Studies in health technology and informatics
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Stud Health Technol Inform · Jan 2015
Multicenter StudyThe Role of Hospital Information Systems in Universal Health Coverage Monitoring in Rwanda.
In this retrospective study, the authors monitored the patient health coverage in 6 Rwandan hospitals in the period between 2011 and 2014. Among the 6 hospitals, 2 are third level hospitals, 2 district hospitals and 2 private hospitals. Patient insurance and financial data were extracted and analyzed from OpenClinic GA, an open source hospital information system (HIS) used in those 6 hospitals. ⋯ The amounts paid by the patients for health services decreased in private hospitals to 25% of the total costs in 2014 (-7.4%) and vary between 14% and 19% in public hospitals. Although the number of insured patients has increased and the patient share decreased over the four years of study, the patients' out-of-pocket payments increased especially for in-patients. This study emphasizes the value of integrated hospital information systems for this kind of health economics research in developing countries.
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Stud Health Technol Inform · Jan 2015
Design and development of an EMR for Ebola Treatment Centers in Sierra Leone using OpenMRS.
Ebola treatment presents unique challenges for medical records because strict infection control requirements rule out most conventional record-keeping systems. We used the OpenMRS platform to rapidly develop an EMR system for the recently opened Kerry Town, Sierra Leone Ebola Treatment Centre. This system addresses the need for recording patient data and communicating it between the infectious and non-infectious zones, and is specifically designed for maximum usability by staff wearing cumbersome protective equipment. This platform is interoperable with other key eHealth systems in the country, and is extensible to other sites and diseases.
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The hearing healthcare scenario is rapidly evolving due to the pervasive use of m-Health solutions, in particular mobile apps. This brings along significant advantages and opportunities (e.g., accessibility, affordability, personalized healthcare, patient empowerment) as well as significant potential risks and threats (e.g., safety, misuse, quality issues, privacy). Our research aims at the identification and assessment of apps in the hearing healthcare domain. In this article we present an overview of the current availability, variety, and penetration of hearing-related apps.
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Stud Health Technol Inform · Jan 2015
Health Care Decision Support System for the Pediatric Emeregency Department Management.
Health organization management is facing a high amount of complexity due to the inherent dynamics of the processes and the distributed organization of hospitals. It is therefore necessary for health care institutions to focus on this issue in order to deal with patients' requirements and satisfy their needs. The main objective of this study is to develop and implement a Decision Support System which can help physicians to better manage their organization, to anticipate the overcrowding feature, and to establish avoidance proposals for it. ⋯ These entities which can be either physical agents representing real actors in the health care institution or software agents allowing the implementation of optimizing tools, cooperate to satisfy the demands of patients while respecting emergency degrees. This paper is concerned with agents' negotiation. It proposes a new approach for multi-skill tasks scheduling based on interactions between agents.
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Stud Health Technol Inform · Jan 2015
Clinical Decision Support Based on Integrated Patient Models: A Vision.
Clinical decision making is non-trivial given the amounts of data and knowledge that needs to be considered. So far, medical knowledge, biological knowledge and patient data are separated from each other and need to be integrated mentally by a physician to form an overarching patient model. In this paper, we describe a vision for future decision support systems that link knowledge about organ functions, biological processes, treatment decisions and clinical data represented in repsective models. Requirements and challenges for realizing this vision will be collected.