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Broadcast and newspaper headlines in the past couple of months have all been about pressures on A&E services. As ever with whirlwind media storms they typically tend to blow out before reasoned conclusions can be drawn.
Which leaves the question, what really does bedevil A&E? Is the problem facing the front-end of acute hospitals down to underfunding of emergency medicine; the ‘downgrading’ or closure of A&E units; fragmented and unresponsive primary care services; NHS 111 (more on this in a future blog); the payment or ‘tariff’ system; unprecedented patient demand; blockages including static or declining bed capacity and delays in transferring patients out of hospital or all of the above?
The direction of NHS England is to ‘centralise’ or ‘standardise’ emergency provision around fewer locations and downgrading others into Minor Injury Units (MIU) or Urgent Care Centres (UCC). To critics this move is one which creates a ‘two-tier’ system. The logic is an economy of scale one and follows where earlier efforts in stroke and heart care have lead; namely centralise medical expertise and services in order to improve outcomes.
Two things are for sure – we shouldn’t expect ‘healthcare professionals’, code in this case for paramedics, doctors and nurses to work ‘harder’, they already are. Nor should we condemn people for using A&E services inappropriately in whatever numbers. Clifford Mann, the President of the Royal College of Emergency Medicine was absolutely right when he said last month: “I don’t think we should blame people for going to the emergency department when we (the system) told them to go there. It’s absurd.”
It is true that over time, we have both incrementally and intentionally designed a reactive, fire-fighting, hospital focused, medical health system, with care very much an after-thought. Hospitals declaring they can’t cope through issuing ‘major incident’ alerts is the predictable consequence of some pretty foolhardy thinking. One such example is the 4-hour waiting time target in A&E. Ludicrously this activity measure is regarded by many as both a good indicator of A&E performance and quality. It is nothing of the sort.
The 4-hour waiting time target is a nationally-derived arbitrary indicator. The NHS in England (or more appropriately individual NHS Trusts) has missed its four-hour A&E waiting time target with performance dropping to its lowest level for a decade. Figures show that from October to December 2014, 92.6% of patients were seen in four hours – below the 95% target. The performance is the worst quarterly result since the target was introduced in 2004. Viewed a different way, 9 out of 10 people who go to A&E get ‘seen and treated’, discharged or admitted within the 4-hour window. Interestingly, whether we actually solved their problem is not measured and consequently never recorded.
The waiting time target’s reductionist logic is simple. It is an arbitrary indicator which is set from outside the immediate organisation (hospital) or business unit (A&E). The hospital and A&E is then expected to achieve the target come hell or high-water, through greater effort by its employees.
The reason this happens is because there is a mistaken belief that it is people themselves who are the limiting constraint on performance within organisations – they need to work harder or refrain from using services for reasons that are deemed ‘inappropriate’. The reality is that people’s performance or usage of a particular service is a consequence of the influence of other parts of the system, of which they form a part – policies, processes, procedures, systems, management.
To use an analogy, if a person enters a 10 mile race, in the knowledge that they can only run 5, the only way they can finish the race is to ‘game’ or ‘cheat’ and catch a bus after the 5 mile mark. This is what happens in organisations. If the A&E arbitrary target for transferring patients to a ward is 4 hours, but the existing capability (if measured) is only 5, then in effect, the bus is a trolley parked in the hospital corridor. Only now it is alleged, the wheels are coming off.
The other problem with arbitrary targets is that they are self-limiting. With no knowledge of patient demand and existing capability to meet that demand, the artificial target actually could be well within the range of what is improvement is possible. Paradoxically, setting a target can actually lead to under-achievement, in both people and organisations.
To compound the problem, along with the deficiencies inherent in setting an arbitrary target, measuring and judging performance at a single static point or over a single period of time is also counter-productive. This ignores the nature of variation that exists in almost everything we do, individually and collectively within an organisation. Here averages distort unless viewed within the context of the overall distribution of performance and the underlying trend, viewed against the demand placed on the system at any given point in time.
Whilst we can’t remove the 4-hour waiting time (the real limiting constraint), we should treat it as unavoidable system limitation but not drive performance solely on achievement of the arbitrary number. The alternative ‘solution’ is very straightforward – establish more insightful information streams and/or make better use of data in a more operationally meaningful way. Asking useful questions will help understand and in-turn resolve problems:
- Before measuring anything, ensure you are measuring the right thing. Is the 4-hour target measuring the right thing? Measure what matters to patients – do we actually know what this is? Is it quick diagnosis, speedy treatment, medical or psychological reassurance or getting help?
- When it comes to measurement we first have to understand patient demand. Do we empirically know why people choose to use A&E (patient demands) in order to understand how A&E can be better designed to deal with these demands (capability)? Simply ranking demands by their perceived inappropriateness without understanding the patient context doesn’t solve any problem.
- Measure your existing capability over time and understand the statistical variation that exists, against an understanding of demand.
- Express what you measure statistically, based on the nature of the distribution of the thing being measured.
- Unless you intend to change something and if you must set a target, understand what is within your current organisational or business unit capability.
