A self-learning nurse call system

Abstract

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 * Corresponding author: Tel.: +32 9 331 49 38, Fax: +32 9 331 48 99 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 used 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 1,050 instances, correct parameter values are discovered very efficiently as the components require at most 100 milliseconds execution time and 20 megabyte memory.

Implementing monitoring technologies in care homes for people with dementia: A qualitative exploration using Normalization Process Theory

“…From a Normalization Process Theory perspective, this again highlights the importance of the construct of Cognitive Participation (involvement of stakeholders): implementation may be facilitated by greater involvement in discussions to deepen understandings, rather than by simply increasing didactic instruction. This point seems important when emerging technologies of potential value to care homes include advanced, self-learning nurse-call systems (Ongenae et al, 2014), as well as the increased market availability of wearable personal technologies and mobile health apps which are imbued with clinical and ethical questions about safety, effectiveness, and their impact upon the human caring role (Powell et al, 2014, Fox, 2017). The implementation of such technologies may be facilitated by careful and well-planned approaches, in which staff training moves beyond functional instruction to include deeper discussion about the expected benefits and challenges.…”

A 2015 Medical Informatics Perspective on Health and Clinical Management: Will Cloud and Prioritization Solutions Be the Future of Health Data Management?

“…One of the main concerns developed this year is the prioritization in health. In a paper proposed by Salman [3] for example, remote triage in telehealth was proposed with prioritization processes with a particular focus on the fusion of data from multisources, another framework for prioritization was proposed by Ongenae et al [4] providing a learning nurse call system that uses ontologies and data-mining techniques in order to regulate the nurse calls in hospital. On the topic of prioritization, we kept one of the best papers of the section entitled "developing and testing a computerized Decision Support System for Nurse-to-Patient assignment" proposed by C. Van Oosteveen et al [5].…”

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“…DECT phones can be utilized, not only for receiving (similar to pocket pagers) and sending text messages, but also for providing full-duplex voice communication. Some proposals have indeed been made to integrate DECT phones into nurse call systems, in an effort to improve the nurse-patient communication and the functionality of the existing systems [3][4][5]. After all, DECT phone integration opens the way to calls to a specific nurse, in charge of a particular patient or bed [3].…”

“…The nurse call system in Ref. [5] is adaptive and assesses the priority of the call, based on certain pre-defined conditions and delivers a call to the most appropriate nurse. The disadvantage of the system is the decision time taken until the right nurse is chosen, one of the causes of the delay in traditional nurse call systems.…”