564 lines
30 KiB
TeX
564 lines
30 KiB
TeX
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% Citations
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\begin{document}
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{\fontfamily{phv}\selectfont}
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\input{cover.tex}
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\section{Background}
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Clinical 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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Early warning scores (EWS) have been widely adopted internationally for preemptive detection of deteriorating patients\cite{downey_strengths_2017}.
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A large body of scientific evidence validates the effectiveness of EWS in assessing severity of illness, and in predicting adverse clinical events,
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such as severe deterioration, likelihood of intensive care unit (ICU) admission, and mortality, both in hospital wards\cite{subbe_validation_2001, buist_association_2004, paterson_prediction_2006, alam_exploring_2015, bilben_national_2016, brekke_value_2019}
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and in ambulatory care \cite{ehara_effectiveness_2019, burgos-esteban_effectiveness_2022, paganelli_conceptual_2022}.
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Two commonly used clinical scores are the \textit{National Early Warning Score 2} (NEWS2) and the
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\textit{Modified Early Warning Score} (MEWS)\cite{burgos-esteban_effectiveness_2022}.
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Both are calculated by capturing various vital parameters from the patient at a specific point in time, followed by numerical aggregation of the
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captured data according to the score being used\cite{subbe_validation_2001, noauthor_national_2017}.
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For MEWS, each measured physiological parameter is assigned an individual score based on which range it is in.
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The ranges for scoring each parameter are shown in Table \ref{mews-table}.
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The individual scores are then added together to produce the final MEWS.
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\begin{table}[!h]
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\noindent\adjustbox{max width=\textwidth}{
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\begin{NiceTabular}{l>{\columncolor{red!15}}c>{\columncolor{orange!15}}c>{\columncolor{yellow!15}}c>{\columncolor{green!15}}c>{\columncolor{yellow!15}}c>{\columncolor{orange!15}}c>{\columncolor{red!15}}c}[hvlines,colortbl-like]
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\hline
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Individual Score & $\mathbf{+3}$ & $\mathbf{+2}$ & $\mathbf{+1}$ & $\mathbf{+0}$ & $\mathbf{+1}$ & $\mathbf{+2}$ & $\mathbf{+3}$ \\
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\hline
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\textbf{Systolic Blood Pressure} [mmHg] & $<70$ & $71-80$ & $81-100$ & $101-199$ & & $\geq 200$ & \\
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\hline
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\textbf{Heart Rate} [bpm] & & $<40$ & $41-50$ & $51-100$ & $101-110$ & $111-129$ & $\geq 130$ \\
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\hline
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\textbf{Respiratory Rate} [bpm] & & $<9$ & & $9-14$ & $15-20$ & $21-29$ & $\geq 30$ \\
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\hline
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\textbf{Temperature} [°C] & & $<35$ & & $35-38.4$ & & $\geq 38.5$ & \\
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\hline
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\textbf{AVPU} & & & & alert & reacting to voice & reacting to pain & unresponsive \\
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\hline
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\end{NiceTabular}
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}
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\caption{\label{mews-table}MEWS calculation ranges}
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\end{table}
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Traditionally, doctors and nursing staff perform collection and evaluation of the data manually, often inputting data into an EWS-calculator by hand.
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However, as Eisenkraft et al. mentioned in 2023, ``some known setbacks of the NEWS and other scales are the frequency of scoring and
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response, integration into practice, and miscalculation by healthcare providers [...]''\cite{eisenkraft_developing_2023}{(p.2)}.
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Remote patient monitoring (RPM) can improve deterioration detection\cite{shaik_remote_2023} by greatly reducing the
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amount of human interaction required to take measurements and perform EWS calculations.
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A number of studies have explored RPM combined with automated EWS calculation in hospitals\cite{eisenkraft_developing_2023, filho_iot-based_2021, un_observational_2021, karvounis_hospital_2021}.
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With hospitals facing overwhelming patient load during the SARS-CoV-2 pandemic, interest in exploring remote patient monitoring options surged,
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and 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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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}.
