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How combining data gives a better understanding

Manchester is one of the first councils in the UK to create a system that, in a few mouse clicks, gives social workers a complete overview of a complex family situation.

Typically, social workers can struggle through reams of assessments in outdated software to get the same level and clarity of information.  

What is it? 

Frontline social workers collect rich and detailed information from their visits, but this is often stored in free-text form in specialist systems that can’t be accessed or assimilated easily. When a new person takes on a case, it can take hours for them to work through this data to fully understand the situation, and important information can be missed.

Manchester’s integrated data set aims to change this. It gives frontline social workers far greater quality and quantity of information about the families they’re working with.

How does it work?

The system comprises 16 data sets from multiple agencies. In just a few clicks, social workers can gain a comprehensive view of a family, including interactions with other agencies, needs and genealogy. 

Through traditional methods, such as case files stored in client management systems, gaining an equivalent understanding would be dependent on which professionals were present at a case conference, hours of painstakingly reading case file notes, or on being able to share information between agencies. As a result, life and death decisions may be based on incomplete or poor quality data.

The software also makes it easier for managers to monitor and check social workers’ cases, and provides a useful set of checks at the point at which a case is closed. 

What are the benefits?

It is estimated that the integrated data set saves key workers approximately three or four hours when completing an assessment. Key workers can undertake 40 assessments a year. The integrated data set saves around two weeks of their time - the equivalent of increasing staffing capacity by 4%. 

Moreover, the system improves the quality of decision-making and helps the team determine whether to commission or stop services. 

The integrated data set is even taking them a step closer to predictive government. Through analysing the data, they can look for factors that are most likely to lead to certain outcomes.

For example, Manchester’s analysis found that increases in unauthorised school absences, or increases in a family’s offending behaviour, are a predictor of a child being classed as a Child in Need. However, exclusions and personal offending were not found to be predictive of a child becoming a Child in Need. 

At the moment, Manchester is only just exploring the full capabilities of this data set. However, in the future, the council may be able to combine data and analytics to build predictive algorithms, helping them identify children most at risk of abuse or neglect at a far earlier stage.

This article was written by Nesta. For the original article, please see here