Applying a complexity lens to policy implementation: how feedback loops help to understand systems change in integrated healthcare

Background
Although a complexity theory lens can help to understand national policy implementation at a local government level, application of this lens often remains at a metaphorical level. We illustrate how complexity concepts, such as adaptation, feedback, emergence and co-evolution can be used practically in research on policy implementation using a UK case study (Future in Mind; FiM), aimed at improving school children's access to mental health services in a metropolitan borough in the North England.

Methods
We re-analysed interview data with staff from local government, National Health Service/mental health, schools, and the voluntary and community sector (n = 31 semi-structured interviews) involved in the implementation of FiM and coded this data in NVivo12 using complexity concepts as sensitising constructs.

Findings
We identified 5 feedback loops: two positives (1. Flexing the training offer; 2. New skills, knowledge and behaviour by non-specialist staff) and three negatives (3. Short termism, 4. Free rider behaviour, 5. Professional boundaries). These loops energised local adaptations of FiM by school and NHS staff, leading to system-level change (emergence), with the school system becoming more responsive to mental wellbeing needs of children and young people, and shifts across systems (co-evolution), by developing joint values and language between schools and NHS.

Conclusions
We demonstrate the importance of positive and negative feedback loops for evidencing system-level change and shifts across systems. Our findings provide new insights into unintended consequences of policy implementation and starting positions for implementers to utilise feedback for generating impact. We argue for a blurring of boundaries between implementation and evaluation in policy research to optimise the use of feedback.

Highlights

• Use a complexity lens to understand unintended policy outcomes & chain reactions

• Blur boundaries between implementation and evaluation in policy research

• Identify feedback loops while implementing policy to timely adjust to co-evolution

• Maximise positive feedback while minimise negative feedback in loops of learning