Data for operational management, such as for simple rotas, logistics, production management, flow management, etc., in healthcare could be significantly improved (Manning 2015). Analytics based on routine data, including outcome measures, would also enable the use of more robust research methodologies for understanding comparative performance different contexts.
“This could challenge the current view, held by many researchers and clinicians, that we can only trust answers if we have a Randomised Controlled Trial and three decimal points.” “In real life, we say, shop A is doing well and shop B is not doing well, so what is shop A doing that shop B isn’t. We can then try different approaches and try different hypothesis in real time. This would have enormous potential for improving quality and safety. Poor care causes significant harm and better routine data could allow a much quicker management response.” (Manning 2015)
This view was echoed by ICHOM, who felt that adoption of findings from traditional comparative effectiveness research is incredibly slow. They believe that people need to experience an innovation in practice and then take it back to their own institution. They seek to compare the outcomes of institution A and institution B rather than treatment A versus treatment B (Stowell 2015):
“If we give clinicians back outcomes data compared to their peers, they will say, ‘hey, why is this place doing so much better than us?’ That motivates them to visit those institutions and pull into their practices the innovations developed there.” (Stowell 2015)
It might also become apparent that organisations are delivering an equally high quality of care, but that some are doing it more cheaply than the others, thus improving value.
This approach to improvement is known as positive deviance and follows these steps (Bradley, Curry et al. 2009):
1. Identify positive deviants, high performing organisations.
2. Study them in depth to identify hypotheses about practices driving their performance.
3. Test hypotheses statistically in larger representative samples of organisations.
4. Work with potential adopters to disseminate evidence about best practice.
The positive deviance approach is particularly appropriate in situations where (Bradley, Curry et al. 2009):
• Organisations can be ranked reliably based on valid performance measures
• There is substantial natural variation in performance within an industry
• Openness about practices to achieve exceptional performance exists
• There is an engaged constituency to promote uptake of discovered practices
These criteria have traditionally been met within a small number of medical conditions and impressive results have been reported (Bradley, Curry et al. 2009). The availability of routine data, including outcomes measures, will enable a much larger proportion of healthcare to meet these criteria and to adopt the approach.
NHS Data Collections as a platform for a Learning Health System
What role for learning health systems in quality improvement within healthcare providers?
Cambridge University Hospitals NHS Foundation Trust (CUH)
Mr Kingsley Manning interview
Dr Caleb Stowell Interview
Research in action: using positive deviance to improve quality of health care.