Quality and Outcomes Framework (QOF) – 2015-16

Summary

This Quality and Outcomes Framework (QOF) publication provides data for the reporting year 1 April 2015 to 31 March 2016. The QOF was introduced as part of the new General Medical Services (GMS) contract on 1 April 2004. The objective of the QOF is to improve the quality of care patients are given by rewarding practices for the quality of care they provide to their patients. The Calculating Quality Reporting Service (CQRS), together with the General Practice Extraction Service (GPES) were used for the extraction of QOF data. There have been changes to QOF coding and indicators. These are referred to throughout this publication. Consideration must be given to changes to indicators and their definitions each year when interpreting differences and comparing data from one year to the next.


Key facts

  • QOF recorded prevalence – Recorded prevalence for 2015-16 is presented for 7,619 general practices in England.
  1.  The highest prevalence rates are for Hypertension (13.8 per cent), Obesity (9.5 per cent) and Depression (8.3 per cent).
  2. Hypertension (7.9 million), Obesity (4.3 million) and Depression (3.8 million) are the conditions reporting the highest register numbers.
  3. The largest year on year differences in register numbers are in Depression (increase of 470,168) and Obesity (increase of 132,222).
qof

Image source: NHS Digital

  •  QOF achievement  – Achievement data for 2015-16 shows that:
  1.  The average achievement score for practices was 532.9 points out of 559
  2. The highest levels of achievement were for Obesity and Chronic Kidney Disease where 99.9 per cent was achieved. The lowest level of achievement was in Osteoporosis at 87.5 per cent.
  3. 640 practices achieved the maximum of 559 points. In 2014-15 there were 448 practices which achieved the maximum of 559 points.
  •  QOF exceptions – Exceptions data for 2015-16 show that:
  1.  The condition with the highest percentage of exceptions is Cardiovascular Disease at 31.3 per cent overall
  2. The measure with the lowest percentage of exceptions is Blood Pressure at 0.5 per cent overall.

Read the full overview here

Read the full report here

Mirror, mirror, on the wall, whose local care is fairest of all?

Cookson, R. Quality Watch. Published online: 31 October 2016

The problem with national data on health equity is that nobody owns it. It isn’t any one person’s problem, and it is easy to explain away bad news. Social inequalities in health and health care are influenced by all sorts of complex social, economic and technological factors, and there is no ‘national control group’ telling us what would have happened if national policy had been different. So it is hard to tell whether particular NHS policies are responsible for particular national equity trends. Policy makers are thus able to take credit for good news, and shift blame for bad news, without anyone learning any useful lessons.

Inequality gradients

Local equity is assessed by comparing the level of local inequality with the national picture and with ten similar CCG areas based on deprivation, age profile, ethnic mix and rurality. The diagrams below illustrate this for two CCGs in 2015. In the diagrams, each dot is a neighbourhood, with bigger dots for bigger neighbourhoods. The lines are linear regression lines through the dots, showing social inequality ‘gradients’ – the steeper the gradient, the greater the health inequality. The solid green line shows local inequality, as compared with the dashed red line showing national inequality and the dotted blue line showing inequality within ten similar CCG areas.

Liverpool:

Liverpool health equity gradient 600px

Brent:

Brent health equity gradient 600px

From the diagrams we can see that Liverpool has statistically significantly ‘worse-than-expected’ equity compared with these benchmarks, whereas Brent has significantly ‘better-than-expected’ equity.

Read the full blog post here

Views from the NHS frontline: Health Visitors

Amid drastic cuts to health visiting services, I’m struggling to help the vulnerable families I see every day | The Guardian Healthcare Network

112-2Health visitors don’t always get good press at the school gates or toddler groups. Among my fellow nursing friends, the standing joke is that I spend my day simply weighing babies. I guess as a result it’s not hard to see why in some areas the value placed on health visiting has fallen so far that the service will be cut completely.

At the moment most councils are reviewing the funding for health visiting amid drastic cuts to public health budgets. Cumbria and Staffordshire are planning on cutting health visiting posts and a number of other NHS trusts have job freezes and have discussed redundancies. NHS Digital reported this year that the number of health visitors dropped in UK by 433 posts.

While perhaps there may be some truth in the comments I so often hear, the reality of health visiting feels very different.

Read the full article here

Empowering primary care to innovate and accelerate the uptake of new models of care

The King’s Fund. Published online: October 2016

Dr Jonathan Serjeant, Co-founder and Clinical Director, Brighton and Hove Integrated Care, shares lessons from NHS Collaborate on how to support leaders to create the right conditions to test, share and learn.

This presentation was filmed at the Emerging models of primary care conference.

