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Practice clinical status

This quarterly report gives you a current status snapshot across your practice. It includes the numbers and percentages recorded/screened for each of the key clinical indicators outlined below. You will know this report as CPI1. View a sample report (pdf 972kb) 

The information is drawn from your Practice Managment System and is captured primarily from READ codes and screening, it should be a useful guide for how you are going with IPIF but is not always a direct match (refer below for details for each indicator).

Graphs:  All the graphs are based on percentages with each bar representing 100% of the relevant patients and the coloured parts indicating how that is broken down.

This spreadsheet has 23 individual sheets and graphs plus the summary sheet. A description of each sheet follows.

Summary

This is a summary of all the information that is contained in the separate spreadsheets that follow. It provides a grand total and percentage across each of the clinical indicators on  one page. Clinical governance will be interested in this snapshot of where the practice is at. It will help to identify where the practice is doing well and where it needs to put its energy and focus. There are clickable links to other pages on the right hand side of the page.

Smoking status

1a. Smoking status recorded spreadsheet

This sheet looks at both the level of smoking status recorded and the actual status for the most recent recorded status for each patient.

The data source is either direct or mapped READ codes stored in the PMS (in Medtech32, these are stored either in the classifiations or history).

Each table is based on all patients aged 15 or over at the end of the quarter:

  • current smoker: patients whose most recent status recorded was as a smoker
  • ex smoker: patients whose most recent status recorded was as an ex smoker
  • non smoker: patients whose most recent status recorded was as a non smoker
  • not recorded: patients with no READ code or mapped code recorded
  • total patients: all funded patients for the quarter aged 15 or over
  • overall percentage: percentage of total patients with a status recorded

Click here for a complete list of the smoking READ codes and how they are mapped.

There are three tables:

  • Status by age group
  • Status by ethnicity
  • Status by age group and gender

 

1b. Smoking status graphs

For each of these graphs the colour:

  • red = level of smokers
  • orange = ex smokers
  • green = non smokers
  • grey = not recorded - the recording work to be done

If there is a lot of grey you know you have lots of work to do to achieve the goal of recording the smoking status of all your patients. If there is little grey, you have a more accurate picture of the smoking status of your community. This will help you determine where to put the emphasis of your health promotion programme.

(i) Smoking recorded status by age gaph: Use this graph to determine the age groups that are not well recorded. If you have a small percentage of grey, you can be more confident about the quality of the data and this will let you know which age groups require focus.

(ii) Smoking recorded status by ethnicity graph: Use this graph to determine the ethnicity of groups that are not well recorded . If you have a small percentage of grey, it will also let you know which ethnicity groups require focus as they have a high red percentage.

(iii) Smoking recorded status by gender graph: Use this graph to determine the gender and age of groups that are not well recorded. This graph is large as it combines both age and gender, but it does enable you to see the area to target in one place.

Diabetes ever recorded

2a. Diabetes ever recorded spreadsheet

This spreadsheet looks at the number of people recorded with diabetes. The data source is either direct or mapped READ codes stored in the PMS.

It will only tell you the number and percentage that you have recorded, so on its own it does not tell you if this is good or bad. The percentage for each practice will vary hugely because the number of people you expect to have diabetes depends on the age, ethnicity and gender of your practice's community.

You may want to check out how your recording of people with diabetes compares to the expected prevalence. This is available from the DHB prevalence spreadsheet received by your PHO. This will help you determine how well you are going when you use the expected prevalence alongside these charts.

For each breakdown, the tables show both the number and the percentage. It is important to look at both as in some cases the numbers are very small and the percentage alone can give a false impression.

There are four tables altogether:

  • Ethnicity and age group numbers
  • Ethnicity and age group percentages (to the right of the first table)
  • Gender and age group numbers
  • Gender and age group percentages (to the right of the first table)

 

2b. Diabetes ever recorded graphs

Each of these graphs shows the percentage of diabetes ever recorded as a proportion of the total population.

