Frequently asked questions

Please note the survey has now closed.

Where can I find the results?

You can view the latest results for a GP practice by entering practice name or postcode here.

All results and reports for the latest, and previous, GP Patient Surveys can be found here.

Presentation of results

Many of the GP Patient Survey outputs show summary results – statistics that provide a quick way of viewing the result for a question. For example, we might use ‘Easy’ to describe a combination of the ‘Very easy and ‘Fairly easy’ response options. The individual response options are available to view in the National results (for National level) or the Excel spreadsheets (for ICS, PCN and practice level). Detailed data at all levels is available to view in the Analysis Tool.

For more information see the Presentation of results document.

How do you calculate where patient experience is best and where patient experience could improve?

These are calculated by comparing a practice’s results with the average results for the practice’s Integrated Care System (ICS).

Where patient experience is best: These are the three results for this practice that are the highest compared with the ICS average. These results may be above the ICS average. If none of the results for a practice are above ICS average, then the results that are closest to the ICS average have been chosen.

Where patient experience could improve: These are the results for this practice that are the lowest compared with the ICS average. If no results are below the ICS average, this is stated.

Is it possible to rank GP practices based on GP Patient Survey data?

No, the GP Patient Survey is not designed to rank GP practices. The survey highlights areas in which practices perform best and areas in which practices could improve, and there is no single metric that can be used to effectively rank GP practices. Furthermore, results are drawn from a survey (a sample of the population) and therefore come with a degree of uncertainty. Ranking practices or other geographies without accounting for this uncertainty can produce misleading results.

What is an Integrated Care System (ICS)?

Integrated Care Systems (ICS) are partnerships between the organisations delivering health and social care in a geographical area, working to plan and coordinate services to improve population health and reduce inequalities between different groups of people. ICS have replaced CCGs (Clinical Commissioning Groups), and as a result GP Patient Survey data is no longer reported at CCG level.

There are 42 ICS covering all parts of England. You can find your local ICS here.

You can find more information about ICSs here: https://www.england.nhs.uk/integratedcare/what-is-integrated-care/

What is a Primary Care Network (PCN)?

Primary Care Networks (PCNs) are groups of GP practices working together, with other local healthcare staff and organisations (such as community, mental health, social care, pharmacy, hospital and voluntary services). PCNs enable greater provision of proactive, personalised, coordinated and more integrated health and social care for people close to home.

You can find more information about PCNs here: https://www.england.nhs.uk/primary-care/primary-care-networks/

Some of the filters are missing or greyed out, why is that?

Filters can only be selected when there is enough data to display results for that option. This may be because there are simply no survey responses from a particular group of patients at a practice. Or it may be because data has been suppressed.

If a filter is greyed out, it means that data for this filter is limited. This may be due to another filter you have added; try removing any previous filter or filters.

Why is data sometimes suppressed?

Suppression is used to prevent individuals and their responses being identifiable in the data, and to ensure results based on very small numbers of respondents are not released.

There are two types of suppression in the GP Patient Survey:

• In cases where a result is based on fewer than 10 people (unweighted), it has been suppressed. For example, where fewer than 10 people answered a question from a particular organisation, the results are not shown.
   
• In addition, for organisations with an eligible population of 1,000 or less, individual response option counts below 5 (but excluding 0) and corresponding percentages have been suppressed for the relevant questions in the 'Some questions about you' section and questions relating to long-term conditions, disabilities, or illnesses. In instances where only one response option is suppressed for these questions, the next lowest response option has also been suppressed to prevent back calculation from the total number of responses. The Technical Annex contains the full list of questions where this applies. Please note due to the technical limitations of the analysis tool and the PCN dashboard it has not been possible to apply this suppression method to the affected organisations and questions. This applies to a very small number of organisations, and results for these questions are not presented. However, the full results for these organisations are available in the Excel and csv files on the Survey and Reports page. 
   

