Ambient-aware continuous care through semantic context dissemination.

BACKGROUND: The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e.g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate.The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data. METHODS: The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability. RESULTS: A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered. CONCLUSIONS: The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results.

Distributed, signal strength-based indoor localization algorithm for use in healthcare environments.

In current healthcare environments, a trend toward mobile and personalized interactions between people and nurse call systems is strongly noticeable. Therefore, it should be possible to locate patients at all times and in all places throughout the care facility. This paper aims at describing a method by which a mobile node can locate itself indoors, based on signal strength measurements and a minimal amount of yes/no decisions. The algorithm has been developed specifically for use in a healthcare environment. With extensive testing and statistical support, we prove that our algorithm can be used in a healthcare setting with an envisioned level of localization accuracy up to room revel (or region level in a corridor), while avoiding heavy investments since the hardware of an existing nurse call network can be reused. The approach opted for leads to very high scalability, since thousands of mobile nodes can locate themselves. Network timing issues and localization update delays are avoided, which ensures that a patient can receive the needed care in a time and resources efficient way.

Probabilistic priority assessment of nurse calls.

Current nurse call systems are very static. Call buttons are fixed to the wall, and systems do not account for various factors specific to a situation. We have developed a software platform, the ontology-based Nurse Call System (oNCS), which supports the transition to mobile and wireless nurse call buttons and uses an intelligent algorithm to address nurse calls. This algorithm dynamically adapts to the situation at hand by taking the profile information of staff and patients into account by using an ontology. This article describes a probabilistic extension of the oNCS that supports a more sophisticated nurse call algorithm by dynamically assigning priorities to calls based on the risk factors of the patient and the kind of call. The probabilistic oNCS is evaluated through implementation of a prototype and simulations, based on a detailed dataset obtained from 3 nursing departments of Ghent University Hospital. The arrival times of nurses at the location of a call, the workload distribution of calls among nurses, and the assignment of priorities to calls are compared for the oNCS and the current nurse call system. Additionally, the performance of the system and the parameters of the priority assignment algorithm are explored. The execution time of the nurse call algorithm is on average 50.333 ms. Moreover, the probabilistic oNCS significantly improves the assignment of nurses to calls. Calls generally result in a nurse being present more quickly, the workload distribution among the nurses improves, and the priorities and kinds of calls are taken into account.

A self-learning nurse call system.

The complexity of continuous care settings has increased due to an ageing population, a dwindling number of caregivers and increasing costs. Electronic healthcare (eHealth) solutions are often introduced to deal with these issues. This technological equipment further increases the complexity of healthcare as the caregivers are responsible for integrating and configuring these solutions to their needs. Small differences in user requirements often occur between various environments where the services are deployed. It is difficult to capture these nuances at development time. Consequently, the services are not tuned towards the users' needs. This paper describes our experiences with extending an eHealth application with self-learning components such that it can automatically adjust its parameters at run-time to the users' needs and preferences. These components gather information about the usage of the application. This collected information is processed by data mining techniques to learn the parameter values for the application. Each discovered parameter is associated with a probability, which expresses its reliability. Unreliable values are filtered. The remaining parameters and their reliability are integrated into the application. The eHealth application is the ontology-based Nurse Call System (oNCS), which assesses the priority of a call based on the current context and assigns the most appropriate caregiver to a call. Decision trees and Bayesian networks are used to learn and adjust the parameters of the oNCS. For a realistic dataset of 1050 instances, correct parameter values are discovered very efficiently as the components require at most 100ms execution time and 20MB memory.

An ontology-based nurse call management system (oNCS) with probabilistic priority assessment.

BACKGROUND: The current, place-oriented nurse call systems are very static. A patient can only make calls with a button which is fixed to a wall of a room. Moreover, the system does not take into account various factors specific to a situation. In the future, there will be an evolution to a mobile button for each patient so that they can walk around freely and still make calls. The system would become person-oriented and the available context information should be taken into account to assign the correct nurse to a call.The aim of this research is (1) the design of a software platform that supports the transition to mobile and wireless nurse call buttons in hospitals and residential care and (2) the design of a sophisticated nurse call algorithm. This algorithm dynamically adapts to the situation at hand by taking the profile information of staff members and patients into account. Additionally, the priority of a call probabilistically depends on the risk factors, assigned to a patient. METHODS: The ontology-based Nurse Call System (oNCS) was developed as an extension of a Context-Aware Service Platform. An ontology is used to manage the profile information. Rules implement the novel nurse call algorithm that takes all this information into account. Probabilistic reasoning algorithms are designed to determine the priority of a call based on the risk factors of the patient. RESULTS: The oNCS system is evaluated through a prototype implementation and simulations, based on a detailed dataset obtained from Ghent University Hospital. The arrival times of nurses at the location of a call, the workload distribution of calls amongst nurses and the assignment of priorities to calls are compared for the oNCS system and the current, place-oriented nurse call system. Additionally, the performance of the system is discussed. CONCLUSIONS: The execution time of the nurse call algorithm is on average 50.333 ms. Moreover, the oNCS system significantly improves the assignment of nurses to calls. Calls generally have a nurse present faster and the workload-distribution amongst the nurses improves.