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The Future NHS Plans: Delivering Transformation and Sustainability
- 1. Evaluating the impact of new care models Tim Gardner 18 May 2017
- 2. About us We shine a light on how to make successful change happen The Health Foundation is an independent charity committed to bringing about better health and health care for people in the UK. We connect what works on the ground with effective policymaking and vice versa.
- 4. Evaluating new models of care Analytical methods have been developed to assess the impacts of new models of care, often (not exclusively) in research settings A common approach is to: • Identify the intervention group within existing data • Retrospectively select a ‘control’ group, e.g. by matching to the characteristics of the intervention group • Compare the outcomes of these groups • Other methods are possible
- 5. Example: Birmingham OwnHealth A telephone-based health coaching trial in Birmingham for people with heart failure, CHD, COPD, diabetes and history of hospital use. Expected to reduce hospital admissions by giving participants personalised care plan and monthly ‘check-in’ phone calls. • 2,698 people recruited to receive the intervention • Retrospectively matched to ‘control group’ with similar characteristics • Looked at emergency hospital admissions, pre- and post- intervention, for each group
- 6. Example: Birmingham OwnHealth Steventon A, Tunkel S, Blunt I, Bardsley M. Effect of telephone health coaching (Birmingham OwnHealth) on hospital use and associated costs: cohort study with matched controls. BMJ. 2013;347:f4585.
- 7. Example: Birmingham OwnHealth Steventon A, Tunkel S, Blunt I, Bardsley M. Effect of telephone health coaching (Birmingham OwnHealth) on hospital use and associated costs: cohort study with matched controls. BMJ. 2013;347:f4585.
- 8. New care models Information on the impact of new care models on patient outcomes and cost needed for: • Local improvement • National policymaking Critical for evaluation arrangements to be ‘baked in’ from the very start to provide timely, regular, high-quality feedback
- 9. The challenge We have the methods, but need to develop an analytical capability to put them into practice in a way that is: • Informed by the needs of the service users • Responsive to emerging questions • Timely • Robust • Able to operate at scale • Sustainable, providing an ongoing resource for the NHS • Underpinned by a rigorous approach to information governance • Able to draw on new analytical methods (and indeed, contribute to them)
- 11. The response The Improvement Analytics Unit, a new partnership between NHS England and the Health Foundation. Our aim: • By 2019, create a unit to provide rapid feedback on the progress being made to improve care and efficiency as part of national programmes in England • Inform both local and national decision making regarding the improvement of new care models
- 12. The Improvement Analytics Unit Data management team Statistical team Leadership and governance Intermediaries with local and national NHS teams performing ‘analytical advisor’ role Exchange with universities to draw on latest methodological thinking Partnership between NHS England and Health Foundation
- 13. The first output Principia is a multi- speciality provider in Rushcliffe Nottinghamshire From April 2014, Principia introduced an enhanced support package for residents of 24 local care homes
- 14. The enhanced support package • Alignment between general practices and care homes • Advocacy and independent support from Age UK Nottingham and Nottinghamshire • Enhanced specification of general practice care for frail older people living in care homes • Improved peer-to-peer support from community nurses for nurses employed within care homes • A programme of work to engage and support care home managers
- 15. Rates of hospital admissions for care home residents per year Note: The figure is based on the linked care home and hospital data for August 2014 to August 2016, and is for residents aged 65 or over in the six comparison areas, namely Harborough, Blaby, Test Valley, South Cambridgeshire, Chelmsford and Brentwood. We excluded Rushcliffe since the aim of this figure is to illustrate baseline admission rates, and admissions in Rushcliffe might
- 16. Similarity of the matched groups
- 19. Results Principia residents (number per person per year) Matched residents (number per person per year) Relative difference (adjusted rate ratio) 95% confidence interval A&E attendances 0.74 1.02 29% lower 11% to 43% lower Emergency admissions 0.64 0.78 23% lower 3% to 39% lower Potentially avoidable admissions 0.22 0.30 28% lower 0% to 49% lower Elective admissions 0.11 0.13 29% higher 36% lower to 163% higher Outpatient attendances 1.99 1.85 11% higher 12% lower to 40% higher
- 20. Interpretation Older residents in Principia care homes experienced fewer A&E attendances and emergency admissions than similar residents in similar care homes elsewhere. The matched comparison group had similar age, gender, health conditions and prior hospital utilisation to the Principia residents, but might have differed in unobserved ways. Assuming the two groups were comparable, the most likely explanation is higher quality care for residents of the Principia care homes. This might be related to the enhanced support, some other aspect of care delivery or local context.
- 21. 2016/ 7 2017/8 2018/9 April 2019 + Feasibility testing with 2 local teams Developing service, with 5- 10 local teams Target: 20 local teams Release to NHS Scale-up
- 22. Thank you
Editor's Notes
- As you may know, THF is an independent charity committed to improving health and healthcare for people in the UK We fund frontline service improvement projects, development programmes and research, as well as undertaking in-house policy, economic and data analysis Here today to talk about some work that we’re currently doing to understand and help guide the development of the NHS’ new care models programme
- This morning you heard about NCMs and some of the other interventions that feature in STPs. These are sophisticated interventions in a health system that is not just complicated, but a complex adaptive system where there is unlikely to be a simple ‘cause and effect’ relationship between interventions and outcomes. Understanding the true impact of these interventions can be difficult: the lack of an obvious control group, for example, as well as identifying the role of intervention (i.e. people normally receive these interventions when they’re ill, and they often get better so is that the intervention or just regression to the mean?) So, hugely important to ensure we can learn about what’s working and where we need to improve.
