Tag Archives: NHS

Patient’s experience of hospital care at weekends

The “weekend effect”, whereby patients admitted to hospitals during weekends appear to be associated with higher mortality compared with patients who are admitted during weekdays, has received substantial attention from the health service community and the general public alike.[1] Evidence of the weekend effect was used to support the introduction of ‘7-day Service’ policy and associated changes to junior doctor’s contracting arrangement by the NHS,[2-4] which have further propelled debates surrounding the nature and causes of the weekend effect.

Members of the CLAHRC West Midlands are closely involved in the HiSLAC project,[5] which is an NIHR HS&DR Programme funded project led by Professor Julian Bion (University of Birmingham) to evaluate the impact of introducing 7-day consultant-led acute medical services. We are undertaking a systematic review of the weekend effect as part of the project,[6] and one of our challenges is to catch up with the rapidly growing literature fuelled by the public and political attention. Despite that hundreds of papers on this topic have been published, there has been a distinct gap in the academic literature – most of the published papers focus on comparing hospital mortality rates between weekends and weekdays, but virtually no study have compared quantitatively the experience and satisfaction of patients between weekends and weekdays. This was the case until we found a study recently published by Chris Graham of the Picker Institute, who has unique access to data not in the public domain, i.e. the dates of admission to hospital given by the respondents.[7]

This interesting study examined data from two nationwide surveys of acute hospitals in 2014 in England: the A&E department patient survey (with 39,320 respondents representing a 34% response rate) and the adult inpatient survey (with 59,083 respondents representing a 47% response rate). Patients admitted at weekends were less likely to respond compared to those admitted during weekdays, but this was accounted for by patient and admission characteristics (e.g. age groups). Contrary to the inference that would be made on care quality based on hospital mortality rates, respondents attending hospital A&E department during weekends actually reported better experiences with regard to ‘doctors and nurses’ and ‘care and treatment’ compared with those attending during weekdays. Patients who were admitted to hospital through A&E during weekends also rated information given to them in the A&E more favourably. No other significant differences in the reported patient experiences were observed between weekend and weekday A&E visits and hospital admissions. [7]

As always, some cautions are needed when interpreting these intriguing findings. First, as the author acknowledged, patients who died following the A&E visits/admissions were excluded from the surveys, and therefore their experiences were not captured. Second, although potential differences in case mix including age, sex, urgency of admission (elective or not), requirement of a proxy for completing the surveys and presence of long-term conditions were taken into account in the aforementioned findings, the statistical adjustment did not include important factors such as main diagnosis and disease severity which could confound patient experience. Readers may doubt whether these factors could overturn the finding. In that case the mechanisms by which weekend admission may lead to improved satisfaction Is unclear. It is possible that patients have different expectations in terms of hospital care that they receive by day of the week and consequently may rate the same level of care differently. The findings from this study are certainly a very valuable addition to the growing literature that starts to unfold the complexity behind the weekend effect, and are a further testament that measuring care quality based on mortality rates alone is unreliable and certainly insufficient, a point that has long been highlighted by the Director of the CLAHRC West Midlands and other colleagues.[8] [9] Our HiSLAC project continues to collect and examine qualitative,[10] quantitative,[5] [6] and economic [11] evidence related to this topic, so watch the space!

— Yen-Fu Chen, Principal Research Fellow


  1. Lilford RJ, Chen YF. The ubiquitous weekend effect: moving past proving it exists to clarifying what causes it. BMJ Qual Saf 2015;24(8):480-2.
  2. House of Commons. Oral answers to questions: Health. 2015. House of Commons, London.
  3. McKee M. The weekend effect: now you see it, now you don’t. BMJ 2016;353:i2750.
  4. NHS England. Seven day hospital services: the clinical case. 2017.
  5. Bion J, Aldridge CP, Girling A, et al. Two-epoch cross-sectional case record review protocol comparing quality of care of hospital emergency admissions at weekends versus weekdays. BMJ Open 2017;7:e018747.
  6. Chen YF, Boyal A, Sutton E, et al. The magnitude and mechanisms of the weekend effect in hospital admissions: A protocol for a mixed methods review incorporating a systematic review and framework synthesis. Systems Review 2016;5:84.
  7. Graham C. People’s experiences of hospital care on the weekend: secondary analysis of data from two national patient surveys. BMJ Qual Saf 2017;29:29.
  8. Girling AJ, Hofer TP, Wu J, et al. Case-mix adjusted hospital mortality is a poor proxy for preventable mortality: a modelling study. BMJ Qual Saf 2012;21(12):1052-56.
  9. Lilford R, Pronovost P. Using hospital mortality rates to judge hospital performance: a bad idea that just won’t go away. BMJ 2010;340:c2016.
  10. Tarrant C, Sutton E, Angell E, Aldridge CP, Boyal A, Bion J. The ‘weekend effect’ in acute medicine: a protocol for a team-based ethnography of weekend care for medical patients in acute hospital settings. BMJ Open 2017;7: e016755.
  11. Watson SI, Chen YF, Bion JF, Aldridge CP, Girling A, Lilford RJ. Protocol for the health economic evaluation of increasing the weekend specialist to patient ratio in hospitals in England. BMJ Open 2018:In press.

