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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2023

Open Access 01-12-2023 | Review

21st century (clinical) decision support in nursing and allied healthcare. Developing a learning health system: a reasoned design of a theoretical framework

Authors: Mark van Velzen, Helen I. de Graaf-Waar, Tanja Ubert, Robert F. van der Willigen, Lotte Muilwijk, Maarten A. Schmitt, Mark C. Scheper, Nico L. U. van Meeteren

Published in: BMC Medical Informatics and Decision Making | Issue 1/2023

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Abstract

In this paper, we present a framework for developing a Learning Health System (LHS) to provide means to a computerized clinical decision support system for allied healthcare and/or nursing professionals. LHSs are well suited to transform healthcare systems in a mission-oriented approach, and is being adopted by an increasing number of countries. Our theoretical framework provides a blueprint for organizing such a transformation with help of evidence based state of the art methodologies and techniques to eventually optimize personalized health and healthcare. Learning via health information technologies using LHS enables users to learn both individually and collectively, and independent of their location. These developments demand healthcare innovations beyond a disease focused orientation since clinical decision making in allied healthcare and nursing is mainly based on aspects of individuals’ functioning, wellbeing and (dis)abilities. Developing LHSs depends heavily on intertwined social and technological innovation, and research and development. Crucial factors may be the transformation of the Internet of Things into the Internet of FAIR data & services. However, Electronic Health Record (EHR) data is in up to 80% unstructured including free text narratives and stored in various inaccessible data warehouses. Enabling the use of data as a driver for learning is challenged by interoperability and reusability.
To address technical needs, key enabling technologies are suitable to convert relevant health data into machine actionable data and to develop algorithms for computerized decision support. To enable data conversions, existing classification and terminology systems serve as definition providers for natural language processing through (un)supervised learning.
To facilitate clinical reasoning and personalized healthcare using LHSs, the development of personomics and functionomics are useful in allied healthcare and nursing. Developing these omics will be determined via text and data mining. This will focus on the relationships between social, psychological, cultural, behavioral and economic determinants, and human functioning.
Furthermore, multiparty collaboration is crucial to develop LHSs, and man-machine interaction studies are required to develop a functional design and prototype. During development, validation and maintenance of the LHS continuous attention for challenges like data-drift, ethical, technical and practical implementation difficulties is required.
Appendix
Available only for authorised users
Footnotes
1
Research data, developed ontologies and developed algorithms are considered as open science and therefor will be published in scientific literature, ontology databases like Bioportal, and all algorithms will be made available to EHR providers, other researchers and developers.
 
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Metadata
Title
21st century (clinical) decision support in nursing and allied healthcare. Developing a learning health system: a reasoned design of a theoretical framework
Authors
Mark van Velzen
Helen I. de Graaf-Waar
Tanja Ubert
Robert F. van der Willigen
Lotte Muilwijk
Maarten A. Schmitt
Mark C. Scheper
Nico L. U. van Meeteren
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2023
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-023-02372-4

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BMC Medical Informatics and Decision Making 1/2023 Go to the issue

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Harmonising electronic health records for reproducible research: challenges, solutions and recommendations from a UK-wide COVID-19 research collaboration

Research

Requirements analysis for an AI-based clinical decision support system for general practitioners: a user-centered design process

Research

Risk prediction of heart failure in patients with ischemic heart disease using network analytics and stacking ensemble learning

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Quality indices for topic model selection and evaluation: a literature review and case study

Research

Development of a novel drug information provision system for Kampo medicine using natural language processing technology

Research

Deep learning approach to detection of colonoscopic information from unstructured reports