Clinical decision support in primary care for better diagnosis and management of retinal disease

Ho, Shu Fen; Kalloniatis, Michael; Ly, Angelica

doi:10.1080/08164622.2021.2008791

Cited by 4 publications

(3 citation statements)

References 69 publications

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“…While there appears to be a dearth of research evaluating similar decision support tools for chronic insomnia, there are several studies on such tools in primary care, although they are primarily focused on the impact of electronic clinical decision support tools (eCDST) [26][27][28]. eCDST allows for providers to enter patient specific information, which is then processed using validated algorithms to make recommendations or issue prompts for the provider to consider [26].…”

Section: Discussionmentioning

confidence: 99%

Assessment and management of chronic insomnia disorder: an algorithm for primary care physicians

Selsick,

Heidbreder,

Ellis

et al. 2024

BMC Prim. Care

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Background Primary care physicians often lack resources and training to correctly diagnose and manage chronic insomnia disorder. Tools supporting chronic insomnia diagnosis and management could fill this critical gap. A survey was conducted to understand insomnia disorder diagnosis and treatment practices among primary care physicians, and to evaluate a diagnosis and treatment algorithm on its use, to identify ways to optimize it specifically for these providers. Methods A panel of experts developed an algorithm for diagnosing and treating chronic insomnia disorder, based on current guidelines and experience in clinical practice. An online survey was conducted with primary care physicians from France, Germany, Italy, Spain, and the United Kingdom, who treat chronic insomnia patients, between January and February 2023. A sub-sample of participants provided open-ended feedback on the algorithm and gave suggestions for improvements. Results Overall, 106 primary care physicians completed the survey. Half (52%, 55/106) reported they did not regularly screen for insomnia and half (51%, 54/106) felt they did not have enough time to address patients’ needs in relation to insomnia or trouble sleeping. The majority (87%,92/106) agreed the algorithm would help diagnose chronic insomnia patients and 82% (87/106) agreed the algorithm would help improve their clinical practice in relation to managing chronic insomnia. Suggestions for improvements were making the algorithm easier to read and use. Conclusion The algorithm developed for, and tested by, primary care physicians to diagnose and treat chronic insomnia disorder may offer significant benefits to providers and their patients through ensuring standardization of insomnia diagnosis and management.

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Section: Discussionmentioning

confidence: 99%

Assessment and management of chronic insomnia disorder: an algorithm for primary care physicians

Selsick,

Heidbreder,

Ellis

et al. 2024

BMC Prim. Care

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“…In community practice, a standard clinical work‐up for AMD begins with a thorough history and symptoms followed by assessment using a battery of clinical tests 17 . Progressing through these stages of decision‐making assists clinicians in arriving at an informed diagnosis 26 . In the context of AMD diagnosis, patient age and information from the fellow eye form key considerations given the definition of AMD as a disease of the elderly and its generally bilateral, though asymmetric, nature 27 .…”

Section: Discussionmentioning

confidence: 99%

“…17 Progressing through these stages of decision-making assists clinicians in arriving at an informed diagnosis. 26 In the context of AMD diagnosis, patient age and information from the fellow eye form key considerations given the definition of AMD as a disease of the elderly and its generally bilateral, though asymmetric, nature. 27 However, the participant community optometrists in this study were only provided with single eye data from one source, a CFP, from which to establish a diagnosis.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Diagnostic accuracy of community optometrists for age‐related macular degeneration using colour fundus photographs: A pilot evaluation

Ho,

Doig,

2023

Ophthalmic Physiologic Optic

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PurposeThe accurate diagnosis of age‐related macular degeneration (AMD) represents an important step in delaying and preventing vision loss and achieving optimal patient care. Therefore, this pilot study aimed to estimate the diagnostic accuracy of community optometrists for identifying AMD using colour fundus photographs (CFPs) to support sample size calculations for subsequent definitive studies.MethodsFive practising community optometrists were invited to classify a total of 1023 CFPs for the (1) presence of AMD, and, if applicable, (2) stage of AMD (early/intermediate/late geographic atrophy/late neovascular AMD). Diagnosis by referral centre clinicians formed the reference standard. Diagnostic accuracy was assessed by the area under the receiver operating characteristic curve (aROC). Sensitivity, specificity, positive and negative predictive values were also calculated.ResultsOf the 1023 CFPs included in the study, 226 images were of AMD and 797 images were of other ocular conditions or no abnormal findings. Participating community optometrists had a mean (SD) age of 30.2 (8.9) years, 60.0% (3/5) were female and the mean number of years practising in primary eye care was 5.4 (5.4) years. Community optometrists demonstrated excellent performance for diagnosing AMD, with an aROC of 0.86 (95% CI 0.83 to 0.89), sensitivity of 84.5% (95% CI 79.1 to 89.0) and specificity of 88.0% (95% CI 85.5 to 90.1). The aROC (95% CI) for diagnosing early, intermediate, late geographic atrophy and late neovascular AMD was 0.82 (0.73 to 0.91), 0.76 (0.72 to 0.81), 0.69 (0.49 to 0.90) and 0.55 (0.34 to 0.75), respectively.ConclusionsThese results justify the need for an appropriately powered definitive study to assess community clinicians' diagnostic accuracy for AMD.

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A few-shot disease diagnosis decision making model based on meta-learning for general practice

Liu,

Tian,

Zhou

et al. 2024

Artificial Intelligence in Medicine

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Clinical decision support in primary care for better diagnosis and management of retinal disease

Cited by 4 publications

References 69 publications

Assessment and management of chronic insomnia disorder: an algorithm for primary care physicians

Assessment and management of chronic insomnia disorder: an algorithm for primary care physicians

Diagnostic accuracy of community optometrists for age‐related macular degeneration using colour fundus photographs: A pilot evaluation

A few-shot disease diagnosis decision making model based on meta-learning for general practice

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