Future Trends in NHS

The future of health care is often conceptualised in terms of improved treatments emerging from the bio-medical science base – for instance increasing the precision with which particular therapies can be targeted. Many of these advances in the effectiveness of care will have supply side consequences in terms of cost and some will require service re-configuration – regenerative medicine and bed-side diagnostics, for example. However the larger challenges are likely to originate from increased demand. The service will have to adapt to these supply and demand side changes. This blog considers the role of applied research in informing these adaptations in order to improve the overall effectiveness and efficiency of services.

We discern three trends which, absent a major perturbation such as international conflict, will alter demand over the medium to long term. The time horizon for our analysis is the next quarter century, given that the longer the time horizon the wider the variance in any predictions.

The trends are as follows:

  1. The population demographic will continue towards higher proportions of elderly people.
  2. The dependency ratio (ratio of working age to young and retired people) will become increasingly adverse.
  3. Demand for services per capita will increase.

None of these assumptions is unarguable as they involve outcomes that have not yet been observed. They are ordered from least to most contentious.

  1. That the population will continue to age is almost a given, but the rate at which it will do is less certain. Some predict that over a third of children alive now will reach a century. However, the rate of increase in life expectancy may slow as the large reductions in smoking related deaths are absorbed into the base-line. Immigration could affect population projections in ways that are hard to predict. The recent sudden increase in mortality among white middle-aged males in the USA,[1] but improvement in survival of low socio-economic group children in the same country,[2] shows how difficult projections can be. A recent demonstration of trends over two decades suggests that age-specific prevalence of dementias are reducing, arguably because risk factors for cardiovascular disease are also risk factors for dementia. This will not reduce the total prevalence of dementia, of course, if life expectancy continues to increase.[3] [4]
  2. The worsening of the dependency ratio is almost a corollary of an ageing society, but again the extent to which this happens is less certain as the work force gradually internalises the notion that 65 years of age is not a biological watershed but a social convention.[5] But delayed retirement will not solve the problem of a deteriorating dependency ratio; absent a method to delay ageing, many types of work, such as aviation and mining, are simply not suitable for older people. In addition, as people work longer at the end of life; so policies are encouraging longer leaves of absence from work outside the home to care for young children. So, all things considered, the dependency ratio will become more adverse as a function of increased longevity. Note, Britain appears to be at an earlier stage in this transition than many other high-income countries, such as Japan and Germany, and the opportunity for immigration to mitigate the tendency is likely to be accentuated given recent events.
  3. Demand for services contingent on an ageing population is somewhat controversial. A reasonable planning assumption is that people will be healthier at a given age but this will not completely mitigate the frailty of older people at a given age. In that case we must assume a rise in demand as the population ages, even if age-specific morbidity declines to some extent.

Implications for the NHS flow from the above. Demand for services will increase relative to resources. That is to say there will be more old people relative to working age people and there will be more frail people relative to the population and demand will outpace economic growth. All of this may be compounded by a tendency for old people to live in remote areas at a distance from major conurbations where health services are concentrated. However, this problem will be less acute than in most other countries.

There are many possible mitigations and the NIHR has a role in all of them; these are listed in the table below.

Factors to help the service cope with increasing demand.

                  Mitigating factor How it might work Caveats Potential impact
Major technical advances that might affect demand. A ‘cure’ or prevention for dementia would both improve the economy (and hence supply) while supressing demand. Probably lies outside our 25 year time horizon. Will prolong life and hence increase the proportion of frail elderly people. Potentially very high but out of scope. Medical advances more generally likely to increase demand by increasing longevity.
Self-care An ‘extreme’ form of skill substitution. Unlike other mitigations there is an extensive research literature. Beneficial for capable patients minimal impact on global demand. The correct answer to improving care, reducing demand will require development of interventions and further research.
Information technology Can make care safer and supply more efficient. Full electronic notes disrupt patient communication in their current form. A lot more needs to be learned about the design and implementation of this deceptively complex technology. Huge benefits in prospect but the socio-technical aspects require extensive development and research.
Robotics May substitute for expensive/scarce human resources.[6] Humans require the care and attention of other humans. Moderate. Likely to assist rather than replace clinical input.
Skill substitution Less expensive staff (physician’s assistants) substitute for more expensive (doctors). Increasingly feasible as health care increasingly codified. Limited by the complexity of decision making in patients with many diseases. Very hard to say without more research. May be modest.
Pro-active community services Prevent deterioration to improve health and decrease admissions. Existing research disappointing – may actually increase demand by identifying self- correcting illness. Potentially great but we are in the foothills of discovery.

