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Angle-closure glaucoma, or closed-angle glaucoma, is an eye condition where the iris bulges out and blocks the iridocorneal angle, resulting in a buildup of aqueous humor and increased intraocular pressure. Immediate medical attention is necessary due to the sudden onset of symptoms. The treatment for angle-closure glaucoma includes short-term and long-term approaches. Short-term treatment involves using eye drops like pilocarpine to lower intraocular pressure by increasing aqueous humor...
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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients
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Development of a Cloud-Based Clinical Decision Support System for Ophthalmology Triage Using Decision Tree Artificial

Stuti M Tanya1, Anne X Nguyen2, Sean Buchanan3

  • 1Department of Ophthalmology & Visual Sciences, McGill University, Montreal, Quebec, Canada.

Ophthalmology Science
|November 28, 2022
PubMed
Summary

A new cloud-based clinical decision support system (CDSS) for teleophthalmology improves diagnostic accuracy and urgency assessment in referrals. This novel system standardizes care and enhances efficiency in on-call ophthalmology consults.

Keywords:
Artificial intelligenceCDSS, clinical decision support systemsCI, confidence intervalClinical decision support systemDecision tree algorithmHIPAA, Health Insurance Portability and Accountability ActTeleophthalmologyTriage and referral

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Area of Science:

  • Ophthalmology
  • Medical Informatics
  • Telemedicine

Background:

  • Clinical decision support systems (CDSS) are increasingly vital in teleophthalmology for optimizing triage and referral processes.
  • Traditional methods for on-call ophthalmology consults can be inefficient and lack standardization.

Purpose of the Study:

  • To develop and implement a novel cloud-based decision tree CDSS for on-call ophthalmology consults.
  • To standardize the triage and referral process, aiming for more accurate provisional diagnoses and urgency assessments.

Main Methods:

  • A web-based decision tree algorithm was created using current guidelines and expert consensus.
  • The CDSS collected specific patient ophthalmic information to generate a provisional diagnosis and urgency level.
  • Statistical analyses, including Spearman-rho correlations and Cohen's kappa, were used to compare CDSS outputs with referring provider and ophthalmologist assessments.

Main Results:

  • The CDSS demonstrated comparable performance to referring providers in determining diagnostic category (κ = 0.5898).
  • The CDSS showed superior performance over referring providers in determining provisional diagnosis (agreement = 53.1%) and urgency (ρ = 0.5014).
  • The CDSS identified a lower urgency level in 22.9% of cases compared to referring providers (6.3%).

Conclusions:

  • This represents the first cloud-based CDSS in ophthalmology designed to enhance triage and referral.
  • The CDSS improves diagnostic accuracy and urgency assessment, standardizes information collection, and moves beyond paper-based consults.
  • Future work may involve machine learning models (e.g., random forest, CNN) to further refine speed and accuracy, particularly sensitivity.