docs(thesis): add lit review to introduction
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@ -27,6 +27,12 @@
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description={User Interface},
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first={User Interface (GUI)}
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}
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\newglossaryentry{iot}{
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type=\acronymtype,
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name={IoT},
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description={Internet of Things},
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first={Internet of Things (IoT)}
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}
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\newglossaryentry{spo2}{
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type=\acronymtype,
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name={SPO\textsubscript{2}},
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@ -123,8 +123,6 @@ A summary must be written in both English and German.
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\subsection{Background}
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% TODO add full lit review
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Clinical \gls{deterioration} is a critical concern in healthcare, particularly for vulnerable populations such as the elderly and chronically
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ill patients. It refers to a decline in a patient's health status and may lead to adverse outcomes, including hospitalization,
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longer stays in intensive care units, and increased healthcare costs.
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@ -183,30 +181,349 @@ With hospitals facing overwhelming patient load during the SARS-CoV-2 pandemic,
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and \Gls{news2} emerged as an effective tool for predicting severe infection outcomes\cite{filho_iot-based_2021, gidari_predictive_2020, otoom_iot-based_2020, carr_evaluation_2021}
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while reducing person-to-person contact during patient monitoring.
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\subsection{Motivation}
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\subsection{Review of existing literature}
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In order to examine the current state of scientific knowledge about the use of wearable devices for automated \Gls{ews} monitoring of
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patients at home, a comprehensive review of the existing literature was conducted.
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By systematically examining and synthesizing the current body of knowledge, this review identified a variety of approaches for
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utilizing smart medical devices in post-discharge patient care, as well as existing limitations and challenges in future research
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in this rapidly evolving field.
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\subsubsection{Search strategy}
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A systematic search strategy was implemented on the Scopus database, aimed to encompass a broad spectrum of literature relevant
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to the use of smart medical devices for automated early warning score monitoring of patients dismissed from ambulant or hospital care.
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The search focused on topics related to the research area, encompassing the examination of \Gls{ews}, hospital admission, care escalation,
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and medical emergencies in combination with IT automation, medical wearables and \Gls{iot}.
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The Scopus database was chosen for its extensive coverage of scholarly literature across multiple disciplines.
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For the search strategy, the following inclusion and exclusion criteria were employed to select relevant articles:
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Inclusion criteria:
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\begin{itemize}
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\item Articles focusing on the utilization of medical wearable devices for remote patient monitoring
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\item Articles addressing the automated calculation of early warning scores
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\item Articles discussing the application of early warning scores outside of medical care facilities
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\end{itemize}
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Exclusion criteria:
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\begin{itemize}
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\item Non-English language articles
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\item Publications for which full-text access was not available
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\item Duplicate articles
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\item Articles outside of the \enquote{Computer Science} subject area
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\end{itemize}
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The following Scopus query was used to identify relevant literature:
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\begin{tcolorbox}[enhanced, center, width=0.95\linewidth, rounded corners=all, colframe=black!75!white, boxrule=0.5pt, colback=black!5!white]
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\begin{lstlisting}[language=SQL]
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TITLE-ABS-KEY(("patient" OR "clinical" OR "medical") AND ("deterioration" OR "instability" OR "decompensation" OR "admission" OR "hospitalization" OR "escalation" OR "triage" OR "emergency")) OR ("early warning" OR "early warning score" OR "warning" OR "score*" OR "EWS") AND TITLE-ABS-KEY("system" OR "automat*" OR "smart*" OR "wearable*" OR "internet of thing*" OR "iot" OR "digital" OR "sensor*" OR "signal" OR "intelligen*" OR "predict*" OR "monitor*" OR "sreen*" OR "remote" OR "it" OR "comput*" OR "mobile" OR "5G" OR "network" (("vital*" OR "bio*") AND ("marker*" OR "sign*" OR "monitor*"))) AND TITLE-ABS-KEY("home" OR "domestic" OR "community" OR "remote" OR "longterm" OR "nursing" OR "rehabilitation" OR "outof*hospital" OR "telemedicine" OR "ehealth" OR "mhealth")
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\end{lstlisting}
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\end{tcolorbox}
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\subsubsection{Results}
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\begin{figure}[h]
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\begin{center}
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\includegraphics[width=.5\textwidth]{./figures/prisma-flowchart.png}
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\caption{\label{prisma-flowchart}PRISMA flowchart showing screening and assessment of identified literature}
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\end{center}
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\end{figure}
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An initial query on Scopus yielded a total of $N=1997$ records.