Mitigating demand is not easy in the face of the demographic factors mentioned above. It is often argued, even in official enquiries, that prevention is the key to reducing demand. While prevention may reduce demand arising from particular diseases, such as diabetes, survivors go on to develop further diseases on their trajectory to death.[7] It is therefore not at all clear that prevention will reduce total demand and it may even be the case that deferred demand is augmented demand. There are some potential mitigating possibilities. A prevention or cure for Alzheimer’s disease would make a massive difference. Less distant is an ‘artificial pancreas’ that might massively simplify diabetes care. Methods to make people independent, such as home telemetry, have had nugatory impact on demand to date,[8] but this may change in the future. Patient self-care is beneficial in improving healthcare and satisfaction,[9] but effects on total demand have been modest.

If supply side measures might help services cope with the consequences and demand continues to rise, then two points should be noticed. First, efficiency gains are notoriously difficult to achieve in service industries. Second, the likely increasingly adverse dependency ratio is likely to limit expansion in skilled staff. Partial solutions may lie in manufacturing, including robotics and information technology. Skill substitution is a future area where it may be possible to improve efficiency.[10] In particular, physicians assistants may reduce costs overall.[11] The research for skills or system substitution is not entirely positive – for example, substituting nurses for doctors may not improve efficiency because consultation times had to increase.[12] There is an international trend to provide more care at ‘grass roots’ by means of Community Health Workers (CHWs) – an area where high-income countries are learning from low- and middle-income countries.[13] CHWs have a large potential role in improving care – helping patients to adhere to medications, providing preventative services, identifying deteriorating patients. Their effect on reducing demand is less certain, and on occasion they may actually increase it.[14]

Readers may think that the CLAHRC WM Director can be rather pessimistic, even nihilistic. Not so, CLAHRC WM has recently conducted an overview (umbrella review) across 50 systematic reviews of different methods to integrate care across hospitals and communities.[15] Discharge planning with post-discharge support is highly effective. Multi-skill teams are much more effective if they include hospital outreach than if they are entirely community-based. Self-management is effective but mainly for single diseases. Case management is of minimal value. Across all intervention types, length of stay was reduced in over half, emergency admissions were reduced in half, and readmissions were reduced in nearly half. In almost no case did the intervention make any of the above outcomes worse. Costs to the service were reduced in over a third of intervention types, but the quality of evidence is poor on this point – a topic that is being addressed across all CLAHRCs. And here is the CLAHRC WM Director’s point; there are no quick wins and no silver bullets. And the solutions are not self-evident. Only by patiently trying out new things and evaluating them methodologically can things improve. It may sound self-serving, but that does not mean it is incorrect – CLAHRCs have an immense contribution to make to improve the effectiveness and cost-effectiveness of health services.

— Richard Lilford, CLAHRC WM Director

I acknowledge advice from Prof Peter Jones (University of Cambridge), Director of CLAHRC East of England, but the views expressed are entirely my own.