Additional presentations are available here

Presentation slides are available here

 

A call for action: improving decision-making in the commissioning of health services

Jones, S. & Turner, A. BMJ Clinical Evidence Blog. Published online: 27 October 2016

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While evidence based medicine (EBM) has been promoted for over 20 years and has influenced other disciplines like nursing, little attention has been paid to decision-making in NHS clinical commissioning: the planning and purchasing of services to meet the healthcare needs of the local population. Where EBM supports clinical decisions on an individual patient basis, there is no equivalent philosophy for commissioning, where decisions are made that can affect hundreds of thousands of people.

The way commissioners access and use evidence is highly variable: the commissioning setting lacks the scientific framework that is present in medicine. Increasing integration means commissioners come from a range of backgrounds with varying skills and experience relating to evidence utilisation. What is meant by evidence in this setting is ill-defined: commissioners use different kinds of ‘evidence’ including data, best practice guidelines, research findings and patient feedback. Research evidence is not always available, of varying quality and often lacks actionable insights to inform decision making. Where these gaps exist, commissioners themselves could contribute but there is no consistent process by which the evidence base can be improved through evaluation findings or co-produced research.

Read the full blog post here

 

Screening and brief intervention for obesity in primary care: a parallel, two-arm, randomised trial

Aveyard, P. et al. The Lancet. Published online: 24 October 2016

Background: Obesity is a common cause of non-communicable disease. Guidelines recommend that physicians screen and offer brief advice to motivate weight loss through referral to behavioural weight loss programmes. However, physicians rarely intervene and no trials have been done on the subject. We did this trial to establish whether physician brief intervention is acceptable and effective for reducing bodyweight in patients with obesity.

Methods: In this parallel, two-arm, randomised trial, patients who consulted 137 primary care physicians in England were screened for obesity. Individuals could be enrolled if they were aged at least 18 years, had a body-mass index of at least 30 kg/m2 (or at least 25 kg/m2 if of Asian ethnicity), and had a raised body fat percentage. At the end of the consultation, the physician randomly assigned participants (1:1) to one of two 30 s interventions. Randomisation was done via preprepared randomisation cards labelled with a code representing the allocation, which were placed in opaque sealed envelopes and given to physicians to open at the time of treatment assignment. In the active intervention, the physician offered referral to a weight management group (12 sessions of 1 h each, once per week) and, if the referral was accepted, the physician ensured the patient made an appointment and offered follow-up. In the control intervention, the physician advised the patient that their health would benefit from weight loss. The primary outcome was weight change at 12 months in the intention-to-treat population, which was assessed blinded to treatment allocation. We also assessed asked patients’ about their feelings on discussing their weight when they have visited their general practitioner for other reasons. Given the nature of the intervention, we did not anticipate any adverse events in the usual sense, so safety outcomes were not assessed. This trial is registered with the ISRCTN Registry, number ISRCTN26563137.

Findings: Between June 4, 2013, and Dec 23, 2014, we screened 8403 patients, of whom 2728 (32%) were obese. Of these obese patients, 2256 (83%) agreed to participate and 1882 were eligible, enrolled, and included in the intention-to-treat analysis, with 940 individuals in the support group and 942 individuals in the advice group. 722 (77%) individuals assigned to the support intervention agreed to attend the weight management group and 379 (40%) of these individuals attended, compared with 82 (9%) participants who were allocated the advice intervention. In the entire study population, mean weight change at 12 months was 2·43 kg with the support intervention and 1·04 kg with the advice intervention, giving an adjusted difference of 1·43 kg (95% CI 0·89–1·97). The reactions of the patients to the general practitioners’ brief interventions did not differ significantly between the study groups in terms of appropriateness (adjusted odds ratio 0·89, 95% CI 0·75–1·07, p=0·21) or helpfulness (1·05, 0·89–1·26, p=0·54); overall, four (<1%) patients thought their intervention was inappropriate and unhelpful and 1530 (81%) patients thought it was appropriate and helpful.

Interpretation: A behaviourally-informed, very brief, physician-delivered opportunistic intervention is acceptable to patients and an effective way to reduce population mean weight.

Read the full article here

Revalidation: Guidance for GPs

RCGP | This page provides resources, links and guidance on appraisal and revalidation to help GPs demonstrate that they meet required standards.

There is a continuing need to ensure that revalidation, delivered through annual appraisal, supports GPs in their personal and professional development and facilitates quality improvements in practice. The new RCGP Guide to Supporting Information for Appraisal and Revalidation aims to reduce inconsistencies in interpretation of requirements and simplify and streamline the appraisal and revalidation process.

The page also features revalidation guidance podcasts by  Dr Susi Caesar, RCGP Medical Director for Revalidation:

Read the full overview & podcasts here

Read the full guide here

View the ‘Mythbusters’ document here