- If you want to set a target outside your current capability, then identify what you are going to change to achieve this objective.
- Do not let the setting of a target act as a system constraint in itself.
- Do not make a business out of it – the aim should be continuous improvement not ranking.
This alternative approach would require us to regularly understand and measure local demand placed on the system (find out from their perspective why people actually come to A&E, don’t assume to know the answers) and the local capability to respond to this demand. Is demand predictable over time? What is the current system capability (staff mix, capability to meet the nature of patient needs and resourcing) to successfully address this demand?
A sophisticated understanding of patient demand and the capability to meet it will provide providers and commissioners with the ‘business intelligence’ they need to have a more effective A&E service. It informs us as to the level and nature of professional expertise required in A&E and when; availability of appropriate test facilities and beds; person-centred processes to effectively meet needs and even how best to approach the design and layout of A&E.
One thing we can forget though: the growing trend to rebrand A&E Emergency Department (ED) – that has zero impact.
The Perceived Problems
Healthcare providers and commissioners face multiple challenges. They increasingly recognise that the NHS must change the way it operates to effectively meet future challenges. Commonly held opinions dominate discussion – from a belief in rising demand for healthcare services, costs associated with technological and treatment advances, increasing public expectations and a funding gap of £30 billion.
Conventional Approach to Change
Conventional approaches adopt an internal activity and cost-reduction focus. They typically involve workshops to agree service models and action plans. These tend to be accompanied by artificial modelling of service capacity and staff resourcing which arrive at ‘optimum levels’ of activity ‘contacts’ that are then tested in workshop environments. Following this type of analysis, work is undertaken to standardise service processes so as to reduce variation and waste.
Inherent within this approach is a belief that there is a capacity problem; solutions can be found via workshops and abstract planning models that determine staff resourcing. Workforce planning often ensues to try and address the perceived problem of the ‘plateaued worker’. The logic equates to stable staffing levels and standardised processes which will lead to activity and cost reduction gains. Standardising processes typically take place in workshop environments, far are removed from where the real work occurs.
Yet, in service organisations, seeking to standardise processes often creates problems. In healthcare there is high variability of patient demand; standardising processes will only cause service performance to fall (as the standard offering fails to meet the natural variation in needs) and costs increase (as the service provider’s standard work leads to more activity creating additional process waste and rework).
Adopting a Different Tact for Better Results
To address these challenges I have pioneered a new and refreshing approach to healthcare analysis – the Consumption Demand Method™. The starting point for improved services at less cost rests on more intelligent use of data to inform future performance improvement through intelligent system and service redesign. This alternative approach to realising better healthcare services and less cost begins with looking at healthcare data differently, not from an activity but patient-centred perspective.
Unlike existing practice, this work establishes time-series data to understand the true nature of person-demand for acute services in order to better understand the root-cause(s) of service challenges facing healthcare commissioners and providers alike. From understanding patient demands it is possible to develop knowledge as to ‘who, where and why’ these demands exist in the first place and how best to meet them in order to provide more effective, person-centred services at less expense.
The Method is directly influencing commissioners and providers, helping to challenge conventional thinking about healthcare demand. A recent study of secondary healthcare demand reveals counter-intuitive truths about the true nature of service demand. Unlike activity-levels, patient-centred demand in secondary care is not rising, but entirely stable and predictable. This is bad but good news.
Rather the root-cause for increases in activity (and associated costs) lay in the inability to successfully design service responses based on genuine understanding of patient needs. This inability drives ‘amplification of demand’ from relatively small numbers of patients – the ‘vital few’.
I have utilised the Method to establish a series of ‘demand-led’ improvement projects. These include work in the following areas:
- Primary care transformation
- Delayed transfers of care (DTOC)
- Accident and emergency
- Referral time to treatment (RTT)
- Integrated diabetes service
- Sustained high-cost users
- Long-term conditions
- Out-of-hours provision
- NHS 111
Moreover, the Method has also been successfully used to perform wide-ranging reviews of medical specialities and existing improvement schemes. One such review of a two-year QIPP scheme in paediatric urgent care, which cost seven figures to resource, found that the local healthcare economy would have saved more money by not doing anything! The work analysed demand for services against stated project aims, proposed changes in both design and process and realised operational savings.
The approach and work also acts as a catalyst in providing knowledge and skills transfer to senior clinicians, commissioners and specialists in the analysis of consumption data and redesign of service models and care systems against patient-level demand as opposed to arbitrary and abstract activity indicators.
In fact, the Method effectively identifies ‘business gaps’, encourages thinking about the real problems, asks intelligent questions and provides the means to sustainably improve performance. It deliberately keeps abstract programme and project management, risk assessments and associated document reporting to a minimum as they impede real change.
More intelligent use of data in this way can better inform future commissioning and operational improvement through system and service redesign. After all the NHS has exhausted all other misguided approaches – standardising; over-medicalising; functionalising and commercialising operations. We need to humanise healthcare and focus as much on care needs as medical treatments.
For more information see Front-to-Back Thinking – humanising healthcare – Dibley Consulting or contact me at email@example.com