Smart hospital conceptualisations by experts in teams

Objective – The concept of Smart hospitals looks to future hospitals as infrastructures for effective and efficient clinical processes as well as infrastructures for supportive social interactions between patients and health professionals with the objective to design places that increase health service quality, productivity and patient's positive experience. This requires teams of experts that bring in knowledge from different disciplines like medicine and healthcare sciences, Information and Communication Technology, Social Sciences and Architecture. Background - One of the biggest challenges in healthcare is the rising demand for services, while there is a decrease in workforce due to an aging society. Given the current budget constraints, healthcare systems are therefore under pressure to provide cost effectively high-quality services which requires fundamental reforms. When healthcare process data becomes more detailed and accurate, leveraging the concept of smart hospitals could contribute to better use of healthcare resources, including the hospital buildings. Research question - What is a smart hospital? How can various disciplines contribute to smart hospitals? How will healthcare processes change by applying smart technologies? Methodology – 5 interdisciplinary student groups of in total 28 students (12 medicine, 5 economy, 5 social sciences and 6 Technology) explored the concepts for future Smart Hospitals during a 4-week intensive course in Experts in Team. The projects included 3 phases: (1) conceptualisation; (2) writing an article based on literature research and; (3) integrating the findings in a proposal for a product. Results - The 5 projects reflect the students’ research on the application of smart technologies in future hospitals, ranging from: (1) the use of drones for acute healthcare: (2) application of artificial intelligence for improving diagnosis; (3) use of Building Information Models to optimise use of healthcare resources; (4) reducing hospital acquired infections by tracking flow of objects and people and; (5) home delivery of diagnostic services to reduce number of patients in the hospital. Conclusion - The link between healthcare services and the physical environment has the potential to be re-invented through digitalisation and analytics of hospital process data leading to predictability and reduction of variation to support decision making. This requires cross-cutting solutions from healthcare management, logistic management and facility management in combination with ICT and social sciences.publishedVersio

The Development of Smart Hospital Masterplan for Teaching Hospital

In the digital and intelligent era, all organization including hospital should transform their bussiness process from manual and paper-based activities to automatic and paperless one to become a so-called Smart Hospital. However, the development of IT services in hospital are considered not optimal due to the absence of guidelines and a foundation in its implementation. This guideline of Smart Hospital Master Plan will be the basis for implementing the concept of smart hospitals, which in practice will manage health services based on Information Technology. Furthermore, this Smart Hospital Master Plan approach applies the COBIT 5 framework to obtain guaranteed value and benefits of smart hospital implementation at the hospital. Three main activities in COBIT 5 that will use in assessing IT support for improving the quality of health services in this teaching hospital, namely evaluation, direction, and supervision

Development HealthCare System of Smart Hospital Based on UML and XML

The convergence of information technology systems in health care system building is causing us to look at more effective integration of technologies. Facing increased competition, tighter spaces, staff retention and reduced reimbursement, today’s traditional hospitals are looking at strategic ways to use technology to manage their systems called smart hospital. The concept of the smart hospital is a useful system for any hospital; about adding intelligence to the traditional hospital system by covering all resources and locations with patient information. Patient’s information is an important component of the patient privacy in any health care system that is based on the overall quality of each patient in the health care system. The main commitment for any health care system is to improve the quality of the patient and privacy of patient’s information. Today, there is a need of such computer environment where treatment to patients can be given on the basis of his/her previous medical history at the time of emergency at any time, on any place and anywhere. Pervasive and ubiquitous environment and UML (unified modeling language) can bring the boon in this field. For this it's needed to develop the ubiquitous health care computing environment using the UML with traditional hospital environment. This paper is based on the ubiquitous and pervasive computing environment based on UML and XML(The Extensible Markup Language)  technology, in which these problems has been tried to improve traditional hospital system into smart hospital in the near future. The key solution of the smart hospital is online identification of all patients, doctors, nurses, staff, medical equipments, medications, blood bags, surgical tools, blankets, sheets, hospital rooms, etc. In this paper efforts is channeled into improving the knowledge-base ontological description for smart hospital system by using UML and XML technology, Our knowledge is represented in XML format from UML modeling(class diagram). Our smart hospital provides access to its system by using a smart card. Finally, the former try to improve health care delivery through development and management of acute care hospital designed; both physically and operationally, for more efficiency and increased patients safety. Keywords: UML; Smart Hospital (SH); Ontology; XML; health care system

AI-based smart sensing and AR for gait rehabilitation assessment

Health monitoring is crucial in hospitals and rehabilitation centers. Challenges can affect the reliability and accuracy of health data. Human error, patient compliance concerns, time, money, technology, and environmental factors might cause these issues. In order to improve patient care, healthcare providers must address these challenges. We propose a non-intrusive smart sensing system that uses a SensFloor smart carpet and an inertial measurement unit (IMU) wearable sensor on the user’s back to monitor position and gait characteristics. Furthermore, we implemented machine learning (ML) algorithms to analyze the data collected from the SensFloor and IMU sensors. The system generates real-time data that are stored in the cloud and are accessible to physical therapists and patients. Additionally, the system’s real-time dashboards provide a comprehensive analysis of the user’s gait and balance, enabling personalized training plans with tailored exercises and better rehabilitation outcomes. Using non-invasive smart sensing technology, our proposed solution enables healthcare facilities to monitor patients’ health and enhance their physical rehabilitation plans.info:eu-repo/semantics/publishedVersio

