Platform for Efficient Switching between Multiple Devices in the Intensive Care Unit

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.

Objectives: Handheld computers, such as tablets and smartphones, are becoming more and more accessible in the clinical care setting and in Intensive Care Units (ICUs). By making the most useful and appropriate data available on multiple devices and facilitate the switching between those devices, staff members can efficiently integrate them in their workflow, allowing for faster and more accurate decisions. This paper addresses the design of a platform for the efficient switching between multiple devices in the ICU. The key functionalities of the platform are the integration of the platform into the workflow of the medical staff and providing tailored and dynamic information at the point of care.

Methods: The platform is designed based on a 3-tier architecture with a focus on extensibility, scalability and an optimal user experience. After identification to a device using Near Field Communication (NFC), the appropriate medical information will be shown on the selected device. The visualization of the data is adapted to the type of the device. A web-centric approach was used to enable extensibility and portability.

Results: A prototype of the platform was thoroughly evaluated. The scalability, performance and user experience were evaluated. Performance tests show that the response time of the system scales linearly with the amount of data. Measurements with up to 20 devices have shown no performance loss due to the concurrent use of multiple devices.

Conclusions: The platform provides a scalable and responsive solution to enable the efficient switching between multiple devices. Due to the web-centric approach new devices can easily be integrated. The performance and scalability of the platform have been evaluated and it was shown that the response time and scalability of the platform was within an acceptable range.

Prototyping Sensor Network System for Automatic Vital Signs Collection

Objective: Development of a clinical sensor network system that automatically collects vital sign and its supplemental data, and evaluation the effect of automatic vital sensor value assignment to patients based on locations of sensors.

Methods: The sensor network estimates the data-source, a target patient, from the position of a vital sign sensor obtained from a newly developed proximity sensing system. The proximity sensing system estimates the positions of the devices using a Bluetooth inquiry process. Using Bluetooth access points and the positioning system newly developed in this project, the sensor network collects vital sign and its 4W (who, where, what, and when) supplemental data from any Blue-tooth ready vital sign sensors such as Continua-ready devices. The prototype was evaluated in a pseudo clinical setting at Kyoto University Hospital using a cyclic paired comparison and statistical analysis.

Results: The result of the cyclic paired analysis shows the subjects evaluated the proposed system is more effective and safer than POCS as well as paper-based operation. It halves the times for vital signs input and eliminates input errors. On the other hand, the prototype failed in its position estimation for 12.6% of all attempts, and the nurses overlooked half of the errors. A detailed investigation clears that an advanced interface to show the system’s “confidence”, i.e. the probability of estimation error, must be effective to reduce the oversights.

Conclusions: This paper proposed a clinical sensor network system that relieves nurses from vital signs input tasks. The result clearly shows that the proposed system increases the efficiency and safety of the nursing process both subjectively and objectively. It is a step toward new generation of point of nursing care systems where sensors take over the tasks of data input from the nurses.

Healthcare in the Smart Home: A Study of Past, Present and Future

Ubiquitous or Pervasive Computing is an increasingly used term throughout the technology industry and is beginning to enter the consumer electronics space in its most recent form under the umbrella term: “Internet of Things”. One area of focus is in augmenting the home with intelligent, networked sensors and computers to create a Smart Home which opens a host of possibilities for the role of tomorrow’s dwelling. As the world’s population continues to live longer and consequently experience more medical-related ailments, at the same time institutional healthcare is struggling to cope, the role of the Smart Home becomes paramount to monitoring a dweller’s health and providing any necessary intervention. This study looks at the history of Smart Home Healthcare, current research areas, and potential areas of future investigation. Unique categorisations are presented in Activities of Daily Living (ADL) and Personal Sensors, and a thorough look at the application of Smart Home Healthcare is presented. Technology can augment traditional methods of healthcare delivery and in some cases completely replace it. Costs can be reduced and medical adherence can be increased, all of which contribute to a more sustainable and effective model of care.

Development and usability of a personalized sensor-based system for pervasive healthcare

Although a plethora of remote health monitoring systems have been proposed for chronic conditions, the challenge posed by the changing patient needs and the requirement for personalization in health monitoring to move beyond proprietary, difficult to extend, and unsustainable solutions still pertains. In this direction, we describe a mobile health system based on a smartphone, portable/wearable sensors for measuring the patient's physiological parameters, and back-end platforms for the health professionals to monitor the patient condition and configure monitoring plans in an individualized manner. A prototype system was developed based on a Service-oriented Architecture and integrating commercially available sensing devices. An experimental study has been conducted with 53 patients in order to investigate the usability of the proposed system. The patients were able to perform the majority of the target tasks successfully (Success Rate = 77%), while the perceived usability using the System Usability Scale (SUS) was found to be above average (SUS score = 73%), indicating that the patients overall perceived the system as both easy to use and useful.

Connected health and integrated care: Toward new models for chronic disease management

The increasingly aging population in Europe and worldwide brings up the need for the restructuring of healthcare. Technological advancements in electronic health can be a driving force for new health management models, especially in chronic care. In a patient-centered e-health management model, communication and coordination between patient, healthcare professionals in primary care and hospitals can be facilitated, and medical decisions can be made timely and easily communicated. Bringing the right information to the right person at the right time is what connected health aims at, and this may set the basis for the investigation and deployment of the integrated care models. In this framework, an overview of the main technological axes and challenges around connected health technologies in chronic disease management are presented and discussed. A central concept is personal health system for the patient/citizen and three main application areas are identified. The connected health ecosystem is making progress, already shows benefits in (a) new biosensors, (b) data management, (c) data analytics, integration and feedback. Examples are illustrated in each case, while open issues and challenges for further research and development are pinpointed.

