APPRAISE-RS: Automated, updated, participatory, and personalized treatment recommender systems based on GRADE methodology

Attention deficit hyperactivity disorder; Evidence-based medicine; Meta-analysisTrastorn per dèficit d'atenció amb hiperactivitat; Medicina basada en l'evidència; MetaanàlisiTrastorno por déficit de atención con hiperactividad; Medicina basada en la evidencia; MetanálisisPurpose: Clinical practice guidelines (CPGs) have become fundamental tools for evidence-based medicine (EBM). However, CPG suffer from several limitations, including obsolescence, lack of applicability to many patients, and limited patient participation. This paper presents APPRAISE-RS, which is a methodology that we developed to overcome these limitations by automating, extending, and iterating the methodology that is most commonly used for building CPGs: the GRADE methodology.Method: APPRAISE-RS relies on updated information from clinical studies and adapts and automates the GRADE methodology to generate treatment recommendations. APPRAISE-RS provides personalized recommendations because they are based on the patient's individual characteristics. Moreover, both patients and clinicians express their personal preferences for treatment outcomes which are considered when making the recommendation (participatory). Rule-based system approaches are used to manage heuristic knowledge.Results: APPRAISE-RS has been implemented for attention deficit hyperactivity disorder (ADHD) and tested experimentally on 28 simulated patients. The resulting recommender system (APPRAISE-RS/TDApp) shows a higher degree of treatment personalization and patient participation than CPGs, while recommending the most frequent interventions in the largest body of evidence in the literature (EBM). Moreover, a comparison of the results with four blinded psychiatrist prescriptions supports the validation of the proposal.Conclusions: APPRAISE-RS is a valid methodology to build recommender systems that manage updated, personalized and participatory recommendations, which, in the case of ADHD includes at least one intervention that is identical or very similar to other drugs prescribed by psychiatrists.This work was supported by European Regional Development Fund (ERDF), the Spanish Ministry of the Economy, Industry and Competitiveness (MINECO) and the Carlos III Research Institute [PI19/00375], Fundació Pascual i Prats & Campus Salut, UdG [AIN2018E], Generalitat de Catalunya [2017 SGR 1551]

Evidence supporting the best clinical management of patients with multimorbidity and polypharmacy: a systematic guideline review and expert consensus

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.The complexity and heterogeneity of patients with multimorbidity and polypharmacy renders traditional disease-oriented guidelines often inadequate and complicates clinical decision making. To address this challenge, guidelines have been developed on multimorbidity or polypharmacy. To systematically analyse their recommendations, we conducted a systematic guideline review using the Ariadne principles for managing multimorbidity as analytical framework. The information synthesis included a multistep consensus process involving 18 multidisciplinary experts from seven countries. We included eight guidelines (four each on multimorbidity and polypharmacy) and extracted about 250 recommendations. The guideline addressed (i) the identification of the target population (risk factors); (ii) the assessment of interacting conditions and treatments: medical history, clinical and psychosocial assessment including physiological status and frailty, reviews of medication and encounters with healthcare providers highlighting informational continuity; (iii) the need to incorporate patient preferences and goal setting: eliciting preferences and expectations, the process of shared decision making in relation to treatment options and the level of involvement of patients and carers; (iv) individualized management: guiding principles on optimization of treatment benefits over possible harms, treatment communication and the information content of medication/care plans; (v) monitoring and follow-up: strategies in care planning, self-management and medication-related aspects, communication with patients including safety instructions and adherence, coordination of care regarding referral and discharge management, medication appropriateness and safety concerns. The spectrum of clinical and self-management issues varied from guiding principles to specific recommendations and tools providing actionable support. The limited availability of reliable risk prediction models, feasible interventions of proven effectiveness and decision aids, and limited consensus on appropriate outcomes of care highlight major research deficits. An integrated approach to both multimorbidity and polypharmacy should be considered in future guidelines.Journal of Internal MedicineKarolinska Institutet Strategic Research Area in Epidemiology (SfoEpi

Inteligência artificial na prestação dos cuidados de saúde e a perspectiva dos médicos portugueses

RESUMO - Inteligência Artificial (IA) já é uma realidade na área da saúde. Sua adopção não é mais uma questão de escolha. A questão principal é como integrá-la no âmbito dos cuidados de saúde, desde a formação académica nas escolas de medicina até o uso de dispositivos baseados em IA na prática clínica em hospitais, centros de saúde, clínicas, entre outros. O acompanhamento da evolução da IA, desde os seus primeiros passos, permite compreender como a mesma se tornou um instrumento valioso para a melhoria dos cuidados de saúde. Além desta abordagem, esta tese apresenta os resultados de um questionário aplicado à classe médica portuguesa, com o propósito de compreender sua perceção e predisposição para usar IA na sua prática clínica. Adicionalmente, há expectativa de que os resultados possam ser úteis para elaboração de políticas públicas, visando a inserção dessa importante tecnologia na área de saúde, sempre em benefício dos utentes, que são a motivação principal de todo esse processo. Para obter sucesso, uma política pública focada na modernização dos cuidados de saúde deve encaminhar com especial atenção a questão da parceria homem-máquina.ABSTRACT - Artificial Intelligence (AI) is already a reality in healthcare. Adopting AI in this field is not a matter of choice anymore. The main issue is how to integrate it in the healthcare whole environment, from medical schools’ academic curriculum to the use of AI-based devices in the front-end of Medicine, such as hospitals, health centers, clinics, among others. Following AI evolution, since its very beginning, helps one to understand how it becomes a valuable instrument to healthcare outputs enhancement. Besides summarizing such outcome, this dissertation presents the results of a survey among Portuguese physicians with the purpose of comprehending their perception on and willingness of using AI in their daily activities. Furthermore, there is an expectation that the findings presented in this paper can be useful to drawing public policies regarding the introduction of this powerful technology in the healthcare area for the benefit of patients – as the center of the whole process. To be successful, a public policy focused on healthcare modernization must address carefully a human-machine partnership