Errors and discrepancies in the administration of intravenous infusions: a mixed methods multihospital observational study

Introduction Intravenous medication administration has traditionally been regarded as error prone, with high potential for harm. A recent US multisite study revealed few potentially harmful errors despite a high overall error rate. However, there is limited evidence about infusion practices in England and how they relate to prevalence and types of error. Objectives To determine the prevalence, types and severity of errors and discrepancies in infusion administration in English hospitals, and to explore sources of variation, including the contribution of smart pumps. Methods We conducted an observational point prevalence study of intravenous infusions in 16 National Health Service hospital trusts. Observers compared each infusion against the medication order and local policy. Deviations were classified as errors or discrepancies based on their potential for patient harm. Contextual issues and reasons for deviations were explored qualitatively during observer debriefs. Results Data were collected from 1326 patients and 2008 infusions. Errors were observed in 231 infusions (11.5%, 95% CI 10.2% to 13.0%). Discrepancies were observed in 1065 infusions (53.0%, 95% CI 50.8% to 55.2%). Twenty-three errors (1.1% of all infusions) were considered potentially harmful; none were judged likely to prolong hospital stay or result in long-term harm. Types and prevalence of errors and discrepancies varied widely among trusts, as did local policies. Deviations from medication orders and local policies were sometimes made for efficiency or patient need. Smart pumps, as currently implemented, had little effect, with similar error rates observed in infusions delivered with and without a smart pump (10.3% vs 10.8%, p=0.8). Conclusion Errors and discrepancies are relatively common in everyday infusion administrations but most have low potential for patient harm. Better understanding of performance variability to strategically manage risk may be a more helpful tactic than striving to eliminate all deviations

Exploring the Current Landscape of Intravenous Infusion Practices and Errors (ECLIPSE): protocol for a mixed-methods observational study

Introduction Intravenous medication is essential for many hospital inpatients. However, providing intravenous therapy is complex and errors are common. ‘Smart pumps’ incorporating dose error reduction software have been widely advocated to reduce error. However, little is known about their effect on patient safety, how they are used or their likely impact. This study will explore the landscape of intravenous medication infusion practices and errors in English hospitals and how smart pumps may relate to the prevalence of medication administration errors. Methods and analysis This is a mixed-methods study involving an observational quantitative point prevalence study to determine the frequency and types of errors that occur in the infusion of intravenous medication, and qualitative interviews with hospital staff to better understand infusion practices and the contexts in which errors occur. The study will involve 5 clinical areas (critical care, general medicine, general surgery, paediatrics and oncology), across 14 purposively sampled acute hospitals and 2 paediatric hospitals to cover a range of intravenous infusion practices. Data collectors will compare each infusion running at the time of data collection against the patient's medication orders to identify any discrepancies. The potential clinical importance of errors will be assessed. Quantitative data will be analysed descriptively; interviews will be analysed using thematic analysis. Ethics and dissemination Ethical approval has been obtained from an NHS Research Ethics Committee (14/SC/0290); local approvals will be sought from each participating organisation. Findings will be published in peer-reviewed journals and presented at conferences for academic and health professional audiences. Results will also be fed back to participating organisations to inform local policy, training and procurement. Aggregated findings will inform the debate on costs and benefits of the NHS investing in smart pump technology, and what other changes may need to be made to ensure effectiveness of such an investment

Analysis of the Nurse’s Turnover Intentions at Private Hospitals in Indonesia

The number of hospitals, especially private hospitals in Indonesia, keeps growing. The hospital provides services as intangible products produced by health workers or HR at the hospital, including nurses. Globally, the rate of turnover among nurses ranges from 10% to 21% per year when the optimum standard of turnover for nurses in a hospital is 10% per year [1]. High turnover rates have a detrimental impact on hospitals, in terms of management, financing, and service quality. The purpose of this study is to determine the direct and indirect effects of transformational leadership, job characteristics, and quality of work life on the desire for a turnover. This study used a cross-sectional design. For data analysis, it used a variant-based Structural Equation Model (SEM) method or generally referred to as Smart Partial Least Square/Smart PLS. The results of this study indicate that the indirect effect of transformational leadership through job characteristics on the desire to retreat (turnover intention) is the highest value influence. Based on that results, the suggestion of this study is to increase leadership management from the hospital to be prioritized. Besides that, hospital management also needs to pay attention to the quality of life of the nurses, in order to achieve the conditions of human resources that can support a realization of high work productivity. Keywords: hospital, nurse, turnover intention