92 lines
5.9 KiB
TeX
92 lines
5.9 KiB
TeX
\documentclass[10pt, a5paper]{article}
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\usepackage[utf8]{inputenc}
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\usepackage[T1]{fontenc}
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%\usepackage[english, ngerman]{babel}
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\usepackage[english]{babel}
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\usepackage{graphicx}
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\usepackage{parskip}
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\usepackage{caption}
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\usepackage{subcaption}
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\usepackage{fancyhdr}
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\usepackage{blindtext}
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\usepackage[left=1cm, right=1cm, top=1.5cm, bottom=1.5cm]{geometry}
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\usepackage[table]{xcolor}
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\usepackage{color}
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\usepackage[colorlinks]{hyperref}
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\pagestyle{plain}
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% Citations
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%\usepackage{cite}
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\usepackage[backend=biber, style=vancouver]{biblatex}
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\addbibresource{../bibliography/bibliography.bib}
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% Colors
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\definecolor{PLRI_Rot}{RGB}{190,30,60}
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\definecolor{grau}{RGB}{120,110,100}
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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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Early warning scores (EWS) have been widely adopted internationally to identify 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 ICU admission, and mortality, both on 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 common implemetations 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 specifically used score.
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Traditionally, doctors and nursing staff perform collection and evaluation of the data manually, inputting data into an EWS-calculator by hand.
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Frequency of scoring, miscalculations and practical integration are known setbacks of NEWS2 and other scores\cite{eisenkraft_developing_2023}.
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% which is limited due to lack of resources\cite{shaik_remote_2023}.
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Remote patient monitoring (RPM) can improve detection of deterioration\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{filho_iot-based_2021, un_observational_2021, karvounis_hospital_2021, eisenkraft_developing_2023}.
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With hospitals facing critical patient demand 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{gidari_predictive_2020, otoom_iot-based_2020, filho_iot-based_2021, carr_evaluation_2021},
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while reducing person-to-person contact during patient monitoring.
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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
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%this study should be taken lightly due to potential bias reporting.
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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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\section{Motivation}
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While the application of EWS in ambulant care facilities and hospitals has been thoroughly investigated, very little research has been done to
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assess their practicability for remote monitoring of at-risk patients at home.
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Some studies have examined monitoring individual vital signs for abnormalities using wearables for at-home-patients in a laboratory setting,
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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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Anzanpour et al. developed a monitoring system which collects vitals data and calculates EWSs in 2015, however due to limited or nonexistent
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availability of remotely operable sensors for all vital signs relevant to EWSs, the work was limited to using a laboratory prototype
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requiring some 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 of real-time EWS calculation using data gathered in the laboratory is inconsistent and was not demonstrated.
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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}, but
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setting up continuous monitoring systems is cumbersome as it involves connecting patients to sensor devices
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with numerous electrodes and cables, which restrict patient activities to the bed area\cite{un_observational_2021}.
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Also, data transmission is highly reliant on in-house telecommunication infrastructure.
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In contrast, wearable devices such as armband or wristband incorporates multiple biosensors in a single form-factor,
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which allows a higher degree of patient mobility without the constraints of physical wirings.
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More importantly, data transmission through cellular network avoids the need of installing additional in-house
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telecommunication infrastructure, allows rapid deployment, and provides versatile and scalable solutions.
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\section{Objectives}
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\section{Tasks}
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\newpage
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\printbibliography
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\end{document}
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