These suppression methods are used to prevent individuals and their responses being identifiable in the data, and to ensure results based on very small numbers of respondents are not released.

The result shows that 0 people answered 'not very easy', but the percentage for this answer is showing as more than 0%. Why is this?

This is because in the unweighted data there may be a single person who has given a response. When weighted, this response has been given a value of greater than zero but less than 0.5 and, therefore, rounded to 0. Because the actual value is still greater than zero, in percentage terms this shows as a %.

There is a message saying that data for my practice is unavailable. Why is this?

Unfortunately, for a few practices data is limited or unavailable due to a small number of returned surveys.

I've seen cases where, when adding up the number of people who have selected each different response for a question, the total does not match the figure in the 'total number of responses' column. Why is this?

This can happen when weighted data is rounded to a whole number.

When weights are applied, decimals are added to the number of responses in each category and the total number of responses. This means that sometimes there can be cases where the number of responses is different from the base size. For example, if a report says that 59 people say ‘yes’ and 14 say ‘no’, but the number of responses is 74 (not 73). This means that the weighted values could actually be 59.345 and 14.456, which add up to 73.801 (which is then rounded up to 74).

I have seen cases where the same numbers of responses are given to two different answer categories, but the percentage values are different. Why?

There are examples in the reports where, for example, it looks like one person has selected 'Other' and one person selected 'I would prefer not to say'. However, their corresponding percentages are 1% and 2%. Again, this happens when the results and number of responses are rounded but the percentages are calculated on un-rounded data.

The Excel reports refer to ‘Local NHS England’ – what is this?

Local health systems are supported by seven integrated regional teams: Local health systems are supported by seven integrated regional NHS England teams: East of England, London, Midlands, North East and Yorkshire, North West, South East and South West. These regional divisions are what we refer to under ‘local NHS England’ in the Excel reports. More details about the seven regions are available here.

What are confidence intervals?

You may notice references to confidence intervals (lower and upper limits) for selected questions within the practice, PCN and ICS Excel reports. These are included to give users an indication of the accuracy of the findings for individual questions.

Because we hear from a sample of people at each practice, rather than all registered patients, we cannot be certain that the results to a question are exactly the same as if everyone had taken part. The confidence interval is a statistical measure that shows the range of values within which the true value is estimated to lie. In other words, what we would have found if everyone had been given and completed the survey.

In this survey, as in most other surveys, we use a “95% confidence interval”. This means that we are 95% sure that the true value lies within the upper and lower limits of the confidence interval. Another way of looking at this is that, if we were to re-run the survey over and over again, we believe we would get a result that falls within the confidence interval 95 times out of 100 (or 19 times out of 20).

For more information on statistical reliability and confidence intervals, please see the latest Technical annex.

How do I use confidence intervals to understand observed differences in results?

Confidence intervals also allow us to compare results between separate groups within our sample, including demographic subgroups or organisations, as well as over time. Observed differences between these groups may be ‘real’ or they may occur by chance (because not everyone in the population has responded to the survey).

We use confidence intervals to describe the accuracy of findings, demonstrating that if we were to re-run the survey, we would get a result that falls within the confidence interval 95% of the time. Confidence intervals also allow us to compare results over time: for GPPS at national level this interval is very narrow, and usually means that even small differences (of less than +0.25 percentage points) are statistically significant.

Looking at how this works in practice, the table below shows the proportion saying they had a ‘good’ overall experience of contacting their GP practice the last time they tried (Q16), along with the lower and upper confidence intervals, for 3 ICSs.

The confidence intervals for ICS 3 do not overlap with those for ICS 1 or 2, which means we say that there is a statistically significant difference in their results. However, the confidence intervals for ICS 1 and 2 do overlap which means that the observed difference is not statistically significant – it may just be by chance, and so we cannot be confident that ICS 1 is performing better than ICS 2 even though their percentage score is higher. A similar approach can be taken if comparing results for a particular organisation over time.

The Technical Annex has more information on statistical reliability and confidence intervals.