• Health Policy and Management
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• v.30 no.3
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• pp.277-285
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• 2020

Chronic diseases as well as a growing population of older adults are currently the leading cause of ill health and economic burden worldwide. Managing those diseases in one-on-one medical consultations poses substantial challenges due to limited time and resources in the current health care system. Various approaches have been taken to manage these conditions, most with limited success. Shared medical appointments (SMAs) are an innovative care delivery option to make the testing of alternative care modalities a prime concern. SMAs are individual medical consultations carried out in a group of patients with similar diseases by providing education, medication management, and disease monitoring. SMAs, since their initial conceptualization in 1998, have gained much popularity and adopted as one of the standard processes in many countries. Accumulated evidence-based studies show outcomes for increasing access to care, behavioral change facilitated through self-management education, maintained/better outcomes, physician productivity, and enhanced resource management. This review summarizes current evidence regarding the existing status of SMAs abroad. An extensive literature search was conducted on major electronic databases including PubMed and Google Scholar. This study suggests to explore and exploit the SMAs which have unique potential as a healthcare delivery innovation in Korea.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.9-13
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• 2023

In this study, we explored the potential of integrating interactive AI callbot technology into the medical consultation domain as part of a broader service development initiative. Aimed at enhancing patient satisfaction, the AI callbot was designed to efficiently address queries from hospitals' primary users, especially the elderly and those using phone services. By incorporating an AI-driven callbot into the hospital's customer service center, routine tasks such as appointment modifications and cancellations were efficiently managed by the AI Callbot Agent. On the other hand, tasks requiring more detailed attention or specialization were addressed by Human Agents, ensuring a balanced and collaborative approach. The deep learning model for voice recognition for this study was based on the Transformer model and fine-tuned to fit the medical field using a pre-trained model. Existing recording files were converted into learning data to perform SSL(self-supervised learning) Model was implemented. The ANN (Artificial neural network) neural network model was used to analyze voice signals and interpret them as text, and after actual application, the intent was enriched through reinforcement learning to continuously improve accuracy. In the case of TTS(Text To Speech), the Transformer model was applied to Text Analysis, Acoustic model, and Vocoder, and Google's Natural Language API was applied to recognize intent. As the research progresses, there are challenges to solve, such as interconnection issues between various EMR providers, problems with doctor's time slots, problems with two or more hospital appointments, and problems with patient use. However, there are specialized problems that are easy to make reservations. Implementation of the callbot service in hospitals appears to be applicable immediately.