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After removing duplicates, $N=952$ records were excluded, resulting in $N=1045$ unique records.
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Upon screening the titles and abstracts, $N=963$ records did not meet the inclusion criteria, leaving $N=82$ articles to be assessed for
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eligibility in full text.
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Finally, after a thorough evaluation, $N=45$ articles were included for the literature review, providing insight into the current state of
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research on the use of smart medical devices for automated early warning score monitoring in patients transitioning from ambulant or
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hospital care.
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Figure \ref{prisma-flowchart} shows the literature assessment process.
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The list of reviewed literature is shown in Tables \ref{tab:inclusion-table-1}, \ref{tab:inclusion-table-2} and \ref{tab:inclusion-table-3}.
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\begin{table}[!ht]
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\centering
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\begin{tcolorbox}[
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enhanced, width=\linewidth, boxrule=2pt, arc=4pt,
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tabularx={
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>{\footnotesize}r
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>{\footnotesize}X
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>{\footnotesize}l
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}
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]
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\textbf{Number} & \textbf{Title} & \textbf{Author(s), Year} \\
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\specialrule{2pt}{0em}{0em}
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1 &
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Internet of things enabled in-home health monitoring system using early warning score\cite{anzanpour_internet_2015} &
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Anzanpour 2015 \\
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\hline
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2 &
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Context-Aware Early Warning System for In-Home Healthcare Using Internet-of-Things\cite{anzanpour_context-aware_2016} &
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Anzanpour 2016 \\
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\hline
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3 &
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An IoT based system for remote patient monitoring\cite{archip_iot_2016} &
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Archip 2016 \\
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\hline
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4 &
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Wireless sensor network-based smart room system for healthcare monitoring\cite{arnil_wireless_2011} &
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Arnil 2011 \\
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\hline
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5 &
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Design and Development of IOT Based Multi-Parameter Patient Monitoring System\cite{athira_design_2020} &
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Athira 2020 \\
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\hline
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6 &
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Medical warning system based on Internet of Things using fog computing\cite{azimi_medical_2016} &
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Azimi 2016 \\
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\hline
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7 &
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Self-aware early warning score system for IoT-based personalized healthcare\cite{azimi_self-aware_2017} &
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Azimi 2017 \\
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\hline
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8 &
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Review on IoT based Healthcare systems\cite{b_v_review_2022} &
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Krishna 2022 \\
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\hline
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9 &
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Effectiveness of Early Warning Scores for Early Severity Assessment in Outpatient Emergency Care: A Systematic Review\cite{burgos-esteban_effectiveness_2022} &
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Burgos-Esteban 2022 \\
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\hline
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10 &
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A QRS Detection and R Point Recognition Method for Wearable Single-Lead ECG Devices\cite{chen_qrs_2017} &
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Chen 2017 \\
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\hline
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11 &
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Adopting the Internet of Things technologies in health care systems\cite{chiuchisan_adopting_2014} &
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Chiuchisan 2014 \\
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\hline
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12 &
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An Efficient Wireless Health Monitoring System\cite{chowdary_efficient_2018} &
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Chowdary 2018 \\
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\hline
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13 &
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DeepSigns: A predictive model based on Deep Learning for the early detection of patient health deterioration\cite{da_silva_deepsigns_2021} &
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da Silva 2021 \\
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\hline
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14 &
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Use of ultra-low cost fitness trackers as clinical monitors in low resource emergency departments\cite{dagan_use_2020} &
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Dagan 2020 \\
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\hline
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15 &
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A data fusion algorithm for clinically relevant anomaly detection in remote health monitoring\cite{de_mello_dantas_data_2020} &
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de Mello Dantas 2020 \\
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\hline
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16 &
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Patient attitudes towards remote continuous vital signs monitoring on general surgery wards: An interview study\cite{downey_strengths_2017} &
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Downey 2018 \\
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\hline
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17 &
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Developing a real-time detection tool and an early warning score using a continuous wearable multi-parameter monitor\cite{eisenkraft_developing_2023} &
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Eisenkraft 2023 \\