Personalized Monitors for Real-Time Detection of Physiological States

The authors introduce an algorithmic framework to process real-time physiological data using nonparametric Bayesian models under the context of developing and testing personalized wellness monitors. A wearable device aggregates signals from various sensors while periodically transmitting the collected data to a backend server, which builds custom user profiles based on inferred hidden Markov states. They discuss how these user profiles can be used in various contexts as proxies for fluctuating physiological states and leveraged for various longitudinal classification tasks. Using data collected in a two-week study hosted at Jaslok Hospital, the authors show how physiological changes induced by different environments with various levels of stress can be quantified by the authors' platform. To minimize the dependence on continuous connectivity with the backend server, they introduce a heuristic to enable real-time state identification using the modest processing capabilities of the wearable device.

A caregiver support platform within the scope of an ambient assisted living ecosystem

The Ambient Assisted Living (AAL) area is in constant evolution, providing new technologies to users and enhancing the level of security and comfort that is ensured by house platforms. The Ambient Assisted Living for All (AAL4ALL) project aims to develop a new AAL concept, supported on a unified ecosystem and certification process that enables a heterogeneous environment. The concepts of Intelligent Environments, Ambient Intelligence, and the foundations of the Ambient Assisted Living are all presented in the framework of this project. In this work, we consider a specific platform developed in the scope of AAL4ALL, called UserAccess. The architecture of the platform and its role within the overall AAL4ALL concept, the implementation of the platform, and the available interfaces are presented. In addition, its feasibility is validated through a series of tests.

Wireless LAN security management with location detection capability in hospitals

OBJECTIVES

In medical institutions, unauthorized access points and terminals obstruct the stable operation of a large-scale wireless local area network (LAN) system. By establishing a real-time monitoring method to detect such unauthorized wireless devices, we can improve the efficiency of security management.

METHODS

We detected unauthorized wireless devices by using a centralized wireless LAN system and a location detection system at 370 access points at the University of Tokyo Hospital. By storing the detected radio signal strength and location information in a database, we evaluated the risk level from the detection history. We also evaluated the location detection performance in our hospital ward using Wi-Fi tags.

RESULTS

The presence of electric waves outside the hospital and those emitted from portable game machines with wireless communication capability was confirmed from the detection result. The location detection performance showed an error margin of approximately 4 m in detection accuracy and approximately 5% in false detection. Therefore, it was effective to consider the radio signal strength as both an index of likelihood at the detection location and an index for the level of risk.

CONCLUSIONS

We determined the location of wireless devices with high accuracy by filtering the detection results on the basis of radio signal strength and detection history. Results of this study showed that it would be effective to use the developed location database containing radio signal strength and detection history for security management of wireless LAN systems and more general-purpose location detection applications.

Recent progress in sensor-enhanced health information systems - slowly but sustainably

The use of health-enabling technologies is regarded as one important means to face some of the challenges which accompany the demographic change with an expected rise in multi-morbidity and an increased need of care. A precondition for the sensible use of these technologies is their integration in existing information system structures, and - preferably - the enhancement of these into sensor-enhanced health information systems (seHIS). The aim of this review is to report on recent progress in seHIS, and thus to identify relevant areas of research that have to be addressed to provide patient-centered services in a semantically interoperable environment. A literature search in PubMed/Medline was combined with a manual search of papers (n = 1004) in three prominent health/medical informatics journals and one biomedical engineering journal starting from the year 2007. Despite a multitude of papers that present advanced systems using health-enabling technologies, only few papers could be identified that explicitly describe the design of seHIS or the integration of health-enabling technologies into health information systems. Recurring statements emphasise the importance of the following areas of research: patient-centered care using all available sources of information, data security, the stringent use of data representation and device connectivity standards, and adequate methods for data fusion and diagnostic analysis. There is a broad range of research in health-enabling technologies, often focused on specific diseases. The transition from current institution-centered health information systems to person-centered seHIS will be gradual, yet unavoidable for tapping the full potential of health-enabling technologies. seHIS is a growing field of research, and many ambitious challenges are still open. This literature review gives a brief outline of the most frequently mentioned research foci.

A clinical study to assess fall risk using a single waist accelerometer

Falls have various causes and are often associated with mobility impairments. Preventive steps to avoid falls may be initiated, if an increasing fall risk could be detected in time. The objective of this article is to identify an automated sensor-based method to determine fall risk of patients based on objectively measured gait parameters. One hundred fifty-one healthy subjects and 90 subjects at risk of falling were measured during a Timed 'Up & Go' test with a single triaxial acceleration sensor worn on a waist belt. The fall risk was assessed using the STRATIFY score. A decision tree induction algorithm was used to distinguish between subjects with high and low risk using the determined gait parameters. The results of the risk classification produce an overall accuracy of 90.4% in relation to STRATIFY score. The sensitivity amount to 89.4%, the specificity to 91.0% and the reliability parameter kappa equals 0.79. The method presented is able to distinguish between subjects with high and low fall risk. It is unobtrusive and therefore may be applied over extended time periods. A subsequent study is needed to confirm the model's suitability for data recorded in patients' everyday lives.