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%Javanbakht et al. found that continuous vitals monitoring is more cost-effective than intermittent monitoring\cite{javanbakht_cost_2020}, however the findings of this study should be taken lightly due to potential bias reporting.
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\section{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 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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\subsection{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 EWS, hospital admission, care escalation,
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and medical emergencies in combination with IT automation, medical wearables and Internet of Things (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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\subsection{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 Table \ref{inclusion-table}.
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\begin{table}[!h]
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\adjustbox{max width=\textwidth}{
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\begin{NiceTabular}{rll}[hvlines,colortbl-like]
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\hline
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\textbf{Number} & \textbf{Title} & \textbf{Author(s), Year} \\
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\hline
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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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\hline
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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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\hline
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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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\hline
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\end{NiceTabular}
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}
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\caption{\label{inclusion-table}List of included articles}
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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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\subsection{Discussion}
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While the application of 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 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 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 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 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 EWS in real-time with laboratory data is both inconsistent and weak.
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Recent studies indicate a growing trend towards investigating automated 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 COVID-19
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pandemic\cite{paganelli_conceptual_2022, filho_iot-based_2021, otoom_iot-based_2020, gronbaek_continuous_2023}.
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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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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 EWS specialized for use outside of medical care facilities.
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\subsection{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 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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|
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However, it is important to acknowledge the lack of research on the use of 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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|
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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 EWS in 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 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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\section{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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and cables, restricting patient mobility to the bed area, and physically tying the monitoring equipment
|
|
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}.
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Therefore, utilizing such devices for RPM is a suitable approach.
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|
|
|
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In summary, with the current availability of wearable, networked biosensors and the validated effectiveness of EWS in medical facilities,
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combining both aspects presents an important and interesting research opportunity which could help reduce mortality and improve clinical
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outcomes for patients at risk of deterioration, both in their homes and on the go.
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|
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%Patients appreciate the face-to-face aspect of early warning score monitoring as it allows for reassurance, social interaction, and gives them further opportunity to ask questions about their medical care\cite{downey_patient_2018}.
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%Taking continuous measurements is superior to measuring intermittently\cite{gronbaek_continuous_2023, shaik_remote_2023}.
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\section{Objectives}
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|
|
|
The objective of this research is to explore the practical feasibility of using an existing, clinically validated EWS to remotely monitor
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patients who are still at risk of deterioration after having been dismissed from medical care facilities,
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|
utilizing smart medical sensor devices.
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|
Taking measurements using the devices should be as easy and unintrusive as possible for the patient, enabling them to take
|
|
vital sign readings easily from the comfort of their home or while out of the house.
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|
|
|
This will be accomplished by developing and subsequently evaluating a digital system capable of capturing, processing and monitoring patient
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|
vitals data.
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|
The system will consist of a network of smart medical sensors and a centralized web application used to store and process the data.
|
|
Patients and, potentially, medical staff can interact with the application to visualize and utilize captured data.
|
|
In addition to monitoring individual physiological parameters for abnormalities, the application will calculate the patient's current
|
|
MEWS, and send alerts when an increased risk of deterioration is detected.
|
|
A visualization depicting the main flow of data in the system is shown in Figure \ref{system-components-macro}.
|
|
\begin{center}
|
|
\begin{figure}[h]
|
|
\includegraphics[width=\textwidth]{../figures/components-macro.png}
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|
\caption{\label{system-components-macro}Data flow of the proposed early warning system}
|
|
\end{figure}
|
|
\end{center}
|
|
|
|
The following vital signs will be captured and processed by the application:
|
|
\begin{itemize}
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|
\item Heart Rate (HR)
|
|
\item Blood Pressure (BP)
|
|
\item Body Temperature (TEMP)
|
|
\item Blood Oxygen Saturation (SPO2)
|
|
\item Respiratory Rate (RR)
|
|
\footnote{
|
|
Determining the respiration rate of a mobile subject accurately using currently available electronic monitoring equipment
|
|
presents a major challenge.
