The COVID-19 pandemic has forced many countries to implement a variety of restrictive measures to prevent it from spreading more widely, including the introduction of medical teleconsultations and the use of various tools in the field of inpatient telemedicine care. Digital technologies provide a wide range of treatment options for patients, and at the same time pose a number of organizational challenges for medical entities. Therefore, the question arises of whether organizations are ready to use modern telemedicine tools during the COVID-19 pandemic. The aim of this article is to examine two factors that impact the level of organizational e-readiness for digital transformation in Polish primary healthcare providers (PHC). The first factor comprises operational capabilities, which are the sum of valuable, scarce, unique, and irreplaceable resources and the ability to use them. The second factor comprises technological capabilities, which determine the adoption and usage of innovative technologies. Contrary to the commonly analyzed impacts of technology on operational capabilities, we state the reverse hypothesis. The verification confirms the significant influence of operational capabilities on technological capabilities. The research is conducted using a questionnaire covering organizational e-readiness for digital transformation prepared by the authors. Out of the 32 items examined, four are related to the operational capabilities and four to the technological capabilities. The result of our evaluation shows that: (i) a basic set of four variables can effectively measure the dimensions of OC, namely the degree of agility, level of process integration, quality of resources, and quality of cooperation; (ii) a basic set of three variables can effectively measure the dimensions of TC, namely adoption and usage of technologies, customer interaction, and process automation; (iii) the empirical results show that OC is on a higher level than TC in Polish PHCs; (iv) the assessment of the relationship between OC and TC reveals a significant influence of operational capabilities on technological capabilities with a structural coefficient of 0.697. We recommend increasing the level of technological capability in PHC providers in order to improve the contact between patients and general practitioners (GPs) via telemedicine in lockdown conditions.
Data processing in robotics is currently challenged by the effective building of multimodal and common representations. Tremendous volumes of raw data are available and their smart management is the core concept of multimodal learning in a new paradigm for data fusion. Although several techniques for building multimodal representations have been proven successful, they have not yet been analyzed and compared in a given production setting. This paper explored three of the most common techniques, (1) the late fusion, (2) the early fusion, and (3) the sketch, and compared them in classification tasks. Our paper explored different types of data (modalities) that could be gathered by sensors serving a wide range of sensor applications. Our experiments were conducted on Amazon Reviews, MovieLens25M, and Movie-Lens1M datasets. Their outcomes allowed us to confirm that the choice of fusion technique for building multimodal representation is crucial to obtain the highest possible model performance resulting from the proper modality combination. Consequently, we designed criteria for choosing this optimal data fusion technique.
Recommendation systems have lately been popularised globally. However, often they need to be adapted to particular data and the use case. We have developed a machine learning-based recommendation system, which can be easily applied to almost any items and/or actions domain. Contrary to existing recommendation systems, our system supports multiple types of interaction data with various modalities of metadata through a multi-modal fusion of different data representations. We deployed the system into numerous e-commerce stores, e.g., food and beverages, shoes, fashion items, and telecom operators. We present our system and its main algorithms for data representations and multi-modal fusion. We show benchmark results on open datasets that outperform the state-of-the-art prior work. We also demonstrate use cases for different e-commerce sites.
The COVID-19 pandemic has had two main consequences for the organization of treatment in primary healthcare: restricted patients’ access to medical facilities and limited social mobility. In turn, these consequences pose a great challenge for patients and healthcare providers, i.e., the limited personal contact with medical professionals. This can be eased by new digital technology. While providing solutions to many problems, this technology poses several organizational challenges for healthcare system participants. As the current global situation and the outbreak of the humanitarian crisis in Europe show, these and other likely emergencies amplify the need to learn the lessons and prepare organizations for exceptional rapid changes. Therefore, a question arises of whether organizations are ready to use modern e-health solutions in the context of a rapidly and radically changing situation, and how this readiness can be verified. The aim of this article is to clarify the organizational e-heath readiness concept of Polish primary healthcare units. This study employs the triangulation of analytical methods, as it uses: (i) a literature review of e-health readiness assessment, (ii) primary data obtained with a survey (random sampling of 371 managers of PHC facilities across Poland) and (iii) the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, employed to estimate the structural model. The evaluation of the model revealed that its concept was adequate for more mature entities that focus on the patient- and employee-oriented purpose of digitization, and on assuring excellent experience derived from a consistent care process. In the context of patients’ restricted access to medical facilities and limited social mobility, a simpler version of the research model assesses the readiness more adequately. Finally, the study increases the knowledge base of assets (resources and capabilities), which will help healthcare systems better understand the challenges surrounding the adoption and scaling of e-health technologies.
This paper investigates determinants of recommendation systems’ performance in an online experiment in a large European Internet footwear store. By combining transactional data and archival customer records, a unique database was compiled from which proxy variables were extracted to represent dimensions of consumer loyalty and shopping involvement. These variables were combined in regression analysis with technical characteristics of two types of algorithms employed for generating recommendations: the EMDE algorithm, relying on the LSH method, and the industry-standard CF-RS. Statistical analysis reveals that recommendations are more successful when visual data modality is combined with behavioural data. Better recommendation performance was found to be associated with lower levels of consumer involvement in shopping, as well as higher levels of trust and engagement with the vendor. Experience with the vendor showed a negative correlation with recommendation performance through both its main effect and by its interactions with other consumer-related variables.
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