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Enregistrement W2585349153 · doi:10.1093/fampra/cmw147

How the ‘omics’ revolution can change primary care

2017· editorial· en· W2585349153 sur OpenAlexaff
Martin Dawes

Notice bibliographique

RevueFamily Practice · 2017
Typeeditorial
Langueen
DomaineMedicine
ThématiqueChronic Disease Management Strategies
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésMedicinePrimary careOmicsPrimary (astronomy)Primary health careData scienceBioinformaticsFamily medicineEnvironmental health

Résumé

récupéré en direct d'OpenAlex

Family medicine has reached a critical stage where we need to rethink how we practice. Patients with, or at risk of getting, multiple chronic diseases being treated with polypharmacy are becoming the norm in most developed, and now in developing, countries (1). Multiple guidelines, some with conflicting advice, are being produced by groups focusing on one disease with occasional exceptions (2). Technological innovations with telehealth, near-patient testing and remote monitoring add to the complexity. Challenges to continuity of care, with multiple specialists often involved with one patient, multiple charts and ineffective information technology all contribute to increasing the difficulty of delivering patient-centred evidence-informed care. The adverse drug reactions from the drugs we prescribe result in 197000 deaths in Europe every year (3) and are responsible for 9.5% of direct health care costs in the USA (4). It is against this background that the new technologies of genetics, genomics, proteomics, metabolomics and the microbiome are starting to become relevant to primary care (5). The next 20 years are going to see a revolution in medicine much like the period of the late 17th and 18th century when diseases were being recognized and described for the first time. Physicians like Charcot and Sydenham were carefully documenting the presentation of symptoms and signs. The importance of their work is critical today even when we are surrounded by so much physiological, biochemical, imaging and genomic data. Their careful observations, leading to the identification of the phenotypes, are still the clinical basis of much of genomic medicine. New understanding of the molecular basis of disease is leading to the description of new phenotypes of diseases such as diabetes that are associated with the DNA of the individual (6). This may lead to targeting of prevention of blindness in one individual and peripheral vascular disease in another based on their genotype. Pharmacogenetics helps predict responsiveness to common therapies. Metabolomics and proteomics will inform the progression of disease and the likelihood of disease. The microbiome will help us understand the development of disease. The data they will produce will be large and complex; without expert systems, it will be almost impossible to use. Are these ‘omics’ technologies a step too far for primary care, or could they be used to address the flaws of the current system and lead to significantly improved processes for care? Adding a pharmacogenetic alert to the inadequate prescribing process is unlikely to improve this as up to 97% of alerts are ignored (7). However, pharmacogenetics can be a trigger for rethinking prescribing. The current system expects the health professional to identify the possible drugs for the patient considering the patient’s current level of disease, their other diseases, their other drugs and their renal and liver function. It expects the professional to remember all the potential significant drug–drug, drug–disease interactions and the impact of renal and liver function on dosing or even prescribing of certain drugs. This is impossible in most patients. It is rather like a pilot flying a modern jet plane only with manual controls. By comparison, the decision support of a modern airplane is smart enough to land the plane safely in most situations. The information to identify the safe and effective drugs for a patient exists. It is the basis of the education given to doctors and pharmacists. It is possible to use this information to create smart algorithms that can identify the suitable drug options for a patient. It is perfectly possible to include the pharmacogenetic information in those algorithms (8). Similarly, it is possible to take diagnostic information to create algorithms that identify the test options for a patient using genomic information, and finally it is possible to use smart systems to identify and even communicate risk, using genomic information. The fact that these systems are only now being developed indicates the significant academic challenges needed to ensure that they work, and can be used in the normal workflow. The introduction of clinically valuable ‘omic’ and genetic information has provided the impetus and funding to make this happen. However, for these systems to be adopted into primary care, we need primary care professionals to be involved. The last thing we need is a smart tool that works just for diabetes but does not include other comorbidities that may influence therapeutics or testing. There needs to be accompanying educational platforms that provide the patient and provider with the confidence and knowledge to use them effectively (9). To make this happen, primary care health professionals need to form a global network to enable these changes. We have several effective organizations that can be the foundation of such a network. The body that represents the academic colleges of family practice, WONCA, the worldwide organizations that represent research in Primary Care, NAPCRG in North America, SAPC in the UK, EGPRN in Europe and AAAPC in Australasia. The challenge of introducing the ‘omic’ technologies is an opportunity for primary care to update many of the decision support systems and make sure that they are effective, unbiased, ethical and equitable. Funding: none. Ethical approval: none. Conflict of interest: I am CEO of a University spin out company, GenXys, that sells pharmacogenetic tests and medication decision support software.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,027
score de la tête « metaresearch » (Gemma)0,111
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,051
Score d'incertitude au seuil0,144

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0270,111
Méta-épidémiologie (sens strict)0,0050,002
Méta-épidémiologie (sens large)0,0060,005
Bibliométrie0,0060,003
Études des sciences et des technologies0,0060,008
Communication savante0,0180,010
Science ouverte0,0060,004
Intégrité de la recherche0,0510,063
Charge utile insuffisante (le modèle a refusé de juger)0,0130,009

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,067
Tête enseignante GPT0,335
Écart entre enseignants0,267 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2017
Routes d'admission1
Résumé présentnon

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