- A number of techniques have been developed to assess the impact of this sort of intervention One approach is to identify the people who have received an intervention and then match them with a control group who haven’t This aims to find a group of people who mirror the intervention group as closely as possible in terms of demographic profile, health characteristics, etc We can then compare what happens to each group and effectively isolate the impact of the intervention
- Example from 2013 Birmingham OwnHealth, a telephone health coaching intervention aiming to reduce hospital admissions by providing people with phone based support to manage their conditions at home Delivered to nearly 2,700 people with heart failure, CHD, COPD or diabetes, who all had history of hospital use
- These are the number of emergency admissions per patient for each of the intervention and control groups, in the months leading up to the intervention group being given the intervention There are some differences between the lines, but they are a relatively good fit So what happens next?
- Emergency admissions for the control group goes down – regression to the mean – but not for the people who receive the intervention This intervention unfortunately didn’t deliver on its objectives on this occasion Not clear why – need further qualitative work to understand whether it’s the intervention or the context in which it’s been applied, and ID how to develop to overcome these Doesn’t mean that thinking behind it and whole concept of health coaching is flawed, but important to identify unintended consequences to inform how initiatives can be developed to be more effective
- And there’s an obvious read across to the new care models programme… Need to understand the impact of NCM on patient outcomes and cost at two levels First is local: understanding what does and what doesn’t work to inform learning and guide the development of service delivery models Second is national: same intelligence can be used to help develop service frameworks, identify and reinforce key enablers and remove barriers to change
- The big challenge here is not developing the methods to understand what impact new care models are having, but having the analytical capability to use them in a way that is meaningful. Feedback needs to be informed by the needs of the services users – otherwise may not be remotely useful. E.g. if my Fitbit tells me I’m not being active enough, I know I need to move more. But it also tells me I’m not getting enough sleep, which is pretty useless without help in understanding why that is Also need to be able to respond and adapt feedback to help answer emerging questions, as well as timely, regular and robust Aim for NCMs to cover 50% of England by 2020, so need to build capacity for the NHS to be able to sustain and develop this analytical capability at scale
- Just as the NCM programme has taken inspiration from the development of Accountable Care Organisations in the USA, we’ve taken inspiration from the approach taken by the Center for Medicare and Medicaid Services which aimed to use rapid cycle evaluation to provide frequent feedback to the people involved in delivering new care models locally while also evaluating the outcomes achieved by the different models being tested
- IAU has been setup as a partnership between NHSE and THF Joint leadership and governance of the Unit, with dedicated data management and statistical teams based at THF Plus intermediaries to link NHSE with local teams to ensure analysis meets local needs And work with universities to ensure we stay in touch with developments in analytical methodologies
- Alignment – one general practice to one care home Advocacy – e.g. to support residents and their families in changing to the aligned general practice Enhanced care – e.g. a named GP visited the care home every 1-2 weeks to meet residents and proactively review medications and care plans Improved peer support – e.g. training courses and signposting to existing specialist community services Engage and support care home managers – care home managers network and regular meetings with the CCG
- We measured hospital activity for as long as possible, following moving to the care home: A&E attendances Emergency admissions A subset of ‘potentially avoidable’ emergency admissions Elective admissions Outpatient attendances The percentage of nights spent as a hospital inpatient Also, for the subset of residents who died, we assessed: The percentage of deaths that occurred outside of hospital (included because the enhanced support included an element of end-of-life care planning)
- Intervention group: 588 residents from 24 care homes, aged 65 years or over, who had moved into care home that cared specifically for frail older residents between August 2014 and July 2016 Not previously residents of care homes, but had experienced inpatient admission to hospital within previous two years before moving to the care home. Control group: Selected six areas of England with similar demographic and socioeconomic characteristics and emergency admission rates to Rushcliffe. Selected a ‘matched group’ of 588 residents on the basis of similar resident and care home characteristics 588 intervention residents and 588 matched comparison residents, each group tracked for just over 200 days on average following moving to the care home.
- Principia residents attended A&E 29% less often than the matched comparison group
- And were admitted to hospital as an emergency 23% less frequently
- Fewer avoidable admissions too, but confidence intervals mean we can’t be confident that was a meaningful difference No evidence of an impact on the number of nights spent in hospital, or the number of elective admissions or outpatient attendances And Principia residents were just as likely to die outside of hospital
- So what to make of this? Principia residents had fewer A&E attendances and emergency admissions than the comparison group It’s possible that the two groups may have differed in ways we didn’t account for, but assuming the two groups were comparable the only explanation of the difference is that Principia residents received better quality of care
- Where next? In process of scaling this up – continue testing the approach with another NMC Vanguard Early lessons include importance of relationships with end users, and that ”unmet need” for analytics is very clear