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\hline
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18 &
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An IoT-Based Healthcare Platform for Patients in ICU Beds During the COVID-19 Outbreak\cite{filho_iot-based_2021} &
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Filho 2021 \\
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\end{tcolorbox}
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\caption{\label{tab:inclusion-table-1}List of reviewed articles \textit{(Part 1 of 3)}}
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\end{table}
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\begin{table}[!ht]
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\centering
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\begin{tcolorbox}[
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enhanced, width=\linewidth, boxrule=2pt, arc=4pt,
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tabularx={
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>{\footnotesize}r
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>{\footnotesize}X
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>{\footnotesize}l
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}
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]
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\textbf{Number} & \textbf{Title} & \textbf{Author(s), Year} \\
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\specialrule{2pt}{0em}{0em}
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19 &
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Patient Monitoring System Based on Internet of Things\cite{gomez_patient_2016} &
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Gomez 2016 \\
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\hline
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20 &
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Continuous monitoring is superior to manual measurements in detecting vital sign deviations in patients with COVID-19\cite{gronbaek_continuous_2023} &
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Gronbaek 2023 \\
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\hline
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21 &
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Secure and lightweight privacy preserving Internet of things integration for remote patient monitoring\cite{imtyaz_ahmed_secure_2022} &
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Imtyaz 2022 \\
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\hline
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22 &
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Remote Continuous Health Monitoring System for Patients\cite{jagadish_remote_2018} &
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Jagadish 2018 \\
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\hline
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23 &
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Cost utility analysis of continuous and intermittent versus intermittent vital signs monitoring in patients admitted to surgical wards\cite{javanbakht_cost_2020} &
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Javanbakht 2020 \\
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\hline
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24 &
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Wearable sensors to improve detection of patient deterioration\cite{joshi_wearable_2019} &
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Joshi 2019 \\
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\hline
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25 &
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Intelligent Healthcare\cite{kale_intelligent_2021} &
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Kale 2021 \\
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\hline
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26 &
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A Hospital Healthcare Monitoring System Using Internet of Things Technologies\cite{karvounis_hospital_2021} &
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Karvounis 2021 \\
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\hline
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27 &
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All-day mobile healthcare monitoring system based on heterogeneous stretchable sensors for medical emergency\cite{lee_all-day_2020} &
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Lee 2020 \\
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\hline
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28 &
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Analysis of the early warning score to detect critical or high-risk patients in the prehospital setting\cite{martin-rodriguez_analysis_2019} &
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Martin-Rodriguez 2019 \\
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\hline
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29 &
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An IoT-based framework for early identification and monitoring of COVID-19 cases\cite{otoom_iot-based_2020} &
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Otoom 2020 \\
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\hline
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30 &
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A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home\cite{paganelli_conceptual_2022} &
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Paganelli 2022 \\
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\hline
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31 &
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Personalized Mobile Health for Elderly Home Care: A Systematic Review of Benefits and Challenges\cite{pahlevanynejad_personalized_2023} &
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Pahlevanynejad 2023 \\
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\hline
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32 &
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CuraBand: Health Monitoring and Warning System\cite{phaltankar_curaband_2021} &
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Phaltankar 2021 \\
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\hline
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33 &
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Internet of Things in Healthcare, A Literature Review\cite{quraishi_internet_2021} &
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Quraishi 2021 \\
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\hline
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34 &
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Vital Sign Monitoring System for Healthcare Through IoT Based Personal Service Application\cite{sahu_vital_2022} &
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Sahu 2022 \\
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\hline
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35 &
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Internet-of-Things-Enabled Early Warning Score System for Patient Monitoring\cite{sahu_internet--things-enabled_2022} &
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Sahu 2022 \\
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\hline
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36 &
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Cloud-Based Remote Patient Monitoring System with Abnormality Detection and Alert Notification\cite{sahu_cloud-based_2022} &
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Sahu 2022 \\
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\end{tcolorbox}
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\caption{\label{tab:inclusion-table-2}List of reviewed articles \textit{(Part 2 of 3)}}
