Blood self-sampling: a missing link for remote patient care

Hügle, Thomas

doi:10.1136/rmdopen-2022-002728

Cited by 2 publications

(2 citation statements)

References 11 publications

(18 reference statements)

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“…Since development of the algorithm that relied on rule-based NLP, large language models (LLMs) such as GPT4 have surged in popularity and are being leveraged in several settings. However, LLMs are associated with their own limitations including black box models, LLM hallucinations and introducing different types of bias 19–22. How they could be implemented in a healthcare setting on outpatient letters that contain sensitive patient information to replace text analytics has not been well established.…”

Section: Discussionmentioning

confidence: 99%

Development and evaluation of a text analytics algorithm for automated application of national COVID-19 shielding criteria in rheumatology patients

Jani,

Alfattni,

Belousov

et al. 2024

Ann Rheum Dis

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IntroductionAt the beginning of the COVID-19 pandemic, the UK’s Scientific Committee issued extreme social distancing measures, termed ‘shielding’, aimed at a subpopulation deemed extremely clinically vulnerable to infection. National guidance for risk stratification was based on patients’ age, comorbidities and immunosuppressive therapies, including biologics that are not captured in primary care records. This process required considerable clinician time to manually review outpatient letters. Our aim was to develop and evaluate an automated shielding algorithm by text-mining outpatient letter diagnoses and medications, reducing the need for future manual review.MethodsRheumatology outpatient letters from a large UK foundation trust were retrieved. Free-text diagnoses were processed using Intelligent Medical Objects software (Concept Tagger), which used interface terminology for each condition mapped to Systematized Medical Nomenclature for Medicine–Clinical Terminology (SNOMED-CT) codes. We developed the Medication Concept Recognition tool (Named Entity Recognition) to retrieve medications’ type, dose, duration and status (active/past) at the time of the letter. Age, diagnosis and medication variables were then combined to calculate a shielding score based on the most recent letter. The algorithm’s performance was evaluated using clinical review as the gold standard. The time taken to deploy the developed algorithm on a larger patient subset was measured.ResultsIn total, 5942 free-text diagnoses were extracted and mapped to SNOMED-CT, with 13 665 free-text medications (n=803 patients). The automated algorithm demonstrated a sensitivity of 80% (95% CI: 75%, 85%) and specificity of 92% (95% CI: 90%, 94%). Positive likelihood ratio was 10 (95% CI: 8, 14), negative likelihood ratio was 0.21 (95% CI: 0.16, 0.28) and F1 score was 0.81. Evaluation of mismatches revealed that the algorithm performed correctly against the gold standard in most cases. The developed algorithm was then deployed on records from an additional 15 865 patients, which took 18 hours for data extraction and 1 hour to deploy.DiscussionAn automated algorithm for risk stratification has several advantages including reducing clinician time for manual review to allow more time for direct care, improving efficiency and increasing transparency in individual patient communication. It has the potential to be adapted for future public health initiatives that require prompt automated review of hospital outpatient letters.

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Section: Discussionmentioning

confidence: 99%

Development and evaluation of a text analytics algorithm for automated application of national COVID-19 shielding criteria in rheumatology patients

Jani,

Alfattni,

Belousov

et al. 2024

Ann Rheum Dis

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“…Eventually, they considered refusing to recognize ChatGPT as an author, in accordance also to Nature announcement [18]. For their part, the authors in [19] provided an overview of the potential use of ChatGPT as a rheumatologist, its capabilities and risks. They concluded that although chatbots will not be able to make clinical decisions for now; they will be able, for example, to design study protocols, streamline information accessibility and contribute to time-saving.…”

Section: Introductionmentioning

confidence: 99%

Harnessing ChatGPT and GPT-4 for Evaluating the Rheumatology Questions of the Spanish Access Exam to Specialized Medical Training

Madrid

Rosales-Rosado

Freites

et al. 2023

Preprint

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The emergence of Large Language Models (LLM) with remarkable performance such as ChatGPT and GPT-4, has led to an unprecedented uptake in the population. One of their most promising and studied applications concerns education due to their ability to understand and generate human-like text, creating a multitude of opportunities for enhancing educational practices and outcomes. The objective of this study is two-fold: to assess the accuracy of ChatGPT/GPT-4 in answering rheumatology questions from the access exam to specialized medical training in Spain (MIR), and to evaluate the medical reasoning followed by these LLM to answer those questions. A dataset, RheumaMIR, of 145 rheumatology-related questions, extracted from the exams held between 2010 and 2023, was created for that purpose, used as a prompt for the LLM, and was publicly distributed. Six rheumatologists with clinical and teaching experience evaluated the clinical reasoning of the chatbots using a 5-point Likert scale and their degree of agreement was analyzed. The association between variables that could influence the models' accuracy (i.e., year of the exam question, disease addressed, type of question and genre) was studied. ChatGPT demonstrated a high level of performance in both accuracy, 66.43%, and clinical reasoning, median (Q1-Q3), 4.5 (2.33-4.67). However, GPT-4 showed better performance with an accuracy score of 93.71% and a median clinical reasoning value of 4.67 (4.5-4.83). These findings suggest that LLM may serve as valuable tools in rheumatology education, aiding in exam preparation and supplementing traditional teaching methods.

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Blood self-sampling: a missing link for remote patient care

Cited by 2 publications

References 11 publications

Development and evaluation of a text analytics algorithm for automated application of national COVID-19 shielding criteria in rheumatology patients

Development and evaluation of a text analytics algorithm for automated application of national COVID-19 shielding criteria in rheumatology patients

Harnessing ChatGPT and GPT-4 for Evaluating the Rheumatology Questions of the Spanish Access Exam to Specialized Medical Training

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