(i) Diabetes ever recorded by age and ethnicity graph: Use this graph alongside the expected prevalence noted above to determine the age ethnicity groups that are well identified and also most at risk.

(ii) Diabetes ever recorded by age and gender graph: Use this graph alongside the expected prevalence to determine the age gender groups that are not well identified and also most at risk.

Diabetes annual review

3a. Diabetes annual review spreadsheet

This spreadsheet looks at the number of people who have had a 'diabetes annual review' or 'diabetes get checked' in the previous 12 months and compares this to the number of people who have diabetes ever recorded. The data source is currently based on the number of screening records in your PMS where the screening code is based on that used for 'diabetes get checked'.

If you believe the numbers in your spreadsheet are lower than the actual number and you are using Enigma/Best Practices/CMDHB Chronic Care Management, please contact us.

The spreadsheet will tell you the percentage of your patients with diabetes recorded who have had an annual review in the last year. This will be a different percentage from that shown in the PHO Performance Progamme which looks for the number of annual reviews carried out based on the expected prevalence of people with diabetes. So the Karo spreadsheet is a more real picture of the work you have done with those people you have already identified as having diabetes.

For each breakdown, the tables show both the number and the percentage. It is important to look at both as in some cases the numbers are very small and the percentage alone can give a false impression.

There are four tables altogether:

  • Ethnicity and age group numbers
  • Ethnicity and age group percentages (to the right of the first table)
  • Gender and age group numbers
  • Gender and age group percentages (to the right of the first table)

 

3b. Diabetes anual review graphs

Each of these graphs shows the percentage of people who have had a diabetes annual review as a proportion of those identified as having diabetes.

(i) Diabetes get checked by age and ethnicity graph: Use this graph to identify the extent to which a combination of ethnicity and age is a significant factor for those people with diabetes recorded who receive the annual review and those who do not.

(ii) Diabetes get checked by age and gender graph:  Use this graph to identify the extent to which a combination of gender and age is a factor as to who receives the annual review and who does not.

CVD risk assessment

4a. CVDRA spreadsheet

This spreadsheet looks at the number of people who have had a CVD risk assessment in the previous five years and compares this to the number of people in the population who are identified for screening. The data source is currently based on the number of screening records in your PMS where the screening code is selected by the person doing the export.

If you believe the numbers in your spreadsheet are lower than the actual number, please contact us.

The spreadsheet will tell you the percentage of your 'eligible patients' who have had an annual review in the five years. This should be the same as that shown in the PHO Performance Progamme spreadsheet for the same quarter.

For each breakdown, the tables show both the number and the percentage. It is important to look at both as in some cases the numbers are very small and the percentage alone can give a false impression.

There are four tables altogether:

  • Ethnicity and age group numbers
  • Ethnicity and age group percentages (to the right of the first table)
  • Gender and age group numbers
  • Gender and age group percentages (to the right of the first table)

 

4b. CVDRA population

This spreadsheet looks at the total population identified as eligible for CVD screening. It is based on the national guidelines and is a direct match with the prevalence level identified by MoH and used as part of IPIF.

There are two tables:

  • Eligible female population by ethnicity and age group
  • Eligible male population by ethnicity and age group

 

4c. CVD risk assessment graphs

These graphs indicate the percentage of the eligible population who have received a CVD risk assessment within the last five years.

(i) CVD risk assessment by ethnicity graph: Use this graph to determine the age/ethnicity of groups that have not had a CVD risk assessment in comparison with those that have. Note that Europeans 35-39 have nil CVD risk assessments as they are not eligible according to the national guidelines. It is therefore important to view this graph alongside the eligible population table.

(ii) CVD risk assessment by gender graph: Use this graph to determine the age/gender of people who have not had a CVD risk assessment in comparison with those who have. Note that all women aged 30-39 will have nil CVD risk assessments as they are not eligible according to the national guidelines. It is therefore important to view this graph alongside the eligible population table.

