Predicting Consultation Success in Online Health Platforms Using Dynamic Knowledge Networks and Multimodal Data Fusion
Online healthcare consultation in virtual health is an emerging industry marked by innovation and fierce competition. Accurate and timely prediction of healthcare consultation success can proactively help online platforms address patient concerns and improve retention rates. However, predicting online consultation success is challenging due to the partial role of virtual consultations in patients' overall healthcare journey and the disconnect between online and in-person healthcare IT systems. Patient data in online consultations is often sparse and incomplete, presenting significant technical challenges and a research gap. To address these issues, we propose the Dynamic Knowledge Network and Multimodal Data Fusion (DyKoNeM) framework, which enhances the predictive power of online healthcare consultations. Our work has important implications for new business models where specific and detailed online communication processes are stored in the IT database, and at the same time, latent information with predictive power is embedded in the network formed by stakeholders' digital traces. It can be extended to diverse industries and domains, where the virtual or hybrid model (e.g., integration of online and offline services) is emerging as a prevailing trend.
Code (2)
Tasks
ManagementMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Employment of Artificial Intelligence Mechanisms for e-Health Systems in Order to Obtain Vital Signs Improving the Processes of Online Consultations and Diagnosis
A large number of web-based e-Health applications have been developed through time, allowing doctors to access different types of functionalities, like knowing which medication the patient has consumed or performing onli…
Employment of artificial intelligence mechanisms for e-Health systems in order to obtain vital signs and detect diseases from medical images improving the processes of online consultations and diagnosis
Nowadays e-Health web applications allow doctors to access different types of features, such as knowing which medication the patient has consumed or performing online consultations. Internet systems for healthcare can be…
Generative AI and Two-Tiered Online Mental Health Communities
Online mental health communities (OMHCs) are tiered platforms that connect patients with licensed counselors through public Q&A forums and paid private consultations. Their two-tier structure creates a strategic dilemma …
MedAI Dialog Corpus (MEDIC): Zero-Shot Classification of Doctor and AI Responses in Health Consultations
Zero-shot classification enables text to be classified into classes not seen during training. In this study, we examine the efficacy of zero-shot learning models in classifying healthcare consultation responses from Doct…
Classificationtext-classificationText ClassificationXLM-R+2An Ensemble Classification Approach in A Multi-Layered Large Language Model Framework for Disease Prediction
Social telehealth has made remarkable progress in healthcare by allowing patients to post symptoms and participate in medical consultations remotely. Users frequently post symptoms on social media and online health platf…
Ensemble Learning