need someone who is familiar with the local population level health data and experienced in its analysis to lead on this for the local Star approach. They will need to be trained in using the Star tool and to attend each workshop to ensure that data is being used consistently, working closely with the facilitator in order to discuss data gaps and weakness of existing data.
Using data to inform decisions
The Star approach enables better use of the available data to inform decisions and shows where data is lacking.
Participants in pilots report that the real value of Star is in the conversations that occur in stakeholder workshops, allowing participants to trace the use of numerical data and challenge the various assumptions that may influence decisions.
Star – socio-technical allocation of resources – is a tool for commissioners.
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