Artificial Intelligence-Supported Development of Health Guideline Questions.

BACKGROUND: Guideline questions are typically proposed by experts. OBJECTIVE: To assess how large language models (LLMs) can support the development of guideline questions, providing insights on approaches and lessons learned. DESIGN: Two approaches for guideline question generation were assessed: 1) identification of questions conveyed by online search queries and 2) direct generation of guideline questions by LLMs. For the former, the researchers retrieved popular queries on allergic rhinitis using Google Trends (GT) and identified those conveying questions using both manual and LLM-based methods. They then manually structured as guideline questions the queries that conveyed relevant questions. For the second approach, they tasked an LLM with proposing guideline questions, assuming the role of either a patient or a clinician. SETTING: Allergic Rhinitis and its Impact on Asthma (ARIA) 2024 guidelines. PARTICIPANTS: None. MEASUREMENTS: Frequency of relevant questions generated. RESULTS: The authors retrieved 3975 unique queries using GT. From these, they identified 37 questions, of which 22 had not been previously posed by guideline panel members and 2 were eventually prioritized by the panel. Direct interactions with LLMs resulted in the generation of 22 unique relevant questions (11 not previously suggested by panel members), and 4 were eventually prioritized by the panel. In total, 6 of 39 final questions prioritized for the 2024 ARIA guidelines were not initially thought of by the panel. The researchers provide a set of practical insights on the implementation of their approaches based on the lessons learned. LIMITATION: Single case study (ARIA guidelines). CONCLUSION: Approaches using LLMs can support the development of guideline questions, complementing traditional methods and potentially augmenting questions prioritized by guideline panels. PRIMARY FUNDING SOURCE: Fraunhofer Cluster of Excellence for Immune-Mediated Diseases.

Embedding patients' values and preferences in guideline development for allergic diseases: The case study of Allergic Rhinitis and its Impact on Asthma 2024.

Recommendations for or against the use of interventions need to consider both desirable and undesirable effects as well as patients' values and preferences (V&P). In the decision-making context, patients' V&P represent the relative importance people place on the outcomes resulting from a decision. Therefore, the balance between desirable and undesirable effects from an intervention should depend not only on the difference between benefits and harms but also on the value that patients place on them. V&P are therefore one of the criteria to be considered when formulating guideline recommendations in the Evidence-to-Decision framework developed by the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) Working Group. Patients' V&P may be quantified through utilities, which can be elicited using direct methods (e.g., standard gamble or time trade-off) or indirect methods (using validated instruments to measure health-related quality of life, such as EQ-5D). The GRADE approach recommends conducting systematic reviews to summarise all the available evidence and assess the degree of certainty on V&P. In this article, we discuss the importance of considering patients' V&P and provide examples of how they are considered in the 2024 person-centred Allergic Rhinitis and its Impact on Asthma (ARIA) guidelines.

Concepts for the Development of Person-Centered, Digitally Enabled, Artificial Intelligence-Assisted ARIA Care Pathways (ARIA 2024).

The traditional healthcare model is focused on diseases (medicine and natural science) and does not acknowledge patients' resources and abilities to be experts in their own lives based on their lived experiences. Improving healthcare safety, quality, and coordination, as well as quality of life, is an important aim in the care of patients with chronic conditions. Person-centered care needs to ensure that people's values and preferences guide clinical decisions. This paper reviews current knowledge to develop (1) digital care pathways for rhinitis and asthma multimorbidity and (2) digitally enabled, person-centered care.1 It combines all relevant research evidence, including the so-called real-world evidence, with the ultimate goal to develop digitally enabled, patient-centered care. The paper includes (1) Allergic Rhinitis and its Impact on Asthma (ARIA), a 2-decade journey, (2) Grading of Recommendations, Assessment, Development and Evaluation (GRADE), the evidence-based model of guidelines in airway diseases, (3) mHealth impact on airway diseases, (4) From guidelines to digital care pathways, (5) Embedding Planetary Health, (6) Novel classification of rhinitis and asthma, (7) Embedding real-life data with population-based studies, (8) The ARIA-EAACI (European Academy of Allergy and Clinical Immunology) strategy for the management of airway diseases using digital biomarkers, (9) Artificial intelligence, (10) The development of digitally enabled, ARIA person-centered care, and (11) The political agenda. The ultimate goal is to propose ARIA 2024 guidelines centered around the patient to make them more applicable and sustainable.

A multistakeholder development process to prioritize and translate COVID-19 health recommendations for patients, caregivers and the public. A case study of the COVID-19 recommendation map.

BACKGROUND AND OBJECTIVES: To develop a digital communication tool to improve the implementation of up-to-date COVID-19 recommendations. Specifically, to improve patient, caregiver and public understanding of healthcare recommendations on prevention, diagnoses and treatment. METHODS: Multi-stakeholder engagement design. In conjunction with the COVID-19 Recommendations and Gateway to Contextualization RecMap, we co-developed a stakeholder prioritization, drafting and editing process to enhance guideline communication and understanding. RESULTS: This paper presents the multi-stakeholder development process with three distinct plain language recommendation formats: formal recommendation, good practice statement, and additional guidance. Our case study of COVID-19 plain language recommendations PLRs addresses both public health interventions (e.g., vaccination, face masks) and clinical interventions (e.g., home pulse oximetry). CONCLUSION: This paper presents a novel approach to engaging stakeholders in improving the communication and understanding of published guidelines during the COVID-19 pandemic.