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Analysing Patterns in Electronic Optometry Referrals: Feasibility and Methodology.

Abdullah F Alotaibi¹, Matthew Jinkinson², Ketan R Parmar³

¹Division of Pharmacy and Optometry, The University of Manchester, Manchester, UK. Abdullah.Alotaibi@postgrad.manchester.ac.uk.

Ophthalmic & Physiological Optics : the Journal of the British College of Ophthalmic Opticians (Optometrists)

|March 6, 2026

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This summary is machine-generated.

Electronic optometry referral data is viable for analysis. This study shows that large datasets can reveal patterns in eye care referrals, including sex-based differences, aiding future research.

Area of Science:

Ophthalmology and Optometry
Health Informatics
Public Health

Background:

Primary care optometry generates significant electronic referral data.
Understanding referral patterns is crucial for optimizing eyecare delivery and resource allocation.
Current analytical methods for large-scale optometry referral data require evaluation.

Purpose of the Study:

To assess the feasibility of utilizing large-scale electronic primary care optometry referral data.
To explore various analytical methods for analyzing this dataset.
To identify potential patterns and associations within optometry referrals.

Main Methods:

Analysis of 12,339 electronic referrals from primary care optometrists.
Data preprocessing including categorization, normalization, and merging of referral conditions.

Keywords:

Electronic referrals Feasibility study Health inequality Optometry referrals Primary eyecare Referral patterns Socioeconomic deprivation

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Descriptive analysis of referral patterns to secondary care ophthalmology and within primary eyecare.

Regression analysis to investigate sex-based differences in referrals.

Spatial analysis to map referral distribution and its relation to social deprivation.

Main Results:

77.3% of referrals were to secondary care ophthalmology; 14.1% within primary eyecare.
Most common referral reasons included cataracts, glaucoma, and YAG laser capsulotomy.
Significant sex differences observed, with higher referral rates for females in certain conditions (e.g., glaucoma).
No significant association found between referral ratio and social deprivation levels.

Conclusions:

Electronic optometry referral data is a viable resource for analysis.
Established analytical methods can be supported by the quality and scope of this data.
Expanding sample size and timeframes could uncover clinically significant referral patterns.
Future research can validate findings and inform understanding of eyecare inequalities.