This is a series of three blogs that relate to the Next Generation NHS.They cover:
1. Systems Change with Data
2. Innovation is all around us
3. Spread- what gets in the way and what works
The slides for the whole of this series are attached. Prof Malby Inaugural FIN
To set the scene at the beginning of the live session I asked people about their view of the future of the NHS
1. Systems Change with Data
What Does the Future Context for the NHS Look Like?
Alec Ross recently wrote a book on the Industries of the Future . He shows us that in the Agricultural age those who owned the land had the power, in the Industrial age it was those who owned Iron, and in our Information age it’s those who own and can work with Data.
He says that 90% of world’s data was produced in the last 2 years. There are 16 billion internet devices in the world and by 2020 there will be 40 billion. I work in townships in South Africa and they have leapfrogged into the data age with micro enterprises and SMEs and health support utilising hand held devises, mobile phones. HIV prevention is a mobile phone service for young people. And these folk don’t have access to electricity or running water.
Ross says harvesting and interpreting this data will create the trillion dollar industries of the future, where robots of the cartoons of the 70s will be the reality. Whilst robots have high up front costs, their but operating costs are much cheaper than labour. Labour will change to cognitive activity. Genetic code will be the next big industry – lengthening life by 5-10 years. But most importantly the pace of change will ramp up
Whilst the NHS will still find rapid change comes from new technology/ clinical discoveries and inventions, this will now be alongside innovation that comes through data.
Hang onto this notion that the future is about Data. I’ve been curious all my career about the relationship between data, information and change. We lag behind in the NHS. It’s not just the care record or a common IT system – in fact we won’t need those in the future as organisations such as Patients Know Best finds ways of integrating data. Data in the NHS’s future will be available to citizens, carers, and NHS professionals – the issue is how we use it
In health care when we talk about data and innovation we tend to be thinking about digital innovations – the technology that makes data available to you.
All systems see change as a change in identity. Systems are set up to maintain themselves – to reproduce their identity. What changes identity is repeated perturbations using data and repeated interactions to generate a new understanding. This is fundamental for a new NHS. There has to be data generated about the system and how it works, with deep repeated conversations to generate understanding of the implications of that data for how the system works, which leads to agreements about change. Repeatedly telling a systems it has to change and how it has to change will not work. Perhaps the Junior doctors strike is a good example of how not to go about it in systems terms let alone in political terms. Data was generated to support an assertion (a partial view) which was then contested. There was no mutual sense-making discussion to find a better co-created solution.
As professionals we are data starved. Whilst there is plethora of data in the NHS and Social Care systems – little of that is available to citizens and professionals to make sense of the quality and process of care and to make fantastic decisions together. Many clinical professionals relay on assumptions and prejudice about what doesn’t work (or what does) in terms of a citizen’s whole experience; or in terms of their own team (or GP practice). In high performing health systems teams have access to demand, activity, acuity, flow, quality, cost and experience and have authority for their collective work. Most NHS teams don’t have that.
So in order to change a system you have to:
- Clarify the boundaries of the system – which is the set of relationships around a shared purpose – so it could be everyone involved in improving services with and for frail elderly people; or it could be a community.
- Provide more/different (or just some..) data
- Create space and time to make sense of that data
- Diversify who is involved (new eyes/ emerging leaders) – connecting the system to itself
A Data Model for Social Innovation in Place
This is the sort of data I’m talking about. The diagram below (developed in our Health Systems Innovation Lab) describes the categories of data capture that you need in order to be able to undertake your local diagnosis.
Your local data is then:
- Compared internally within the system (comparisons between viewpoints: Localities/ GP practices/ Consultants/ Speciality).
- Benchmarked against similar places elsewhere.
- Described both in terms of the categories listed but also in terms of what it means to population segments (e.g. people managing diabetes; young adults – using segments that are meaningful locally).
- Described at the big picture level and at the personal level in terms of people’s stories.
|Data Theme||Data sources (high level)||Process|
|1. Demographic/ Geographical – our health needs||Population data (which includes health inequalities by locality).
But needs benchmarking at local (community/locality) up to whole system level
|2. How the system currently operates to meet needs.||Capacity, Access, Flow, Demand on GPs (calls into GPs), GP referrals, A&E patterns, Elective care patterns, Care home admits, Carer and patient stories.
The data can be presented in relation no 1 above i.e. to cohorts of patients; to geographical localities; to service providers.
Example real stories
|Again from local to whole system.
Harvest from current data and find the killer patterns.
Everyone interviews patients in every part of the system.
|3. How people behave in the system to meet their own needs and that of their community.||Mapping what people can and do use – and from where (third sector, community, as well as statutory)
Example real stories
|Citizens mapping of local/ locality/ population cluster/ whole.
Professionals walking alongside citizens.
|4. How data is currently used for change||Assessing how data is used between professionals and patients to inform decisions; between health professionals for peer review; between health professionals and managers for strategic decisions
Is it used for improvement and change?
Example real stories
|Survey clinicians, managers and citizens to generate the data.|
|5. How this system learns||Take a series of place based changed governing documents||Analyse governance papers linguistically.|
|6. How adaptive and resilient is this system||1. How well the management enables the system to adapt:
· The rate of information flow – problem based scenarios using real time data
· The nature of the interconnections – mapping
· Cognitive Diversity (diversity of thought) – reviewing membership of groups and amount of time given to different views
· Energy and license? – the ability of people to enact change
|Generated primarily by people in the system in real time.
There is value in small data sets and observational data here.
|7. Is this system fit for the future?||Taking the whole of the data above and interpret through the lens of 3 possible futures||Using scenarios from the data to tell the current story; model how that plays out in future scenarios|
© Health Systems Innovation Lab, London South Bank University
- Without data we rely on personal preference, assumptions, and prejudices
- Without data power rests with the few
- Without data the system won’t change
- But data is all around us
- Perturb the system…
- Generate data that helps us know what is going on here, that in turn generates relationships and curiosity and frustration with the status quo
The next blog post is on the innovations that are all around us. The final one is on what gets in the way and what we need to do. They follow weekly.
Hope you enjoy
 Anderson, R. A., Issel, L. M., & McDaniel, R. R. (2003). Nursing Homes as Complex Adaptive Systems: Relationship between Management Practice and Resident Outcomes. Nursing Research, 52(1), 12–21.