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When our practice team discussed the GMS markers on identifying carers, it emerged that memory and discussion were the only tools we had. As the clinical effectiveness lead in the LHCC (local health care co-operative) I raised this matter, and one of the LHCC staff who had previously been involved in audit started to research the area with the aim of drawing up a practical guide for the identification of carers.

The problem

Census information indicates there are about half a million unpaid carers in Scotland ­ about one in 10 of the population. The figures show 60 per cent of these people are likely to be female with an average age of about 47. The majority of carers do not recognise themselves as carers and often become isolated as they attempt to manage the many conflicting roles they have to fulfil.

The search also indicates about 80 per cent of carers admit to the caring role having an adverse effect on their health. But very few of them mention this to their GPs, putting the cared-for person's needs before their own.

The LHCC is fortunate in having a carer's co-ordinator (Madeleine Martin) whose job is to highlight the issue within the LHCC and to find and develop services in the community for carers. So for us, identifying carers is not a stale tick-box exercise, as we have services and agencies that will help and

advise. Having said that, the literature we used on our noticeboard and to hand out to patients was all in the public domain from voluntary organisations, so practices without access to a carer co-ordinator should nevertheless be aware of the crucial link they can be in directing carers to organisations that may prove to be their lifeline.

What we did

We did many searches on the computer to identify patients who were under the age of 75, had a significant medical condition and who had someone living in the same address. Carers were spread across a range of diseases.

We searched obvious areas such as patients with Alzheimer's, diabetes, chronic renal failure, cerebral palsy and stroke. We did less obvious searches for patients with ME and MS, learning difficulties, rheumatoid arthritis and visual impairment.

We did numerous other searches just to see what might turn up but as with all good ideas the first ones threw up the most number of patients. Oddly enough Alzheimer's disease turned up very few people because all the searches were for patients under 75.

We also decided to continue with the existing system we had operating for the over-75s' health checks where patients are asked whether they are a carer or are cared for.

The most productive fields for searches were multiple sclerosis, ME, cerebral palsy,

diabetic neuropathy, chronic renal failure, learning difficulties, rheumatoid arthritis and osteoarthritis (see box for results of some searches). Once a potential carer was identified, a practice team member would contact them to confirm their carer status and ask whether they needed support.

We have coded the cared-for and the carers so that we can have a register (918A ­ carer; 918G ­ is a carer; 918F ­ has a carer). This register needs constant updating as people's circumstances change constantly. It is unlike coding for a disease, which once contracted remains part of the summary. The carer codes are there to identify people not a disease, and like all social coding need to be regularly updated and altered.

Regular team discussion is needed to keep the register updated and accurate so staff can advise who has died or been newly diagnosed. It is also important to make a member of staff responsible for this project ­ without it, the project will get stuck and not move forward.

What we have achieved

A register now exists and so far we have identified an extra 10 carers, bringing the total to 40. We may be doing more than is demanded by the contract but we are finally addressing the issue in a systematic way to the carer's benefit.

These figures exclude patients >75 years and those patients in long-term residential care

Condition/disease Read code used No. of patients Patients with Percentage

identified someone else reached for

at address this disease

area/condition

Multiple sclerosis F20.. 13 7 85%

Rheumatoid arthritis NO4.. 14 7 50%

KJL/cerebral palsy F23.. 5 5 100%

Osteoarthritis NO5.. 79 51 65%

Chronic renal failure K05.. 8 5 63%

ME F286. 27 16 60%

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