is Data Enough to save Health & Social Care?

 

 

Everywhere I look there seems to be a demand for data analysts throughout the health, social care and voluntary sector. Most people will say we need this data to find and deal with health inequalities and help us plan and support what we’re doing.

The push to make use of the internet for information sharing and capturing information is also being pushed as a way to make savings and improve things in such organisations.

All very exciting, and I'm a firm believer in data and its infrastructure, I love technology. However, we have been down this road before and will probably go round again,  unless we learn from pur past mistakes. Here are some learning points from previous experiences from working with and in similar fields.

Data is not enough. You need to add local knowledge, specialist knowledge of particular protected characteristics and their needs and aspirations. If we go down the route of just using data we will end up doing and achieving little as in the past.

Using Data to help with inequalities is not new. It's been around since around the Equality Act. This Act which came in 2010 whilst working in this area there came  some great plans and ideas that came out of it. Unfortunately a lot of it was ignored or just not utilised.

A couple of these ideas were focused primarily on using data to make plans and justify decisions based on population statistics. For example, if a specific area had a greater number of protected characteristics, expenditure and services should be targeted for the most deprived.

Unfortunately most found the statistics just weren’t available. As a result of this, our own authority even created an organisation to collect and disseminate this data. Either it was found to be too patchy or too old to have relevance. An example of this is, A lot of the data presents minority ethnics as a large population, but Brexit has changed those facts. There is also an influx, as noted locally, of foreign students from India and Asia, again not in the data.

Sometimes with data it's what you don't see, During World War II, planes come back from battle with bullet holes. People initially sought to strengthen the most  shot or commonly damaged parts of the planes to increase survival rates. A mathematician, Abraham Wald, pointed out that perhaps the reason certain areas of the planes weren’t covered in bullet holes was that planes that were shot in certain critical areas did not make it back. This  led to the armor being stregthened  on the parts of returning planes where there were no bullet holes.  This shows that the r missing data may be more important than the what your actualy seeing  when looking at  data it's important to look and  listen also to what is not being  seen and therefore the need ask and talk to peopleiseven more important. 

Furthermore, there are numerous disabilities each with their own definitions and criteria. Again, with the increase in awareness of non-visible disabilities and neuro-diversities the data just doesn't match up or clumps these together. The data is not matching needs or the local picture.

Therefore data is just not enough. We need to add local knowledge, specialist knowledge of particular protected characteristics and their needs and aspirations. If we go down the route of just using data we will end up doing and achieving little as in the past.

Another aspect is even when you have data, it's what you do with it that that makes the difference. If you use data collect it, add local knowledge and do nothing it won't make a difference. I’ve seen many brilliant and probably quite costly reports with great data, that goes nowhere or isn’t utilised.

To be affective you have to take the gunning principles (case law) from sec 149 of the Equality Act and put these in use. Are the people making the decisions aware of it? Have they consciously used it to make decisions and not ticked boxed it? Where there are discrepancies what has been done to mitigate these. On this last one be careful I have seen many reports in mitigation saying where services have been closed. Creating a shortage in local facilities,  then mitigating by saying things like ‘just can catch the bus’, but not actually looking at the availability of buses, the cost, or the access of such say in rural areas.

Data is good but it is only one tool it must be analysed, it needs adding local knowledge and then using it to make a difference. In affect data can only be apart of the answer not the whole picture. To see that, you need to add different perspectives. Collate, check localy ( go talk to people) and use.