  1. Deaton A, Lubotsky D. Mortality, inequality and race in American cities and states. Soc Sci Med. 2003;56(6):1139-53.
  2. Chetty R HN, Katz LF. The Effects of Exposure to Better Neighbourhoods on Children: New Evidence from the Moving to Opportunity Experiment. Am Econ Rev. 2016.
  3. Matthews FE, Stephan BC, Robinson L, Jagger C, Barnes LE, Arthur A, Brayne C; Cognitive Function and Ageing Studies (CFAS) Collaboration. A two decade dementia incidence comparison from the Cognitive Function and Ageing Studies I and II. Nat Commun. 2016; 7: 11398.
  4. Matthews FE, Arthur A, Barnes LE, Bond J, Jagger C, Robinson L, Brayne C; Medical Research Council Cognitive Function and Ageing Collaboration. A two-decade comparison of prevalence of dementia in individuals aged 65 years and older from three geographical areas of England: results of the Cognitive Function and Ageing Study I and II. Lancet. 2013; 382(9902): 1405-12.
  5. Lilford R. Robotic hotels today – nursing homes tomorrow? NIHR CLAHRC West Midlands News Blog. March 6 2015.
  6. Lilford R. Medical Technology – Separating the Wheat from the Chaff. NIHR CLAHRC West Midlands News Blog. February 26 2016.
  7. Lilford R. Improving Diabetes Care. NIHR CLAHRC West Midlands News Blog. November 11 2016.
  8. Henderson C, Knapp M, Fernández J-L, Beecham J, Hirani SP, Cartwright M, et al. Cost effectiveness of telehealth for patients with long term conditions (Whole Systems Demonstrator telehealth questionnaire study): nested economic evaluation in a pragmatic, cluster randomised controlled trial. BMJ. 2013; 346: f1035.
  9. Tricco AC, Ivers NM, Grimshaw JM, Moher D, Turner L, Galipeau J, et al. Effectiveness of quality improvement strategies on the management of diabetes: a systematic review and meta-analysis. Lancet. 2012; 379: 2252–61.
  10. Lilford R. The Future of Medicine. NIHR CLAHRC West Midlands News Blog. October 23 2015.
  11. Lilford R. Improving Hospital Care: Not easy when budgets are pressed. NIHR CLAHRC West Midlands News Blog. January 23 2015.
  12. Laurant M, Reeves D, Hermens R, Braspenning J, Grol R, Sibbald B. Substitution of doctors by nurses in primary care. Cochrane Database Syst Rev. 2005; 2(2).
  13. Lilford R. Lay Community Health Workers. NIHR CLAHRC West Midlands News Blog. April 10 2015.
  14. Roland M, Abel G. Reducing emergency admissions: are we on the right track? BMJ. 2012; 345: e6017.
  15. Damery S, Flanagan S, Combes G. Does integrated care reduce hospital activity for patients with chronic diseases? An umbrella review of systematic reviews. BMJ Open. 2016; 6: e011952.

Vanguards: Truly at the Forefront?

The NHS Expo 2015 saw much attention and enthusiasm for the new Vanguard sites for the NHS. NHS England and the Department of Health have put a lot of trust in these sites to deliver better connected care and improved efficiency. One of the phrases repeated was “care without walls” and there is clear intent to break up organisational barriers as part of this process.

The NHS as a whole is often accused of having a defensive mind-set and of deliberately obstructing change. This is undoubtedly true, but the counterargument runs that this insulates the NHS against the worst of knee-jerk political decision making. This has meant that as policy makers have become more frustrated at the resistance of the NHS to change, they have resorted to more deliberately disruptive approaches to change management. Some of the earlier incarnations of this arguably gained momentum under New Labour in the 2000s with the promotion of increased competition and a marketplace for healthcare as a policy lever for change, and was followed up by perhaps one of the most deliberately disruptive pieces of legislation in healthcare, the 2012 Health and Social Care Act, described by Sir David Nicholson as “so big you can see it from space”.[1]

Undoubtedly these Vanguards could be truly innovative and efficient and this continues a policy move towards greater integration between health and social care as seen in the devolution of healthcare to Manchester and other regions. The mandate to be disruptive could deliver real ground-breaking change, except that no exemption has been given on the myriad of performance targets that all of these organisations face and the additional finance is very limited due to the current spending plans. So perhaps the mandate for change is more towards Eleanor Clift’s summary that “People want change but not too much change. Finding that balance is tricky for every politician”.[2]

So to the root of these new models of care: what is a Vanguard? Well, interestingly, one Chief Executive was brave enough to admit that despite being underway with the process, he was not really sure what it would ultimately look like, but that staff response to greater integration had been enthusiastic. Another described the process of being a Vanguard as making changes that they would have made anyway, but that the additional funding was allowing them to do it more quickly. The range of projects and integrations being undertaken under the Vanguard banner is very broad, which is laudable and ambitious, but the evaluation of the changes and proof of causality will be challenging with such a complex and inter-related and multi-provider environment. The launch documents for Vanguards spoke of “intensive evaluation” and many CLAHRCS and Academic Health Science Networks will be involved in trying to deliver the ‘logic model’ based evaluations.[3] It will be interesting to see the initial outputs from these evaluations.

— Paul Bird, CLAHRC Head of Programme Delivery (Engagement)


  1. Timmins N. Never Again? The Story of the Health and Social Care Act 2012. London: The King’s Fund. 2012
  2. Clift E & Brazaitis T. War Without Bloodshed – the Art of Politics. New York, NY: Touchstone. 1997.
  3. NHS England. The Forward View into Action: New Care Models: Update and Initial Support. 2015.

