241 lines
14 KiB
TeX
241 lines
14 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{csquotes}
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\usepackage[english]{babel}
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\usepackage{graphicx}
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\usepackage{parskip}
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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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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, a 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{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
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to a single location.
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Conversely, battery-powered, wireless vitals monitoring devices, such as wearable armbands or smartwatches, can combine several
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biometric sensors into one device, allowing for a much higher degree of patient mobility, faster deployment and better
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scalability\cite{un_observational_2021}.
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Therefore, utilizing such devices for RPM is a suitable approach.
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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 vital signs of at-home-patients for abnormalities in an experimental 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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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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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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%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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\newpage
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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
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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.
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Patients and, potentially, medical staff can interact with the application to visualize and utilize captured data.
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In addition to monitoring individual physiological parameters for abnormalities, the application will calculate the patient's current
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MEWS, and send alerts when an increased risk of deterioration is detected.
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A visualization depicting the main flow of data in the system is shown in Figure \ref{system-components-macro}.
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\begin{center}
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\begin{figure}[h]
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\includegraphics[width=\textwidth]{../figures/components-macro.png}
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\caption{\label{system-components-macro}Data flow of the proposed early warning system}
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\end{figure}
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\end{center}
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The following vital signs will be captured and processed by the application:
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\begin{itemize}
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\item Heart Rate (HR)
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\item Blood Pressure (BP)
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\item Body Temperature (TEMP)
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\item Blood Oxygen Saturation (SPO2)
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\item Respiratory Rate (RR)
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\footnote{
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Determining the respiration rate of a mobile subject accurately using currently available electronic monitoring equipment
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presents a major challenge.
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Leveraging available SPO2 readings alongside asking the subject whether they are experiencing any shortness of breath
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may, however, provide a suitable compromise.
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}
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\item AVPU Score
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\footnote{
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Determining the AVPU score of a patient requires examination by qualified medical staff, but prompting
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the user to answer a simple question coherently to determine whether they are alert or not may be a suitable option.
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}
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\end{itemize}
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The devices listed in Table \ref{device-table} will be used to measure the patient's vital signs, while
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the web application and its alert system prompts the patient periodically to take new measurements.
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\begin{table}[!h]
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\noindent\adjustbox{max width=\textwidth}{
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\begin{NiceTabular}{lll}[hvlines,colortbl-like]
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\hline
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\textbf{Device Name} & \textbf{Device Type} & \textbf{Captured Vitals Parameter} \\
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\hline
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\href{https://www.withings.com/de/en/scanwatch}{Withings Scanwatch} & Wearable Smartwatch & HR, SPO2, RR (while asleep) \\
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\hline
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\href{https://www.withings.com/de/en/thermo}{Withings Thermo} & Handheld Smart Thermometer & TEMP \\
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\hline
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\href{https://www.withings.com/de/en/bpm-core}{Withings BPM Core} & Smart Blood Pressure Cuff & BP, HR \\
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\hline
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Patient's phone & Smartphone & AVPU \\
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\hline
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\end{NiceTabular}
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}
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\caption{\label{device-table}Smart devices used for data capture}
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\end{table}
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Following the technical implementation of the described system, its day-to-day usability and effectiveness will be evaluated in
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a case study.
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Over the course of a week, a test subject, representing a patient recently dismissed from an accident and emergency hospital department
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(A\&E) will be using the system both at home and while out and about.
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While awake, the patient will be prompted by the system via smartphone notifications to take new measurements every two hours.
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The captured data and resulting MEWS records will be periodically reviewed by another person representing medical staff during
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this time.
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Overall, the proposed research is aimed at answering the following scientific inquiries:
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\begin{enumerate}
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\item What are the challenges of developing and utilizing a remote patient monitoring system using smart medical sensors, given the currently available technology?
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\item Can smart medical sensors be used effectively to determine MEWS remotely for patients discharged from A\&E, hospital wards and
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ambulant care?
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\end{enumerate}
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\newpage
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\section{Tasks}
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The following milestones are defined for the research project:
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\begin{enumerate}
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\item Application design
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\begin{itemize}
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\item Detailed software architecture design, data model design
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\end{itemize}
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\item Application development and unit testing
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\begin{itemize}
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\item Database, API, authentication
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\item MEWS algorithm, alerts
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\item User interface
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\end{itemize}
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\item Application integration and deployment
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\begin{itemize}
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\item SSL certificate installation, deployment to public webserver
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\end{itemize}
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\item Case study data collection
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\item Case study data analysis and interpretation
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\item Written compilation of findings
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\item Reviews and adjustments
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\end{enumerate}
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The total available time for the project is 12 weeks.
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A timeline for each defined milestone is displayed in Figure \ref{gantt}.
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\begin{center}
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\begin{figure}[h]
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\includegraphics[width=\textwidth]{../figures/gantt.png}
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\caption{\label{gantt}Project Timeline}
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\end{figure}
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\end{center}
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\newpage
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\printbibliography
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\end{document}
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