With evolvement in technologies, the healthcare industry is shifting towards value-based care services, where advanced techniques are deployed to provide assistance to care delivery. However, certain aspects such as the way care should be provided, and technology deployment should be analyzed from the scratch throughout a clinical carrier for better medical services. Cathy Wolfe, a research analyst, practice CEO and president of Wolters Kluwer health learning, stated that both the outcome and staff retention are the critical parts of the healthcare sector, and should be driven dynamically along with system-wide learning to enhance skills and knowledge.
In the study perspective, technology-based learning is the common practice followed in the medical sector these days where internet-based devices are used for instant information retrieval and sharing amongst peers. But limitation arises concerning lack of recognition, knowledge on learner’s requirements and different level of understanding. By leveraging adaptive learning models, one can experience individualized learning with learner’s strength and weakness determined through AI and machine learning technologies.
At present, formal type of research are employed by adaptive learning users, but it is likely to be changed with progress in time. Adaptive learning platform powered by Cerego was recently deployed by Elsevier Publications to assist health science users. At the most basic level, the individual strengths on that particular subject are recognized through the artificial intelligence or machine learning-based platform, and the learning material is modified to ensure better focus on individual limitation. This adaption process is limited to a particular type and content for learners. Other processes involve evidence-based learning and disrupting medical education.
In evidence-based training, clinical educators provide on-class-evidence-based training sessions and orientation programs, while ascertaining its requirement and high turnover among clinicians. In disruptive-based medical education, adaptive quizzing with several case studies and virtual anatomy are employed to engage students in studies. Adaptive quizzing employs machine-learning algorithms to evaluate the understanding level of a student. If the student understands the topic better, then minimum questions can be expected or vice versa.
Hospitals are depending more on adaptive learning models to identify students prepared to transition into practice. Clinicians are allowed to study and train without interruption of their daily routine. As a step forward to future, augmented reality and virtual reality will change the way of education by allowing students to study through a wide variety of real-time clinical experiences.