Recent advancements in Artificial intelligence (AI) have led to its widespread adoption in healthcare applications and services. The global pandemic has further hastened the integration of AI into telehealth services, such as service quality improvement and new models of care. This paper focuses on the service quality improvement of telehealth, specifically, cancer information and telephone support services, in terms of its human system interaction. We present an AI framework for the detection of patient emotions and emotion transitions based on call recordings of telehealth services. The call recordings are typically a conversation between the caller (patient or carer) and the healthcare practitioner (nurse or counsellor). All primary emotions are expressed across diverse topics during these calls. It is anticipated that the caller emotion state improves during the call due to the information and support received. The proposed AI framework is designed to detect emotions expressed at all stages of the call and based on these expressions, formulate the emotion transition during the call. We have evaluated the proposed AI framework on a large real-world dataset of 60,000 call recordings of cancer information and telephone support services provided by Cancer Council Victoria, Australia. The results confirm the effectiveness of this AI framework in detection of emotions and emotion transitions during the provision of telehealth services.

SAM (Sequential Access through Meta-data) is the data access and job management system for the D0 high energy physics experiment at Fermilab. The SAM system is being developed and used to handle the Petabyte-scale experiment data, accessed by hundreds of D0 collaborators scattered around the world. In this paper, we present solutions to some of the distributed data processing problems from the perspective of real experience dealing with mission-critical data. We concentrate on the distributed disk caching, resource management and job control. The system has elements of Grid computing and has features applicable to data-intensive computing in general.

Presents the Sequential Access Model (SAM), which is the data-handling system for D0, one of two primary high-energy experiments at Fermilab. During the next several years, the D0 experiment will store a total of about 1 PByte of data, including raw detector data and data processed at various levels. The design of SAM is not specific to the D0 experiment and carries few assumptions about the underlying mass storage level; its ideas are applicable to any sequential data access. By definition, in the sequential access mode, a user application needs to process a stream of data by accessing each data unit exactly once, the order of the data units in the stream being irrelevant. The units of data are laid out sequentially in files. The adopted model allows for a significant optimization of system performance, a reduction in user file latency and an increase in the overall throughput. In particular, caching is done with the knowledge of all the files that are needed "in the near future", which is defined as all the files being used by already-running or submitted jobs. The bulk of the data is stored in files on tape in the mass storage system Enstore. All of the data managed by SAM is cataloged in great detail in a relational database (Oracle).