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\end{table}
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\begin{table}[!ht]
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\centering
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\begin{tcolorbox}[
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enhanced, width=\linewidth, boxrule=2pt, arc=4pt,
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tabularx={
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>{\footnotesize}r
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>{\footnotesize}X
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>{\footnotesize}l
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}
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]
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\textbf{Number} & \textbf{Title} & \textbf{Author(s), Year} \\
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\specialrule{2pt}{0em}{0em}
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37 &
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Remote patient monitoring using artificial intelligence: Current state, applications, and challenges\cite{shaik_remote_2023} &
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Shaik 2023 \\
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\hline
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38 &
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Prototype development of continuous remote monitoring of ICU patients at home\cite{thippeswamy_prototype_2021} &
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Thippeswamy 2021 \\
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\hline
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39 &
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IoT based Smart Healthcare Monitoring Systems: A Review\cite{tiwari_iot_2021} &
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Tiwari 2021 \\
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\hline
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40 &
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Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients\cite{un_observational_2021} &
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Un 2021 \\
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\hline
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41 &
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Adaptive threshold-based alarm strategies for continuous vital signs monitoring\cite{van_rossum_adaptive_2022} &
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van Rossum 2022 \\
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\hline
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42 &
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A retrospective comparison of the Modified Early Warning Score (MEWS) and machine learning approach\cite{wu_predicting_2021} &
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Wu 2021 \\
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\hline
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43 &
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IoT based Real Time Health Monitoring\cite{yeri_iot_2020} &
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Yeri 2020 \\
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\hline
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44 &
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Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology\cite{youssef_ali_amer_vital_2020} &
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Youssef Ali Amer 2020 \\
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\hline
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45 &
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Features of electronic Early Warning systems which impact clinical decision making\cite{zarabzadeh_features_2012} &
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Zarabzadeh 2012 \\
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\end{tcolorbox}
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\caption{\label{tab:inclusion-table-3}List of reviewed articles \textit{(Part 3 of 3)}}
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\end{table}
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% TODO for all outcomes, present and compare the findings of each study
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\subsubsection{Discussion}
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While the application of \Glspl{ews} in ambulant care facilities and hospitals has been thoroughly investigated,
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very little research has been done to assess their practicability for remote monitoring of at-risk patients at home.
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Furthermore, it was observed that previous research on the use of \Gls{iot}-devices for this purpose was largely conducted in
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experimental settings, limiting the generalizability of the results.
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Some studies have examined monitoring vital signs of at-home-patients for abnormalities,
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however in most of them, no automated \Gls{ews} calculations were made\cite{archip_iot_2016, azimi_medical_2016, chowdary_efficient_2018, yeri_iot_2020, lee_all-day_2020, athira_design_2020, phaltankar_curaband_2021, thippeswamy_prototype_2021}.
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however in most of them, no automated EWS calculations were made\cite{archip_iot_2016, azimi_medical_2016, chowdary_efficient_2018, yeri_iot_2020, lee_all-day_2020, athira_design_2020, phaltankar_curaband_2021, thippeswamy_prototype_2021}.
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In 2015, Anzanpour et al. developed a monitoring system which collects vitals data and calculates an \Gls{ews}, however due to limited or nonexistent
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availability of wireless sensors for all relevant vital signs, the work was limited to using a laboratory prototype
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and required manual interaction in transferring vitals data\cite{anzanpour_internet_2015}.
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Sahu et al. documented their development of an \Gls{ews}-supported digital early warning system using the PM6750\cite{sahu_internet--things-enabled_2022},
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an experimental vitals data monitoring device capable of taking continuous measurements in a laboratory setting\cite{noauthor_pm6750_nodate}.
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However, the methodology they used to calculate an \Gls{ews} in real-time with laboratory data is both inconsistent and weak.
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However, the methodology they used to calculate the \Gls{ews} in real-time with laboratory data is both inconsistent and weak.
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The availability of comprehensive, mobile vital signs monitoring equipment has seen a significant increase, especially in the wake of the SARS-CoV-2
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Recent studies indicate a growing trend towards investigating automated \Gls{ews} calculations in real-world scenarios\cite{downey_strengths_2017, karvounis_hospital_2021, b_v_review_2022, dagan_use_2020}.
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Notably, the availability of comprehensive, mobile vital signs monitoring equipment has seen a significant increase, especially in the wake of the SARS-CoV-2
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pandemic\cite{paganelli_conceptual_2022, filho_iot-based_2021, otoom_iot-based_2020, gronbaek_continuous_2023}.