Cervical screening

5a. Cervical screening spreadsheet

This spreadsheet looks at the number of women aged 18-70 who have had a cervical smear in the last three years and the outcome of that smear. The data in the spreadsheet and the graphs that follow comes from the screening part of the PMS. It will differ from the percentage shown in IPIF.  IPIF receives data from the national screening unit (matched by NHI) to determine how well your PHO is meeting the cervical screening indicator.

This Karo spreadsheet gathers information from your PMS. It is therefore also an indicator of how well you are recording the outcomes you receive from the National Cervical Screening Unit.

The spreadsheet outcome categories are:

  • abnormal (screening outcome A or AB)
  • normal (screening outcome blank or N or NO)
  • exempt (screening outcome EX or X)
  • refused (RE)
  • no screening recorded

 

There are two tables:

  • Outcome by age group: numbers and percentages
  • Outcome by ethnicity: numbers and percentages

 

Note that if you have a large percentage of outcomes in the exempt category, check that your practices have changed the third recall outcome in the PMS system as Medtech has previously automatically inserted 'exempt' once the third recall is sent.

 

5b. Cervical screening graphs

Both graphs show the percentage of people who have had cervical screening and the outcomes of their screening.

For each of these graphs, the colour:

  • red = abnormal
  • orange = exempt
  • green = normal
  • blue = refused
  • grey = not recorded - the recording work to be done

 

The grey area indicates the work to be done either to record the outcome of smears received from the National Screening Unit and/or those women who have not had a smear in the last three years.

(i)  Cervical screening by age graph: Use this graph to identify the extent to which age is correlated with both the outcome for cervical screening and whether women are screened. Note that a low level of not recorded (grey) is a more reliable indicator of the accuracy of the outcomes.

(ii) Cervical screening by ethnicity graph: Use this graph to identify the extent to which ethnicity is a correlated with both the outcome for cervical screening and whether women are screened . As above, a low level of not recorded (grey) is a more reliable indicator of the accuracy of the outcomes.

Breast Screening

6a. Breast screening spreadsheet

This spreadsheet looks at the number of women aged 45-70 who have had a mammogram in the last two years and the outcome of their mammography.

The data in this spreadsheet and the graphs that follow comes from the screening part of your PMS. The percentages will differ from that shown in IPIF.  IPIF receives data from the National Breast Screening data base to determine how well your practice is meeting the breast screening indicator.

This Karo spreadsheet gathers information from the screening part of your PMS. Mammography is not initiated by the practice so the level of match with the National Breast Screening Unit will be as good as the accuracy of the inbox document mapping within the PMS. Unlike IPIF, private mammography is included if a screening has been mapped into the PMS.

As with many of the above indicators, if practices are recording well, this will be a "good" indicator.

The spreadsheet outcome categories are:

  • abnormal (screening outcome A or AB)
  • normal (screening outcome blank or N or NO)
  • exempt (screening outcome EX or X)
  • refused (RE)
  • no screening recorded

 

There are two tables:

  • Outcome by age group: numbers and percentages
  • Outcome by ethnicity: numbers and percentages

 

Note that if you have a large percentage of outcomes in the exempt category, check your practices have changed the third recall outcome in the PMS system as Medtech has previously automatically inserted 'exempt' once the third recall is sent.

 

6b. Breast screening graphs

Both graphs show the percentage of people who have had a mammogram and the outcome of the mammography.

For each of these graphs, the colour:

  • red = abnormal
  • orange = exempt
  • green = normal
  • blue = refused
  • grey = not recorded - the recording work to be done

 

(i) Breast screening by age graph: Use this graph to identify the extent to which age is correlated with  whether women have either had a mammogram or not or whether their mammogram has been recorded or not. Comparison with the IPIF indicator for breast screening will identify the extent to which recording is the main concern.

(ii) Breast screening by ethnicity graph: Use this graph to identify the extent to which ethnicity is correlated with whether women have either had a mammogram or not or whether their mammogram has been recorded or not. Comparison with the IPIF indicator for breast screening will identify the extent to which recording is the main concern.

Population

This is the raw data that is used for calculating the population figures in all of the sheets.