Updating diabetic retinopathy screening lists using automatic extraction from GP patient records

Area of Science:

• Ophthalmology
• Public Health
• Health Informatics

Background:

• Diabetic retinopathy screening is crucial for preventing sight loss in diabetes patients.
• Rising diabetes incidence necessitates efficient screening list management.
• Manual list compilation can be inaccurate and time-consuming.

Purpose of the Study:

• To evaluate the effectiveness of electronic data extraction for updating diabetic eye screening lists.
• To assess the accuracy and timeliness of patient identification using primary care electronic records.
• To improve the Gloucestershire Diabetic Eye Screening Programme's (GDESP) patient registry.

Main Methods:

• A pilot study was conducted in Gloucestershire involving 54 general practices using EMIS software.
• An existing screening list of 14,209 patients was audited against a list generated by automatic extraction of patients coded with 'diabetes' (Read Code C10).
• Patients identified were followed up for screening and referral to the Hospital Eye service.

Main Results:

• The manual list contained 14,771 patients.
• Automatic extraction identified an additional 709 patients (4.8%) with diabetes code C10.
• This included patients diagnosed over five years ago and young patients diagnosed over a year ago.

Conclusions:

• Automatic electronic data extraction significantly enhances the identification of eligible patients for diabetic eye screening.
• This method improves the completeness of screening lists compared to manual compilation.
• Implementing electronic extraction can optimize resource allocation and patient care in diabetic retinopathy screening programs.