This research addresses a major policy concern for the NHS– how best to manage patients in the community to avoid unnecessary, disruptive and costly emergency admissions. In 2012–13 there were 5.3 million emergency admissions to hospitals costing approximately £12.5B.3Over 2 million unplanned admissions per
year are for those aged≥ 65 years – accounting for 68% of hospital bed-days – and the use of more than 51,000 acute beds at any one time.85In 2009/10 the average length of stay was approximately 3 days for
patients aged< 65 years, but 9 days for patients aged ≥ 65 years.7Approximately half of all bed-days are
attributable to just 5% of the population– typically older people with multimorbidity.3,86An emergency
admission to hospital is a disruptive and unsettling experience which can have a negative impact on a patient’s life as they are exposed to clinical and psychological risks and likely to have increased their dependency on discharge.7It is widely recognised that there are opportunities to improve management of
care for older patients with multimorbidity in primary and community settings– and that doing so is necessary to reduce avoidable emergency admissions.3
The use of EARP tools is widely advocated16to support the identification of vulnerable patients for proactive
targeted care aimed at preventing emergency hospitalisation– a ‘triple fail’ event that is harmful, costly and results in poor patient satisfaction.6‘It is now well recognised in the NHS that predictive risk tools
are essential to use if high quality care is to be offered’ (Jennifer Dixon, Chief Executive of the Health Foundation).87EARP tools can support the allocation of scarce resources through the identification of
patients in need who can be proactively targeted with support appropriate to their needs. The use of EARP tools is widely advocated in academic, policy and clinical literature and is, for example, a core component of both the English and Welsh chronic/long-term conditions models.5,14
Provisional indications from a UK-wide survey led by one of the co-applicants are that> 70% of UK practices now have access to an EARP tool. The development and validity of the tools has been widely researched,26but little research has been undertaken into their effectiveness.15,26The 2015 NHS England
paper Next Steps for Risk Stratification in the NHS notes a‘pressing need for further research’ about the effectiveness of hospital-avoidance interventions.15
There is some debate about how best to use EARP tools– and whether or not focus should be on those at the highest level of risk. Lewis,15for instance, suggests that at this level there is little scope for improvement,
whereas Wallace et al. suggest that numbers at this level are so small that overall impact is limited when following this approach.88However, UK health policy is clearly focused on the management of patients at
the highest level of risk. GP contracts have incentivised EARP use for case management of patients at high risk of hospitalisation, with over £480M allocated for the Avoiding Unplanned Admissions Enhanced Service in England between 2014 and 2017.18Participation in the Enhanced Services (ES) is widespread with around
7500 practices taking part in the ES– over 95% of all practices (NHS Digital, 2015, personal communication). Their responsibilities are to use a risk prediction model or alternative to identify vulnerable older people, high-risk patients and patients needing end-of-life care who are at risk of unplanned admission, and create a register of at least 2% of patients aged> 18 years.18
For those patients on the register, the practice is expected to offer care co-ordination and active care planning and review, involving patients as partners in the management of their health. The ES is aligned with NHS policy guidance for patient-centred care and supporting self-management, with GPs encouraged to involve patients in their care planning through shared decision-making.18,89The King’s Fund’s 2013 list
of priorities for sustainable health services begins with active support for self-management and primary prevention, including the targeting of high-risk groups.85
The reach and scale of ES and the potential impact on patients and health and care resources is considerable, with over 800,000 patients across the whole of England on the case management register in each of the intervention years (2014/15 and 2015/16).
Incentivisation of the targeting of those at the very highest levels of risk has become routine practice in Wales through QOF measures and in England through the funding of an enhanced service, although originally predictive risk stratification tools were seen as a way of managing care across the spectrum of risk. In 2013, Geraint Lewis, Chief Data Officer, NHS England, drew attention to this:
It is important to remember that patients at very high-predicted risk of hospitalisation only account for a modest proportion of all unplanned admissions. Therefore, to have a meaningful impact on admission rates at the population level, it will be important to consider less-intensive, lower-cost interventions for patients at moderately high-predicted risk. Indeed, there is a danger that by focusing exclusively on the integration of care for very high-risk patients, virtual wards may be diverting attention away from the integration of care for lower risk patients.
Lewis et al.90This work is licensed under a Creative Commons Attribution
3.0 Unported Licence (http://creativecommons.org/licenses/by/3.0)
There has been an assumption that, if EARP tools predict those at high risk of emergency admission to hospital with reasonable accuracy, patients will benefit from targeted care to improve their health and reduce unplanned episodes of care. However, this is based on assumptions– with little empirical evidence to date– about how risk prediction tools and incentivisation of care work in practice. Our findings have shown effects that are opposite to those intended. Although GPs reported that the use of the PRISM tool overall was low, they also reported carrying out reviews of the PRISM-generated lists of people predicted to be at the highest level of risk of emergency admission to hospital. They reported that they felt very stretched without additional resources to allocate specifically to the care of these patients. We have found an increase in emergency admissions and ED attendances at all risk levels, associated with introduction of the PRISM tool, and once we had adjusted for all significant covariates, including age, season and trend over time. This is challenging to explain, and is an effect that would not have been detected in a traditional evaluation that focused on relatively small numbers of those at the highest level of risk. Our use of anonymised linked routine data has allowed us to detect effects across the whole population. We refer to recently published results showing a similar unexpected effect on emergency episodes related to introduction of an air pollution alert system.91We can hypothesise that PRISM, alongside QOF targets to
target care at those at highest risk of emergency hospital admission, both sensitised GPs and practice staff to need at this level, while shifting the focus from others at lower risk, thereby missing opportunities to avoid emergency admission to hospital in those seen as less frail.
Conclusions
We have carried out the first large study of what happens when you introduce EARP in the real world. Use of anonymised data linkage has allowed us to carry out an experimental study with a randomised design at population level, with a very high rate of inclusion of primary and secondary routine outcomes, as well as self-reported outcomes for a sample of patients and health economic analysis.
Our results are surprising and alarming– increases in activity and costs across the board. We do not fully understand how or why this is happening; possible reasons include sensitisation of GPs and identification of unmet need; lack of resources to respond to need apart from hospitalisation; and concentration on those at highest predicted risk may mean that attention slips from those with lower predicted risk scores. Despite low reported use of PRISM, we found clinically and operationally important effects of the
introduction of the new risk stratification tool alongside contractual (QOF) incentives to target those at the highest risk of emergency admission to hospital. These effects were unexpected and in the opposite direction to those intended. We are unable to disentangle the effects of the introduction of the PRISM tool from those of the QOF targets, but this reflects practice across the UK, where emergency admission predictive risk stratification tools have been introduced alongside an incentivised enhanced service.
DISCUSSION AND CONCLUSIONS
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We see no reason to expect that findings would be different from those found across this mixed urban and semi-urban population in south Wales.