The NHS “Five Year Review”. View from the West Midlands CLAHRC

We were delighted to note the emphasis on controlled studies and operational research in the NHS’s “Five Year Forward View”. CLAHRCs have taken the lead in supporting the development of new interventions and in evaluating them, and have been at the forefront of evaluations of whole-scale service change and ‘combinatorial innovation’ heralded by the report. For example, our particular CLAHRC has:

  • Documented improved access to mental health following an intervention designed in collaboration with service users.[1]
  • Developed and then evaluated an intervention to support women with social risk factors over the peri-natal period.[2]
  • Evaluated a £400m intervention to bring social housing up to minimum standards.[3]

CLAHRCs often take the lead in pilot studies that are then rolled out into national evaluations funded by competitive grants; local examples include evaluations of new IT platforms as they are introduced into NHS hospitals,[4] methods to increase access to mental health services,[5] and of increased consultant provision over weekends.[6]

CLAHRCs played a large role in applications for the AHSNs and continue to work in close alignment with these bodies, for example in developing, adapting and evaluating projects to enhance patient safety. CLAHRCs have combined intellectual rigour with the need to respond rapidly to the service timetable and have become international leaders in imaginative designs such as step wedge trials.[7]

We also applaud the emphasis on prevention and reducing disparities in the review; here again CLAHRCs are making a substantial contribution at many levels, providing state of the science evidence through systematic reviews, option appraisal (through economic models),[4] [8] [9] intervention development incorporating expertise in subjects as diverse as behavioural economics and organisational theory, alpha testing in off-line simulations and large scale intervention through randomised trials. CLAHRCs are all working with local authorities in this work and are therefore well positioned to lead evaluations where local evaluations are rolled out more widely. Our CLAHRC has contributed to the development of economic models to evaluate service change, as well as individual technologies such as regenerative medicine,[10] [11] thereby strengthening both the supply and demand sides of the health economy.

— Richard Lilford, CLAHRC WM Director


  1. Birchwood M, Connor C, Lester H, Patterson P, Freemantle N, Marshall M, Fowler D, Lewis S, Jones P, Amos T, Everard L, Singh SP. Reducing duration of untreated psychosis: care pathways to early intervention in psychosis services. Br J Psychiatry. 2013; 203(1): 58-64.
  2. Kenyon S, Jolly K, Hemming K, Ingram L, Blissett J, Dann S-A, Chambers J, MacArthur C. Evaluation of Lay Support in Pregnant women with Social risk (ELSIPS): a randomised controlled trial. BMC Pregnancy Childbirth. 2012. 12: 11.
  3. Sandwell Homes. Sandwell Homes Business and Delivery Action Plan 2010/2011. 2009.
  4. Lilford RJ, Girling AJ, Sheikh A, Coleman JJ, Chilton PJ, Burn SL, Jenkinson DJ, Blake L, Hemming K. Protocol for evaluation of the cost-effectiveness of ePrescribing systems and candidate prototypes for other related health information technologies. BMC Health Serv Res. 2014. 14: 314.
  5. Marshall M, Husain N, Bork N, Chaudhry IB, Lester H, Everard L, Singh SP, Freemantle N, Sharma V, Jones PB, Fowler D, Amos T, Tomenson B, Birchwood M. Impact of early intervention services on duration of untreated psychosis: Data from the National EDEN prospective cohort study. Schizophr Res. 2014; 159(1): 1-6.
  6. Bion J, Dixon J, Evans T, et al. Stepping Up: A Phased Evaluation of the Impact of High-Intensity Specialist-Led Acute Care (HiSLAC) of Emergency Medical Admissions to NSH Hospitals. HS&DR 12/128/17.
  7. Hemming K, Lilford RJ, Girling AJ. Stepped-wedge cluster randomised controlled trials: a generic framework including parallel and multiple-level designs. Stat Med. 2014;[ePub].
  8. Lilford RJ, Chilton PJ, Hemming K, Girling AJ, Taylor CA, Barach P. Evaluating policy and service interventions: framework to guide selection and interpretation of study end points. BMJ. 2010: c4413.
  9. Yao GL, Novielli N, Manaseki-Holland S, Chen YF, van der Klink M, Barach P, Chilton PJ, Lilford RJ; European HANDOVER Research Collaborative. Evaluation of a predevelopment service delivery intervention: an application to improve clinical handovers. BMJ Qual Saf. 2012;21 (s1): i29-38.
  10. Girling AJ, Young TP, Brown CA, Lilford RJ. Early-stage valuation of medical devices: the role of developmental uncertainty. Value Health. 2010; 13(5): 585-91.
  11. Girling AJ, Lilford RJ, Young TP. Pricing of medical devices under coverage uncertainty – a modelling approach. Health Econ. 2012; 21(12): 1502-1507.