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Since then, a variety of wearable medical sensors capable of continuously recording vital parameters have been developed and are
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commercially available\cite{noauthor_visi_nodate, noauthor_equivital_nodate, noauthor_vitls_nodate, noauthor_caretaker_nodate, noauthor_medtronic_nodate, noauthor_bpm_nodate, noauthor_worlds_nodate, noauthor_smart_nodate}.
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This surge in accessibility has paved the way for more extensive and continuous monitoring of patients in non-medical care settings.
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This demonstrates the evolving landscape of \Gls{rpm}, aiming to improve clinical outcomes and alleviate the burden on hospital wards.
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Moreover, there is a growing interest in incorporating machine learning algorithms to enhance the predictive capabilities of
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deterioration detection\cite{un_observational_2021, da_silva_deepsigns_2021, de_mello_dantas_data_2020}.
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This demonstrates the evolving landscape of remote patient monitoring, aiming to improve clinical outcomes and alleviate the
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burden on hospital wards.
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Despite the wealth of literature reviewed, no existing empirical studies evaluating the use of early warning scores for
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patients at home were identified.
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This highlights a crucial research gap and prompts the need for further investigation in this area, potentially warranting the development
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of an \Gls{ews} specialized for use outside of medical care facilities.
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\subsubsection{Interpretation of Results}
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Based on the findings, several key implications can be drawn.
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Firstly, the improved availability of smart sensors and the demonstrated effectiveness of \Glspl{ews} in predicting deterioration in direct
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medical care settings warrant research into their utilization at home.
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By remotely monitoring patients, it may be possible to identify early signs of deterioration, enabling earlier dismissal from
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hospital care and thereby freeing up valuable resources.
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Additionally, this approach holds the potential to reduce mortality rates and minimize the frequency of adverse clinical outcomes.
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However, it is important to acknowledge the lack of research on the use of \Glspl{ews} at home, which calls for a feasibility study in this
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specific context.
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This study would need to address challenges such as the frequency of measurements required and the absence of immediate diagnosis
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from qualified medical staff.
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Overcoming these obstacles is essential to ensure the safety and efficacy of automated remote patient monitoring in home-based settings.
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In conclusion, the literature review highlights the increasing interest in using smart medical devices and EWS for remote patient
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monitoring, particularly in real-world scenarios.
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The absence of studies evaluating the application of \Glspl{ews} for patients at home underscores the need for further investigation in this area.
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Conducting a feasibility study to explore the practicality and challenges of implementing \Glspl{ews} in home-based care would contribute
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significantly to the existing body of knowledge and help advance the field of automated early warning score monitoring in
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non-medical care settings.
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\subsection{Motivation}
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% TODO EWS makes prediction value better than monitoring abnormalities in single vital signs
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Installing and operating traditional continuous monitoring systems, like the vital sign monitors used in medical facilities, demands
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specialized equipment and technical expertise.
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Furthermore, these systems are cumbersome for patients, as they involve connecting patient and sensor device with numerous electrodes
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@ -215,16 +532,16 @@ to a single location.
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Conversely, battery-powered, wireless vitals monitoring devices, such as wearable armbands or smartwatches, can combine several
|
||||
biometric sensors into one device, allowing for a much higher degree of patient mobility, faster deployment and better
|
||||
scalability\cite{un_observational_2021}.
|
||||
Therefore, utilizing such devices for \Gls{rpm} is a suitable approach.
|
||||
|
||||
In summary, with the current availability of wearable, networked biosensors and the validated effectiveness of \Glspl{ews} in medical facilities,
|
||||
In summary, with the current availability of wearable, networked biosensors and the validated effectiveness of EWS in medical facilities,
|
||||
combining both aspects presents an important and interesting research opportunity which could help reduce mortality and improve clinical
|
||||
outcomes for patients at risk of deterioration, both in their homes and on the go.
|
||||
Conducting a feasibility study to explore the practicality and challenges of implementing a system capable of remote \Gls{ews} calculation for mobile patients
|
||||
would contribute significantly to the existing body of knowledge and help advance the field of automated early warning score monitoring in
|
||||
non-medical care settings.
|
||||
|
||||
\subsection{State of the problem}
|
||||
|
||||
% Merge with Motivation?
|
||||
|
||||
% There is a lack of software calculating MEWS with RPM
|
||||
|
||||
The rapid advancements in wearable, networked biosensors have expanded the horizons of \Gls{rpm}.
|
||||
|
||||
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Reference in New Issue
Block a user