|
|
Leveraging available SPO2 readings alongside asking the subject whether they are experiencing any shortness of breath
|
|
may, however, provide a suitable compromise.
|
|
}
|
|
\item AVPU Score
|
|
\footnote{
|
|
Determining the AVPU score of a patient requires examination by qualified medical staff, but prompting
|
|
the user to answer a simple question coherently to determine whether they are alert or not may be a suitable option.
|
|
}
|
|
\end{itemize}
|
|
The devices listed in Table \ref{device-table} will be used to measure the patient's vital signs, while
|
|
the web application and its alert system prompts the patient periodically to take new measurements.
|
|
\begin{table}[!h]
|
|
\noindent\adjustbox{max width=\textwidth}{
|
|
\begin{NiceTabular}{lll}[hvlines,colortbl-like]
|
|
\hline
|
|
\textbf{Device Name} & \textbf{Device Type} & \textbf{Captured Vitals Parameter} \\
|
|
\hline
|
|
\href{https://www.withings.com/de/en/scanwatch}{Withings Scanwatch} & Wearable Smartwatch & HR, SPO2, RR (while asleep) \\
|
|
\hline
|
|
\href{https://www.withings.com/de/en/thermo}{Withings Thermo} & Handheld Smart Thermometer & TEMP \\
|
|
\hline
|
|
\href{https://www.withings.com/de/en/bpm-core}{Withings BPM Core} & Smart Blood Pressure Cuff & BP, HR \\
|
|
\hline
|
|
Patient's phone & Smartphone & AVPU \\
|
|
\hline
|
|
\end{NiceTabular}
|
|
}
|
|
\caption{\label{device-table}Smart devices used for data capture}
|
|
\end{table}
|
|
|
|
Following the technical implementation of the described system, its day-to-day usability and effectiveness will be evaluated in
|
|
a case study.
|
|
Over the course of a week, a test subject, representing a patient recently dismissed from an accident and emergency hospital department
|
|
(A\&E) will be using the system both at home and while out and about.
|
|
While awake, the patient will be prompted by the system via smartphone notifications to take new measurements every two hours.
|
|
The captured data and resulting MEWS records will be periodically reviewed by another person representing medical staff during
|
|
this time.
|
|
|
|
Overall, the proposed research is aimed at answering the following scientific inquiries:
|
|
\begin{enumerate}
|
|
\item What are the challenges of developing and utilizing a remote patient monitoring system using smart medical sensors, given the currently available technology?
|
|
\item Can smart medical sensors be used effectively to determine MEWS remotely for patients discharged from A\&E, hospital wards and
|
|
ambulant care?
|
|
\end{enumerate}
|
|
|
|
|
|
\newpage
|
|
\section{Tasks}
|
|
|
|
The following milestones are defined for the research project:
|
|
\begin{enumerate}
|
|
\item Application design
|
|
\begin{itemize}
|
|
\item Detailed software architecture design, data model design
|
|
\end{itemize}
|
|
\item Application development and unit testing
|
|
\begin{itemize}
|
|
\item Database, API, authentication
|
|
\item MEWS algorithm, alerts
|
|
\item User interface
|
|
\end{itemize}
|
|
\item Application integration and deployment
|
|
\begin{itemize}
|
|
\item SSL certificate installation, deployment to public webserver
|
|
\end{itemize}
|
|
\item Case study data collection
|
|
\item Case study data analysis and interpretation
|
|
\item Written compilation of findings
|
|
\item Reviews and adjustments
|
|
\end{enumerate}
|
|
|
|
The total available time for the project is 12 weeks.
|
|
A timeline for each defined milestone is displayed in Figure \ref{gantt}.
|
|
|
|
\begin{center}
|
|
\begin{figure}[h]
|
|
\includegraphics[width=\textwidth]{../figures/gantt.png}
|
|
\caption{\label{gantt}Project Timeline}
|
|
\end{figure}
|
|
\end{center}
|
|
|
|
\newpage
|
|
\printbibliography
|
|
|
|
\end{document}
|