Disruptive innovations in the clinical laboratory: catching the wave of precision diagnostics
Notice bibliographique
Résumé
Disruptive innovation is an invention that disrupts an existing market and creates a new one by providing a different set of values, which ultimately overtakes the existing market. Typically, when disruptive innovations are introduced, their performance is initially less than existing standard technologies, but because of their ability to bring the cost down, and with gradual improvement, they end up replacing established service standards.Disruptive technologies have their fingerprints in health care. Pathology and laboratory medicine are fertile soils for disruptive innovations because they are heavily reliant on technology. Disruptive innovations have resulted in a revolution of our diagnostic ability and will take laboratory medicine to the next level of patient care. There are several examples of disruptive innovations in the clinical laboratory. Digitizing pathology practice is an example of disruptive technology, with many advantages and an extended scope of applications. Next-generation sequencing can be disruptive in two ways. The first is by replacing an array of laboratory tests, which each requires expensive and specialized instruments and expertise, with a single cost-effective technology. The second is by disrupting the current paradigm of the clinical laboratory as a diagnostic service by taking it into a new era of preventive or primary care pathology. Other disruptive innovations include the use of dry chemistry reagents in chemistry analyzers and also point of care testing. The use of artificial intelligence is another promising disruptive innovation that can transform the future of pathology and laboratory medicine. Another emerging disruptive concept is the integration of two fields of medicine to create an interrelated discipline such as "histogenomics and radiohistomics." Another recent disruptive innovation in laboratory medicine is the use of social media in clinical practice, education, and publication.There are multiple reasons to encourage disruptive innovations in the clinical laboratory, including the escalating cost of health care, the need for better accessibility of diagnostic care, and the increased demand on the laboratory in the era of precision diagnostics. There are, however, a number of challenges that need to be overcome such as the significant resistance to disruptive innovations by current technology providers and governmental regulatory bodies. The hesitance from health care providers and insurance companies must also be addressed.Adoption of disruptive innovations requires a multifaceted approach that involves orchestrated solutions to key aspects of the process, including creating successful business models, multidisciplinary collaborations, and innovative accreditation and regulatory oversight. It also must be coupled with successful commercialization plans and modernization of health care structure. Fostering a culture of disruptive innovation requires establishing unique collaborative models between academia and industry. It also requires uncovering new sources of unconventional funding that are open to high-risk high-reward projects. It should also be matched with innovative thinking, including new approaches for delivery of care and identifying novel cohorts of patients who can benefit from disruptive technology.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,056 | 0,145 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,000 | 0,013 |
| Études des sciences et des technologies | 0,000 | 0,004 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,005 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».