Commissioning: Person-centred intelligence

In previous issues I explored the importance of needs assessments in securing appropriate placements, and the need for commissioners to have a reliable and comprehensive assessment strategy that will generate the data and insights required to inform placement decision making, provide reliable outcome monitoring and improve sufficiency strategies.

This month, I’m focusing on the final piece of the solution: technology. When we have reliable and comprehensive assessment data, how can we use it in commissioning to inform placement decisions and progress? Questions that commissioners need answers to include:

• “I have all of this information about the child, demonstrating their complexity, but what type of specialist placement do they need?”
• “The child is in a specialist residential placement with a plan to step down to fostering, but how will I know when the time is right and have the confidence it will work?”

There is a need to understand and learn from outcomes relating to past care decisions and care journeys. For example, what has worked for children with particular needs profiles of previously? These insights should inform future decisions. Technology has evolved to support this and has been implemented in many aspects of life. However, social care has been slow to harness the possibilities.

The reliability of Child and Adolescent Needs and Strengths assessment (CANS) data combined with sufficient sample sizes, allows us to use well-established statistical techniques such as “latent class analysis” to produce groups of children that share the same profile of needs, strengths and risks. We can then use the regular assessment data to identify which interventions and services improved outcomes for each “class” (profile) of children. Previously these insights and learnings could only be provided through analysis of historical data in single research programmes. However, the sector needs this analysis to happen automatically, accessibly and in real time.

In the United States, CANS assessment tools have been in place for many years along with technology that maximises the insights this data provides (see box). As well as being able to introduce the CANS assessments as part of TCOM England, we have also been able to identify advanced software platforms. Transformational Collaborative Outcomes Management (TCOM) is an assessment-based approach to decision-making, planning and management in complex care systems.

Technological anxieties

There are anxieties about giving technology a critical role in a sector where professional judgment will always be key. Standard artificial intelligence (AI) or machine learning can encourage repeat decision-making patterns and entrench biases, rather than providing insight to support positive change. This was a key consideration when seeking a technology solution for CANS data.

After an extensive review of the options we identified a software application called P-CIS (Person-Centred Intelligence Solution), to transform longitudinal assessment data into meaningful insights that support decision-making in real time – as a constant companion in management and practice.

CANS results provide a detailed profile based on clusters of needs, strengths and risks. Let’s say a young person comes into care with very little information but is demonstrating challenging behaviours. The team are unsure what placement will work. Using P-CIS, the results of the young person’s CANS are compared with the assessments and care journeys of thousands of other children to identify similar profiles. Once similar cases are identified, the team gains a reliable view of what placements or interventions worked – or did not work – for those children. They can then make a more informed decision on where to place the young person. Such analysis takes seconds.

Mark Kerr is chief executive of the Centre for Outcomes of Care

TRANSFORMING ASSESSMENT DATA INTO USEFUL INFORMATION

Dr. Kate Cordell is chief executive, chief scientific officer and co-founder of Opeeka, which is a partner in the TCOM England initiative and supporting implementation of the CANS assessment tool in England.

“I’ve always been particularly interested in improving the way data can tell individual and population stories but also enable critical learning about what works for whom. This means turning complex data into something that’s accessible daily and meaningful within the busy work of care.

As an online assessment software, P-CIS transforms all sources of assessment data into useful information about a person’s story and their changes within services. These insights are then accessible in real-time to inform care-planning and decision-making in practice.

As an outcomes analysis tool that supports commissioning and management of services, P-CIS learns from and illustrates an agency’s population needs, practitioner successes and crucially, the drivers of positive outcomes.

Person-centred intelligence is very different to standard machine learning or standard artificial intelligence. It learns the drivers of positive outcomes and offers insights that enable professional judgment, rather than restricting it. This process reinforces good care decisions, reducing and preventing individual or institutional bias.

P-CIS connects to any case management system to streamline assessment collection – from any number of assessment tools used – and outcome reporting.”

The journey to evidence-based care

Goal one
Embrace an evidence-based approach that recognises the importance of comprehensive assessment of needs and strengths over time

Goal two
Implement a universal assessment strategy that will support decision-making and generate meaningful longitudinal data through consistent assessment over time

Goal Three
Implement software to transform that assessment data into person-centred intelligence – insights that are personally meaningful, actionable and accessible within the daily work of both management and practice.